Thursday, December 31, 2009
Happy new year and decade
Unfortunately I can't send each of you a card from my favorite e-card site, but here's the unpersonalized view. I wish everyone a peaceful, healthy, and prosperous year--geez, even decade!
Wednesday, December 30, 2009
Losing to win, investment in loss, not repeating mistakes
At the age of eight Josh Waitzkin was defeated in his first bid for a national chess championship. He had been the favorite, but ultimately he fell short. He took the summer off to go fishing with his parents. It was during this time that he “questioned everything and decided to come back strong, [that he] arrived at a commitment to chess that was about much more than fun and glory. It was about love and pain and passion and pushing [himself] to overcome.” He had confronted his chess mortality and “responded to heartbreak with hard work.” (p. 23)
Years later came a new challenge—Push Hands from Tai Chi that seeks “to defeat a thousand pounds with four ounces.” The only way that Push Hands students can progress “is to release the ego enough to allow themselves to be tossed around while they learn how not to resist.” (p. 107) It’s what his instructor calls investment in loss—giving yourself to the learning process. Waitzkin also describes it as humility training. The less successful students were “frozen in place, repeating their errors over and over, unable to improve because of a fear of releasing old habits. . . They were locked up by the need to be correct.”
Waitzkin suggests that “if a student of virtually any discipline could avoid ever repeating the same mistake twice—both technical and psychological—he or she would skyrocket to the top of their field. Of course such a feat is impossible—we are bound to repeat thematic errors, if only because many themes are elusive and difficult to pinpoint.” In his own chess career, for instance, he didn’t realize that he was “faltering in transitional moments until many months of study brought the pattern to light.” (pp. 107-108) And let me quote the passage that describes this faltering in full because it should resonate with traders. “For a period of time, almost all my chess errors came in a moment immediately following or preceding a big change. For example, if I was playing a positional chess game, with complex maneuvering, long-term strategical planning, and building tension, and suddenly the struggle exploded into concrete tactics, I would sometimes be slow to accommodate the new scenario. Or, if I was playing a very tactical position that suddenly transformed into an abstract endgame, I would keep on calculating instead of taking a deep breath and making long-term plans.” (p. 75) With awareness and action, he reports, his weakness was transformed into a strength.
My follow-up post, on using adversity, will continue this general theme. As a sidenote, I just finished reading T. J. Stiles’s biography of Vanderbilt, The First Tycoon, winner of the National Book Award for non-fiction, a prize apparently worth the non-Vanderbiltian sum of $10,000. The commodore had horrific losses, adversities that beset him like clockwork, but he was always revitalized to push on harder and smarter than ever. He’s not nearly so endearing as Josh Waitzkin (he certainly wasn’t a Push Hands kind of competitor), but he’s a model of the power of relentless perseverance.
Years later came a new challenge—Push Hands from Tai Chi that seeks “to defeat a thousand pounds with four ounces.” The only way that Push Hands students can progress “is to release the ego enough to allow themselves to be tossed around while they learn how not to resist.” (p. 107) It’s what his instructor calls investment in loss—giving yourself to the learning process. Waitzkin also describes it as humility training. The less successful students were “frozen in place, repeating their errors over and over, unable to improve because of a fear of releasing old habits. . . They were locked up by the need to be correct.”
Waitzkin suggests that “if a student of virtually any discipline could avoid ever repeating the same mistake twice—both technical and psychological—he or she would skyrocket to the top of their field. Of course such a feat is impossible—we are bound to repeat thematic errors, if only because many themes are elusive and difficult to pinpoint.” In his own chess career, for instance, he didn’t realize that he was “faltering in transitional moments until many months of study brought the pattern to light.” (pp. 107-108) And let me quote the passage that describes this faltering in full because it should resonate with traders. “For a period of time, almost all my chess errors came in a moment immediately following or preceding a big change. For example, if I was playing a positional chess game, with complex maneuvering, long-term strategical planning, and building tension, and suddenly the struggle exploded into concrete tactics, I would sometimes be slow to accommodate the new scenario. Or, if I was playing a very tactical position that suddenly transformed into an abstract endgame, I would keep on calculating instead of taking a deep breath and making long-term plans.” (p. 75) With awareness and action, he reports, his weakness was transformed into a strength.
My follow-up post, on using adversity, will continue this general theme. As a sidenote, I just finished reading T. J. Stiles’s biography of Vanderbilt, The First Tycoon, winner of the National Book Award for non-fiction, a prize apparently worth the non-Vanderbiltian sum of $10,000. The commodore had horrific losses, adversities that beset him like clockwork, but he was always revitalized to push on harder and smarter than ever. He’s not nearly so endearing as Josh Waitzkin (he certainly wasn’t a Push Hands kind of competitor), but he’s a model of the power of relentless perseverance.
Tuesday, December 29, 2009
Aldridge, High-Frequency Trading
First, what Irene Aldridge’s High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (Wiley, 2010) is not. It’s not an idiot’s guide to high-frequency trading, and it’s not a do-it-yourself manual for the small fry self-directed trader who wants to transform himself into the next Renaissance Technologies. (At least not unless he gets a pretty hefty infusion of cash into his trading business and has highly developed quant skills.) Instead, this book is a description of some of the key elements of high-frequency trading—order execution, ways to find trading opportunities, backtesting, portfolio optimization, and risk management. It draws on academic research (some 20 pages of bibliography) and explicates many ideas with mathematical and statistical formulas.
Aldridge’s book is, I think, particularly valuable for the intraday trader who will often have a high-frequency trading system on the other side of his trade. It’s imperative to know how these systems operate and how they can sometimes in what seems a blink of the eye exploit a market inefficiency and, presto, return the market to efficiency. The book also offers useful pointers for those seeking to develop, automate, and monitor their lower-frequency trading strategies.
There’s a lot of meat on the bones of this book. To profit from it directly you have to be quantitatively savvy. To profit indirectly, you need only be intellectually curious and not mathematically challenged.
For the latter group (yes, I include myself) let me share two of Aldridge’s broad-based hypotheses. I suspect that if a trader truly understands and acts on the issues involved in these two hypotheses, whatever his reasoned conclusion as to their validity, he will leapfrog ahead of his competition, high frequency or snail-paced.
First, Aldridge claims that “in the long term, none of the markets is a zero-sum game. The diverse nature of market participants ensures that all players are able to extract value according to their own metrics.” (p. 47)
Second, all traders seek to differentiate predictable price moves from random moves. Aldridge describes some of the tests that can be performed to determine market efficiency—for instance, non-parametric runs tests, autoregression-based tests, tests based on the martingale hypothesis, and cointegration-based tests. She concludes that “the same security may be predictable at one frequency and fully random at another frequency. Various combinations of securities may have different levels of efficiency. While price changes of two or more securities may be random when securities are considered individually, the price changes of a combination of these securities may be predictable, and vice versa.” (p. 89)
I will never be a high-frequency trader, but even for the “slower and duller” Aldridge’s book has a lot to offer. What works at warp speed is sometimes, slightly modified, a winner for those who are entering their trades (even manually) via a cable modem far from the exchange.
Aldridge’s book is, I think, particularly valuable for the intraday trader who will often have a high-frequency trading system on the other side of his trade. It’s imperative to know how these systems operate and how they can sometimes in what seems a blink of the eye exploit a market inefficiency and, presto, return the market to efficiency. The book also offers useful pointers for those seeking to develop, automate, and monitor their lower-frequency trading strategies.
There’s a lot of meat on the bones of this book. To profit from it directly you have to be quantitatively savvy. To profit indirectly, you need only be intellectually curious and not mathematically challenged.
For the latter group (yes, I include myself) let me share two of Aldridge’s broad-based hypotheses. I suspect that if a trader truly understands and acts on the issues involved in these two hypotheses, whatever his reasoned conclusion as to their validity, he will leapfrog ahead of his competition, high frequency or snail-paced.
First, Aldridge claims that “in the long term, none of the markets is a zero-sum game. The diverse nature of market participants ensures that all players are able to extract value according to their own metrics.” (p. 47)
Second, all traders seek to differentiate predictable price moves from random moves. Aldridge describes some of the tests that can be performed to determine market efficiency—for instance, non-parametric runs tests, autoregression-based tests, tests based on the martingale hypothesis, and cointegration-based tests. She concludes that “the same security may be predictable at one frequency and fully random at another frequency. Various combinations of securities may have different levels of efficiency. While price changes of two or more securities may be random when securities are considered individually, the price changes of a combination of these securities may be predictable, and vice versa.” (p. 89)
I will never be a high-frequency trader, but even for the “slower and duller” Aldridge’s book has a lot to offer. What works at warp speed is sometimes, slightly modified, a winner for those who are entering their trades (even manually) via a cable modem far from the exchange.
Monday, December 28, 2009
Two approaches to learning
Sometime back in August, in a comment on one of my posts, Jorge suggested that I read Josh Waitzkin’s The Art of Learning (Free Press, 2007). I got as far as obtaining a copy of the book and then somehow got distracted—too many books, too little time. When Linda Raschke referenced it in a recent webinar I refocused. I curled up with The Art of Learning, a truly marvelous book for anyone seeking to improve her performance.
Josh Waitzkin, in case his name doesn’t resonate, was an eight-time national chess champion in his youth. He was the subject of Searching for Bobby Fischer, a book written by his father and subsequently made into a movie. He then went on to become a martial arts champion with 21 national championship titles and several world championship titles. He also helped to develop Chessmaster, the best-selling chess-playing computer game series, now owned by UbiSoft. You might describe him as an overachiever.
I’m going to mine his book for insights over a series of posts, but there’s no way my desiccated summaries can begin to capture the passion of the book. You get what you pay for. Today’s theme is essentially the difference between statis and process.
There is little room at the top in competitive worlds. Most people who try will be disappointed. So, Waitzkin asks, what separates out the winners from the also-rans and the outright losers? And, “if ambition spells probable disappointment, why pursue excellence?” He suggests that “the answer to both questions lies in a well-thought-out approach that inspires resilience, the ability to make connections between diverse pursuits, and day-to-day enjoyment of the process. The vast majority of motivated people, young and old, make terrible mistakes in their approach to learning.” (pp. 29-30)
Drawing on the work of developmental psychologists Waitzkin describes two differing views of intelligence—entity and incremental. The first view looks upon overall intelligence or skill at a certain task or set of tasks as fixed; the kid is inherently smart or dumb, is good at math but is a bad speller. The second view might adopt the old Avis slogan “we try harder” as its mantra; with hard work, incrementally, the bad speller can improve and perhaps even become a crack speller.
When challenged, those who have an entity view of intelligence are “brittle and prone to quit” because they have a “learned helplessness orientation”; the incrementalists keep plugging away. It is easy to destroy the self-confidence of the first group because “they feel the need to live up to and maintain a perfectionist image that is easily and inevitably shattered.” They find it difficult to come back from defeat.
Fortunately it seems that we can reprogram ourselves to view particular tasks/challenges as part of an overall learning process in which we are trying to achieve mastery rather than as stand-alone exercises that will be judged or graded. This reprogramming is not only good for the soul; it also improves performance on these tasks. Kids who received “mastery-oriented” instructions outperformed those who received “helplessness-producing” instructions.
Josh Waitzkin, in case his name doesn’t resonate, was an eight-time national chess champion in his youth. He was the subject of Searching for Bobby Fischer, a book written by his father and subsequently made into a movie. He then went on to become a martial arts champion with 21 national championship titles and several world championship titles. He also helped to develop Chessmaster, the best-selling chess-playing computer game series, now owned by UbiSoft. You might describe him as an overachiever.
I’m going to mine his book for insights over a series of posts, but there’s no way my desiccated summaries can begin to capture the passion of the book. You get what you pay for. Today’s theme is essentially the difference between statis and process.
There is little room at the top in competitive worlds. Most people who try will be disappointed. So, Waitzkin asks, what separates out the winners from the also-rans and the outright losers? And, “if ambition spells probable disappointment, why pursue excellence?” He suggests that “the answer to both questions lies in a well-thought-out approach that inspires resilience, the ability to make connections between diverse pursuits, and day-to-day enjoyment of the process. The vast majority of motivated people, young and old, make terrible mistakes in their approach to learning.” (pp. 29-30)
Drawing on the work of developmental psychologists Waitzkin describes two differing views of intelligence—entity and incremental. The first view looks upon overall intelligence or skill at a certain task or set of tasks as fixed; the kid is inherently smart or dumb, is good at math but is a bad speller. The second view might adopt the old Avis slogan “we try harder” as its mantra; with hard work, incrementally, the bad speller can improve and perhaps even become a crack speller.
When challenged, those who have an entity view of intelligence are “brittle and prone to quit” because they have a “learned helplessness orientation”; the incrementalists keep plugging away. It is easy to destroy the self-confidence of the first group because “they feel the need to live up to and maintain a perfectionist image that is easily and inevitably shattered.” They find it difficult to come back from defeat.
Fortunately it seems that we can reprogram ourselves to view particular tasks/challenges as part of an overall learning process in which we are trying to achieve mastery rather than as stand-alone exercises that will be judged or graded. This reprogramming is not only good for the soul; it also improves performance on these tasks. Kids who received “mastery-oriented” instructions outperformed those who received “helplessness-producing” instructions.
Sunday, December 27, 2009
Goldman Sachs VIP stocks
Toward the end of August Goldman Sachs published a hedge fund trend monitor.
Among the data collected were the fifty stocks that “matter most” to hedge funds—that is, the fifty stocks that most frequently appear among the largest ten holdings of hedge funds. Here are the top ten as of June 30, 2009. I calculated (first column) the rate of return between June 30 and December 10 and (second column) the rate of return between June 30 and the highest close after June 30 and before December 10.
As you can see, as of December 10 the top stocks on average underperformed the SPY. By contrast, at their height they outperformed the SPY by about 9%. So hedge funds had to be nimble to get outsized returns from this basket of stocks. (Not that any one fund owned the whole basket.) Looking at the ten stocks individually, six were outperforming the S&P 500 as of December 10 and eight outperformed at their height, some dramatically so. As is so often the case, timing made the difference.
AlphaClone also slices and dices hedge fund holdings and publishes leaderboard. This clone fund is made up of the largest equity holding for each hedge fund in their database, 50% hedged and rebalanced quarterly.
Among the data collected were the fifty stocks that “matter most” to hedge funds—that is, the fifty stocks that most frequently appear among the largest ten holdings of hedge funds. Here are the top ten as of June 30, 2009. I calculated (first column) the rate of return between June 30 and December 10 and (second column) the rate of return between June 30 and the highest close after June 30 and before December 10.
| BAC | 15.23% | 40.83% | MSFT | 25.66% | 25.56% | AAPL | 37.91% | 45.33% | GOOG | 40.30% | 40.30% | JPM | 20.99% | 38.26% | PFE | 22.13% | 25.67% | QCOM | 0.80% | 7.19% | RIG | 8.63% | 24.78% | CVS | -1.16% | 19.26% | V | 32.22% | 33.26% | Average | 20.27% | 30.04% |
| SPY | 20.33% | 21.13% |
As you can see, as of December 10 the top stocks on average underperformed the SPY. By contrast, at their height they outperformed the SPY by about 9%. So hedge funds had to be nimble to get outsized returns from this basket of stocks. (Not that any one fund owned the whole basket.) Looking at the ten stocks individually, six were outperforming the S&P 500 as of December 10 and eight outperformed at their height, some dramatically so. As is so often the case, timing made the difference.
AlphaClone also slices and dices hedge fund holdings and publishes leaderboard. This clone fund is made up of the largest equity holding for each hedge fund in their database, 50% hedged and rebalanced quarterly.
Wednesday, December 23, 2009
World’s most unusual Christmas trees
As Charlie Munger said, “Invert, always invert.” Why the upside-down tree? So specialty stores could display ornaments while using as little floor space as possible.
If you have a soft spot for the pathetic, Charlie Brown’s Christmas tree should really resonate.
And what do you do with all those trading books and magazines? Make a bookshelf tree.
From a Singapore jeweler comes this tree with 21,798 diamonds totaling 913 carats and 3,762 crystal beads. Worth a million bucks!
These images are among the ten featured on the Neatorama site (admittedly two years old, but fun anyway).
Is trading self-poisoning?
I’ve started reading Peter Ward’s The Medea Hypothesis: Is Life on Earth Ultimately Self-Destructive? (Princeton University Press, 2009). I doubt that I’ll read it cover to cover, but its premise is intriguing in a Malthusian, doomsday sort of way.
Contrary to Gaia hypotheses that envisioned “Mother Nature” as a kindly, nurturing force, Ward offers us Medea. She, of course, was the quintessential bad mother who murdered all her children after she found out that husband Jason, who could cast a spell over any woman (and bewitched her), was a cad—and an unlikable one at that. Ward’s Medea hypothesis is that “the overall effect of life has been and will be to reduce the longevity of the Earth as a habitable planet. Life itself, because it is inherently Darwinian, is biocidal, suicidal, and creates a series of positive feedbacks to Earth systems . . . that harm later generations. Thus it is life that will cause the end of itself, on this or any planet inhabited by Darwinian life. . . .” (p. 35) Only human intelligence and engineering, Ward suggests, can delay this fate.
Okay, you ask, what does this have to do with trading and investing? Let’s look at two characteristics of Medean life.
First, species keep increasing in population, ultimately outstripping resources vital to their continuance. Take the classic example of putting a breeding pair of insects in a closed jar with some food. The insects multiply, the food disappears, and the bugs die off from starvation, “usually with some last phase of cannibalism preceding the complete extinction.” I couldn’t help thinking of competing hedge funds when picturing the insects in the jar. Fortunately for some funds “angels” often add food to the jar. “But the point here,” says Ward, “is that . . . there are always too many bugs for the amount of food, and some are always dying of starvation or being killed by other bugs as they fight for food.” (p. 36) Simple Darwinism at work. Just ask the presumably dwindling number of real estate agents in Greenwich, CT.
Second, life is self-poisoning in closed systems. We need not linger over the image of increasing amounts of carbon dioxide in the air and liquid and solid waste in the ground. Instead let’s jump directly to the analogous question: is trading self-poisoning? We know that it can be. Just think of Frank Norris’s 1903 novel The Pit. (If you haven’t read this tale of the Chicago wheat pit, it’s available for free download online via the Gutenberg Project.) Perhaps Jesse Livermore’s suicide is another testament to the self-poisoning character of trading.
But there’s no reason a trader has to encapsulate himself in a world that excludes everything that isn’t market related. Moreover, isn’t trading, virtually by definition, an open system—that is, a system that continuously interacts with its environment and that has supplies of energy, however metaphorically defined, that cannot be depleted? Well, in some sense yes, but unfortunately we can’t extend the notion of energy supplies, no matter how hard we try, to include financial resources. They certainly can be depleted through trading.
I suspect that trading, at least for those who are successful at it, is both self-sustaining and self-poisoning. I also suspect that most trading systems start to degrade not only because markets change but also because they change markets; that is, they themselves have “self-poisoning” qualities. I have no proof of any of this, but it’s something to think about. And, oh yes, happy holidays! Ho-ho-ho.
Contrary to Gaia hypotheses that envisioned “Mother Nature” as a kindly, nurturing force, Ward offers us Medea. She, of course, was the quintessential bad mother who murdered all her children after she found out that husband Jason, who could cast a spell over any woman (and bewitched her), was a cad—and an unlikable one at that. Ward’s Medea hypothesis is that “the overall effect of life has been and will be to reduce the longevity of the Earth as a habitable planet. Life itself, because it is inherently Darwinian, is biocidal, suicidal, and creates a series of positive feedbacks to Earth systems . . . that harm later generations. Thus it is life that will cause the end of itself, on this or any planet inhabited by Darwinian life. . . .” (p. 35) Only human intelligence and engineering, Ward suggests, can delay this fate.
Okay, you ask, what does this have to do with trading and investing? Let’s look at two characteristics of Medean life.
First, species keep increasing in population, ultimately outstripping resources vital to their continuance. Take the classic example of putting a breeding pair of insects in a closed jar with some food. The insects multiply, the food disappears, and the bugs die off from starvation, “usually with some last phase of cannibalism preceding the complete extinction.” I couldn’t help thinking of competing hedge funds when picturing the insects in the jar. Fortunately for some funds “angels” often add food to the jar. “But the point here,” says Ward, “is that . . . there are always too many bugs for the amount of food, and some are always dying of starvation or being killed by other bugs as they fight for food.” (p. 36) Simple Darwinism at work. Just ask the presumably dwindling number of real estate agents in Greenwich, CT.
Second, life is self-poisoning in closed systems. We need not linger over the image of increasing amounts of carbon dioxide in the air and liquid and solid waste in the ground. Instead let’s jump directly to the analogous question: is trading self-poisoning? We know that it can be. Just think of Frank Norris’s 1903 novel The Pit. (If you haven’t read this tale of the Chicago wheat pit, it’s available for free download online via the Gutenberg Project.) Perhaps Jesse Livermore’s suicide is another testament to the self-poisoning character of trading.
But there’s no reason a trader has to encapsulate himself in a world that excludes everything that isn’t market related. Moreover, isn’t trading, virtually by definition, an open system—that is, a system that continuously interacts with its environment and that has supplies of energy, however metaphorically defined, that cannot be depleted? Well, in some sense yes, but unfortunately we can’t extend the notion of energy supplies, no matter how hard we try, to include financial resources. They certainly can be depleted through trading.
I suspect that trading, at least for those who are successful at it, is both self-sustaining and self-poisoning. I also suspect that most trading systems start to degrade not only because markets change but also because they change markets; that is, they themselves have “self-poisoning” qualities. I have no proof of any of this, but it’s something to think about. And, oh yes, happy holidays! Ho-ho-ho.
Monday, December 21, 2009
Goldman's new skyscraper
For those who think that Goldman Sachs gets more breaks than they do, here's an article from today's Bloomberg.com: "Taxpayers Help Goldman Reach Height of Profit in New Skyscraper."
Trading, the buying and selling of inventory
Some time back Ira (I’m embarrassed to say that I have forgotten his last name) was active on a list I read; he then got inspired to launch a web site: http://delta100.com/wordpress/. I saved one of his posts, from which I have lifted a passage that may help other traders. (Call it the holiday spirit of sharing, except in this case I’m sharing someone else’s thoughts. That seems somewhat Scrooge-like, doesn’t it?)
“Trading and investing are two different animals. Investing is a plan that involves the allocation of assets in various areas that are supposed to increase in value over a period of time. During this time the individual is earning a living in his/her chosen field of endeavor. Trading is the generation of cash flow. In the stock, futures and options markets a trader has the ability to make substantially more money than an investor. Trading is like any other retail business. You are doing nothing more than buying and selling inventory. If you are a buyer you are hoping to buy at one price and sell at a higher price. Isn’t that exactly what every retail and wholesale business is doing? If a store cannot sell an item at the price listed it goes on sale. The same should happen in trading. If the stock, option or future doesn’t do what it is supposed to do, sell it and buy something else that will produce a profit. Safeway and Tiffany have different methods of operation. One is a high volume low margin business with a rapid inventory turnover and the other generates a higher margin with a lower turnover rate. At this time they are both profitable. The one thing you don’t see in these businesses is a stagnant inventory. They are not investors; they are in business to buy and sell inventory. If you go into a Chevy agency you won’t find one 1957 Corvette convertible or other vintage car. The Corvette sold new for $3500 and in good condition it could bring between $65,000 and $125,000 at auction in today’s market. The purpose of the agency is to buy and sell cars not invest in them. Businesses buy and sell inventory. Investors are collectors of assets. If you are an investor you own the building and if you are in business you rent that building and generate cash flow by buying and selling inventory. Both should make money.”
“Trading and investing are two different animals. Investing is a plan that involves the allocation of assets in various areas that are supposed to increase in value over a period of time. During this time the individual is earning a living in his/her chosen field of endeavor. Trading is the generation of cash flow. In the stock, futures and options markets a trader has the ability to make substantially more money than an investor. Trading is like any other retail business. You are doing nothing more than buying and selling inventory. If you are a buyer you are hoping to buy at one price and sell at a higher price. Isn’t that exactly what every retail and wholesale business is doing? If a store cannot sell an item at the price listed it goes on sale. The same should happen in trading. If the stock, option or future doesn’t do what it is supposed to do, sell it and buy something else that will produce a profit. Safeway and Tiffany have different methods of operation. One is a high volume low margin business with a rapid inventory turnover and the other generates a higher margin with a lower turnover rate. At this time they are both profitable. The one thing you don’t see in these businesses is a stagnant inventory. They are not investors; they are in business to buy and sell inventory. If you go into a Chevy agency you won’t find one 1957 Corvette convertible or other vintage car. The Corvette sold new for $3500 and in good condition it could bring between $65,000 and $125,000 at auction in today’s market. The purpose of the agency is to buy and sell cars not invest in them. Businesses buy and sell inventory. Investors are collectors of assets. If you are an investor you own the building and if you are in business you rent that building and generate cash flow by buying and selling inventory. Both should make money.”
Saturday, December 19, 2009
A holiday slowdown
The two TV shows that are the backbone of my “keep going, you can’t stop until you find out what happens” incentive to exercise on my stationary bicycle are on an extended holiday break. I don’t intend to cut you off so rudely, but postings over the holidays will be somewhat sporadic. This is the time I reconnect with people I haven’t seen in a while, write to those whom I probably wouldn’t recognize if I met them in the grocery store but who nonetheless etched a place in my life, and reflect on the passing of yet another year. It’s not just a flip of the standard calendar but also a flip of my personal calendar; I was born soon after everyone was all partied out. I might even read a book or two that have nothing whatsoever to do with the financial markets or the art/science of trading. Then again, as you know, I can find parallels practically everywhere.
Friday, December 18, 2009
Some trading rules from Dennis Gartman
I’ve never taken it upon myself to collect trading rules, but somewhere along the line I saved thirteen trading rules from Dennis Gartman. As we look forward to the holiday season and yet another new year and, of course, the seemingly mandatory resolutions that we break almost as soon as we make, here are five.
Learn to trade like a mercenary guerilla. “We must . . . learn to fight/invest on the winning side, and we must be willing to change sides immediately when one side has gained the upper hand.”
Don’t hold on to losing positions. “Holding on to losing positions costs real capital as one’s account balance is depleted, but it can exhaust one’s mental capital even more seriously as one holds to the losing trade, becoming more and more fearful with each passing minute, day and week, avoiding potentially profitable trades while one nurtures the losing position.”
Trading runs in cycles; some are good, some are bad, and there is nothing we can do about that other than accept it and act accordingly. “Thus, when things are going well, trade often, trade large, and try to maximize the good fortune that is being bestowed upon you. However, when trading poorly, trade infrequently, trade very small, and continue to get steadily smaller until the winds have changed and the trading ‘gods’ have chosen to smile upon you once again.”
Keep your technical systems simple. “The greatest traders/investors we’ve had the honor to know over the years continue to employ the simplest trading schemes. They draw simple trend lines, they see and act on simple technical signals, they react swiftly, and they attribute it to their knowledge gained over the years that complexity is the home of the young and untested.”
And, finally, what Gartman considers the most important rule of all:
Do more of that which is working and do less of that which is not. “This is a simple rule in writing; this is a difficult rule to act upon. However, it synthesizes all the modest wisdom we’ve accumulated over thirty years of watching and trading in markets. Adding to a winning trade while cutting back on losing trades is the one true rule that holds—and it holds in life as well as in trading/investing.”
Learn to trade like a mercenary guerilla. “We must . . . learn to fight/invest on the winning side, and we must be willing to change sides immediately when one side has gained the upper hand.”
Don’t hold on to losing positions. “Holding on to losing positions costs real capital as one’s account balance is depleted, but it can exhaust one’s mental capital even more seriously as one holds to the losing trade, becoming more and more fearful with each passing minute, day and week, avoiding potentially profitable trades while one nurtures the losing position.”
Trading runs in cycles; some are good, some are bad, and there is nothing we can do about that other than accept it and act accordingly. “Thus, when things are going well, trade often, trade large, and try to maximize the good fortune that is being bestowed upon you. However, when trading poorly, trade infrequently, trade very small, and continue to get steadily smaller until the winds have changed and the trading ‘gods’ have chosen to smile upon you once again.”
Keep your technical systems simple. “The greatest traders/investors we’ve had the honor to know over the years continue to employ the simplest trading schemes. They draw simple trend lines, they see and act on simple technical signals, they react swiftly, and they attribute it to their knowledge gained over the years that complexity is the home of the young and untested.”
And, finally, what Gartman considers the most important rule of all:
Do more of that which is working and do less of that which is not. “This is a simple rule in writing; this is a difficult rule to act upon. However, it synthesizes all the modest wisdom we’ve accumulated over thirty years of watching and trading in markets. Adding to a winning trade while cutting back on losing trades is the one true rule that holds—and it holds in life as well as in trading/investing.”
Thursday, December 17, 2009
Why reinvent the wheel?
Traders are often exhorted not to reinvent the wheel. That is, don’t waste your time doing something that has already been done by other people when you could be doing something more worthwhile. Like making money using the wheels invented earlier.Fran Briggs, a peak performance coach, asked a group of elementary school students why we should reinvent the wheel, more precisely why they would reinvent the wheels on their bicycles. Among the more imaginative answers--you can’t see inside, they slow down when on grass, and they don’t glow in the dark. I would give a gold star to the second answer.
The wheel was a brilliant solution to a set of local problems. For instance, round trumps other alternatives on surfaces that are, for lack of a more scientific description, pretty hard. Round isn’t a great solution in deep snow, in sand, in the water (despite the Mississippi steamboats), or in the air. The wheel doesn’t solve all transportation problems.
So the first problem is that the wheel isn’t all it’s cracked up to be. It has its limitations. It doesn’t work under all conditions and it’s not adaptive.
Even where the wheel should work it needs a lot of re-engineering. Racing tires, for example, are a very distant relative of the old stone wheel. And racing tires are useless for driving through snow; tread “localizes” the tire.
In brief, innovators have two tasks. First, they have to keep reinventing the wheel to make it better at the tasks for which it was designed. Second, they have to realize the limitations of the wheel and come up with alternatives when the environment changes.
It’s all too easy to get lulled into a false complacency. We are told that we can be highly successful simply by applying the tools that others have created. Perhaps. But would you want to enter the Indy 500 with stone tires? Would you want to enter the Iditarod with a sled equipped with racing tires?
The folks with the latest and greatest don’t always win, but it’s hard to compete with antediluvian or irrelevant models. Sometimes the best use of one’s time is reinventing the wheel.
Wednesday, December 16, 2009
Technical analysis and the repeatability of history, some incoherent ramblings
According to John Murphy (Technical Analysis of the Financial Markets, 1999, and included in the online knowledge base of the Market Technicians Association) technical analysis is based on three premises: (1) market action discounts everything, (2) prices move in trends, and (3) history repeats itself. The first premise is grounded in economics, the second in both mathematics and physics, and the third in psychology.
Today I want to take a very brief, subjective look at the third premise, that history repeats itself. The premise was famously stated by Jesse Livermore: “There is nothing new on Wall Street or in stock speculation. What has happened in the past will happen again, and again, and again. This is because human nature does not change, and it is human emotion that always gets in the way of human intelligence. Of this I am sure.” (Richard Smitten, Trade Like Jesse Livermore, p. 167)
There is no question that markets exhibit broad cyclical patterns, some of which follow business cycles and many of which result from the fear and greed of traders and investors. For the longer-term trader these trending patterns are vitally important. But no events ever repeat exactly; this time always is different. As chaos theorists would say, markets exhibit aperiodic behavior. (By the way, I have a visceral reaction every time I hear someone repeat Mark Twain’s vapid statement that history rhymes, but that’s a rant for another day.) The question is whether history repeats itself closely enough for technical analysis to be a useful tool or whether, as Bill Williams argued in Trading Chaos (Wiley, 1995), “Not only is technical analysis based on the false assumption that the future will be like the past, but it uses inappropriate linear techniques for analysis.” (p. 43)
Williams adopts a fractal view of the markets, citing Mandelbrot’s study of cotton prices. “Each particular price change was random and unpredictable. But the sequence of changes was independent of scale: curves for the daily and monthly price changes matched perfectly. Incredibly, analyzed Mandelbrot’s way, the degree of variation had remained constant over a tumultuous sixty-year period that saw two World Wars and a depression.” (p. 36, quoting James Gleick, Chaos, 1987) By the way, those interested in Mandelbrot’s work should read The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin & Reward, co-authored with the journalist Richard L. Hudson (Basic Books, 2004). I myself haven’t read it, so I’m going to leave fractals behind and move on.
Instead of confronting the question of the repeatability of history head on (or the more interesting question of the extent to which each of us shapes the history we purport to know) I’m just going to interject a personal bias for which I offer no defense. And note that this is not a wholesale rejection of technical analysis as a tool. I personally take absolutely no solace in knowing that over a certain period of time in 62 percent of the cases the market closed up the day after scenario XYZ occurred. I keep thinking that the scenario has been framed too simplistically, so I have no confidence in this alleged historical probability. I also don’t know whether, even if over time this same statistical scenario continues to play out, I would be in the markets long enough to make the statistical strategy profitable. You could say that in my case the subjectivist theory of probability (that is, that probability is a measure of confidence or degree of belief) has swamped the objectivist theory of probability. Now that shouldn’t be the case for anyone with a scientific bent of mind, but unfortunately I can’t overcome it.
* * *
I’ve just reread the draft of this post and would consign it to the dustbin save for the fact that, however embarrassingly illogically it meanders through some difficult and important concepts, there are a few ideas that I will try to develop more rationally at a later time. My defense is that I wrote it in the netherworld between being awake and dosing off. The heat went out in the house around 7 p.m. and instead of just sucking it up and waiting until the next morning I made the mistake of calling the heating oil company for service (I’m on a contract so they are obligated to come 24/7 at no per/call charge). The technician arrived in the wee small hours of the morning when, despite being on a caffeine high, I was still struggling to stay awake. Of course, when he finally left and I went to bed I was too juiced up to drop off instantaneously as is my wont. So another day was trashed. There’s merit in stoicism.
Today I want to take a very brief, subjective look at the third premise, that history repeats itself. The premise was famously stated by Jesse Livermore: “There is nothing new on Wall Street or in stock speculation. What has happened in the past will happen again, and again, and again. This is because human nature does not change, and it is human emotion that always gets in the way of human intelligence. Of this I am sure.” (Richard Smitten, Trade Like Jesse Livermore, p. 167)
There is no question that markets exhibit broad cyclical patterns, some of which follow business cycles and many of which result from the fear and greed of traders and investors. For the longer-term trader these trending patterns are vitally important. But no events ever repeat exactly; this time always is different. As chaos theorists would say, markets exhibit aperiodic behavior. (By the way, I have a visceral reaction every time I hear someone repeat Mark Twain’s vapid statement that history rhymes, but that’s a rant for another day.) The question is whether history repeats itself closely enough for technical analysis to be a useful tool or whether, as Bill Williams argued in Trading Chaos (Wiley, 1995), “Not only is technical analysis based on the false assumption that the future will be like the past, but it uses inappropriate linear techniques for analysis.” (p. 43)
Williams adopts a fractal view of the markets, citing Mandelbrot’s study of cotton prices. “Each particular price change was random and unpredictable. But the sequence of changes was independent of scale: curves for the daily and monthly price changes matched perfectly. Incredibly, analyzed Mandelbrot’s way, the degree of variation had remained constant over a tumultuous sixty-year period that saw two World Wars and a depression.” (p. 36, quoting James Gleick, Chaos, 1987) By the way, those interested in Mandelbrot’s work should read The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin & Reward, co-authored with the journalist Richard L. Hudson (Basic Books, 2004). I myself haven’t read it, so I’m going to leave fractals behind and move on.
Instead of confronting the question of the repeatability of history head on (or the more interesting question of the extent to which each of us shapes the history we purport to know) I’m just going to interject a personal bias for which I offer no defense. And note that this is not a wholesale rejection of technical analysis as a tool. I personally take absolutely no solace in knowing that over a certain period of time in 62 percent of the cases the market closed up the day after scenario XYZ occurred. I keep thinking that the scenario has been framed too simplistically, so I have no confidence in this alleged historical probability. I also don’t know whether, even if over time this same statistical scenario continues to play out, I would be in the markets long enough to make the statistical strategy profitable. You could say that in my case the subjectivist theory of probability (that is, that probability is a measure of confidence or degree of belief) has swamped the objectivist theory of probability. Now that shouldn’t be the case for anyone with a scientific bent of mind, but unfortunately I can’t overcome it.
* * *
I’ve just reread the draft of this post and would consign it to the dustbin save for the fact that, however embarrassingly illogically it meanders through some difficult and important concepts, there are a few ideas that I will try to develop more rationally at a later time. My defense is that I wrote it in the netherworld between being awake and dosing off. The heat went out in the house around 7 p.m. and instead of just sucking it up and waiting until the next morning I made the mistake of calling the heating oil company for service (I’m on a contract so they are obligated to come 24/7 at no per/call charge). The technician arrived in the wee small hours of the morning when, despite being on a caffeine high, I was still struggling to stay awake. Of course, when he finally left and I went to bed I was too juiced up to drop off instantaneously as is my wont. So another day was trashed. There’s merit in stoicism.
Tuesday, December 15, 2009
Faith, Trading from Your Gut
I have an advance reader’s copy of Trading from Your Gut: How to Use Right Brain Instinct & Left Brain Smarts to Become a Master Trader by Curtis Faith. It’s scheduled for January publication by FT Press.
Let me say up front that I was hesitant to ask for a review copy because I suspected that I, the “left brain smarts” reader, would pan it. From the title it sounded like yet another throwaway self-help book. Well, I confess I was wrong. I found Trading from Your Gut stimulating, even somewhat therapeutic. Unfortunately, a brief summary of its major premises doesn’t do it justice. Stripped to its bare bones it sounds at best pedestrian. It’s far better than that.
The book is written for the experienced left-brained trader who could improve her performance by tapping into the power of her right brain. What does the right brain have to offer? It notices, it reads patterns, it is speedy; in fact, it can make decisions almost instantaneously. It generates ideas and recognizes opportunities; it gets the big picture.
The intuition that comes from the right brain is radically different from emotion. It is, in fact, profoundly logical, even though it does not lend itself to rational analysis. And, Faith argues, it is an absolutely essential ingredient in successful trading. Trading requires decisiveness in the face of uncertainty; both hesitation and panic can kill you. To be decisive you need to stop analyzing and call on your intuition to pull the trigger with conviction and confidence.
How does the trader train the right brain? The same way you get to Carnegie Hall—practice, practice, practice. The key is to force yourself to make trading decisions quickly; this requires you to keep things simple. “Simplicity enables speed, and speed forces simplicity.” (p. 136)
I have focused on intuition over analysis in this review because this is where Faith makes his contribution to the literature. Of course, he contends that the successful trader has to use both hemispheres of his brain, essentially cycling between them. Analysis gives the trader the confidence to trust his gut.
Faith draws on examples from such diverse experiences as car racing, tangoing, fishing, and sky diving to illustrate his theses. He spends some time discussing findings from behavioral finance. And he demonstrates the proper way to use right-brain intuition and left-brain smarts in what he calls the rebound swing method.
All in all, there are lots of goodies in this book. It’s a very fast read (that it’s under 200 pages helps), but it’s not easy to summarize because there are too many subtexts. I thoroughly enjoyed it.
Let me say up front that I was hesitant to ask for a review copy because I suspected that I, the “left brain smarts” reader, would pan it. From the title it sounded like yet another throwaway self-help book. Well, I confess I was wrong. I found Trading from Your Gut stimulating, even somewhat therapeutic. Unfortunately, a brief summary of its major premises doesn’t do it justice. Stripped to its bare bones it sounds at best pedestrian. It’s far better than that.
The book is written for the experienced left-brained trader who could improve her performance by tapping into the power of her right brain. What does the right brain have to offer? It notices, it reads patterns, it is speedy; in fact, it can make decisions almost instantaneously. It generates ideas and recognizes opportunities; it gets the big picture.
The intuition that comes from the right brain is radically different from emotion. It is, in fact, profoundly logical, even though it does not lend itself to rational analysis. And, Faith argues, it is an absolutely essential ingredient in successful trading. Trading requires decisiveness in the face of uncertainty; both hesitation and panic can kill you. To be decisive you need to stop analyzing and call on your intuition to pull the trigger with conviction and confidence.
How does the trader train the right brain? The same way you get to Carnegie Hall—practice, practice, practice. The key is to force yourself to make trading decisions quickly; this requires you to keep things simple. “Simplicity enables speed, and speed forces simplicity.” (p. 136)
I have focused on intuition over analysis in this review because this is where Faith makes his contribution to the literature. Of course, he contends that the successful trader has to use both hemispheres of his brain, essentially cycling between them. Analysis gives the trader the confidence to trust his gut.
Faith draws on examples from such diverse experiences as car racing, tangoing, fishing, and sky diving to illustrate his theses. He spends some time discussing findings from behavioral finance. And he demonstrates the proper way to use right-brain intuition and left-brain smarts in what he calls the rebound swing method.
All in all, there are lots of goodies in this book. It’s a very fast read (that it’s under 200 pages helps), but it’s not easy to summarize because there are too many subtexts. I thoroughly enjoyed it.
Monday, December 14, 2009
Double-sort strategies
Today I’m going to draw on a May 2008 EDHEC paper entitled “Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals” by Ana-Maria Fuertes, Joëlle Miffre, and Georgios Rallis. It’s a double-sort strategy that in its very broadest terms I think could serve as a model for traders and investors trying to gain an edge.
The individual strategies are simple. The momentum strategy sorts commodity futures contracts into quintiles at the end of each month based on their average return over the previous R months—that is, the ranking period. For purposes of the study it is assumed that the futures contracts in each quintile are equally weighted. The investor then buys the top quintile, shorts the bottom quintile, and holds the long-short position for H months—that is, the holding period. The authors focus on thirteen permutations that did well in a 2007 relative strength study: four with a 1-month ranking period (1-1, 1-3, 1-6, 1-12), four with a 3-month ranking period (3-1, 3-3, 3-6, 3-12), three with a 6-month ranking period (6-1, 6-3, 6-6) and two with a 12-month ranking period (12-1, 12-3).
The authors then explore various term-structure strategies. The most basic buys the quintile of commodities with the highest positive roll-returns, shorts the quintile with the most negative roll-returns and holds the positions for a month. For our purposes here it’s not important to explore the variations, especially since the basic strategy tweaked just a tad performs better than strategies with more frequent rebalancing.
The results of the authors’ studies are as follows. “On average, the trend-following strategies and the term-structure strategies that are profitable at the 5% level earn, respectively, annualized alpha of 10.14% and 12.66% whereas over the same period, a passive long-only portfolio yields alpha of only 2.48%. Second, with net returns above 13.5% a year, three momentum strategies (1-1, 3-1 and 12-1) and one term structure strategy [the basic] stand out as conveying the best signals for tactical allocation.”
Now comes the task of combining the strategies. First, it is important to ascertain whether there is enough difference in the strategy signals to make a combination worthwhile. “Term structure trading strategies in commodity futures select, by definition, the most backwardated and contangoed contracts. Even though momentum strategies are not designed per se to overtly shortlist the commodities with the steepest term structures, it has been show that their long portfolios tend to contain backwardated contracts, while their short portfolios are heavily tilted towards contangoed commodities.” Although the correlations between the returns from the two strategies are positive, they vary between 10.92% and 56.96%, with the mean correlation being 31.26%.
Since the correlations are low, a double-sort approach makes sense. Here’s how it works. “First, we compute the roll-returns at the end of each month and their 1/3 breakpoints to split the cross-section of futures contracts into 3 portfolios, labeled Low, Med, and High. We then sort the commodities in the High portfolio into 2 sub-portfolios (High-Winner and High-Loser) based on the mean return of the commodities over the past R months. In effect, the High-Winner and High-Loser portfolios contain 50% of the cross-section that was selected with the first term-structure sort or 50% x 33.3% of the initial cross-section that was available at the end of a given month. Intuitively, High-Winner is thus made of the commodities that have both the highest roll-returns at the time of portfolio construction and the best past performance. Similarly, we sort the commodities in the Low portfolio into 2 sub-portfolios (Low-Winner and Low-Loser) based on their mean return over the past R months. Low-Loser contains therefore commodities that have both the lowest roll-returns at the time of portfolio construction and the worst past performance. The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio, and holds this position for one month.”
The authors analyze six double-sort strategies—three that are sorted first on time-structure with ranking periods set to 1, 3, and 12 months and three that are sorted first on momentum with the same ranking periods. The most profitable strategy sorts first on time-structure and has a ranking period of one month (average return of 23.55% a year); the least profitable again sorts first on time-structure but has a ranking period of twelve months (18.81%).
The double-sort strategies are much riskier than the passive benchmark, but their reward-to-risk ratios and Sortino ratios are consistently higher. So “the higher risk of the double-sort strategies is more than rewarded by the market.” The authors go through the standard statistical checklist for robustness and conclude that “the abnormal returns uncovered are not an artifact of liquidity risk, data snooping, additional non-investable macroeconomic risk factors or time-variation in risks.”
* * *
I have only skimmed the surface of this paper, but I wanted to give some idea of how one might develop a double-sort strategy. It doesn’t have to be a long-short strategy, it doesn’t have to focus on commodities. In place of term structure one could substitute a whole host of measurable variables. For instance, one could develop strategies incorporating volatility or open interest. Another idea, shamelessly stolen from David Varadi at CSS Analytics, is to include an element of cognitive dissonance. Let your informed imagination soar and see what you come up with. (Of course, if you want to share your wildly successful double-sort strategy with this humble blogger she would be very grateful!)
The individual strategies are simple. The momentum strategy sorts commodity futures contracts into quintiles at the end of each month based on their average return over the previous R months—that is, the ranking period. For purposes of the study it is assumed that the futures contracts in each quintile are equally weighted. The investor then buys the top quintile, shorts the bottom quintile, and holds the long-short position for H months—that is, the holding period. The authors focus on thirteen permutations that did well in a 2007 relative strength study: four with a 1-month ranking period (1-1, 1-3, 1-6, 1-12), four with a 3-month ranking period (3-1, 3-3, 3-6, 3-12), three with a 6-month ranking period (6-1, 6-3, 6-6) and two with a 12-month ranking period (12-1, 12-3).
The authors then explore various term-structure strategies. The most basic buys the quintile of commodities with the highest positive roll-returns, shorts the quintile with the most negative roll-returns and holds the positions for a month. For our purposes here it’s not important to explore the variations, especially since the basic strategy tweaked just a tad performs better than strategies with more frequent rebalancing.
The results of the authors’ studies are as follows. “On average, the trend-following strategies and the term-structure strategies that are profitable at the 5% level earn, respectively, annualized alpha of 10.14% and 12.66% whereas over the same period, a passive long-only portfolio yields alpha of only 2.48%. Second, with net returns above 13.5% a year, three momentum strategies (1-1, 3-1 and 12-1) and one term structure strategy [the basic] stand out as conveying the best signals for tactical allocation.”
Now comes the task of combining the strategies. First, it is important to ascertain whether there is enough difference in the strategy signals to make a combination worthwhile. “Term structure trading strategies in commodity futures select, by definition, the most backwardated and contangoed contracts. Even though momentum strategies are not designed per se to overtly shortlist the commodities with the steepest term structures, it has been show that their long portfolios tend to contain backwardated contracts, while their short portfolios are heavily tilted towards contangoed commodities.” Although the correlations between the returns from the two strategies are positive, they vary between 10.92% and 56.96%, with the mean correlation being 31.26%.
Since the correlations are low, a double-sort approach makes sense. Here’s how it works. “First, we compute the roll-returns at the end of each month and their 1/3 breakpoints to split the cross-section of futures contracts into 3 portfolios, labeled Low, Med, and High. We then sort the commodities in the High portfolio into 2 sub-portfolios (High-Winner and High-Loser) based on the mean return of the commodities over the past R months. In effect, the High-Winner and High-Loser portfolios contain 50% of the cross-section that was selected with the first term-structure sort or 50% x 33.3% of the initial cross-section that was available at the end of a given month. Intuitively, High-Winner is thus made of the commodities that have both the highest roll-returns at the time of portfolio construction and the best past performance. Similarly, we sort the commodities in the Low portfolio into 2 sub-portfolios (Low-Winner and Low-Loser) based on their mean return over the past R months. Low-Loser contains therefore commodities that have both the lowest roll-returns at the time of portfolio construction and the worst past performance. The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio, and holds this position for one month.”
The authors analyze six double-sort strategies—three that are sorted first on time-structure with ranking periods set to 1, 3, and 12 months and three that are sorted first on momentum with the same ranking periods. The most profitable strategy sorts first on time-structure and has a ranking period of one month (average return of 23.55% a year); the least profitable again sorts first on time-structure but has a ranking period of twelve months (18.81%).
The double-sort strategies are much riskier than the passive benchmark, but their reward-to-risk ratios and Sortino ratios are consistently higher. So “the higher risk of the double-sort strategies is more than rewarded by the market.” The authors go through the standard statistical checklist for robustness and conclude that “the abnormal returns uncovered are not an artifact of liquidity risk, data snooping, additional non-investable macroeconomic risk factors or time-variation in risks.”
* * *
I have only skimmed the surface of this paper, but I wanted to give some idea of how one might develop a double-sort strategy. It doesn’t have to be a long-short strategy, it doesn’t have to focus on commodities. In place of term structure one could substitute a whole host of measurable variables. For instance, one could develop strategies incorporating volatility or open interest. Another idea, shamelessly stolen from David Varadi at CSS Analytics, is to include an element of cognitive dissonance. Let your informed imagination soar and see what you come up with. (Of course, if you want to share your wildly successful double-sort strategy with this humble blogger she would be very grateful!)
Sunday, December 13, 2009
The psychophysiology of trading
The paper is old (2002) but still interesting. Andrew W. Lo and Dmitry V. Repin in “The Psychophysiology of Real-Time Financial Risk Processing” report the results of their experiment to measure the emotional responses of ten traders—five highly experienced and five with low to moderate experience. They wired up these traders to plot real-time changes in their skin conductance, blood volume pulse, heart rate, electromyographical signals, respiration, and body temperature.
Although the sample is very small and hence just a first stab, the authors noted some significant differences between the two types of traders. The less experienced traders, for instance, seem to be more sensitive to short-term changes in such market variables as deviations and trend reversals. Both sets of traders, however, saw spikes in their blood volume pulse in the face of volatility events.
Lo and Repin conclude that “emotion is a significant determinant of the evolutionary fitness of financial traders.”
If you want a more complete synopsis of the Lo and Repin paper, there’s a digest entitled “Measuring the Stress of Financial Traders" on the NBER site. The only problem is that the site seems to be down right now.
Although the sample is very small and hence just a first stab, the authors noted some significant differences between the two types of traders. The less experienced traders, for instance, seem to be more sensitive to short-term changes in such market variables as deviations and trend reversals. Both sets of traders, however, saw spikes in their blood volume pulse in the face of volatility events.
Lo and Repin conclude that “emotion is a significant determinant of the evolutionary fitness of financial traders.”
If you want a more complete synopsis of the Lo and Repin paper, there’s a digest entitled “Measuring the Stress of Financial Traders" on the NBER site. The only problem is that the site seems to be down right now.
Saturday, December 12, 2009
Trade adjustments
Here’s an interesting piece, originally posted in the OptionsClub Yahoo group, comparing options trade adjustments to betting on a horse race where you can continue to modify your bet throughout the race.
Friday, December 11, 2009
Augen, Day Trading Options
Jeff Augen’s Day Trading Options: Profiting from Price Distortions in Very Brief Time Frames (FT Press, 2009) is tough sledding. Although it’s a slim volume (under 200 pages), it took me the better part of a day to digest it—or, more accurately, most of it. But I consider it time well spent. The odds that I will ever adopt Augen’s strategies as my own are slim to none. No matter. What is most valuable about this book is that it demonstrates how, with imagination and careful crafting, the trader can maintain his edge even as markets change. In this respect the book is both challenging and inspiring.
Let me say up front that if you’re looking for a get rich quick scheme you’ll be sorely disappointed. The book’s title may evoke a variation on, or an update of, the day trading bubble days when guts trumped brains. But this book is more like the revenge of the nerds. Well, even that’s not accurate. Augen is not dueling against the unschooled but against the likes of high frequency traders. As short-term markets have evolved to the overwhelming advantage of those with outsized capital investments in both talent and technology the individual trader needs to retool. Augen points out one way to do this.
The first order of business is to identify the enemy. For Augen the problem is that “the combined activities of automated high-performance trading systems extinguish market inefficiencies almost immediately.” For instance, “studies reveal that large price changes do not result in persistent trends—even at the single-minute level. Stated differently, the recent price history of a stock does not contain enough information to predict the direction.” (p. 35) The retail trader, whatever his time frame, will almost always lose to the institutions who avoid the effects of volatility and optimize their use of capital “with very brief trades placed at just the right time” (p. 44) over a variety of instruments.
So as financial markets have become increasingly efficient and as “the war is being fought between rival computer systems, with speed and precision being the most important considerations,” (p. 77) how can the individual trader compete? He needs to find “statistical advantages that cannot be extinguished by the market as simple inefficiencies.” (p. 78)
Augen offers several examples of how to exploit volatility distortions in options. The advantage of this approach is twofold. First, the trader doesn’t have to make a directional bet and, second, he is playing in a space (volatility) that high frequency traders avoid.
Take, for instance, the sometimes dramatically different measures of historical volatility depending on the slice of the 24-hour period one looks at. Traditionally, historical volatility is calculated by using a month’s worth of close-to-close price changes. But what if we separately calculate overnight volatility (close-to-open), intraday volatility (open-to-close), and weekend volatility? (Augen shows how to calculate the annualization factors for daily, intraday, and overnight volatility. For instance, intraday volatility is the standard deviation of the 20 [or whatever number the trader wants to plug into the formula] most recent open-to-close price changes multiplied by 30.5.) We will find that options are often underpriced intraday because their prices are based on close-to-close calculations. Moreover, we might find that “two stocks with similarly priced options have significantly different profiles with regard to intraday and overnight volatility. . . . The first reacts to overnight events around the world; the second reacts to news events that occur during the trading day. The first stock might be a candidate for overnight long straddles initiated near the closing bell; the second might be a candidate for the same trade placed after the market stabilizes around 10:00 in the morning.” (p. 93)
Augen takes the reader through a series of challenging ways to capitalize on volatility distortions. Although Augen writes clearly and uses only the rudimentary math that all options traders should be familiar with, these examples are not for traders who struggle with how to structure a credit spread. Moreover, they are not formulaic by nature; the trader still has to seek out potential anomalies, still has to use his brains. But I think they are important case studies in how to think imaginatively, intelligently, and profitably about financial markets.
Let me say up front that if you’re looking for a get rich quick scheme you’ll be sorely disappointed. The book’s title may evoke a variation on, or an update of, the day trading bubble days when guts trumped brains. But this book is more like the revenge of the nerds. Well, even that’s not accurate. Augen is not dueling against the unschooled but against the likes of high frequency traders. As short-term markets have evolved to the overwhelming advantage of those with outsized capital investments in both talent and technology the individual trader needs to retool. Augen points out one way to do this.
The first order of business is to identify the enemy. For Augen the problem is that “the combined activities of automated high-performance trading systems extinguish market inefficiencies almost immediately.” For instance, “studies reveal that large price changes do not result in persistent trends—even at the single-minute level. Stated differently, the recent price history of a stock does not contain enough information to predict the direction.” (p. 35) The retail trader, whatever his time frame, will almost always lose to the institutions who avoid the effects of volatility and optimize their use of capital “with very brief trades placed at just the right time” (p. 44) over a variety of instruments.
So as financial markets have become increasingly efficient and as “the war is being fought between rival computer systems, with speed and precision being the most important considerations,” (p. 77) how can the individual trader compete? He needs to find “statistical advantages that cannot be extinguished by the market as simple inefficiencies.” (p. 78)
Augen offers several examples of how to exploit volatility distortions in options. The advantage of this approach is twofold. First, the trader doesn’t have to make a directional bet and, second, he is playing in a space (volatility) that high frequency traders avoid.
Take, for instance, the sometimes dramatically different measures of historical volatility depending on the slice of the 24-hour period one looks at. Traditionally, historical volatility is calculated by using a month’s worth of close-to-close price changes. But what if we separately calculate overnight volatility (close-to-open), intraday volatility (open-to-close), and weekend volatility? (Augen shows how to calculate the annualization factors for daily, intraday, and overnight volatility. For instance, intraday volatility is the standard deviation of the 20 [or whatever number the trader wants to plug into the formula] most recent open-to-close price changes multiplied by 30.5.) We will find that options are often underpriced intraday because their prices are based on close-to-close calculations. Moreover, we might find that “two stocks with similarly priced options have significantly different profiles with regard to intraday and overnight volatility. . . . The first reacts to overnight events around the world; the second reacts to news events that occur during the trading day. The first stock might be a candidate for overnight long straddles initiated near the closing bell; the second might be a candidate for the same trade placed after the market stabilizes around 10:00 in the morning.” (p. 93)
Augen takes the reader through a series of challenging ways to capitalize on volatility distortions. Although Augen writes clearly and uses only the rudimentary math that all options traders should be familiar with, these examples are not for traders who struggle with how to structure a credit spread. Moreover, they are not formulaic by nature; the trader still has to seek out potential anomalies, still has to use his brains. But I think they are important case studies in how to think imaginatively, intelligently, and profitably about financial markets.
Thursday, December 10, 2009
Trading efficiency
Van K. Tharp is a prolific writer, trading coach, and workshop promoter. His latest book, Super Trader: Make Consistent Profits in Good and Bad Markets (McGraw-Hill, 2009), repeats themes familiar to anyone who has read his earlier works. But in passing he has a new target—economists who subscribe to the tenets of behavioral finance and who ask “If markets are not efficient because humans are inefficient, how can we use what we now know about human inefficiencies to predict what the markets will do?” This, he contends, is lunacy. (p. 215) Perhaps, but I would refer my readers back to my post on Richard Lehman’s Far from Random (12/4); I think there’s merit in including a behavioral component in modeling the markets.
Tharp says that he practices applied behavioral finance but claims to take a different approach. “If most human beings are inefficient in the way they process information,” he asks, “what would happen if you started to make them efficient?” Put simply, in his recasting of the notion of efficiency, what would happen if traders made fewer mistakes? Tharp defines a mistake as a trader not following his written trading rules. Common mistakes include doing anything because of an emotional reaction, risking too much money on any particular trade, and not having a predetermined exit when you enter the trade. (pp. 222-23) Personally, I agree that risking too much money on a trade is a mistake, but emotions can provide powerful clues and a predetermined exit can cut profits short. These criticisms, however, are peripheral to today’s post. What I want to take away from Tharp’s attempt to keep an arm’s length away from behavioral finance is the idea of trading efficiency.
What would happen if we used the common notion of economic efficiency and said that our goal is to use our resources in such a way as to maximize our profits? In one sense, of course, this is moronically obvious. But if we drill down just a bit, we can say that our goal is to reach a state where (to quote the Wikipedia article) “more output cannot be obtained without increasing the amount of inputs” and “production proceeds at the lowest possible per-unit cost.” (We need not address the zero-sum notion of the economic efficiency of markets that says “no one can be made better off without making someone else worse off.”) Of course, trading and investing are very different beasts from, say, the manufacture of cars, but I think that we might profit from trying to apply the notion of efficiency to our trading and investing plans. For example, we might try to figure out what combination of options and stocks most efficiently creates our desired risk profile. Or we might use Kaufman’s fractal efficiency to assess our performance. Keep thinking imaginatively; the folks making billions are.
Tharp says that he practices applied behavioral finance but claims to take a different approach. “If most human beings are inefficient in the way they process information,” he asks, “what would happen if you started to make them efficient?” Put simply, in his recasting of the notion of efficiency, what would happen if traders made fewer mistakes? Tharp defines a mistake as a trader not following his written trading rules. Common mistakes include doing anything because of an emotional reaction, risking too much money on any particular trade, and not having a predetermined exit when you enter the trade. (pp. 222-23) Personally, I agree that risking too much money on a trade is a mistake, but emotions can provide powerful clues and a predetermined exit can cut profits short. These criticisms, however, are peripheral to today’s post. What I want to take away from Tharp’s attempt to keep an arm’s length away from behavioral finance is the idea of trading efficiency.
What would happen if we used the common notion of economic efficiency and said that our goal is to use our resources in such a way as to maximize our profits? In one sense, of course, this is moronically obvious. But if we drill down just a bit, we can say that our goal is to reach a state where (to quote the Wikipedia article) “more output cannot be obtained without increasing the amount of inputs” and “production proceeds at the lowest possible per-unit cost.” (We need not address the zero-sum notion of the economic efficiency of markets that says “no one can be made better off without making someone else worse off.”) Of course, trading and investing are very different beasts from, say, the manufacture of cars, but I think that we might profit from trying to apply the notion of efficiency to our trading and investing plans. For example, we might try to figure out what combination of options and stocks most efficiently creates our desired risk profile. Or we might use Kaufman’s fractal efficiency to assess our performance. Keep thinking imaginatively; the folks making billions are.
Wednesday, December 9, 2009
Kamich, Chart Patterns
Chart Patterns by Bruce M. Kamich is the third volume in Bloomberg Press’s series Market Essentials: Technical Analysis. Kamich, a vice president at Morgan Stanley Smith Barney’s Technical Analysis Group, has written a very clear introduction to chart patterns for the uninitiated. He takes the reader on a journey from major tops and bottoms to such formations as triangles, flags, pennants, and wedges. For most of the patterns, he outlines targets as well as tactics and trading strategy.
Not surprisingly, Kamich begins his book by asking why we should study patterns. He suggests that “with so many traders and so much money going toward black-box systems and sophisticated math-driven programs to get their ‘edge,’ . . . the new edge in predicting and trading the markets should circle back to the early days of charting.” (p. 7) By way of analogy he points to the movement away from processed foods to the “old ways” of preparing food.
One of the problems with analyzing chart patterns today is that volume is elusive as it disappears from trading floors and exchanges into dark pools. So we don’t have the volume that is supposed to confirm a price pattern. “Oddly,” Kamich writes, “history may be coming full circle in that the chart books from the 1930s do not display the volume below the chart. In the 1930s, we did not have the data collection and spreadsheet capabilities of today to follow weekly and monthly volume stock by stock.” (p. 23) Kamich suggests that in the future the focus will be exclusively on price and that traders will have to adapt to the lack of volume data. For instance, they might replace the “strong volume” filter with a “percentage move” filter.
Another potentially more serious problem is that patterns may be changing since somewhere between half and three-quarters of trading volume in listed stocks is being executed by quantitative programs and algorithmic formula trading. And these programs tend to be similar. So “volume might expand when the programs perceive the same thing in the marketplace, as opposed to tape watchers and chartists watching a pattern develop and then anticipating or acting on the breakout at different points, depending on their time horizon and risk tolerance.” (p. 23)
Chart reading, Kamich maintains, “takes a bit of intuition.” (p. 8) As a result it has proven difficult to apply math and computer programming to chart reading. Although Kamich doesn’t close the door on such enterprises, he notes that humans can recognize a new pattern faster than a computer.
Kamich’s book is a perfect introduction to chart patterns, complete with some 115 Bloomberg charts. And here and there the book even provides nuggets for those who are familiar with the literature on charts. For instance, Kamich cites a rare study on the long-base pattern that found that when a stock breaks out from a base that lasts at least eleven calendar quarters the median price gain was 300%. This might be no more than a case of data mining, but then again it might turn out to have some merit.
Not surprisingly, Kamich begins his book by asking why we should study patterns. He suggests that “with so many traders and so much money going toward black-box systems and sophisticated math-driven programs to get their ‘edge,’ . . . the new edge in predicting and trading the markets should circle back to the early days of charting.” (p. 7) By way of analogy he points to the movement away from processed foods to the “old ways” of preparing food.
One of the problems with analyzing chart patterns today is that volume is elusive as it disappears from trading floors and exchanges into dark pools. So we don’t have the volume that is supposed to confirm a price pattern. “Oddly,” Kamich writes, “history may be coming full circle in that the chart books from the 1930s do not display the volume below the chart. In the 1930s, we did not have the data collection and spreadsheet capabilities of today to follow weekly and monthly volume stock by stock.” (p. 23) Kamich suggests that in the future the focus will be exclusively on price and that traders will have to adapt to the lack of volume data. For instance, they might replace the “strong volume” filter with a “percentage move” filter.
Another potentially more serious problem is that patterns may be changing since somewhere between half and three-quarters of trading volume in listed stocks is being executed by quantitative programs and algorithmic formula trading. And these programs tend to be similar. So “volume might expand when the programs perceive the same thing in the marketplace, as opposed to tape watchers and chartists watching a pattern develop and then anticipating or acting on the breakout at different points, depending on their time horizon and risk tolerance.” (p. 23)
Chart reading, Kamich maintains, “takes a bit of intuition.” (p. 8) As a result it has proven difficult to apply math and computer programming to chart reading. Although Kamich doesn’t close the door on such enterprises, he notes that humans can recognize a new pattern faster than a computer.
Kamich’s book is a perfect introduction to chart patterns, complete with some 115 Bloomberg charts. And here and there the book even provides nuggets for those who are familiar with the literature on charts. For instance, Kamich cites a rare study on the long-base pattern that found that when a stock breaks out from a base that lasts at least eleven calendar quarters the median price gain was 300%. This might be no more than a case of data mining, but then again it might turn out to have some merit.
Tuesday, December 8, 2009
Trendsetting
In the world of trading and investing we are all familiar with trend following methods. I decided to look at trends differently and therefore plunged into Robyn Waters’ book The Trendmaster’s Guide: Get a Jump on What Your Customer Wants Next (Portfolio [Penguin], 2005). Waters spent over ten years with Target, eventually leaving her post as Vice President of Trend Merchandising to strike out on her own. Her book is organized along the lines of a reading primer—that is, A is for ANTENNAE, B is for BIG PICTURE, C is for CONNECT THE DOTS, etc. In this post I’m going to pull out some insights from the world of merchandising that I think are applicable to trading and investing.
A: Looking for the next big thing? “Chances are you’ve already seen it. What you need to do is let yourself recognize it. . . . tune in to the little things, the trivial nuances, and the irrelevant data that everyone else misses.”
B: There’s a deluge of confusing options. “Combine this overwhelming input with type-A personalities who obsess over details, and you have a bad retail recipe: over-designed products that spring from over-conceived strategies.”
C: “An interesting fact catches your attention. A related tidbit pops up out of nowhere. A random comment reinforces a budding thought. . . . Connected, a pattern emerges that often points to a developing trend—in time to do something about it.”
F: “Trends with real staying power are often a series of smaller trends fused together.”
I: “. . . all analyses measure results only after something has already happened. Trend tracking speculates about what might happen based on things that are constantly changing. Following your intuition and listening to your instincts have been all but forgotten in today’s corporate environment. If it weren’t for visionaries who knew how to go with their instincts, we’d be living in a world without Post-it notes, FedEx, and Starbucks double tall skim lattes. . . . Einstein was a pretty smart guy. He believed that ‘not everything that can be counted counts, and not everything that counts can be counted.’ Remember that the next time you are plowing through piles of data and feel out of touch with your gut.”
K: Once again, keep it simple.
L: Lighten up. “When you’re stuck in a dark place void of ideas, lighten up! Have some fun. . . . Deep-six your standard procedures and try operating with a little humor.”
O: “Learn to let go of your preconceptions. Practice unlearning. Stop looking for the answers you expect to find, and instead, identify and pay attention to the signposts and the indicators. Let them lead you to where the minute is going.”
T: “Avoid literal translations of any trend concept or hot idea. It’s hard to differentiate yourself when you merely copy what’s already out there. Think about how a musical score translates notes and sounds into emotion. There are a limited number of notes, but musicians have been arranging them into endless versions of original music for centuries.”
V: “Trend trackers and creative types tend to be very curious people. . . . In the knowledge economy, if you’re not learning on a continual basis, chances are you won’t be earning much either. . . . Read, learn, do, explore, experience, but most of all read. And I mean books.”
Y: “Y is for YUM, YUCK, AND YAWN.” “Yum, yuck, and yawn are critical elements of what I call the Trend Taste Test. They are descriptions of how our gut feels when we are struggling with an imminent decision. It’s been proved that in the realm of complexity, good decisions come from the informed gut. In other words, once you’ve done your trend homework and something feels right, that’s your intuition saying Yum—go with it! If it doesn’t feel right, that’s a Yuck—forget it. And a yawn? If you’re bored, don’t you think your customers will be too?”
And finally Z (for Zen). “Just when you think you have a trend figured out, beware!” For instance, sales of video games and old-fashioned board games skyrocketed at the same time. And women who put only the best organic foods into their stomachs think nothing of getting Botox injections. “Don’t get caught up in absolutes. Learn to be comfortable with utterly opposed trends. . . . F. Scott Fitzgerald said, ‘The test of a first-class mind is the ability to hold two opposing ideas in the head at the same time and still be able to function.’ Learn to practice the Zen of Trend.”
A: Looking for the next big thing? “Chances are you’ve already seen it. What you need to do is let yourself recognize it. . . . tune in to the little things, the trivial nuances, and the irrelevant data that everyone else misses.”
B: There’s a deluge of confusing options. “Combine this overwhelming input with type-A personalities who obsess over details, and you have a bad retail recipe: over-designed products that spring from over-conceived strategies.”
C: “An interesting fact catches your attention. A related tidbit pops up out of nowhere. A random comment reinforces a budding thought. . . . Connected, a pattern emerges that often points to a developing trend—in time to do something about it.”
F: “Trends with real staying power are often a series of smaller trends fused together.”
I: “. . . all analyses measure results only after something has already happened. Trend tracking speculates about what might happen based on things that are constantly changing. Following your intuition and listening to your instincts have been all but forgotten in today’s corporate environment. If it weren’t for visionaries who knew how to go with their instincts, we’d be living in a world without Post-it notes, FedEx, and Starbucks double tall skim lattes. . . . Einstein was a pretty smart guy. He believed that ‘not everything that can be counted counts, and not everything that counts can be counted.’ Remember that the next time you are plowing through piles of data and feel out of touch with your gut.”
K: Once again, keep it simple.
L: Lighten up. “When you’re stuck in a dark place void of ideas, lighten up! Have some fun. . . . Deep-six your standard procedures and try operating with a little humor.”
O: “Learn to let go of your preconceptions. Practice unlearning. Stop looking for the answers you expect to find, and instead, identify and pay attention to the signposts and the indicators. Let them lead you to where the minute is going.”
T: “Avoid literal translations of any trend concept or hot idea. It’s hard to differentiate yourself when you merely copy what’s already out there. Think about how a musical score translates notes and sounds into emotion. There are a limited number of notes, but musicians have been arranging them into endless versions of original music for centuries.”
V: “Trend trackers and creative types tend to be very curious people. . . . In the knowledge economy, if you’re not learning on a continual basis, chances are you won’t be earning much either. . . . Read, learn, do, explore, experience, but most of all read. And I mean books.”
Y: “Y is for YUM, YUCK, AND YAWN.” “Yum, yuck, and yawn are critical elements of what I call the Trend Taste Test. They are descriptions of how our gut feels when we are struggling with an imminent decision. It’s been proved that in the realm of complexity, good decisions come from the informed gut. In other words, once you’ve done your trend homework and something feels right, that’s your intuition saying Yum—go with it! If it doesn’t feel right, that’s a Yuck—forget it. And a yawn? If you’re bored, don’t you think your customers will be too?”
And finally Z (for Zen). “Just when you think you have a trend figured out, beware!” For instance, sales of video games and old-fashioned board games skyrocketed at the same time. And women who put only the best organic foods into their stomachs think nothing of getting Botox injections. “Don’t get caught up in absolutes. Learn to be comfortable with utterly opposed trends. . . . F. Scott Fitzgerald said, ‘The test of a first-class mind is the ability to hold two opposing ideas in the head at the same time and still be able to function.’ Learn to practice the Zen of Trend.”
Monday, December 7, 2009
Dollar bills and Lévy flights
Florin Diacu’s book Mega Disasters: The Science of Predicting the Next Catastrophe (Princeton University Press, 2010) surveys a wide range of catastrophes—tsunamis, earthquakes, volcanic eruptions, whirlwinds (hurricanes, cyclones, and typhoons), rapid climate change, cosmic impacts, financial crashes, and pandemics. As a mathematician, he sets out to study what sorts of calamities can be predicted, especially since many dynamic systems exhibit the property of chaos. “To mathematicians,” he writes, “chaos is another name for high instability: similar starts don’t guarantee similar outcomes.” (p. xii)
Not surprisingly, Diacu concludes that few catastrophes can currently be predicted with any degree of accuracy or with much lead time. For now the best we can do in most cases is (1) to mitigate risk (for instance, imposing strict building codes in earthquake-prone areas or immunizing at-risk populations) and (2) to have procedures in place to manage the fallout from catastrophes.
As readers of this blog should know by now, I’m always on the lookout for new ways to frame familiar concepts, be they metaphors or models. Diacu has provided both an analogy and a statistical model for understanding the relationship between randomness and trend in financial markets.
The analogy comes from a 2006 study of how a potential virus would spread. The researchers decided to simulate the spread of the virus by tracking the movements of about half a million one-dollar bills in the U.S. They went to the well-known web site (by now over ten years old) Where's George. “What they found was not too surprising: the money moved chaotically at both the local and global levels. But it was interesting that they could characterize the dispersion in terms of what mathematicians call “Lévy flights,” named after the French mathematician Paul Pierre Lévy (1886-1971). These movements are characteristic to random walks with many short steps mixed with rare long jumps. In other words, the bills changed many hands in the same city before showing up in some other part of the United States.” (p. 160)
As usual, I’m late to the game: financial mathematics has long since incorporated modified versions of Lévy flights in its simulations because Lévy distributions allow for big jumps and have fat tails. But I figure I’m not alone in my tardiness, so here is an illustration of the difference between Brownian motion (left) and Lévy flights (right). Lévy flights, by the way, can also be characterized as random power law trajectories.
Diacu points to the fact that there is a close connection between Lévy flights and the Mandelbrot sets that Sornette used in his work on market catastrophes. (See my posts of October 23 and 26 on Sornette’s Why Stock Markets Crash, though you won’t find any mention of fractals there). One connection between the two is the absence of a characteristic scale; in fact, Lévy flights have been called scale-invariant fractals.
By now I’m in the deep end of the pool without my water wings. So let me climb out ungracefully and simply say that I find the Lévy flight model a fascinating way to combine the random walk theory of markets with the presence of trends.
Not surprisingly, Diacu concludes that few catastrophes can currently be predicted with any degree of accuracy or with much lead time. For now the best we can do in most cases is (1) to mitigate risk (for instance, imposing strict building codes in earthquake-prone areas or immunizing at-risk populations) and (2) to have procedures in place to manage the fallout from catastrophes.
As readers of this blog should know by now, I’m always on the lookout for new ways to frame familiar concepts, be they metaphors or models. Diacu has provided both an analogy and a statistical model for understanding the relationship between randomness and trend in financial markets.
The analogy comes from a 2006 study of how a potential virus would spread. The researchers decided to simulate the spread of the virus by tracking the movements of about half a million one-dollar bills in the U.S. They went to the well-known web site (by now over ten years old) Where's George. “What they found was not too surprising: the money moved chaotically at both the local and global levels. But it was interesting that they could characterize the dispersion in terms of what mathematicians call “Lévy flights,” named after the French mathematician Paul Pierre Lévy (1886-1971). These movements are characteristic to random walks with many short steps mixed with rare long jumps. In other words, the bills changed many hands in the same city before showing up in some other part of the United States.” (p. 160)
As usual, I’m late to the game: financial mathematics has long since incorporated modified versions of Lévy flights in its simulations because Lévy distributions allow for big jumps and have fat tails. But I figure I’m not alone in my tardiness, so here is an illustration of the difference between Brownian motion (left) and Lévy flights (right). Lévy flights, by the way, can also be characterized as random power law trajectories.
Diacu points to the fact that there is a close connection between Lévy flights and the Mandelbrot sets that Sornette used in his work on market catastrophes. (See my posts of October 23 and 26 on Sornette’s Why Stock Markets Crash, though you won’t find any mention of fractals there). One connection between the two is the absence of a characteristic scale; in fact, Lévy flights have been called scale-invariant fractals.
By now I’m in the deep end of the pool without my water wings. So let me climb out ungracefully and simply say that I find the Lévy flight model a fascinating way to combine the random walk theory of markets with the presence of trends.
American democracy in action--the trader tax
Like many of you, I contacted my senators and representative about the trader tax, using the form letter provided on many websites. I heard nothing from my representative, but since she was one of the co-sponsors of the original DeFazio bill I didn't expect an answer. Here is what Joe Lieberman wrote.
Dear Ms. Jubin:
Thank you for contacting me in support of an extension of unemployment benefits. I am pleased to hear from you on this issue.
As you know, Congress, with my support, passed the American Recovery and Reinvestment Act (ARRA; P.L. 111-5). This legislation extended the Emergency Unemployment Compensation (EUC08) program, which provides federal funds for an additional 20 weeks of unemployment compensation to all qualified job seekers and an additional 13 weeks of benefits in states, like Connecticut, with high unemployment. The EUC08 program was originally scheduled to expire on December 26, 2009.
Despite efforts to restore economic growth, unemployment continues to rise and currently is above ten percent. Although Connecticut's unemployment rate is slightly below the national average at 8.4 percent, it is my belief that we must continue to support programs to assist families, small businesses, and communities through these tough economic times.
To that end, I am pleased to inform you that I strongly supported the Worker, Home Ownership, and Business Assistance Act (P.L. 111-92). This Act allows recipients of unemployment compensation benefits whose benefits have expired or are about to expire an additional 14 weeks of benefits. Plus, if Connecticut's unemployment rate rises above 8.5 percent, qualified residents may receive another six weeks of unemployment benefits. This legislation also includes several other provisions to spur job creation, including an extension of the first-time home buyer's tax credit, extension of the net operating loss carry back deduction for small businesses, and expansion of the net operating loss carry back deduction to businesses and firms with gross receipts greater than $15 million. President Obama signed this legislation into law on November 6, 2009.
I will continue to support efforts to ensure that Congress is considering policies that provide an adequate safety net for workers who have lost their jobs and increase economic productivity and job creation. Please be assured that I will keep your specific views in mind as Congress continues to debate ways to spur economic growth.
Thank you again for sharing your views and concerns with me. I hope you will continue to visit my website at http://lieberman.senate.gov for updated news about my work on behalf of Connecticut and the nation. Please contact me if you have any additional questions or comments about our work in Congress.
Sincerely,
Joseph I. Lieberman
UNITED STATES SENATOR
JIL:kht
Oh well, perhaps if I'm concerned about the proposed transaction tax I also care about the extension of unemployment benefits. Or perhaps his staff is just proactive: if Congress passes the tax I'll need to apply for unemployment benefits. That would of course be tricky since I don't have an employer.
At least Chris Dodd was on target, though not encouraging.
Dear Ms. Jubin:
Thank you for contacting me regarding H.R. 1068, the Let Wall Street Pay for Wall Street's Bailout Act of 2009. I appreciate hearing from you on this important issue.
As you are no doubt aware, the United States Government has been obligated to act in bold and aggressive ways to address our current economic crisis. These responses have been necessary to stave off a total collapse of our economy, and have required an unprecedented amount of federal resources. This, coupled with the fiscal strains placed on the government by many of the policy choices of the Bush Administration over the last eight years has resulted in our projected deficit reaching a record-high level of more than a trillion dollars. In light of this, and the tremendous burden which these actions have placed on taxpayers, various members of Congress have offered plans to help repay this debt.
One such proposal, the Let Wall Street Pay for Wall Street's Bailout Act was introduced in the House of Representatives by Congressman Peter DeFazio (D-OR). This legislation would impose a 0.25 percent transaction tax on the sale and purchase of stocks and other similar financial products, a fee which would be phased out once the cost of financial assistance appropriated under the Emergency Economic Stabilization Act, and subsequent Federal commitments entered into by the Federal Reserve, are repaid. While companion legislation has not been introduced in the United States Senate, please be assured that I will keep your views in mind should this or similar legislation come before the Senate for consideration.
Again, thank you for contacting me. If you would like to stay in touch with me on this or other issues of importance, please visit my website at http://dodd.senate.gov and sign up to receive my regular e-mail issue alerts. If you also would like information on my work as Chairman of the Senate Committee on Banking, Housing and Urban Affairs, please visit the Committee's website at http://banking.senate.gov. Please do not hesitate to contact me in the future if I may be of assistance to you in any way.
Sincerely,
CHRISTOPHER J. DODD
United States Senator
Dear Ms. Jubin:
Thank you for contacting me in support of an extension of unemployment benefits. I am pleased to hear from you on this issue.
As you know, Congress, with my support, passed the American Recovery and Reinvestment Act (ARRA; P.L. 111-5). This legislation extended the Emergency Unemployment Compensation (EUC08) program, which provides federal funds for an additional 20 weeks of unemployment compensation to all qualified job seekers and an additional 13 weeks of benefits in states, like Connecticut, with high unemployment. The EUC08 program was originally scheduled to expire on December 26, 2009.
Despite efforts to restore economic growth, unemployment continues to rise and currently is above ten percent. Although Connecticut's unemployment rate is slightly below the national average at 8.4 percent, it is my belief that we must continue to support programs to assist families, small businesses, and communities through these tough economic times.
To that end, I am pleased to inform you that I strongly supported the Worker, Home Ownership, and Business Assistance Act (P.L. 111-92). This Act allows recipients of unemployment compensation benefits whose benefits have expired or are about to expire an additional 14 weeks of benefits. Plus, if Connecticut's unemployment rate rises above 8.5 percent, qualified residents may receive another six weeks of unemployment benefits. This legislation also includes several other provisions to spur job creation, including an extension of the first-time home buyer's tax credit, extension of the net operating loss carry back deduction for small businesses, and expansion of the net operating loss carry back deduction to businesses and firms with gross receipts greater than $15 million. President Obama signed this legislation into law on November 6, 2009.
I will continue to support efforts to ensure that Congress is considering policies that provide an adequate safety net for workers who have lost their jobs and increase economic productivity and job creation. Please be assured that I will keep your specific views in mind as Congress continues to debate ways to spur economic growth.
Thank you again for sharing your views and concerns with me. I hope you will continue to visit my website at http://lieberman.senate.gov for updated news about my work on behalf of Connecticut and the nation. Please contact me if you have any additional questions or comments about our work in Congress.
Sincerely,
Joseph I. Lieberman
UNITED STATES SENATOR
JIL:kht
Oh well, perhaps if I'm concerned about the proposed transaction tax I also care about the extension of unemployment benefits. Or perhaps his staff is just proactive: if Congress passes the tax I'll need to apply for unemployment benefits. That would of course be tricky since I don't have an employer.
At least Chris Dodd was on target, though not encouraging.
Dear Ms. Jubin:
Thank you for contacting me regarding H.R. 1068, the Let Wall Street Pay for Wall Street's Bailout Act of 2009. I appreciate hearing from you on this important issue.
As you are no doubt aware, the United States Government has been obligated to act in bold and aggressive ways to address our current economic crisis. These responses have been necessary to stave off a total collapse of our economy, and have required an unprecedented amount of federal resources. This, coupled with the fiscal strains placed on the government by many of the policy choices of the Bush Administration over the last eight years has resulted in our projected deficit reaching a record-high level of more than a trillion dollars. In light of this, and the tremendous burden which these actions have placed on taxpayers, various members of Congress have offered plans to help repay this debt.
One such proposal, the Let Wall Street Pay for Wall Street's Bailout Act was introduced in the House of Representatives by Congressman Peter DeFazio (D-OR). This legislation would impose a 0.25 percent transaction tax on the sale and purchase of stocks and other similar financial products, a fee which would be phased out once the cost of financial assistance appropriated under the Emergency Economic Stabilization Act, and subsequent Federal commitments entered into by the Federal Reserve, are repaid. While companion legislation has not been introduced in the United States Senate, please be assured that I will keep your views in mind should this or similar legislation come before the Senate for consideration.
Again, thank you for contacting me. If you would like to stay in touch with me on this or other issues of importance, please visit my website at http://dodd.senate.gov and sign up to receive my regular e-mail issue alerts. If you also would like information on my work as Chairman of the Senate Committee on Banking, Housing and Urban Affairs, please visit the Committee's website at http://banking.senate.gov. Please do not hesitate to contact me in the future if I may be of assistance to you in any way.
Sincerely,
CHRISTOPHER J. DODD
United States Senator
Sunday, December 6, 2009
When ½ + ½ ≠1, a lesson from decision theory
This advice comes from James Stein’s book The Right Decision (McGraw-Hill, 2010). The book is a primer in the basics of decision theory—more appropriate for the business school type trying to learn how to make decisions than for someone interested in decision theory per se.
The book proceeds by way of case studies. Here’s one that caught my eye because it addresses one of my own flaws.
Thomas Edison has to make a decision. He has some seed money with which to start his own company, but it’s a modest amount. He wants to give his business the best chance of becoming prosperous. He currently has two inventions at the top of his “to do” list: a stock quotations printer and an electric light. He has three choices in Stein’s matrix. (1) Develop the printer first because there’s an immediate demand for it although the market is small. And, I would add, it’s a natural extension of Edison’s work in telegraphy. (2) Develop the electric light first because the potential market is huge (although, as we know, it turned out to be a daunting project). (3) Share work on both projects initially and then concentrate on whichever project is coming along best.
Stein opts for choice (1). “The first order of business is to stay in business. To do that, you need money. This may not make you a fortune, but it’s quick money.” The worst choice is (3). “It is important that you realize that by choosing this action you may run out of money before you have a marketable product because the arithmetic of inventions is that two half-inventions do not make a whole.” (p. 23)
Stein continues: “The arithmetic of projects is very similar to the arithmetic of inventions. One nearly finished project plus one half-finished project plus one project in development equals no completed projects.”
And: “Many projects require a critical mass of ideas in order to explode in fully developed glory. Think of all the storefronts you’ve seen empty because the tenants ran out of money before the store could become operational.” (p. 24)
I don’t know whether this resonates with you, but it may just form the basis of a New Year’s resolution for me.
The book proceeds by way of case studies. Here’s one that caught my eye because it addresses one of my own flaws.
Thomas Edison has to make a decision. He has some seed money with which to start his own company, but it’s a modest amount. He wants to give his business the best chance of becoming prosperous. He currently has two inventions at the top of his “to do” list: a stock quotations printer and an electric light. He has three choices in Stein’s matrix. (1) Develop the printer first because there’s an immediate demand for it although the market is small. And, I would add, it’s a natural extension of Edison’s work in telegraphy. (2) Develop the electric light first because the potential market is huge (although, as we know, it turned out to be a daunting project). (3) Share work on both projects initially and then concentrate on whichever project is coming along best.
Stein opts for choice (1). “The first order of business is to stay in business. To do that, you need money. This may not make you a fortune, but it’s quick money.” The worst choice is (3). “It is important that you realize that by choosing this action you may run out of money before you have a marketable product because the arithmetic of inventions is that two half-inventions do not make a whole.” (p. 23)
Stein continues: “The arithmetic of projects is very similar to the arithmetic of inventions. One nearly finished project plus one half-finished project plus one project in development equals no completed projects.”
And: “Many projects require a critical mass of ideas in order to explode in fully developed glory. Think of all the storefronts you’ve seen empty because the tenants ran out of money before the store could become operational.” (p. 24)
I don’t know whether this resonates with you, but it may just form the basis of a New Year’s resolution for me.
Saturday, December 5, 2009
Off topic
A couple of off-topic items today. First, a T. S. Eliot quotation found not in my volume of Eliot poetry but on the Strategic Economic Decisions site. Isn’t that a sad commentary on my life? Anyway, here it is.
“Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?”
--T. S. Eliot, Choruses from The Rock (1934)
* * *
Second, Maira Kalman’s wonderful monthly New York Times blog And the Pursuit of Happiness, normally a blend of photographs, illustrations, and hand-printed text. The link here is to the first post from May 3, 2006; meander through the series. The last one, a tribute to "slow" food, appeared just after Thanksgiving this year.
“Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?”
--T. S. Eliot, Choruses from The Rock (1934)
* * *
Second, Maira Kalman’s wonderful monthly New York Times blog And the Pursuit of Happiness, normally a blend of photographs, illustrations, and hand-printed text. The link here is to the first post from May 3, 2006; meander through the series. The last one, a tribute to "slow" food, appeared just after Thanksgiving this year.
Friday, December 4, 2009
Lehman, Far from Random
Richard Lehman’s Far from Random: Using Investor Behavior and Trend Analysis to Forecast Market Movement (Bloomberg Press, 2009) is a deceptively easy read and, for anyone steeped in market literature, covers some familiar ground. But Lehman applies behavioral finance to the markets in an imaginative, thought-provoking way and offers some important if tentative first steps toward constructing a new model for valuing indexes and ETFs.
His argument, in brief, is that every stock price has two components—fundamental value and subjective value. Fundamental value is similar to the value assigned to private companies; that is, it does not include the earnings multiples that analysts tack on to the intrinsic value of the company “on behalf of the market.” (p. 50) Subjective value includes such components as going concern premium, convenience premium, popularity premium, and anticipation premium. Subjective value, Lehman argues, is driven by the kinds of decisions described by behavioral finance. (Lehman devotes a chapter to an overview of behavioral heuristics.) Moreover, subjective value contributes more than fundamental value in determining the price of a stock; it also accounts for much of the risk in the stock.
Lehman quickly moves on to overall market value. If you diversify away individual stock risk, you are left with market risk (which Lehman measures using volatility). This is essentially the subjective value of the market. And it is this subjective component that explains why “markets actually exhibit nonrandom characteristics in both the short and long term.” (p. 84) Behavioral deviations from the rational actor model lead to market anomalies.
The challenge is how to measure the subjective value of markets. Lehman’s answer is to use trend channel analysis. As is usually the case with any transition from theory to practice, from thinking about the markets to making money in the markets, this is the weakest part of the book. Trend channel analysis may be useful, but it is not an adequate measure of the subjective value of markets.
One could criticize Lehman for not providing rigorous arguments for his theses and for blurring the boundaries between concepts. But I think this is a challenging book that should prompt more, undoubtedly much less readable research.
His argument, in brief, is that every stock price has two components—fundamental value and subjective value. Fundamental value is similar to the value assigned to private companies; that is, it does not include the earnings multiples that analysts tack on to the intrinsic value of the company “on behalf of the market.” (p. 50) Subjective value includes such components as going concern premium, convenience premium, popularity premium, and anticipation premium. Subjective value, Lehman argues, is driven by the kinds of decisions described by behavioral finance. (Lehman devotes a chapter to an overview of behavioral heuristics.) Moreover, subjective value contributes more than fundamental value in determining the price of a stock; it also accounts for much of the risk in the stock.
Lehman quickly moves on to overall market value. If you diversify away individual stock risk, you are left with market risk (which Lehman measures using volatility). This is essentially the subjective value of the market. And it is this subjective component that explains why “markets actually exhibit nonrandom characteristics in both the short and long term.” (p. 84) Behavioral deviations from the rational actor model lead to market anomalies.
The challenge is how to measure the subjective value of markets. Lehman’s answer is to use trend channel analysis. As is usually the case with any transition from theory to practice, from thinking about the markets to making money in the markets, this is the weakest part of the book. Trend channel analysis may be useful, but it is not an adequate measure of the subjective value of markets.
One could criticize Lehman for not providing rigorous arguments for his theses and for blurring the boundaries between concepts. But I think this is a challenging book that should prompt more, undoubtedly much less readable research.
Thursday, December 3, 2009
Some guidelines from the world of marketing
Jack Trout, in his new book Repositioning: Marketing in an Era of Competition, Change, and Crisis (McGraw-Hill, 2010), maintains that the concept of long-term planning “has finally been put to rest.” (p. 113) Its fatal flaw is the simple fact that we can’t predict the future. Taking its place are two short-term directives—“stay flexible and seize the opportunity.” (p. 115) And, Trout adds, start every campaign with your competition in mind. “It’s not what you want to do; it’s what your competition will let you do.” (p. 121)
Since we don’t know who is on the other side of our trades we can’t identify the competition. But we know that in the markets everybody is after everybody else’s money. Moreover, the world of trading and investing is even more cutthroat than the world of business. Businesses can expand not just their market share but the very size of the overall market. PCs and Macs are not locked in a zero-sum game.
Even though we don’t know exactly who’s on the other side of our trades, we can invent a persona for our competition. The competition has to be worthy; it’s foolhardy to assume that our competitor is a rank novice who probably won’t be around for long. We can then give this worthy opponent some salient characteristics, including trading strengths and weaknesses, and (depending on what motivates us) perhaps a thoroughly unlikable personality. Does our opponent trade the same time frame as we do? Does he fold at the slightest wobble in his position? Does he hang on too long? Is he brilliant at trade execution? We don’t want to compete directly against the opponent’s strengths. “When a competitor is known for one thing, you have to be known for something else.” (p. 121)
The good thing about inventing our adversary is that we can change him at will. If either we or the markets change and we need an adversary with a different set of qualities, our imagination can produce him in a jiffy. Just remember: stay flexible, seize the opportunity, and you may just trounce your imaginary opponent.
Since we don’t know who is on the other side of our trades we can’t identify the competition. But we know that in the markets everybody is after everybody else’s money. Moreover, the world of trading and investing is even more cutthroat than the world of business. Businesses can expand not just their market share but the very size of the overall market. PCs and Macs are not locked in a zero-sum game.
Even though we don’t know exactly who’s on the other side of our trades, we can invent a persona for our competition. The competition has to be worthy; it’s foolhardy to assume that our competitor is a rank novice who probably won’t be around for long. We can then give this worthy opponent some salient characteristics, including trading strengths and weaknesses, and (depending on what motivates us) perhaps a thoroughly unlikable personality. Does our opponent trade the same time frame as we do? Does he fold at the slightest wobble in his position? Does he hang on too long? Is he brilliant at trade execution? We don’t want to compete directly against the opponent’s strengths. “When a competitor is known for one thing, you have to be known for something else.” (p. 121)
The good thing about inventing our adversary is that we can change him at will. If either we or the markets change and we need an adversary with a different set of qualities, our imagination can produce him in a jiffy. Just remember: stay flexible, seize the opportunity, and you may just trounce your imaginary opponent.
Wednesday, December 2, 2009
Wilkinson, Technically Speaking
There are quite a few technical analysis interview books on the market, but Technically Speaking: Tips and Strategies from 16 Top Analysts by Chris Wilkinson (Traders Press, 1997) is by far one of the best, especially for those who want to hold positions for more than ten minutes. The author, it is important to stress, interviewed top analysts, not top traders. They may not have made the most money or achieved the status of heroes, but since they earned their living analyzing and advising, they had to be a bit more cerebral. The interviewees were Stan Berge, Ralph Bloch, John Bollinger, Marc Chaikin, Paul Desmond, Peter Eliades, Bob Gabele, Paul Montgomery, John Murphy, Martin Pring, Phil Roth, Lance Stonecypher, Dan Sullivan, Jim Tillman, Stan Weinstein, and Newton Zinder. Some of these names should be familiar to technical traders; others, I have to admit, I had never heard of.
I started with the most intriguing name and discovered that Lance Stonecypher is an analyst for Ned Davis Research. Now that’s a very familiar name. And in one brilliant paragraph he summarizes the four characteristics that are the basis of the philosophy at Ned Davis Research and that the top investing winners, despite often contradictory styles, share. Let me quote it in full.
“First, they all used objectively determined indicators rather than gut emotions to trade. Ned has a riddle he shares with clients relating to this. There is a room with three people in it. One is a high-priced lawyer, another is a low-priced lawyer, and the third is the tooth fairy. In the middle of them is a one hundred dollar bill. Suddenly, the lights go out and then come back on. The $100.00 has disappeared. The question is, ‘Who took the one hundred dollar bill?’ The answer is the high-priced lawyer because the other two are simply figments of the imagination. The point is that we want to make sure that what our indicators are saying [is] really factual rather than a figment of our imagination. It is absolutely critical that we stick with objectively determined indicators. The second characteristic is that all of these winners are very disciplined in their approach. They stay with their system through good times as well as bad. In classical Greek tragedies, the hero is always ruined in the end by some psychological character flaw. We often use this as an analogy because a common flaw of many investors is that they let their ego or personal feelings become involved in their market views, which makes it extremely difficult to admit mistakes. Discipline allows us to control our mistakes. The third key characteristic is that while disciplined, all of these people were flexible enough in their approach to change their minds whenever the evidence shifted. I remember back to the crash of 1987. Marty Zweig had done a study which showed that in every case, following a stock market decline of 30% or more, the economy always sank into a depression or severe recession. And yet within days after the crash, when his indicators turned very bullish, Marty turned bullish as well despite a lot of his fears. So he was flexible enough in his mindset that he shifted his outlook based on his objective indicators. The fourth characteristic is that all of these people are extremely risk averse. Ned Davis once asked Paul Tudor Jones what he did at work all day. He said, ‘The first thing I do is try and figure out what could go wrong and then I spend the rest of the day trying to cut my risks.’ Now, here is a commodity trader who is known as a risk taker, but I say he is risk averse. So, these four tenets: objectivity, discipline, flexibility, and risk management are the cornerstones of our investment philosophy.” (pp. 354-55)
To me that one paragraph alone is worth the $35 price of the book. But this nearly 500-page 8 1/2” x 11” book is jam packed with valuable information for investor and trader alike. The interviewer has done a masterful job of steering the conversations to bring out the best in each of these analysts. No cookie-cutter model here. She seems equally at home with those who rely on cycles, interest rate models, or volatility indicators. And, by the way, when you’re reading this book, don’t pick and choose your way through. There are valuable insights even for the experienced trader and investor in virtually every interview.
I started with the most intriguing name and discovered that Lance Stonecypher is an analyst for Ned Davis Research. Now that’s a very familiar name. And in one brilliant paragraph he summarizes the four characteristics that are the basis of the philosophy at Ned Davis Research and that the top investing winners, despite often contradictory styles, share. Let me quote it in full.
“First, they all used objectively determined indicators rather than gut emotions to trade. Ned has a riddle he shares with clients relating to this. There is a room with three people in it. One is a high-priced lawyer, another is a low-priced lawyer, and the third is the tooth fairy. In the middle of them is a one hundred dollar bill. Suddenly, the lights go out and then come back on. The $100.00 has disappeared. The question is, ‘Who took the one hundred dollar bill?’ The answer is the high-priced lawyer because the other two are simply figments of the imagination. The point is that we want to make sure that what our indicators are saying [is] really factual rather than a figment of our imagination. It is absolutely critical that we stick with objectively determined indicators. The second characteristic is that all of these winners are very disciplined in their approach. They stay with their system through good times as well as bad. In classical Greek tragedies, the hero is always ruined in the end by some psychological character flaw. We often use this as an analogy because a common flaw of many investors is that they let their ego or personal feelings become involved in their market views, which makes it extremely difficult to admit mistakes. Discipline allows us to control our mistakes. The third key characteristic is that while disciplined, all of these people were flexible enough in their approach to change their minds whenever the evidence shifted. I remember back to the crash of 1987. Marty Zweig had done a study which showed that in every case, following a stock market decline of 30% or more, the economy always sank into a depression or severe recession. And yet within days after the crash, when his indicators turned very bullish, Marty turned bullish as well despite a lot of his fears. So he was flexible enough in his mindset that he shifted his outlook based on his objective indicators. The fourth characteristic is that all of these people are extremely risk averse. Ned Davis once asked Paul Tudor Jones what he did at work all day. He said, ‘The first thing I do is try and figure out what could go wrong and then I spend the rest of the day trying to cut my risks.’ Now, here is a commodity trader who is known as a risk taker, but I say he is risk averse. So, these four tenets: objectivity, discipline, flexibility, and risk management are the cornerstones of our investment philosophy.” (pp. 354-55)
To me that one paragraph alone is worth the $35 price of the book. But this nearly 500-page 8 1/2” x 11” book is jam packed with valuable information for investor and trader alike. The interviewer has done a masterful job of steering the conversations to bring out the best in each of these analysts. No cookie-cutter model here. She seems equally at home with those who rely on cycles, interest rate models, or volatility indicators. And, by the way, when you’re reading this book, don’t pick and choose your way through. There are valuable insights even for the experienced trader and investor in virtually every interview.
Tuesday, December 1, 2009
Trading and the problem of random reinforcement
In Beating the Financial Futures Market (Wiley, 2006) Art Collins makes a case for systems trading over discretionary trading. His argument is that the very nature of markets works against the discretionary trader since they are “overwhelmingly random noise with a small trend component. The latter is what mechanical traders largely hang their hopes on, but it’s not perceivable on an individual trade basis. You need many trials to expose biases the same way Vegas needs many bets to exemplify the fact that their long-term edges are ultimately insurmountable.” (p. 1) Discretionary traders are attuned to the noise and very, very few can extract anything meaningful from it.
Moreover, Collins argues, the discretionary trader is rewarded in an arbitrary fashion. Unlike most areas of life where repeated experience results in learning, “trading doesn’t conform because good ideas (whatever they are) won’t necessarily produce winning trades any more than bad decisions will become losses. People get frustrated trying to form rules around an overload of information.”
That is, the discretionary trader is essentially receiving random feedback. And “researchers have scientifically demonstrated that the hardest behavior to break or modify is that which is rewarded in an arbitrary fashion. A lab rat that only occasionally receives food, a drug rush, and so forth when he pushes a lever will continue to hit it until his paw is raw.” (p. 2)
Ah, you might reply, but what about random reinforcement in dog training? Didn’t you quote Scott Page in your October 12 post as saying that when you’re teaching a dog to sit you shouldn’t give him a treat every time as a reward? Yes, but here’s the big difference. First, the dog trainer rewards only the sought-after behavior; she doesn’t give the dog a treat if he doesn’t sit. Second, the dog trainer rewards the appropriate response most of the time, so there’s not a lot of randomness. The element of randomness that is introduced into the training process supposedly strengthens the link in the dog’s brain between the trainer’s command and the dog’s response; it doesn’t turn him into a compulsive wreck.
By contrast, the market (especially in a small time horizon) rewards both bad behavior and good behavior, punishes both good behavior and bad behavior, and may do this more or less randomly. And, contrary to Collins, I would suggest that it doesn’t single out the discretionary trader for this random reinforcement. The market is a terrible, perhaps even deranged teacher! You can’t take your cues from how this teacher reacts to your behavior.
There’s been a lot written about replacing an external reward system with an internal one—rewarding yourself for sticking to your plan, following your rules, being disciplined. There’s no doubt that this task is psychologically extraordinarily difficult because it flies in the face of a lifetime of experience where, at least for the most part, a person is rewarded by the outside world for doing the “right” thing and punished for doing the “wrong” thing. I for one don’t take much solace in giving myself an expensive “A.”
We need to get out of this random feedback loop. And I don't think we can do it by substituting less powerful feedback for more powerful feedback. We have to substitute for this debilitating random feedback something that’s even more powerful. Taking our cue from Art Collins (and, yes, I understand it’s not exactly original), one solution is to substitute the continuous for the discrete. The market gives us discrete feedback; it can recognize only one trade at a time. If we shift our focus from these discrete data points to a line that connects them in a meaningful way—to wit, our equity curve, we presumably have something that no longer exhibits the properties of randomness. (Or, if we do, we know that we’re doing something very wrong.) In the simplest terms, if our equity curve is sloping upward, we know we’re doing the right thing and are being rewarded. If it’s sloping downward, we know we’re doing the wrong thing and are being punished. Yes, there will always be some random reinforcement in the equity curve, but with any luck it will make us more obedient (and wealthier) dogs—oops, traders.
Moreover, Collins argues, the discretionary trader is rewarded in an arbitrary fashion. Unlike most areas of life where repeated experience results in learning, “trading doesn’t conform because good ideas (whatever they are) won’t necessarily produce winning trades any more than bad decisions will become losses. People get frustrated trying to form rules around an overload of information.”
That is, the discretionary trader is essentially receiving random feedback. And “researchers have scientifically demonstrated that the hardest behavior to break or modify is that which is rewarded in an arbitrary fashion. A lab rat that only occasionally receives food, a drug rush, and so forth when he pushes a lever will continue to hit it until his paw is raw.” (p. 2)
Ah, you might reply, but what about random reinforcement in dog training? Didn’t you quote Scott Page in your October 12 post as saying that when you’re teaching a dog to sit you shouldn’t give him a treat every time as a reward? Yes, but here’s the big difference. First, the dog trainer rewards only the sought-after behavior; she doesn’t give the dog a treat if he doesn’t sit. Second, the dog trainer rewards the appropriate response most of the time, so there’s not a lot of randomness. The element of randomness that is introduced into the training process supposedly strengthens the link in the dog’s brain between the trainer’s command and the dog’s response; it doesn’t turn him into a compulsive wreck.
By contrast, the market (especially in a small time horizon) rewards both bad behavior and good behavior, punishes both good behavior and bad behavior, and may do this more or less randomly. And, contrary to Collins, I would suggest that it doesn’t single out the discretionary trader for this random reinforcement. The market is a terrible, perhaps even deranged teacher! You can’t take your cues from how this teacher reacts to your behavior.
There’s been a lot written about replacing an external reward system with an internal one—rewarding yourself for sticking to your plan, following your rules, being disciplined. There’s no doubt that this task is psychologically extraordinarily difficult because it flies in the face of a lifetime of experience where, at least for the most part, a person is rewarded by the outside world for doing the “right” thing and punished for doing the “wrong” thing. I for one don’t take much solace in giving myself an expensive “A.”
We need to get out of this random feedback loop. And I don't think we can do it by substituting less powerful feedback for more powerful feedback. We have to substitute for this debilitating random feedback something that’s even more powerful. Taking our cue from Art Collins (and, yes, I understand it’s not exactly original), one solution is to substitute the continuous for the discrete. The market gives us discrete feedback; it can recognize only one trade at a time. If we shift our focus from these discrete data points to a line that connects them in a meaningful way—to wit, our equity curve, we presumably have something that no longer exhibits the properties of randomness. (Or, if we do, we know that we’re doing something very wrong.) In the simplest terms, if our equity curve is sloping upward, we know we’re doing the right thing and are being rewarded. If it’s sloping downward, we know we’re doing the wrong thing and are being punished. Yes, there will always be some random reinforcement in the equity curve, but with any luck it will make us more obedient (and wealthier) dogs—oops, traders.
Monday, November 30, 2009
Akerlof and Shiller, Animal Spirits
Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism by George A. Akerlof and Robert J. Shiller (Princeton University Press, 2009) tackles a broad swath of issues, both in the economy and in the discipline of economics. For instance, it takes aim at unfettered capitalism, at the model of the purely rational economic agent, and at economists who throw out everything that cannot be quantified. It stresses the role of animal spirits in the everyday economy and describes five of its aspects: confidence, fairness, corruption and antisocial behavior, money illusion, and stories.I’m going to focus here on two interrelated themes: confidence and market volatility. Confidence, the authors contend, is not a simple binary idea that the future will be either rosy or bleak. (They therefore question the value of most measurements of consumer confidence. Instead of measuring animal spirits, these surveys “may only be reflecting consumers’ expectations regarding current and future income.” [p. 17]) Etymologically confidence also encompasses the notions of trust and belief. And trust and belief are not necessarily the outcomes of rational deliberation; indeed, “the very meaning of trust is that we go beyond the rational.” (p. 12)
Confidence varies over time. “In good times, people trust. They make decisions spontaneously. They know instinctively that they will be successful. They suspend their suspicions. Asset values will be high and perhaps also increasing. As long as people remain trusting, their impulsiveness will not be evident. But then, when the confidence disappears, the tide goes out. The nakedness of their decisions stands revealed.” (pp. 12-13)
The authors argue that, contrary to standard economic theory that portrays people as rational agents who weigh the pros and cons of all the options available to them before making a decision, most people base their decisions on what feels right or, as Jack Welch wrote, act “straight from the gut.” Decisions made in the face of uncertainty (where, as we know, uncertainty—unlike risk—cannot be measured) are intuitive. They hinge on whether or not we have confidence. Most business decisions, including our personal decisions about what assets to buy, “involve decision-making processes that are closer to what we do when we flip a pancake or hit a golf ball.” (p. 13)
Let me pause here to stress that the authors claim no relationship between confidence and a positive outcome. We know from behavioral finance that people tend to be overconfident in their ability to make correct decisions. That is, they may make decisions based on confidence, but (one level removed) their confidence in their ability to make decisions based on confidence is often unwarranted.
A second point that should be emphasized: the intuitive decisions that the authors laud are not uninformed decisions. They are the result of accumulated experience, often reflecting expertise. Take the pancake flipping example. I don’t particularly like pancakes and I am a singularly untalented cook. So, for me, deciding when to flip the pancake would be a pseudo-rational exercise that would probably have a sad end. By contrast, the person who had learned over time about the intricacies of making pancakes would “just know” when to flip it. The person trying to flip a house, on the other hand, might be confident but have no experiential basis for that confidence; he’s just riding the wave of general euphoria. His investment is likely to end as badly as my pancake.
And this brings us to the phenomenon of market volatility. No one, the authors begin, has ever made sense of the wild gyrations of markets. Not only can the experts not forecast market movements; “no one can even explain why these events rationally ought to have happened even after they have happened.” (p. 131) They are clearly not explained by fundamentals. The authors suggest that we have to look at the multiplier effect of feedback. First, and most obviously, there is price-to-price feedback, more popularly known as momentum. But “price-to-price feedback itself may not be strong enough to create the major asset price bubbles we have seen.” It is, however, complemented and reinforced by “feedbacks between the asset prices in the bubble and the real economy. This additional feedback increases the length of the cycle and amplifies the price-to-price effects.” (pp. 134-35) Animal spirits are at work everywhere.
And you want to know what animal spirits look like? The cover art for this book, reproduced above, is marvelous.
Sunday, November 29, 2009
Cyclical, secular prognostications
Two links for today. First, Ned Davis claims that the cyclical bull rally is not over but that most of the indicators that correspond to secular market lows are not in place. He bases his analysis on a synthesis of technical, fundamental, and macroeconomic indicators.
Second, a new free offering from The Chart Store--five monthly charts adjusted for inflation by the CPI showing secular cycles of the S&P composite.
Second, a new free offering from The Chart Store--five monthly charts adjusted for inflation by the CPI showing secular cycles of the S&P composite.
Saturday, November 28, 2009
Volatility, Dow Awards
On the Condor Options site there’s an excellent set of presentation slides from a webinar on “Risks Taken Unintentionally: Volatility and the Lessons of the 2008 Financial Crisis” by Jared Woodard.
And if you have a little time on your hands, I suggest going to the Market Technicians Association site. It has .pdf files of all the papers that have won the annual Charles H. Dow Award. If you have still more time on your hands, you have until February 1, 2010 to submit a paper for consideration for next year’s award.
And if you have a little time on your hands, I suggest going to the Market Technicians Association site. It has .pdf files of all the papers that have won the annual Charles H. Dow Award. If you have still more time on your hands, you have until February 1, 2010 to submit a paper for consideration for next year’s award.
Friday, November 27, 2009
Anticipating correlations
Anyone who either tries to construct a portfolio with determinable risk parameters or tries to trade by taking cues from other markets faces a seemingly insurmountable problem: correlations vary over time. For instance, sometimes the bond and stock markets are negatively correlated, but there are times that they move in lockstep. Sometimes U.S. equities move up when the dollar is weak, other times when it is strong. What’s a person to do?
Robert F. Engle gave a three-day lecture series at the Econometric Institute of the Erasmus School of Economics in Rotterdam, subsequently published as Anticipating Correlations: A New Paradigm for Risk Management by Princeton University Press (2009). The Dutch are known for their command of the English language, but they probably didn’t have to work too hard here since the English text serves largely to connect statistical formulas. Since I am not comfortable in the world of econometrics, I’ll stitch together a little post from the linking text and some general observations.
The risk of a portfolio as well as its optimal hedge depends in large part on the future correlations and volatilities of its constituents. For example, in the case of the notorious CDOs correlations between defaults were the key determinants of valuation, yet virtually nobody who bought CDOs or tranches of CDOs had a reliable method for figuring out just how correlated these defaults were.
Engle hypothesizes that shifting correlations are responses to fundamental news. Even when it would seem that trading rather than news moves correlations—for instance, when hedge funds unwind similar positions or deleverage their holdings, Engle claims that this distinction is semantic rather than real.
Engle explores and evaluates a range of models for predicting correlations and comes down on the side of the FACTOR DCC model, analysis for quants only. I’ll skip to the end where the author concludes: “The models developed in this book have the potential to adapt to unforeseen changes in the financial environment and hence give a dynamic picture of correlations and volatilities. These methods are naturally short-run methods focusing on what can happen in the near future. Risk management must necessarily also be concerned with the longer run. Conveniently, the factor versions of these models allow us to model the determinants of factor volatilities, which are the primary determinants of longer-term volatilities and correlations.” (p. 140)
For those who don’t aspire to be risk managers at an investment bank but who want to manage risk in their portfolio, it is important to monitor both volatility and correlation on an ongoing basis. We may not have fancy models, we may not be able to predict with any measurable probability where volatility and correlation will be in the future, but we can devise matrixes to know where we are. Are we in a high volatility or a low volatility environment? Are traditional correlations shifting? What are the fundamental reasons for these shifts? Once we know the answers to these questions, we can start to position size and hedge accordingly.
Robert F. Engle gave a three-day lecture series at the Econometric Institute of the Erasmus School of Economics in Rotterdam, subsequently published as Anticipating Correlations: A New Paradigm for Risk Management by Princeton University Press (2009). The Dutch are known for their command of the English language, but they probably didn’t have to work too hard here since the English text serves largely to connect statistical formulas. Since I am not comfortable in the world of econometrics, I’ll stitch together a little post from the linking text and some general observations.
The risk of a portfolio as well as its optimal hedge depends in large part on the future correlations and volatilities of its constituents. For example, in the case of the notorious CDOs correlations between defaults were the key determinants of valuation, yet virtually nobody who bought CDOs or tranches of CDOs had a reliable method for figuring out just how correlated these defaults were.
Engle hypothesizes that shifting correlations are responses to fundamental news. Even when it would seem that trading rather than news moves correlations—for instance, when hedge funds unwind similar positions or deleverage their holdings, Engle claims that this distinction is semantic rather than real.
Engle explores and evaluates a range of models for predicting correlations and comes down on the side of the FACTOR DCC model, analysis for quants only. I’ll skip to the end where the author concludes: “The models developed in this book have the potential to adapt to unforeseen changes in the financial environment and hence give a dynamic picture of correlations and volatilities. These methods are naturally short-run methods focusing on what can happen in the near future. Risk management must necessarily also be concerned with the longer run. Conveniently, the factor versions of these models allow us to model the determinants of factor volatilities, which are the primary determinants of longer-term volatilities and correlations.” (p. 140)
For those who don’t aspire to be risk managers at an investment bank but who want to manage risk in their portfolio, it is important to monitor both volatility and correlation on an ongoing basis. We may not have fancy models, we may not be able to predict with any measurable probability where volatility and correlation will be in the future, but we can devise matrixes to know where we are. Are we in a high volatility or a low volatility environment? Are traditional correlations shifting? What are the fundamental reasons for these shifts? Once we know the answers to these questions, we can start to position size and hedge accordingly.
Thursday, November 26, 2009
Wednesday, November 25, 2009
Calculating the risk of ruin
In anticipation of Thanksgiving, I with my gallows sense of humor decided to devote today’s post to the risk of ruin. That is, what are the odds that we will blow out our trading account? There are alternative ways to calculate the risk of ruin. Let me start with a simple formula provided by Nauzer Balsara in Money Management Strategies for Futures Traders (Wiley, 1992). In those cases where the average win equals the average loss, the probability of ruin is the probability of failure divided by the probability of success raised to a power equal to the percent of our account at risk, with the result multiplied by 100. If it’s a tossup whether we win or lose—that is, the probability of winning is 0.50 and the probability of losing is 0.50—and we risk 10% of our capital, the formula reads “(1 raised to the power of 10) * 100.” The probability of ruin is 100%. Ruin is ensured. It is significant to note, as Balsara stresses, that “when the probability of success increases marginally to 0.55, with the same payoff ratio and exposure fraction, the probability of ruin drops dramatically to (0.45/0.55)10” or 13.4%. “Therefore,” he stresses, “it certainly does pay to invest in improving the odds of success for any given trading system.” (p. 15) It also pays, of course, to position size wisely.
If the parameters are the same as in the first example but the payoff ratio is 2 (that is, the average win is twice as large as the average loss) we can still quantify the risk of ruin by means of a mathematical formula. Put in its simplest terms, if we stand only a one in three chance of winning, even though our winners are twice as large as our losers, the risk of ruin is, yet again, certain. Once the payoff ratio exceeds 2, however, we have to turn to simulators (some available at no cost online; Equity Monaco from NeoTicker is one) to calculate the risk of ruin.
These techniques for calculating the risk of ruin assume that we can estimate both the probability of winning and the payoff ratio. That is, they assume that (1) we have a backtest in which we have confidence and (2) we believe that the future will closely resemble the past. It’s leap of faith time.
Moreover, not all trading strategies (and this includes many successful strategies) can be meaningfully quantified. For instance, any strategy that is event-dependent or that relies to any extent on qualitative considerations will resist simple risk:reward calculations, despite what analysts might claim.
But let’s assume that the trader is fairly consistent in his approach and has a track record that spans at least a few months. Another way to calculate the risk of ruin is by using the mean and standard deviation of past returns. David E. Chamness wrote a piece in August 2009 for Futures Magazine that provides some formulas for calculating the risk of ruin this way. He also compares constant position sizing with fixed fractional position sizing and shares an elegant equation for expressing the fixed fractional risk of ruin.
If the parameters are the same as in the first example but the payoff ratio is 2 (that is, the average win is twice as large as the average loss) we can still quantify the risk of ruin by means of a mathematical formula. Put in its simplest terms, if we stand only a one in three chance of winning, even though our winners are twice as large as our losers, the risk of ruin is, yet again, certain. Once the payoff ratio exceeds 2, however, we have to turn to simulators (some available at no cost online; Equity Monaco from NeoTicker is one) to calculate the risk of ruin.
These techniques for calculating the risk of ruin assume that we can estimate both the probability of winning and the payoff ratio. That is, they assume that (1) we have a backtest in which we have confidence and (2) we believe that the future will closely resemble the past. It’s leap of faith time.
Moreover, not all trading strategies (and this includes many successful strategies) can be meaningfully quantified. For instance, any strategy that is event-dependent or that relies to any extent on qualitative considerations will resist simple risk:reward calculations, despite what analysts might claim.
But let’s assume that the trader is fairly consistent in his approach and has a track record that spans at least a few months. Another way to calculate the risk of ruin is by using the mean and standard deviation of past returns. David E. Chamness wrote a piece in August 2009 for Futures Magazine that provides some formulas for calculating the risk of ruin this way. He also compares constant position sizing with fixed fractional position sizing and shares an elegant equation for expressing the fixed fractional risk of ruin.
Tuesday, November 24, 2009
Hirsch & Person, Commodity Trader’s Almanac 2010
The Commodity Trader’s Almanac (Wiley, 2010) is the very young sibling of the Stock Trader’s Almanac. While stocks have had their own almanac for over forty years, this is only the fourth edition of the commodity almanac. I bought the first edition (2007) and found it useful. This edition, however, seems to be genuinely “new and improved.” For one thing, it now includes information on S&P 500 and 30-Year Treasury futures. (Earlier, currency futures were added.) Moreover, it provides not only specifications for commodity future contracts but also for their related ETFs and stocks. In the calendar section are charts that overlay about a year’s worth of data comparing a commodity to an ETF or stock and that show the commodity’s long-term seasonal pattern. The calendar itself highlights some seasonal trades that have had a high accuracy rate. The almanac also draws on John Person’s work on candlestick and pivot point trading triggers.
Understandably, commodities have long been viewed as seasonal markets. Even if they stray from seasonal patterns during times of turbulence, it is never wise to trade commodities without knowing their seasonal track records. In this almanac there are three tables for each commodity (plus currencies and the new equity and fixed income futures). The first provides annual highs, lows, and closes of the near-term contract going back at least 25 years, sometimes longer. The second displays the near-term contract monthly closing prices, and the third looks at the near-term contract monthly percent changes. There is also a one-year chart comparing the 25-year pattern to the 5-year pattern. And, for those who like text, there are descriptions of the seasonal tendencies of the commodities both in the calendar section and in the data section.
All in all, this is a treasure trove of material both for futures traders and for those who prefer to trade ETFs.
Understandably, commodities have long been viewed as seasonal markets. Even if they stray from seasonal patterns during times of turbulence, it is never wise to trade commodities without knowing their seasonal track records. In this almanac there are three tables for each commodity (plus currencies and the new equity and fixed income futures). The first provides annual highs, lows, and closes of the near-term contract going back at least 25 years, sometimes longer. The second displays the near-term contract monthly closing prices, and the third looks at the near-term contract monthly percent changes. There is also a one-year chart comparing the 25-year pattern to the 5-year pattern. And, for those who like text, there are descriptions of the seasonal tendencies of the commodities both in the calendar section and in the data section.
All in all, this is a treasure trove of material both for futures traders and for those who prefer to trade ETFs.
Monday, November 23, 2009
Kaufman’s fractal efficiency ratio and market noise
I’ve been toying with Kaufman’s fractal efficiency ratio to see whether some time frames are noisier than others, as is generally asserted. The fractal efficiency ratio is derived by dividing the net change in price movement over n periods by the sum of all component moves, taken as positive numbers, over the same n periods. If the ratio approaches the value 1, the movement is smooth; if the ratio approaches 0, there is great inefficiency or noise.
I’m not talented at number crunching, and I haven’t taken this analysis very far. But here’s some not quite raw data. Click on the graphics to enlarge them.
The first spreadsheet shows the ten-minute price change on YM divided by the sum of all one-minute absolute price movements for the period between 10/14 and 11/2/2009. I have arbitrarily defined the trading day as starting at 8:30 EST and ending at 4:00. The particularly noisy 10-minute segments are colored yellow, the smooth segments are colored green. Beneath the rule is the average fractal efficiency ratio for each trading day and the standard deviation of the ratios. And then follows the 8:30 open and the 4:00 close, with the directional change, for each day. The average of the 14 average fractal efficiency ratios is 0.32; the average standard deviation is 0.22.
I then compared this very small sample with daily, weekly, and monthly SPY, GLD, and EEM data over the entire course of their trading history. Once again, I calculated the ten-period price change divided by the sum of its ten component moves. I highlighted the smoothest daily, weekly, and monthly price series with green, the smallest standard deviations with yellow.
Although I know that the tiny YM study isn’t statistically significant, I do find it interesting that its one-minute intraday price movements are very similar in terms of fractal efficiency to the one-day price movements on the SPY. This might offer some ammunition to day traders who are often accused of merely trading noise.
By the way, it seems that great minds think alike (and quite independently). David Varadi at CSS Analytics is experimenting with using fractal efficiency as the key component in a performance statistic. Of course, he knows what he’s doing, and I’m just mucking about.
I’m not talented at number crunching, and I haven’t taken this analysis very far. But here’s some not quite raw data. Click on the graphics to enlarge them.
The first spreadsheet shows the ten-minute price change on YM divided by the sum of all one-minute absolute price movements for the period between 10/14 and 11/2/2009. I have arbitrarily defined the trading day as starting at 8:30 EST and ending at 4:00. The particularly noisy 10-minute segments are colored yellow, the smooth segments are colored green. Beneath the rule is the average fractal efficiency ratio for each trading day and the standard deviation of the ratios. And then follows the 8:30 open and the 4:00 close, with the directional change, for each day. The average of the 14 average fractal efficiency ratios is 0.32; the average standard deviation is 0.22.
I then compared this very small sample with daily, weekly, and monthly SPY, GLD, and EEM data over the entire course of their trading history. Once again, I calculated the ten-period price change divided by the sum of its ten component moves. I highlighted the smoothest daily, weekly, and monthly price series with green, the smallest standard deviations with yellow.
Although I know that the tiny YM study isn’t statistically significant, I do find it interesting that its one-minute intraday price movements are very similar in terms of fractal efficiency to the one-day price movements on the SPY. This might offer some ammunition to day traders who are often accused of merely trading noise.
By the way, it seems that great minds think alike (and quite independently). David Varadi at CSS Analytics is experimenting with using fractal efficiency as the key component in a performance statistic. Of course, he knows what he’s doing, and I’m just mucking about.
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