Wednesday, June 30, 2010
Schilit and Perler, Financial Shenanigans
With Jeff Skilling (and now Richard Scrushy) back in the news it seems fitting to revisit the world of “creative” accounting. In fact, the third edition of Financial Shenanigans by Howard M. Schilit and Jeremy Perler (McGraw-Hill, 2010) starts with Enron. What were some telltale signs that Enron was engaged in a massive accounting fraud? The simplest was that Enron’s revenue growth (from under $10 billion to over $100 billion in five years) defied reality. Moreover, Enron’s profits never kept pace with its sales. For instance, while sales grew by more than 150% in 2000, profits increased by less than 10%. In the five-year period that witnessed the tenfold growth in sales, profits didn’t even double.
The authors outline a series of financial shenanigans that Enron engaged in, from recording revenue too soon and recording bogus revenue to cash flow antics—e.g., shifting financing cash inflows to the operating section and shifting normal operating cash outflows to the investing section. And we all remember the so-called key metrics shenanigans—showcasing misleading metrics that overstate performance and distorting balance sheet metrics to avoid showing deterioration. (p. 8)
In introducing some warning signs that even those unskilled in deciphering balance sheets can heed, the authors quote from a Warren Buffett annual letter. In it Buffett describes a conversation between a patient, whose X rays showed him to be seriously ill, and his doctor: “I can’t afford the operation, but would you accept a small payment to touch up the X rays?” Buffett continued, “In the long run . . . trouble awaits managements that paper over operating problems with accounting maneuvers. Eventually, managements of this kind achieve the same result as the seriously-ill patient.” (p. 23)
One way that companies fudge their books is by tinkering with time. It’s common practice for salesmen to push hard, often offering incentives to customers so they can meet or beat their quarterly quotas. But if this legal strategy doesn’t work, why not just extend the quarter? It seems that, among its many other sins, Computer Associates “regularly stretched out the last month of the quarter to as much as 35 days.” (p. 48) And Computer Associates was not alone in this practice. Both Sunbeam and Peregrine were known to keep their books open well past the official end of the quarter. At Peregrine this practice became something of a joke, with late transactions described as having been completed on “the thirty-seventh of December.” (p. 50)
Then there are those companies who book revenue prematurely, sometimes very prematurely. Krispy Kreme profited by selling donut-making equipment to its franchisees. But in 2003 the company entered the world of make-believe by “pretending to ship equipment to franchisees. It actually shipped the equipment out, but to company-owned trailers to which the franchisees had no access. Krispy Kreme still recorded the revenue, even though the franchisees had failed to take possession of the machines shipped.” (p. 63)
AIG was involved in bogus transactions long before the most recent financial crisis. They marketed a product known as finite insurance, sometimes used to paper over companies’ earnings shortfalls. In 1998 Brightpoint was coming up short by some $15 million in its December quarter. AIG had a “perfect world” solution. It created a $15 million retroactive insurance policy to cover Brightpoint’s unreported losses, which meant that Brightpoint could immediately book the $15 million as income (“insurance recovery”) and report in line with the guidance it had provided Wall Street at the beginning of the quarter. In turn, it paid “insurance premiums” to AIG over the next three years. As the authors note, “Economic sense dictates that this transaction was not an insurance contract because no real risk had been transferred. Indeed, the transaction was nothing more than a financing agreement.” (p. 77)
Financial Shenanigans is a fascinating book. In more than 300 pages it not only explains accounting ruses, it illustrates them with real-life examples. It shows how companies, even ostensibly reputable ones, try to hide their problems and how the savvy investor can ferret them out. It’s an ideal handbook for short sellers.
The authors outline a series of financial shenanigans that Enron engaged in, from recording revenue too soon and recording bogus revenue to cash flow antics—e.g., shifting financing cash inflows to the operating section and shifting normal operating cash outflows to the investing section. And we all remember the so-called key metrics shenanigans—showcasing misleading metrics that overstate performance and distorting balance sheet metrics to avoid showing deterioration. (p. 8)
In introducing some warning signs that even those unskilled in deciphering balance sheets can heed, the authors quote from a Warren Buffett annual letter. In it Buffett describes a conversation between a patient, whose X rays showed him to be seriously ill, and his doctor: “I can’t afford the operation, but would you accept a small payment to touch up the X rays?” Buffett continued, “In the long run . . . trouble awaits managements that paper over operating problems with accounting maneuvers. Eventually, managements of this kind achieve the same result as the seriously-ill patient.” (p. 23)
One way that companies fudge their books is by tinkering with time. It’s common practice for salesmen to push hard, often offering incentives to customers so they can meet or beat their quarterly quotas. But if this legal strategy doesn’t work, why not just extend the quarter? It seems that, among its many other sins, Computer Associates “regularly stretched out the last month of the quarter to as much as 35 days.” (p. 48) And Computer Associates was not alone in this practice. Both Sunbeam and Peregrine were known to keep their books open well past the official end of the quarter. At Peregrine this practice became something of a joke, with late transactions described as having been completed on “the thirty-seventh of December.” (p. 50)
Then there are those companies who book revenue prematurely, sometimes very prematurely. Krispy Kreme profited by selling donut-making equipment to its franchisees. But in 2003 the company entered the world of make-believe by “pretending to ship equipment to franchisees. It actually shipped the equipment out, but to company-owned trailers to which the franchisees had no access. Krispy Kreme still recorded the revenue, even though the franchisees had failed to take possession of the machines shipped.” (p. 63)
AIG was involved in bogus transactions long before the most recent financial crisis. They marketed a product known as finite insurance, sometimes used to paper over companies’ earnings shortfalls. In 1998 Brightpoint was coming up short by some $15 million in its December quarter. AIG had a “perfect world” solution. It created a $15 million retroactive insurance policy to cover Brightpoint’s unreported losses, which meant that Brightpoint could immediately book the $15 million as income (“insurance recovery”) and report in line with the guidance it had provided Wall Street at the beginning of the quarter. In turn, it paid “insurance premiums” to AIG over the next three years. As the authors note, “Economic sense dictates that this transaction was not an insurance contract because no real risk had been transferred. Indeed, the transaction was nothing more than a financing agreement.” (p. 77)
Financial Shenanigans is a fascinating book. In more than 300 pages it not only explains accounting ruses, it illustrates them with real-life examples. It shows how companies, even ostensibly reputable ones, try to hide their problems and how the savvy investor can ferret them out. It’s an ideal handbook for short sellers.
Tuesday, June 29, 2010
The growth curve of highly successful businesses
Since trading is a business, it makes sense to look at business management books now and again. David G. Thomson’s Mastering the 7 Essentials of High-Growth Companies: Effective Lessons to Grow Your Business (Wiley, 2010) doesn’t have a lot to offer the individual trader. But here’s an interesting takeaway.
More than 60% of the companies that went public in the last three decades no longer exist; 4% now have more than $1 billion in annual revenue. The highly successful 4% have a common growth curve. First, an entrepreneurial phase where an idea is transformed into a viable business model and growth is modest; the average length of this “runway” is five years. Second, an inflection point “where revenue breaks out into an exponential trajectory.” And, finally, variable growth rates to reach $1 billion in revenue.
Ratchet down the dollars, but the growth curve (or equity curve) of highly successful traders should be somewhat similar. Lots of time figuring out a personal style and an edge, honing the craft, and carving out modest profits. And then, with very careful money management, increasing size and letting the powers of compounding work their exponential magic.
But doesn’t this presume the best of all possible worlds? No, according to Thomson. A “bad” market environment is no excuse for a major drawdown in the equity curve. “America’s exponential-growth companies . . . have a consistent track record of growing through recessions.” (p. 23)
What separates the winners from the losers? Although almost all companies had passionate management teams, the failing companies, Thomson contends, demonstrated “blind passion—they never knew when to quit. . . . These teams fail to self-correct.” (p. 21) In brief, we’re back to the themes of flexibility, agility, nimbleness. Only traders who are flexible, who can self-correct, stand a chance of joining and staying in the top 4%.
More than 60% of the companies that went public in the last three decades no longer exist; 4% now have more than $1 billion in annual revenue. The highly successful 4% have a common growth curve. First, an entrepreneurial phase where an idea is transformed into a viable business model and growth is modest; the average length of this “runway” is five years. Second, an inflection point “where revenue breaks out into an exponential trajectory.” And, finally, variable growth rates to reach $1 billion in revenue.
Ratchet down the dollars, but the growth curve (or equity curve) of highly successful traders should be somewhat similar. Lots of time figuring out a personal style and an edge, honing the craft, and carving out modest profits. And then, with very careful money management, increasing size and letting the powers of compounding work their exponential magic.
But doesn’t this presume the best of all possible worlds? No, according to Thomson. A “bad” market environment is no excuse for a major drawdown in the equity curve. “America’s exponential-growth companies . . . have a consistent track record of growing through recessions.” (p. 23)
What separates the winners from the losers? Although almost all companies had passionate management teams, the failing companies, Thomson contends, demonstrated “blind passion—they never knew when to quit. . . . These teams fail to self-correct.” (p. 21) In brief, we’re back to the themes of flexibility, agility, nimbleness. Only traders who are flexible, who can self-correct, stand a chance of joining and staying in the top 4%.
Monday, June 28, 2010
Patel, Trading with Ichimoku Clouds
Ichimoku Kinko Hyo is billed as an extension, perhaps even an evolution, of candlestick charting. I personally have never used it, so I approached Manesh Patel’s Trading with Ichimoku Clouds (Wiley, 2010) as a humble yet always skeptical novice. Since Ichimoku clouds are a price overlay, they clutter charts. Is the clutter worth it?
To enlighten my fellow novices here’s Ichimoku in a nutshell, extracted from Patel’s book. The system is made up of five components: (1) the 9-period average of (highest high + lowest low)/2; (2) the 26-period average of the same formula; (3) the current price shifted back 26 periods; (4) (formula 1 + formula 2)/2 shifted forward in time 26 periods; and (5) the 52-period average of (highest high + highest low)/2 shifted forward by 26 periods. The fourth and fifth indicators combine to make up the Kumo cloud.
The clouds are indications of current and future sentiment and the strength of that sentiment. They also provide support and resistance levels.
In the longest chapter of the book, complete with 135 TradeStation charts, Patel takes the reader through a two-year backtest of trading the EUR/USD cross following a set of bullish and bearish entry and money management rules. There were a total of eight trades. Although the system was profitable, it had a poor risk to reward ratio: the entry risk was 2,249 and the profit was 1,507, not exactly ideal. So Patel shows how one could optimize the strategy.
Patel rounds out his discussion by looking at other Ichimoku strategies (mainly crossovers and breakouts) and time elements in Ichimoku. Patel admits that he uses Gann’s time elements instead of Ichimoku’s.
For anyone interested in Ichimoku trading, this book sets out guiding principles that the systems trader could test, modify, meld into another system—the opportunities are, as always, seemingly infinite. With practice the Kumo clouds might offer up some insights to the discretionary trader as well. I admit I remained unconvinced, but for me this is primarily a question of style.
Perhaps I’m unconvinced in part because Patel has written such a refreshingly honest book. He could easily have cherrypicked his trade to make the strategy look like the holy grail. Instead, he offered up a profitable yet possibly flawed strategy (the sample size was too small to determine whether it was in fact flawed). He’s clearly not trying to sell snake oil.
To enlighten my fellow novices here’s Ichimoku in a nutshell, extracted from Patel’s book. The system is made up of five components: (1) the 9-period average of (highest high + lowest low)/2; (2) the 26-period average of the same formula; (3) the current price shifted back 26 periods; (4) (formula 1 + formula 2)/2 shifted forward in time 26 periods; and (5) the 52-period average of (highest high + highest low)/2 shifted forward by 26 periods. The fourth and fifth indicators combine to make up the Kumo cloud.
The clouds are indications of current and future sentiment and the strength of that sentiment. They also provide support and resistance levels.
In the longest chapter of the book, complete with 135 TradeStation charts, Patel takes the reader through a two-year backtest of trading the EUR/USD cross following a set of bullish and bearish entry and money management rules. There were a total of eight trades. Although the system was profitable, it had a poor risk to reward ratio: the entry risk was 2,249 and the profit was 1,507, not exactly ideal. So Patel shows how one could optimize the strategy.
Patel rounds out his discussion by looking at other Ichimoku strategies (mainly crossovers and breakouts) and time elements in Ichimoku. Patel admits that he uses Gann’s time elements instead of Ichimoku’s.
For anyone interested in Ichimoku trading, this book sets out guiding principles that the systems trader could test, modify, meld into another system—the opportunities are, as always, seemingly infinite. With practice the Kumo clouds might offer up some insights to the discretionary trader as well. I admit I remained unconvinced, but for me this is primarily a question of style.
Perhaps I’m unconvinced in part because Patel has written such a refreshingly honest book. He could easily have cherrypicked his trade to make the strategy look like the holy grail. Instead, he offered up a profitable yet possibly flawed strategy (the sample size was too small to determine whether it was in fact flawed). He’s clearly not trying to sell snake oil.
Sunday, June 27, 2010
Amazon links
At the request of a reader I’m going to start including links to Amazon in my book reviews. I want to assure everyone that I am doing this for my readers’ convenience, not as part of a get rich quick scheme. The non-mandatory disclosure: I get a 4% referral fee when people go to Amazon via a link on my blog and buy something there. Alas, my own Amazon purchases are excluded.
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Saturday, June 26, 2010
The bee’s knees
For those of you who have watched the ubiquitous Geico ads (and perhaps have a soft spot for them since, after all, Geico is a subsidiary of Berkshire Hathaway), here is a pedantic footnote. What in the world does the rightfully rejected ad phrase “It’s the bee’s knees” mean?
The folks at phrases.org (UK) help us out. Start with the basic meaning of “excellent—the highest quality.” But then dig deeper, going on a trip to New Zealand, Zane Grey’s rural America, and the roaring 20s.
And perhaps then you’ll decide that I’m writing pollywoppus.
The folks at phrases.org (UK) help us out. Start with the basic meaning of “excellent—the highest quality.” But then dig deeper, going on a trip to New Zealand, Zane Grey’s rural America, and the roaring 20s.
And perhaps then you’ll decide that I’m writing pollywoppus.
Friday, June 25, 2010
Underhill, The Handbook of Infrastructure Investing
Infrastructure has been a hot investing topic for some time. Emerging markets are building it out, developed markets are working at fixing or upgrading it. In The Handbook of Infrastructure Investing (Wiley 2010) Michael D. Underhill has collected a set of papers that look at opportunities for venture capitalists, private equity, institutional investors, and even the lowly individual investor.
I’m going to confine myself to opportunities for the individual investor. Even though the chapter on valuing the Pennsylvania Turnpike might be interesting (and in fact, having driven this road more often than I’d like to remember, I read this piece first) it doesn’t have much relevance for those of us who aren’t at the top of the financial food chain.
But first, a sobering report card on U.S. infrastructure issued in 2009 by the American Society of Civil Engineers. Aviation D, bridges C, dams D, drinking water D-, energy D+, hazardous waste D, inland waterways D-, levees D-, public parks and recreation C-, rail C-, roads D-, schools D, solid waste C+, transit D, wastewater D-. Not exactly Ivy League material!
So, aside from infrastructure ETFs and the standard energy, utilities, and engineering stocks, what else is out there for the individual investor? And what can we reasonably expect from infrastructure investments?
Underhill’s own paper “What Is Listed Infrastructure?” suggests that infrastructure investments offer “long-term stable cash flows that have the potential for inflation hedging. In addition, infrastructure assets tend to have high barriers to entry, providing monopoly-like features. . . . They behave somewhat like a bond with their stable cash flows and provide investors the ability to participate in capital appreciation or equity upside potential as the underlying assets appreciate in value.” (p. 164) Between November 30, 2001 and December 31, 2008 (I assume that the starting date is linked to the launch of the S&P Global Infrastructure Index and hence isn’t arbitrary) global infrastructure stocks outperformed global stocks by a wide margin (10.86% vs. 0.83%). Their annualized volatility, however, was only slightly better: 14.95% vs. 15.31%.
For investors who want to invest in traditional energy, master limited partnerships are an option. The folks at Chickasaw Capital Management (four authors) provide a thorough review of MLPs. Currently there are 78 publicly traded energy MLPs. They’re structured along the lines of REITs, paying quarterly cash distributions. Between January 2000 and December 2008 the Citigroup MLP Index trumped every other significant index (equities, commodities, real estate) with a low correlation (between 24% and 37%) to every one of them. Drawdowns weren’t pretty, but on balance they were less than those of the other indexes.
This book is not a must-have, but for those who are seeking to diversify their portfolios it shines a light on a path often not taken.
I’m going to confine myself to opportunities for the individual investor. Even though the chapter on valuing the Pennsylvania Turnpike might be interesting (and in fact, having driven this road more often than I’d like to remember, I read this piece first) it doesn’t have much relevance for those of us who aren’t at the top of the financial food chain.
But first, a sobering report card on U.S. infrastructure issued in 2009 by the American Society of Civil Engineers. Aviation D, bridges C, dams D, drinking water D-, energy D+, hazardous waste D, inland waterways D-, levees D-, public parks and recreation C-, rail C-, roads D-, schools D, solid waste C+, transit D, wastewater D-. Not exactly Ivy League material!
So, aside from infrastructure ETFs and the standard energy, utilities, and engineering stocks, what else is out there for the individual investor? And what can we reasonably expect from infrastructure investments?
Underhill’s own paper “What Is Listed Infrastructure?” suggests that infrastructure investments offer “long-term stable cash flows that have the potential for inflation hedging. In addition, infrastructure assets tend to have high barriers to entry, providing monopoly-like features. . . . They behave somewhat like a bond with their stable cash flows and provide investors the ability to participate in capital appreciation or equity upside potential as the underlying assets appreciate in value.” (p. 164) Between November 30, 2001 and December 31, 2008 (I assume that the starting date is linked to the launch of the S&P Global Infrastructure Index and hence isn’t arbitrary) global infrastructure stocks outperformed global stocks by a wide margin (10.86% vs. 0.83%). Their annualized volatility, however, was only slightly better: 14.95% vs. 15.31%.
For investors who want to invest in traditional energy, master limited partnerships are an option. The folks at Chickasaw Capital Management (four authors) provide a thorough review of MLPs. Currently there are 78 publicly traded energy MLPs. They’re structured along the lines of REITs, paying quarterly cash distributions. Between January 2000 and December 2008 the Citigroup MLP Index trumped every other significant index (equities, commodities, real estate) with a low correlation (between 24% and 37%) to every one of them. Drawdowns weren’t pretty, but on balance they were less than those of the other indexes.
This book is not a must-have, but for those who are seeking to diversify their portfolios it shines a light on a path often not taken.
Thursday, June 24, 2010
The CRB Commodity Yearbook 2010
I have long daydreamed about owning CRB commodity yearbooks; the problem is that they don’t come cheap and I don’t have a compelling need for them. This year, with the opportunity to review The CRB Commodity Yearbook 2010 (Wiley), my fantasy became reality.
The latest yearbook is about 375 pages long and measures 8 ½” x 11.” In addition to coverage of world commodity markets and the CRB indices, it contains information on more than 100 commodities, including such marginal items as lard and onions. To give a feel for its offerings let me summarize the data for gold.
The five pages devoted to gold include some text about the commodity followed by nine tables and six charts. The tables show the world mine production of gold (2001-2009), salient statistics about gold in the U.S. (2000-2009), monthly average gold price (2000-2009), volume of trading of gold futures (COMEX, 2000-2009), average open interest of gold (COMEX, 2000-2009), COMEX depository warehouse stocks of gold (2000-2009), central gold bank reserves (2000-2009), mine production of recoverable gold in the U.S. (1999-2008), and consumption of gold by end-use in the U.S. (1989-1998). There are charts of the monthly average price of gold (1830-2009), the weekly close of gold futures (NYMEX, 2000-2009), and the weekly close of gold in British pounds, euros, yen, and Swiss francs (2000-2009).
The yearbook comes with a bonus CD, and this one really is a bonus. It includes feature articles from yearbooks back to 1965. For gold there is text back to the 1970 yearbook, graphs back to 1975, and statistics back to 1970. In brief, more historical data than any commodity junkie could ever want. You’re essentially getting about forty yearbooks for the price of one.
My major complaint about this book is that the charts need some major refurbishing. They look as if they were set up years ago, when computerized typography was in its infancy. The kerning, for instance, is atrocious.
Nonetheless, I’m delighted to be the proud owner of The CRB Commodity Yearbook 2010.
The latest yearbook is about 375 pages long and measures 8 ½” x 11.” In addition to coverage of world commodity markets and the CRB indices, it contains information on more than 100 commodities, including such marginal items as lard and onions. To give a feel for its offerings let me summarize the data for gold.
The five pages devoted to gold include some text about the commodity followed by nine tables and six charts. The tables show the world mine production of gold (2001-2009), salient statistics about gold in the U.S. (2000-2009), monthly average gold price (2000-2009), volume of trading of gold futures (COMEX, 2000-2009), average open interest of gold (COMEX, 2000-2009), COMEX depository warehouse stocks of gold (2000-2009), central gold bank reserves (2000-2009), mine production of recoverable gold in the U.S. (1999-2008), and consumption of gold by end-use in the U.S. (1989-1998). There are charts of the monthly average price of gold (1830-2009), the weekly close of gold futures (NYMEX, 2000-2009), and the weekly close of gold in British pounds, euros, yen, and Swiss francs (2000-2009).
The yearbook comes with a bonus CD, and this one really is a bonus. It includes feature articles from yearbooks back to 1965. For gold there is text back to the 1970 yearbook, graphs back to 1975, and statistics back to 1970. In brief, more historical data than any commodity junkie could ever want. You’re essentially getting about forty yearbooks for the price of one.
My major complaint about this book is that the charts need some major refurbishing. They look as if they were set up years ago, when computerized typography was in its infancy. The kerning, for instance, is atrocious.
Nonetheless, I’m delighted to be the proud owner of The CRB Commodity Yearbook 2010.
Tuesday, June 22, 2010
Reducing uncertainty
In How to Measure Anything (Wiley, 2010) Douglas Hubbard confronts a common measurement myth—that when you have a lot of uncertainty, you need a lot of data to tell you something useful. The fact is that “if you have a lot of uncertainty now, you don’t need much data to reduce uncertainty significantly. When you have a lot of certainty already, then you need a lot of data to reduce uncertainty significantly.” (p. 110)
I think that traders and investors should take this to heart. In a world of great uncertainty we shouldn’t overanalyze a situation assuming that the results will be proportional to the effort. A single price bar or data point might be all that’s necessary to reduce uncertainty significantly. Waiting for the next three price bars or data points may in fact only increase uncertainty.
There’s a reason that good traders often act first and think later. Moreover, when they start thinking after they enter a trade, they’re not usually looking for more reasons to justify the trade or to make a favorable outcome more certain. (One exception might be if a trader is looking to scale into a position.) They’re thinking about all the things that could go wrong. Here again, they don’t need much to reduce their uncertainty about the viability of the trade. Any data that suggest the trade is ill advised should be taken seriously and not rationalized away. Act, then reassess.
Good traders can only try to reduce uncertainty, not attain certainty. It’s wise to do this in the most efficient and cost effective way possible.
I think that traders and investors should take this to heart. In a world of great uncertainty we shouldn’t overanalyze a situation assuming that the results will be proportional to the effort. A single price bar or data point might be all that’s necessary to reduce uncertainty significantly. Waiting for the next three price bars or data points may in fact only increase uncertainty.
There’s a reason that good traders often act first and think later. Moreover, when they start thinking after they enter a trade, they’re not usually looking for more reasons to justify the trade or to make a favorable outcome more certain. (One exception might be if a trader is looking to scale into a position.) They’re thinking about all the things that could go wrong. Here again, they don’t need much to reduce their uncertainty about the viability of the trade. Any data that suggest the trade is ill advised should be taken seriously and not rationalized away. Act, then reassess.
Good traders can only try to reduce uncertainty, not attain certainty. It’s wise to do this in the most efficient and cost effective way possible.
Monday, June 21, 2010
Wilson, The Hedge Fund Book
Richard C. Wilson’s The Hedge Fund Book: A Training Manual for Professionals and Capital-Raising Executives (Wiley, 2010) is a meat and potatoes how-to book for hedge fund managers and wannabe managers. Although it covers a range of topics, its primary focus is on what it takes to set up and run a hedge fund as a business. The extensive use of interviews with representatives from the industry keeps the tone of the book conversational.
Wilson’s strength is marketing. It’s fascinating to see not only how he suggests hedge funds should market themselves, especially to raise capital, but how he markets himself and his assorted businesses. He follows the SKAR formula (specialized knowledge + authority + results = huge growth opportunities and faster development within your career or business). Here are some bullet points under establishing yourself as an authority in your niche area: (1) Publish your own newsletter or blog. (2) Interview one professional each month for your newsletter or blog. (3) Self-publish a short book based on what you have written for your newsletter or blog. (4) Speak at conferences.
There’s little talk about actual trading in the book, but one of the interviewees offered his four top pieces of trading advice. (1) Risk management is everything. (2) Understand behavioral finance. (3) Keep a detailed trading log. (4) For quantitative traders, “fit the security to the model, not the model to the security.” (p. 88)
The Hedge Fund Book (and note the tone of authority even in the title) should be useful for anyone who wants to launch a hedge fund. Even for those of us without such ambitions it’s an interesting read. It certainly dispels any illusions that all one needs to be a successful hedge fund manager is a winning trading strategy!
Wilson’s strength is marketing. It’s fascinating to see not only how he suggests hedge funds should market themselves, especially to raise capital, but how he markets himself and his assorted businesses. He follows the SKAR formula (specialized knowledge + authority + results = huge growth opportunities and faster development within your career or business). Here are some bullet points under establishing yourself as an authority in your niche area: (1) Publish your own newsletter or blog. (2) Interview one professional each month for your newsletter or blog. (3) Self-publish a short book based on what you have written for your newsletter or blog. (4) Speak at conferences.
There’s little talk about actual trading in the book, but one of the interviewees offered his four top pieces of trading advice. (1) Risk management is everything. (2) Understand behavioral finance. (3) Keep a detailed trading log. (4) For quantitative traders, “fit the security to the model, not the model to the security.” (p. 88)
The Hedge Fund Book (and note the tone of authority even in the title) should be useful for anyone who wants to launch a hedge fund. Even for those of us without such ambitions it’s an interesting read. It certainly dispels any illusions that all one needs to be a successful hedge fund manager is a winning trading strategy!
Saturday, June 19, 2010
The seven habits of highly ineffective people
This piece by Dan Ariely would be funny if it weren't so true.
Friday, June 18, 2010
Mathemagical black holes
Here’s an early kickoff to the weekend from the e-book Mathemagic: Advanced Math Puzzles, available for free download on Scribd. I am shamelessly lifting material from Michael W. Ecker’s paper “Number Play, Calculators, and Card Tricks: Mathemagical Black Holes.”
First, the Sisyphus string: 123. “Suppose we start with any natural number, regarded as a string, such as 9,288,759. Count the number of even digits, the number of odd digits, and the total number of digits. These are 3 (three evens), 4 (four odds), and 7 (seven is the total number of digits), respectively. So, use these digits to form the next string or number, 347. Now repeat with 347, counting events, odds, total number, to get 1, 2, 3, so write down 123. If we repeat with 123, we get 123 again. The number 123 with respect to this process and the universe of numbers is a mathemagical black hole. All numbers in this universe are drawn to 123 by this process, never to escape.” (p. 41)
Second, words to numbers: 4. “Take any whole number and write out its numeral in English, such as FIVE for the usual 5. Count the number of characters in the spelling. In this case, it is 4—or FOUR. So, work now with the 4 or FOUR. Repeat with 4 to get 4 again. As another instance, try 163. To avoid ambiguity, I’ll arbitrarily say that we will include spaces and hyphens in our count. Then, 163 appears as ONE HUNDRED SIXTY-THREE for a total count of 23. In turn, this gives 12, then 6, then 3, then 5, and finally 4.” (p. 44)
Third, narcissistic numbers: 153. This one’s a little tougher. “It is well known that, other than the trivial examples of 0 and 1, the only natural numbers that equal the sum of the cubes of their digits are 153, 370, 371, and 407. Of these, just one has a black-hole property. . . . We start with any positive whole number that is a multiple of 3. Recall that there is a special shortcut to test whether you have a multiple of 3. Just add up the digits and see whether that sum is a multiple of 3. . . . Write down your multiple of 3. One at a time, take the cube of each digit. Add up the cubes to form a new number. Now repeat the process. You must reach 153. And once you reach 153, one more iteration just gets you 153 again. Let’s test just one initial instance. Using the sum of the cubes of the digits, if we start with 432—a multiple of 3—we get 99, which leads to 1458, then 702, which yields 351, finally leading to 153, at which point future iterations keep producing 153. Note also that this operation or process preserves divisibility by 3 in the successive numbers.” (pp. 44-45)
Fourth, Kaprekar’s constant: 6174. “Take any four-digit number except an integral multiple of 1111 (i.e., don’t take one of the nine numbers with four identical digits). Rearrange the digits of your number to form the largest and smallest strings possible. That is, write down the largest permutation of the number, the smallest permutation (allowing initial zeros as digits), and subtract. Apply this same process to the difference just obtained. Within the total of seven steps, you always reach 6174. At that point, further iteration with 6174 is pointless: 7641-1467 = 6174. Example: Start with 8028. The largest permutation is 8820, the smallest is 0288, and the difference is 8532. Repeat with 8532 to calculate 8532-2358 = 6174.”
Ecker gives more examples of mathemagical black holes and provides mathematical explanations, so math puzzlers can indulge themselves.
First, the Sisyphus string: 123. “Suppose we start with any natural number, regarded as a string, such as 9,288,759. Count the number of even digits, the number of odd digits, and the total number of digits. These are 3 (three evens), 4 (four odds), and 7 (seven is the total number of digits), respectively. So, use these digits to form the next string or number, 347. Now repeat with 347, counting events, odds, total number, to get 1, 2, 3, so write down 123. If we repeat with 123, we get 123 again. The number 123 with respect to this process and the universe of numbers is a mathemagical black hole. All numbers in this universe are drawn to 123 by this process, never to escape.” (p. 41)
Second, words to numbers: 4. “Take any whole number and write out its numeral in English, such as FIVE for the usual 5. Count the number of characters in the spelling. In this case, it is 4—or FOUR. So, work now with the 4 or FOUR. Repeat with 4 to get 4 again. As another instance, try 163. To avoid ambiguity, I’ll arbitrarily say that we will include spaces and hyphens in our count. Then, 163 appears as ONE HUNDRED SIXTY-THREE for a total count of 23. In turn, this gives 12, then 6, then 3, then 5, and finally 4.” (p. 44)
Third, narcissistic numbers: 153. This one’s a little tougher. “It is well known that, other than the trivial examples of 0 and 1, the only natural numbers that equal the sum of the cubes of their digits are 153, 370, 371, and 407. Of these, just one has a black-hole property. . . . We start with any positive whole number that is a multiple of 3. Recall that there is a special shortcut to test whether you have a multiple of 3. Just add up the digits and see whether that sum is a multiple of 3. . . . Write down your multiple of 3. One at a time, take the cube of each digit. Add up the cubes to form a new number. Now repeat the process. You must reach 153. And once you reach 153, one more iteration just gets you 153 again. Let’s test just one initial instance. Using the sum of the cubes of the digits, if we start with 432—a multiple of 3—we get 99, which leads to 1458, then 702, which yields 351, finally leading to 153, at which point future iterations keep producing 153. Note also that this operation or process preserves divisibility by 3 in the successive numbers.” (pp. 44-45)
Fourth, Kaprekar’s constant: 6174. “Take any four-digit number except an integral multiple of 1111 (i.e., don’t take one of the nine numbers with four identical digits). Rearrange the digits of your number to form the largest and smallest strings possible. That is, write down the largest permutation of the number, the smallest permutation (allowing initial zeros as digits), and subtract. Apply this same process to the difference just obtained. Within the total of seven steps, you always reach 6174. At that point, further iteration with 6174 is pointless: 7641-1467 = 6174. Example: Start with 8028. The largest permutation is 8820, the smallest is 0288, and the difference is 8532. Repeat with 8532 to calculate 8532-2358 = 6174.”
Ecker gives more examples of mathemagical black holes and provides mathematical explanations, so math puzzlers can indulge themselves.
Thursday, June 17, 2010
Forming beliefs and trading strategies
I just started looking at The Place of Probability in Science, ed. by Ellery Eells and James H. Fetzer (Springer, 2010). There’s a potentially interesting paper “Learning to Network” by Brian Skyrms and Robin Pemantle that uses the principles of game theory to theorize about the evolution of interaction networks. But I got sidetracked by the following passage:
“In the simplest belief learning model, Cournot dynamics, an individual assumes that others will do what they did last time and performs the act that has the highest payoff on that assumption. More sophisticated individuals might form their beliefs more carefully, by applying inductive reasoning to some or all of the available evidence. Less confident individuals might hedge their bet on Cournot dynamics with some probabilistic version of the rule. Strategically minded individuals might predict the effect of their current choice on future choices of the other agents involved, and factor this into their decision.” (pp. 278-79)
I trust that most of my readers can analogize from this paragraph to the formulation of various trading strategies and don’t need me to connect the dots. We don’t have to buy into the notion that game theory provides an accurate model of how trading decisions should be made. Just take the passage at face value and act as if you never heard of John Nash, game theory, or a Nash equilibrium. And then, as an exercise, bracket your knowledge of trading strategies as well. From this hypothetical tabula rasa develop reasonable guidelines for each learning/trading model (the simple, sophisticated, less confident, and strategic). In some cases you’ll undoubtedly be reinventing the wheel. But who knows? Perhaps you’ll come up with a new idea or two.
“In the simplest belief learning model, Cournot dynamics, an individual assumes that others will do what they did last time and performs the act that has the highest payoff on that assumption. More sophisticated individuals might form their beliefs more carefully, by applying inductive reasoning to some or all of the available evidence. Less confident individuals might hedge their bet on Cournot dynamics with some probabilistic version of the rule. Strategically minded individuals might predict the effect of their current choice on future choices of the other agents involved, and factor this into their decision.” (pp. 278-79)
I trust that most of my readers can analogize from this paragraph to the formulation of various trading strategies and don’t need me to connect the dots. We don’t have to buy into the notion that game theory provides an accurate model of how trading decisions should be made. Just take the passage at face value and act as if you never heard of John Nash, game theory, or a Nash equilibrium. And then, as an exercise, bracket your knowledge of trading strategies as well. From this hypothetical tabula rasa develop reasonable guidelines for each learning/trading model (the simple, sophisticated, less confident, and strategic). In some cases you’ll undoubtedly be reinventing the wheel. But who knows? Perhaps you’ll come up with a new idea or two.
Wednesday, June 16, 2010
Mazur, What’s Luck Got to Do with It?
Joseph Mazur’s new book What’s Luck Got to Do with It? The History, Mathematics, and Psychology of the Gambler’s Illusion (Princeton University Press, 2010) engagingly summarizes a broad spectrum of literature on gambling—and, yes, he considers investing a form of gambling. A former math professor, Mazur grew up in an environment where betting on horses and playing the numbers were family pastimes. He has fond memories, but he puts his money on probability theory.
Mazur first takes us on a journey from the Neanderthals to the world markets in 2008. He interweaves the histories of gambling and probability theory. For instance, he notes that under Henry VII gambling was forbidden save during the twelve days of Christmas when the public “was not only permitted to gamble but encouraged to do so in church.” (p. 17) And, in addition to writing about the best known early work in probability theory such as Bernoulli’s Ars Conjectandi, he illustrates how the trigram system from the I-Ching can be viewed as an early stab at combinatorial mathematics.
The second part of Mazur’s book introduces some of the basics of probability theory and uses them to show how gamblers so often delude themselves, sometimes with a little help from the house. In a primitive example, if a blinking neon sign over a slot machine reads “This bank pays 99 percent,” the naïve (and wishful) interprets it to mean that he is almost certain to win. In fact, of course, “all it means is that over an infinite number of plays of the slot machine the player will receive 99 percent of all the money he or she already spent.” (p. 149)
Mazur then looks at compulsive gambling and why we have such a hard time psychomanaging risk. He shares insights from behavioral economics and psychology. In the final analysis, however, he argues that “some—if not most—gambling behavior is primarily connected to an intrinsic desire to manipulate luck in order to validate life, to test the forces of uncertainty under a fantasy of knowing something unknowable or to experiment with the new. Making choices based on scant knowledge is an essential function of consciousness.” (p. 216)
We can improve our odds by understanding principles that underlie the “choices based on scant knowledge” that we make, at least when these choices can be analyzed statistically. For instance, gamblers are inclined to embrace the Monte Carlo fallacy as a guiding principle. This fallacy presumes that an event remembers its history; the roulette player therefore mistakenly believes that he should bet on black after a long run of red. (A roulette wheel clearly has no memory; traders do. So is the Monte Carlo fallacy really a fallacy in the financial world?)
We can also try to tilt the odds in our favor by engaging in activities that require both skill and luck, not just luck (as in lotteries or slot machines). For instance, there are blackjack strategies that give the player a 3 percent advantage over the house. A good poker player, keenly aware of the basic odds of various hands, will be able to make an intuitive probability calculation that will at least in part inform his decision to drop, call, or raise.
Mazur’s book is a quick read and thoroughly enjoyable. Even though he’s not particularly knowledgeable about the markets and in his eagerness to bring investing and trading under the mantle of gambling succumbs to reductionist thinking (“investments should be viewed as simply poker games based on a risk-reward evaluation” [p. 59]), he’s shed light on the enduring appeal of luck. Just think of the comment that Jim Simons of Renaissance Technologies, renowned for its phenomenally successful quant trading, made to a gathering of potential investors: “Luck is largely responsible for my reputation for genius. I don’t walk into the office in the morning and say, ‘Am I smart today?’ I walk in and wonder, ‘Am I lucky today?’” (Institutional Investor, November 2000)
Mazur first takes us on a journey from the Neanderthals to the world markets in 2008. He interweaves the histories of gambling and probability theory. For instance, he notes that under Henry VII gambling was forbidden save during the twelve days of Christmas when the public “was not only permitted to gamble but encouraged to do so in church.” (p. 17) And, in addition to writing about the best known early work in probability theory such as Bernoulli’s Ars Conjectandi, he illustrates how the trigram system from the I-Ching can be viewed as an early stab at combinatorial mathematics.
The second part of Mazur’s book introduces some of the basics of probability theory and uses them to show how gamblers so often delude themselves, sometimes with a little help from the house. In a primitive example, if a blinking neon sign over a slot machine reads “This bank pays 99 percent,” the naïve (and wishful) interprets it to mean that he is almost certain to win. In fact, of course, “all it means is that over an infinite number of plays of the slot machine the player will receive 99 percent of all the money he or she already spent.” (p. 149)
Mazur then looks at compulsive gambling and why we have such a hard time psychomanaging risk. He shares insights from behavioral economics and psychology. In the final analysis, however, he argues that “some—if not most—gambling behavior is primarily connected to an intrinsic desire to manipulate luck in order to validate life, to test the forces of uncertainty under a fantasy of knowing something unknowable or to experiment with the new. Making choices based on scant knowledge is an essential function of consciousness.” (p. 216)
We can improve our odds by understanding principles that underlie the “choices based on scant knowledge” that we make, at least when these choices can be analyzed statistically. For instance, gamblers are inclined to embrace the Monte Carlo fallacy as a guiding principle. This fallacy presumes that an event remembers its history; the roulette player therefore mistakenly believes that he should bet on black after a long run of red. (A roulette wheel clearly has no memory; traders do. So is the Monte Carlo fallacy really a fallacy in the financial world?)
We can also try to tilt the odds in our favor by engaging in activities that require both skill and luck, not just luck (as in lotteries or slot machines). For instance, there are blackjack strategies that give the player a 3 percent advantage over the house. A good poker player, keenly aware of the basic odds of various hands, will be able to make an intuitive probability calculation that will at least in part inform his decision to drop, call, or raise.
Mazur’s book is a quick read and thoroughly enjoyable. Even though he’s not particularly knowledgeable about the markets and in his eagerness to bring investing and trading under the mantle of gambling succumbs to reductionist thinking (“investments should be viewed as simply poker games based on a risk-reward evaluation” [p. 59]), he’s shed light on the enduring appeal of luck. Just think of the comment that Jim Simons of Renaissance Technologies, renowned for its phenomenally successful quant trading, made to a gathering of potential investors: “Luck is largely responsible for my reputation for genius. I don’t walk into the office in the morning and say, ‘Am I smart today?’ I walk in and wonder, ‘Am I lucky today?’” (Institutional Investor, November 2000)
Tuesday, June 15, 2010
Harvard Business Review links
A couple of links from Peter Bregman’s blog on the Harvard Business Review site. First, one that’s a year old but still fresh: "Play the Game You Know You Can Win."
Second, and much more recent, "A Ritual to Help You Keep Your Focus and Your Temper." One caveat: if you’re a short-term trader don’t perform this ritual on the hour.
Second, and much more recent, "A Ritual to Help You Keep Your Focus and Your Temper." One caveat: if you’re a short-term trader don’t perform this ritual on the hour.
Monday, June 14, 2010
Hooke, Security Analysis on Wall Street
I rarely venture into the realm of fundamental analysis on this blog, but I am pleased to make an exception for Jeffrey C. Hooke’s book Security Analysis on Wall Street: A Comprehensive Guide to Today’s Valuation Methods (Wiley). A second edition of the original 1998 text has just been released. For some bizarre reason the publisher sent me the first edition as a review copy. I assume that the second edition updates the stocks used as examples and addresses both newer methodologies and recent market concerns. If it improves in any way on the 1998 text, that’s great. All I have to assume for this review is that it doesn’t diminish the qualities of the first edition.
To many traders and investors security analysis seems like one big yawn. The most they do is plug some values into an online stock screener and, voilà , out comes a list of great trade ideas that for some strange reason seldom pan out. Hooke’s book demonstrates beyond a shadow of a doubt that security analysis is not boring. In clear, easy to understand prose it takes the reader from the basics to special cases such as natural resource stocks and distressed securities.
Throughout Hooke is commonsensical and hands-on. He shows how analysts work and some of the practical and theoretical difficulties they encounter. Let me highlight one problem here—figuring out how to forecast future sales. Sales projection techniques, he states, fall into three categories: time series, causal, and qualitative.
The time series method assumes that the future will be like the past and hence is popular in calculating projections for stable industries like food and utilities. Analysts use tools familiar to technical analysts such as moving averages and trendlines. But there are obvious weaknesses in this method. First, it cannot predict turning points in a company’s performance and, second, it does not take into account business cycles.
Causal techniques “forecast a company’s sales by establishing relationships between sales and variables that are independent of the corporation” (p. 204) such as housing starts or demographics. Quantifying these relationships requires regression formulas and econometric calculations. Causal forecasting is useful when analyzing established companies with a reasonably long operating record.
Finally, there are qualitative techniques that should be used in developing projections for every company. They are mandatory in the case of pioneer or growth companies offering new products and services where “the sales forecaster is left with expert opinions, market research studies, and historical analogies as his analytical tools. Sometimes, the result is nothing more than educated guesswork.” (p. 205) But at least it’s educated.
I particularly enjoyed Hooke’s chapters on modern approaches to valuation (intrinsic value, relative value, and acquisition value) even though I suspect that they have been significantly updated in the second edition. But I doubt that he will change the math in the section “How High Is Up?” He writes: “If you buy a 50 P/E stock today and plan on selling it at a 20 P/E (which is still above average) in five years (when the issuer’s business is likely maturing), the earnings per share of the company must quintuple for you to realize a market-type [that is, 14%] return.” (pp. 240-41) Investors in the dot.com bubble, where P/Es could easily get into the 100s, might have saved themselves a lot of financial grief by reading this single sentence from Hooke’s book. I’m certain that readers of the second edition will be able to profit (or avoid downside risk) equally well.
I can highly recommend this book for anyone who does position trading or investing, even the confirmed technical trader. It provides methodologies for finding investment opportunities and continually reassessing positions. With some educated guesswork the reader might even outperform those analysts who wait until a stock has cratered before changing their buy recommendation.
To many traders and investors security analysis seems like one big yawn. The most they do is plug some values into an online stock screener and, voilà , out comes a list of great trade ideas that for some strange reason seldom pan out. Hooke’s book demonstrates beyond a shadow of a doubt that security analysis is not boring. In clear, easy to understand prose it takes the reader from the basics to special cases such as natural resource stocks and distressed securities.
Throughout Hooke is commonsensical and hands-on. He shows how analysts work and some of the practical and theoretical difficulties they encounter. Let me highlight one problem here—figuring out how to forecast future sales. Sales projection techniques, he states, fall into three categories: time series, causal, and qualitative.
The time series method assumes that the future will be like the past and hence is popular in calculating projections for stable industries like food and utilities. Analysts use tools familiar to technical analysts such as moving averages and trendlines. But there are obvious weaknesses in this method. First, it cannot predict turning points in a company’s performance and, second, it does not take into account business cycles.
Causal techniques “forecast a company’s sales by establishing relationships between sales and variables that are independent of the corporation” (p. 204) such as housing starts or demographics. Quantifying these relationships requires regression formulas and econometric calculations. Causal forecasting is useful when analyzing established companies with a reasonably long operating record.
Finally, there are qualitative techniques that should be used in developing projections for every company. They are mandatory in the case of pioneer or growth companies offering new products and services where “the sales forecaster is left with expert opinions, market research studies, and historical analogies as his analytical tools. Sometimes, the result is nothing more than educated guesswork.” (p. 205) But at least it’s educated.
I particularly enjoyed Hooke’s chapters on modern approaches to valuation (intrinsic value, relative value, and acquisition value) even though I suspect that they have been significantly updated in the second edition. But I doubt that he will change the math in the section “How High Is Up?” He writes: “If you buy a 50 P/E stock today and plan on selling it at a 20 P/E (which is still above average) in five years (when the issuer’s business is likely maturing), the earnings per share of the company must quintuple for you to realize a market-type [that is, 14%] return.” (pp. 240-41) Investors in the dot.com bubble, where P/Es could easily get into the 100s, might have saved themselves a lot of financial grief by reading this single sentence from Hooke’s book. I’m certain that readers of the second edition will be able to profit (or avoid downside risk) equally well.
I can highly recommend this book for anyone who does position trading or investing, even the confirmed technical trader. It provides methodologies for finding investment opportunities and continually reassessing positions. With some educated guesswork the reader might even outperform those analysts who wait until a stock has cratered before changing their buy recommendation.
Sunday, June 13, 2010
Securities Analysts
Tomorrow or Tuesday I’m going to write about Hooke’s Security Analysis on Wall Street. By way of historical introduction, here’s a piece that Charles Ellis wrote for The Investment Professional. It’s entitled “Coming of Age: A Brief History of the Changing Role of the Securities Analyst.”
Friday, June 11, 2010
Harris, Short-term Trading with Price Patterns
I have admitted more than once on this blog that I am no systems designer. To be a good at creating trading systems you must be imaginative, comfortable in the world of probability and statistics, and have basic or better programming skills. I may have imagination, but that’s about it. The best blend that I’ve found among active bloggers is CSS Analytics.
But let’s go back ten years, to Michael Harris’s Short-term Trading with Price Patterns (Traders Press, 2000). The book is obviously dated and some of its methodology is—I’m looking for a more scientific description but what comes to mind is—clunky. Nonetheless, this book is an interesting attempt to combine price patterns and probability. Harris focuses on the futures markets.
The basic idea is to identify profitable short-term trading patterns, normally between three and seven bars, and then combine them into a trading system. That is, an entry would be triggered if Pattern A or Pattern B or Pattern C, etc. was identified.
Harris goes beyond simple pattern trading, however, by introducing his p-Indicator. It is in effect a weighted probability function. Let’s say that your system consists of three patterns. You first hypothesize a target and a stop for each pattern. (Harris normally uses the same figure for both the target and the stop, measured in points.) You then look at the entire price history of the particular futures market, analyze both long and short trades, and determine how often each pattern occurred and the percentage of the time that each hypothetical trade met its target before it was stopped out. The formula will return a weighted profitability percentage. The closer the value is to 100%, the better the trading results. Harris says that 60-70% offer good results. The p-Indicator, he claims, provides both an entry signal and a way to size trades.
There are many ways that one could improve on Harris’s methodology, which I’ve sketched in only the broadest strokes here. But I like the idea of having an entry signal which is not simply binary. I know that others have swum in these non-binary waters. For instance, MarketSci Blog had a post more than a year ago about transactional vs. confidence-based trading strategies and Condor Options distinguished between binary and polynary strategies. But unless there’s a shark or a tar ball alert there should be ample opportunity for many more to join in the fun.
But let’s go back ten years, to Michael Harris’s Short-term Trading with Price Patterns (Traders Press, 2000). The book is obviously dated and some of its methodology is—I’m looking for a more scientific description but what comes to mind is—clunky. Nonetheless, this book is an interesting attempt to combine price patterns and probability. Harris focuses on the futures markets.
The basic idea is to identify profitable short-term trading patterns, normally between three and seven bars, and then combine them into a trading system. That is, an entry would be triggered if Pattern A or Pattern B or Pattern C, etc. was identified.
Harris goes beyond simple pattern trading, however, by introducing his p-Indicator. It is in effect a weighted probability function. Let’s say that your system consists of three patterns. You first hypothesize a target and a stop for each pattern. (Harris normally uses the same figure for both the target and the stop, measured in points.) You then look at the entire price history of the particular futures market, analyze both long and short trades, and determine how often each pattern occurred and the percentage of the time that each hypothetical trade met its target before it was stopped out. The formula will return a weighted profitability percentage. The closer the value is to 100%, the better the trading results. Harris says that 60-70% offer good results. The p-Indicator, he claims, provides both an entry signal and a way to size trades.
There are many ways that one could improve on Harris’s methodology, which I’ve sketched in only the broadest strokes here. But I like the idea of having an entry signal which is not simply binary. I know that others have swum in these non-binary waters. For instance, MarketSci Blog had a post more than a year ago about transactional vs. confidence-based trading strategies and Condor Options distinguished between binary and polynary strategies. But unless there’s a shark or a tar ball alert there should be ample opportunity for many more to join in the fun.
Wednesday, June 9, 2010
Schwartz, Micro Markets
Back when you took Econ 101 did you ever think about applying microeconomics to the markets? Probably not. And I would guess that even today most traders and investors are content to repeat the mantra that markets are driven by supply and demand even if they’re not quite sure how supply and demand actually work in the markets. Robert A. Schwartz in Micro Markets: A Market Structure Approach to Microeconomic Analysis (Wiley, 2010) has written a text that should bring everybody up to speed. Given some familiarity with microeconomics and the markets it’s not a difficult read, but it definitely fills some gaps.
Schwartz addresses several major themes: supply and demand (which not surprisingly takes up more than half the book), competition, market efficiency, and regulation. Here I’m going to confine myself to a few points that Schwartz makes about supply and demand.
Let’s assume a continuous electronic, order-driven market. How does the limit-order book function in such a market? “How do the forces of supply and demand guide the continuous order-driven market in the direction of an unobservable, underlying equilibrium price?” (p. 154) First, both limit and market orders are available to the trader, and “transactions are triggered by the arrival of market orders that execute against the posted limit orders.” (p. 155) Limit orders leave the book if there is a transaction or if they are cancelled for one reason or another. Limit orders can also be repriced—upward if sentiment turns positive, downward if it turns negative. In the former case the buy side of the book will start to fill in at higher prices; in the latter, the sell side will fill in at lower prices.
Bids and offers are made at discrete prices, so any graph of the limit-order book will look like ascending and descending staircases, almost meeting at the bid-ask spread. But we know that supply and demand are not graphed as discrete step functions; they are curves and, as such, are continuous linear functions. How, then, can we claim that they model the limit-order book?
Schwartz explains that “because trading is costly, not all potential investors participate in each trading session, and each individual order is only a partial representation of what an individual trader’s complete demand curve might look like. Consequently, on both sides of the market, much of the desire to trade is not expressed, it is just latent.” Expressed desires look like stairsteps; “the complete individual demand curves of everybody who is in the market” give us the Econ 101 graph.
Because not all buy and sell desires are expressed at any given moment we can’t identify a true equilibrium point. At best, we can say that “the natural market forces of demand and supply tend to push prices in the right direction (at least some of the time).” (p. 160)
One more point: the deeper the order book the flatter the cumulated buy and sell order curves. Or, put in the language of the markets and of microeconomics, the deeper the book the more liquid it is and the more elastic the buy and sell curves are.
Here I’ve offered only a glimpse into Micro Markets. It would make an excellent college text, but it also provides the perfect opportunity for the trader or investor to gain a new perspective on his world. And it won’t take you a semester to get through it.
Schwartz addresses several major themes: supply and demand (which not surprisingly takes up more than half the book), competition, market efficiency, and regulation. Here I’m going to confine myself to a few points that Schwartz makes about supply and demand.
Let’s assume a continuous electronic, order-driven market. How does the limit-order book function in such a market? “How do the forces of supply and demand guide the continuous order-driven market in the direction of an unobservable, underlying equilibrium price?” (p. 154) First, both limit and market orders are available to the trader, and “transactions are triggered by the arrival of market orders that execute against the posted limit orders.” (p. 155) Limit orders leave the book if there is a transaction or if they are cancelled for one reason or another. Limit orders can also be repriced—upward if sentiment turns positive, downward if it turns negative. In the former case the buy side of the book will start to fill in at higher prices; in the latter, the sell side will fill in at lower prices.
Bids and offers are made at discrete prices, so any graph of the limit-order book will look like ascending and descending staircases, almost meeting at the bid-ask spread. But we know that supply and demand are not graphed as discrete step functions; they are curves and, as such, are continuous linear functions. How, then, can we claim that they model the limit-order book?
Schwartz explains that “because trading is costly, not all potential investors participate in each trading session, and each individual order is only a partial representation of what an individual trader’s complete demand curve might look like. Consequently, on both sides of the market, much of the desire to trade is not expressed, it is just latent.” Expressed desires look like stairsteps; “the complete individual demand curves of everybody who is in the market” give us the Econ 101 graph.
Because not all buy and sell desires are expressed at any given moment we can’t identify a true equilibrium point. At best, we can say that “the natural market forces of demand and supply tend to push prices in the right direction (at least some of the time).” (p. 160)
One more point: the deeper the order book the flatter the cumulated buy and sell order curves. Or, put in the language of the markets and of microeconomics, the deeper the book the more liquid it is and the more elastic the buy and sell curves are.
Here I’ve offered only a glimpse into Micro Markets. It would make an excellent college text, but it also provides the perfect opportunity for the trader or investor to gain a new perspective on his world. And it won’t take you a semester to get through it.
Tuesday, June 8, 2010
Warm-up to my trading day
I don’t and won’t write about my trading, the good, the bad, or the ugly. Suffice it to say that in order to trade successfully intraday I have to dial my left brain way down, which is difficult for me to do. But I think I’ve come as close to a solution as possible. For me it’s primarily in the warm-up to the trading day.
Let me begin with four failed strategies.
First and most disastrous for me, market analysis, defining a bias for the day (long or short, trend or mean reversion, breakout levels—lots of possibilities). It seems so logical to prepare for the trading day this way; after all, as Louis Pasteur said, chance favors the prepared mind. But it hogties me.
Second, physical exercise. Quite simply, it doesn’t make a difference one way or the other.
Third, puzzles. If I can do them relatively easily this “mental exercise” numbs my brain, my whole brain. Market action could just as well be Brady Bunch reruns; I find it difficult to become engaged.
Fourth, CNBC. On balance I find CNBC a distraction. I do, however, read headline business news (Bloomberg, FT, WSJ) and I know when economic and earnings reports are going to be released. I don’t go into the day unaware of the news flow.
And, what you’ve all been waiting for, the solution that can be yours for only $19.99 plus shipping and handling. Unfortunately, I suspect it has a very limited market.
For me (and realize that I get up at least four hours before U.S. floor trading begins, though I don’t consider 9:30 ET a sacrosanct market open) the absolutely best way I’ve found to warm up for the trading day is to stress my left brain to the point that it has to take a lengthy rest. The MIT linear algebra course is a good example. After I listen to a lecture or two and even take notes like a good college student, I’m primed for the trading day. My left brain has had all the stimulation it needs for several hours, and it can turn over command to the autopilot brain.
My solution may be totally idiosyncratic; then again it might help at least a couple of traders in the world. There, I’ve bared my soul!
Let me begin with four failed strategies.
First and most disastrous for me, market analysis, defining a bias for the day (long or short, trend or mean reversion, breakout levels—lots of possibilities). It seems so logical to prepare for the trading day this way; after all, as Louis Pasteur said, chance favors the prepared mind. But it hogties me.
Second, physical exercise. Quite simply, it doesn’t make a difference one way or the other.
Third, puzzles. If I can do them relatively easily this “mental exercise” numbs my brain, my whole brain. Market action could just as well be Brady Bunch reruns; I find it difficult to become engaged.
Fourth, CNBC. On balance I find CNBC a distraction. I do, however, read headline business news (Bloomberg, FT, WSJ) and I know when economic and earnings reports are going to be released. I don’t go into the day unaware of the news flow.
And, what you’ve all been waiting for, the solution that can be yours for only $19.99 plus shipping and handling. Unfortunately, I suspect it has a very limited market.
For me (and realize that I get up at least four hours before U.S. floor trading begins, though I don’t consider 9:30 ET a sacrosanct market open) the absolutely best way I’ve found to warm up for the trading day is to stress my left brain to the point that it has to take a lengthy rest. The MIT linear algebra course is a good example. After I listen to a lecture or two and even take notes like a good college student, I’m primed for the trading day. My left brain has had all the stimulation it needs for several hours, and it can turn over command to the autopilot brain.
My solution may be totally idiosyncratic; then again it might help at least a couple of traders in the world. There, I’ve bared my soul!
Monday, June 7, 2010
DeFalco, The Profit Hunter
The Profit Hunter: Beating the Bulls, Taming the Bears, and Slaughtering the Pigs (Wiley, 2010) by Neil DeFalco is a survey of strategies for small- to medium-sized investors, most of which rely on the use of derivatives. About half of the book is devoted to options—the basics and a catalog of strategies (when to use, profit potential, risk, and breakeven points). DeFalco then moves on to FOREX, commodities, single stock futures, and international investing.
Any investor who seeks to profit from this book should be reminded that a little knowledge is a dangerous thing. No retail trader can compete against the big boys after reading a basic survey. He will end up on the wrong side of the trade, being beaten, tamed, or slaughtered.
The value of DeFalco’s book lies in the fact that it lays out a range of possibilities for the investor to pursue and practice trading in a paper account. Perhaps as a result over time the investor will find his niche and become truly skilled.
Any investor who seeks to profit from this book should be reminded that a little knowledge is a dangerous thing. No retail trader can compete against the big boys after reading a basic survey. He will end up on the wrong side of the trade, being beaten, tamed, or slaughtered.
The value of DeFalco’s book lies in the fact that it lays out a range of possibilities for the investor to pursue and practice trading in a paper account. Perhaps as a result over time the investor will find his niche and become truly skilled.
Friday, June 4, 2010
Collins, Market Rap
Schadenfreude is a powerful emotion, which probably accounts for the fact that Art Collins’ Market Rap: The Odyssey of a Still-Struggling Commodity Trader (Traders Press, 2000) remains an enjoyable read. We follow him as he leaves his father’s car agency where he had in effect served an eight-year sentence as finance manager, through his stint as a failed rock star, to his arrival on the floor of the CBOT. We understand why, with a little help from his shrink, he abandons the pit for a home office. And we know from the very first page of the book that he isn’t a supertrader, that although he makes money overall “at the end of most years [his] bottom line performance isn’t too far afield of break-even.”
Collins is the author of several books, including When Supertraders Meet Kryptonite, which I wrote about back on July 27, 2009. He continues to share his market insights at Tiger Shark Trading and other trading sites.
Why should we care about this guy’s struggles? After all, might not Tolstoy’s opening line to Anna Karenina—“Happy families are all alike. Every unhappy family is unhappy in its own way.”—be equally true of traders? I suspect not. On the contrary, my hunch is that it is struggling traders who tend to be alike and successful traders who are each successful in his/her own way. But Collins has a knack for describing his struggles in sometimes memorable images, lending them an element of uniqueness.
Take, for instance, the matzo ball trade. “A matzo ball trade (the term loosely borrowed from a comment on Seinfeld), is a kind of chain reaction from hell. It’s one tiny discrepancy between a theoretical and actual execution that culminates, not just in a win turning into a loss, but a WINNN turning into a LOSSS! Like maybe the best trade of your month becoming the worst. . . . Matzos are usually near-afterthoughts; a small incorrect decision/mental lapse that could have just as easily gone one way as the other—a near-instantaneous event that punishes you for the better part of forever.” (p. 84)
Collins gives several examples of matzo ball trades, only some his own. Here’s one that occurred in the overnight yen market. An insomniac trader was poised to sell the yen. He asked for a bid/offer spread, didn’t like it, and hung up. “The next morning, the market was already off to the races toward the netherworld. In the ensuing two years, it lost over half its value; more than 50 full points, each one representing $1250. Needless to say, the trader never participated.”
Collins himself is a mechanical systems trader, and he shares some of his own creations. In style they are similar to one for the British pound: If c > average(c,10), then buy the next bar at open of tomorrow + average(range,4); exit long next bar at c-0.32. Or, an optimized n-day breakout with an exit at the n-day median. Or an S&P day fade: If the close is below the previous eight-day close, buy the next open plus 30% of the previous range on a stop. If it’s above the eight-day average, sell it at the open minus 45% of the range.
For sheer fun, he includes a chapter entitled “Fourteen Things Wrong with Trading Places.”
All in all, Market Rap is perfect summer reading for traders, all of whom struggle from time to time.
Collins is the author of several books, including When Supertraders Meet Kryptonite, which I wrote about back on July 27, 2009. He continues to share his market insights at Tiger Shark Trading and other trading sites.
Why should we care about this guy’s struggles? After all, might not Tolstoy’s opening line to Anna Karenina—“Happy families are all alike. Every unhappy family is unhappy in its own way.”—be equally true of traders? I suspect not. On the contrary, my hunch is that it is struggling traders who tend to be alike and successful traders who are each successful in his/her own way. But Collins has a knack for describing his struggles in sometimes memorable images, lending them an element of uniqueness.
Take, for instance, the matzo ball trade. “A matzo ball trade (the term loosely borrowed from a comment on Seinfeld), is a kind of chain reaction from hell. It’s one tiny discrepancy between a theoretical and actual execution that culminates, not just in a win turning into a loss, but a WINNN turning into a LOSSS! Like maybe the best trade of your month becoming the worst. . . . Matzos are usually near-afterthoughts; a small incorrect decision/mental lapse that could have just as easily gone one way as the other—a near-instantaneous event that punishes you for the better part of forever.” (p. 84)
Collins gives several examples of matzo ball trades, only some his own. Here’s one that occurred in the overnight yen market. An insomniac trader was poised to sell the yen. He asked for a bid/offer spread, didn’t like it, and hung up. “The next morning, the market was already off to the races toward the netherworld. In the ensuing two years, it lost over half its value; more than 50 full points, each one representing $1250. Needless to say, the trader never participated.”
Collins himself is a mechanical systems trader, and he shares some of his own creations. In style they are similar to one for the British pound: If c > average(c,10), then buy the next bar at open of tomorrow + average(range,4); exit long next bar at c-0.32. Or, an optimized n-day breakout with an exit at the n-day median. Or an S&P day fade: If the close is below the previous eight-day close, buy the next open plus 30% of the previous range on a stop. If it’s above the eight-day average, sell it at the open minus 45% of the range.
For sheer fun, he includes a chapter entitled “Fourteen Things Wrong with Trading Places.”
All in all, Market Rap is perfect summer reading for traders, all of whom struggle from time to time.
Thursday, June 3, 2010
The paradoxical character of creative people
In his book Creativity Unlimited Micael Dahlén outlines some paradoxical qualities that creative people exhibit and that the plodding can be trained to strengthen.
First, creative people are both conventional and rebellious.
Second, they are both divergent and convergent thinkers. That is, on the one hand they think differently and hence break patterns and, on the other, they try to collect thoughts (“even the maddest ideas”) into a pattern.
Third, they have both abundant energy and a great need for relaxation. “They often work harder and for longer than other people, but they also take more time off.”
Fourth, they are both humble and proud.
Fifth, they are both introverted and extroverted.
And finally, they use both sides of their brains.
How do you measure up? I fall decidedly short on the third quality.
First, creative people are both conventional and rebellious.
Second, they are both divergent and convergent thinkers. That is, on the one hand they think differently and hence break patterns and, on the other, they try to collect thoughts (“even the maddest ideas”) into a pattern.
Third, they have both abundant energy and a great need for relaxation. “They often work harder and for longer than other people, but they also take more time off.”
Fourth, they are both humble and proud.
Fifth, they are both introverted and extroverted.
And finally, they use both sides of their brains.
How do you measure up? I fall decidedly short on the third quality.
Wednesday, June 2, 2010
Diebold et al., The Known, the Unknown, and the Unknowable in Financial Risk Management
In 1995 Ralph Gomory wrote an essay in Scientific American that read, in part, “We are all taught what is known, but we rarely learn about what is not known, and we almost never learn about the unknowable. That bias can lead to misconceptions about the world around us.”
The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice (Princeton University Press, 2010), a collection of fifteen papers edited by Wharton professors Francis X. Diebold, Neil A. Doherty, and Richard J. Herring, applies the KuU conceptual framework to the financial world. It is a bold book, tackling both theory and practice and spanning the worlds of (among others) banking, insurance, real estate, and investment. It is also utterly engrossing.
There is no way I can do justice to the wealth of material in this book. It incorporates ideas from behavioral finance, statistics, and epistemology; it addresses a range of questions from portfolio management and crisis management (and, no, portfolio management should not be a case study in crisis management) to corporate governance. For the “idea junkie” it’s a real high.
Let me simply touch on a few salient points.
In their introduction the editors suggest that although K situations are often relevant, u and U are of equal or greater relevance in financial risk management. But how can we shed light on the unknown and how can we even begin to approach the unknowable? The editors propose, defining the framework of the book, that we need better data and better theory—and that the two are mutually reinforcing. As we expand our knowledge u can slowly be brought under the umbrella of K. Even U can sometimes be “tamed.” The authors write, “To the extent that U represents a failure of imagination . . . the collection and analysis of data regarding near misses—disasters that were narrowly averted—may provide a window into the domain of U and alternative outcomes.” (p. 7)
Richard Zeckhauser advocates investing wisely in the unknown and unknowable (and, he adds, the unique). He proposes that as long as an investor has a complementary skill—for instance, unusual judgment à la Warren Buffett—he can reap outsized returns because competition is often limited and prices way out of line. And if the investor doesn’t have that skill, he can “ride along in a sidecar pulled by a powerful motorcycle.” (p. 314) He cautions, however, against jumping on the quant bandwagon. The quant world is currently overpopulated, which means that quants may become the new shoeshine boys. That is, “when your math whiz finance Ph.D. tells you that he and his peers have been hired to work in the XYZ field, the spectacular returns in XYZ field have probably vanished forever.” (p. 321)
A Mandelbrot and Taleb thesis: “Large moves beget large moves; markets keep in memory the volatility of past deviations. A subtle concept, fractal memory provides an intrinsic way of modeling both the clustering of large events and the phenomenon of regime switching, which refers to phases when markets move from low to high volatility.” (p. 55)
And, finally, a geek note from the fascinating paper by Colacito and Engle on the term structure of risk. It’s obviously not new since it dates to a 1998 paper, but it was new to me. Anyone who trades volatility should note that “the common practice of converting 1-day volatilities to T-day estimates by scaling by [the square root of T] is inappropriate and produces overestimates of the variability of long-horizon volatility.” (p. 66)
Although this book is most obviously addressed to risk managers and regulators, I think it should be read by every intellectually curious person with skin in the financial game. If the investor or trader doesn’t come away with at least one or two ideas of practical importance to his financial life, he is a “sleepreader.”
The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice (Princeton University Press, 2010), a collection of fifteen papers edited by Wharton professors Francis X. Diebold, Neil A. Doherty, and Richard J. Herring, applies the KuU conceptual framework to the financial world. It is a bold book, tackling both theory and practice and spanning the worlds of (among others) banking, insurance, real estate, and investment. It is also utterly engrossing.
There is no way I can do justice to the wealth of material in this book. It incorporates ideas from behavioral finance, statistics, and epistemology; it addresses a range of questions from portfolio management and crisis management (and, no, portfolio management should not be a case study in crisis management) to corporate governance. For the “idea junkie” it’s a real high.
Let me simply touch on a few salient points.
In their introduction the editors suggest that although K situations are often relevant, u and U are of equal or greater relevance in financial risk management. But how can we shed light on the unknown and how can we even begin to approach the unknowable? The editors propose, defining the framework of the book, that we need better data and better theory—and that the two are mutually reinforcing. As we expand our knowledge u can slowly be brought under the umbrella of K. Even U can sometimes be “tamed.” The authors write, “To the extent that U represents a failure of imagination . . . the collection and analysis of data regarding near misses—disasters that were narrowly averted—may provide a window into the domain of U and alternative outcomes.” (p. 7)
Richard Zeckhauser advocates investing wisely in the unknown and unknowable (and, he adds, the unique). He proposes that as long as an investor has a complementary skill—for instance, unusual judgment à la Warren Buffett—he can reap outsized returns because competition is often limited and prices way out of line. And if the investor doesn’t have that skill, he can “ride along in a sidecar pulled by a powerful motorcycle.” (p. 314) He cautions, however, against jumping on the quant bandwagon. The quant world is currently overpopulated, which means that quants may become the new shoeshine boys. That is, “when your math whiz finance Ph.D. tells you that he and his peers have been hired to work in the XYZ field, the spectacular returns in XYZ field have probably vanished forever.” (p. 321)
A Mandelbrot and Taleb thesis: “Large moves beget large moves; markets keep in memory the volatility of past deviations. A subtle concept, fractal memory provides an intrinsic way of modeling both the clustering of large events and the phenomenon of regime switching, which refers to phases when markets move from low to high volatility.” (p. 55)
And, finally, a geek note from the fascinating paper by Colacito and Engle on the term structure of risk. It’s obviously not new since it dates to a 1998 paper, but it was new to me. Anyone who trades volatility should note that “the common practice of converting 1-day volatilities to T-day estimates by scaling by [the square root of T] is inappropriate and produces overestimates of the variability of long-horizon volatility.” (p. 66)
Although this book is most obviously addressed to risk managers and regulators, I think it should be read by every intellectually curious person with skin in the financial game. If the investor or trader doesn’t come away with at least one or two ideas of practical importance to his financial life, he is a “sleepreader.”
Tuesday, June 1, 2010
The channel of happiness
In Creativity Unlimited: Thinking Inside the Box for Business Innovation (Wiley, 2008) Micael Dahlén reacquaints us with the work of Mihály CsÃkszentmihályi and his thesis that people are happy when they stretch their creative capacity, avoiding the pitfalls of stress and boredom.
If they set tasks for themselves for which they do not possess the requisite skills they’ll be stressed; if the tasks are too easy in relation to their skills they’ll be bored. “The area between the parallels represents happiness. As we can tell from the model, happiness arises when challenges and skills are roughly at the same level, but they can never be at exactly the same level, because as soon as you have learned to meet a challenge, two things occur: (1) the challenge is no longer as great (the arrow points downwards in the model); and (2) your skill has increased (the arrow in the model moves to the right). If you do not accept new and greater challenges, then boredom is the result.” (p. 44)
Staying inside the happiness channel is tough for the trader. Initially his skills are not up to the task, so he is stressed. Eventually his skills more or less match the task at hand, and he becomes bored. Yes, we’ve all read that trading should be boring, but who really wants to be bored all day long? So the trader has to keep defining new challenges for himself. Perhaps it’s to increase size, perhaps it’s to fine tune execution skills, perhaps it’s to develop a new strategy. The list could go on and on.
The fact is that we’re never as good as we could be. Moreover, since the markets are ever changing, we should realize that just about the time we start being really bored we’re likely to get kicked in the gut. If we want to continue to be profitable (and happy) we have to keep our creative juices flowing.
If they set tasks for themselves for which they do not possess the requisite skills they’ll be stressed; if the tasks are too easy in relation to their skills they’ll be bored. “The area between the parallels represents happiness. As we can tell from the model, happiness arises when challenges and skills are roughly at the same level, but they can never be at exactly the same level, because as soon as you have learned to meet a challenge, two things occur: (1) the challenge is no longer as great (the arrow points downwards in the model); and (2) your skill has increased (the arrow in the model moves to the right). If you do not accept new and greater challenges, then boredom is the result.” (p. 44)
Staying inside the happiness channel is tough for the trader. Initially his skills are not up to the task, so he is stressed. Eventually his skills more or less match the task at hand, and he becomes bored. Yes, we’ve all read that trading should be boring, but who really wants to be bored all day long? So the trader has to keep defining new challenges for himself. Perhaps it’s to increase size, perhaps it’s to fine tune execution skills, perhaps it’s to develop a new strategy. The list could go on and on.
The fact is that we’re never as good as we could be. Moreover, since the markets are ever changing, we should realize that just about the time we start being really bored we’re likely to get kicked in the gut. If we want to continue to be profitable (and happy) we have to keep our creative juices flowing.
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