Those who are convinced they can consistently beat the market will find Charles D. Ellis’s book depressing reading. The underlying thesis of Winning the Loser’s Game: Timeless Strategies for Successful Investing, 6th ed. (McGraw-Hill, 2013) is that in recent decades investment management has evolved from a winner’s game to a loser’s game because “the market came to be dominated by the very institutions that were striving to win by outperforming the market. No longer is the active investment manager competing with overly cautious custodians or overly confident amateurs who are out of touch with the fast-moving market. Now he or she competes with other hardworking investment experts in a loser’s game where the secret to ‘winning’ is to lose less than the others lose.” (pp. 5-6)
Put another way, and here I invoke Michael Mauboussin’s The Success Equation, we are faced with the paradox of skill: “As skill improves, performance becomes more consistent, and therefore luck becomes more important.” That is, as the population of skilled investors rises, with individual retail investors moving to the sidelines, the variation in skill narrows and luck plays a greater role in outperformance. And, as we all know, luck is not predictable. That an investor manager has outperformed the market in the past two or three—or even fifteen—years in no way guarantees that his luck will continue. Bill Miller of Legg Mason is, of course, the poster child for this truism.
However you slice it, Ellis argues that the individual investor must reconcile himself to the fact that he will not get outsized returns year after year, whether he hands his money over to a fund manager or (normally even worse) pursues the do-it-yourself route. Instead, he must plod along, trying to minimize mistakes. Ellis puts it in less tortoise-like language: “As all grandparents and most parents know—and as most grandchildren will come to know—the real test of a good driver is simple: no serious accidents. And as all flyers know, safe, dull—even boring—is the essence of a good flight. The secret to success in investing is not in beating the market any more than success in driving is going 10 MPH over the posted speed limit. Success in driving is being on the right road and moving at a reasonable speed.” (p. 51)
What are some of the mistakes investors make? They pay too much in fees, they trade too often, they ignore tax considerations, they fail to diversify, and they have no clearly defined objectives.
And if you still think that active management is the way to go, Ellis offers a couple of sobering statistics: “Over 10-year periods, two out of three active managers’ results fall below the market averages—and below index funds. Over longer periods, more fall below.” And “Those managers who fall short fall short by nearly twice as much as those who do better do better.” (p. 153) He adds, “Yes, Virginia, some managers will always beat the market, but we have no reliable way of determining in advance which specific managers will be the lucky ones.” (p. 166)
There are more than 500,000 copies of Winning the Loser’s Game in print. Whether readers acted on Ellis’s advice or promptly ignored what they were told I have no way of knowing. Creators of mutual funds and ETFs certainly listened. There is now a virtual supermarket of index and quasi-index funds and ETFs available. Some have higher fees than others, some trade more frequently than others, some (beware!) are pegged to bizarre indexes. All in all, the investor still has his work cut out for him. Ellis is standing by to lend him a helping hand.
Wednesday, June 26, 2013
Monday, June 24, 2013
Dreyfuss, Hedge Hogs
Unlike its namesake “eternal” flower, the Amaranth hedge fund was all too mortal. It opened its doors in May 2000 and, in an epic downfall, shuttered them in the fall of 2006. “At its peak in August, Amaranth LLC had assets totaling $9.668 billion. But four weeks later, more than $6 billion had vanished.”
Barbara T. Dreyfuss investigates the “high stakes battle” that led to the fund’s demise in Hedge Hogs: The Cowboy Traders Behind Wall Street’s Largest Hedge Fund Disaster (Random House, 2013). It’s a gripping tale of two natural gas traders whose activities roiled energy markets, “costing utilities, small companies, schools, hospitals, and homes millions of dollars,” and whose “contest ended when one collapsed a multibillion-dollar firm and the other became a billionaire.”
Prior to joining Amaranth Brian Hunter had had a rocky career at Deutsche Bank where, after losing $53 million in two weeks, he was demoted from head of natural gas trading to research analyst and “due to his lack of integrity and immaturity” got no bonus. But Amaranth, eager to expand its commodity business, scooped him up nonetheless. Two and a half years later, when news of Amaranth’s demise became public, executives at Deutsche Bank felt vindicated. As one said, using a reference to cricket players practicing before a game, “he was merely playing in the nets when he was here.”
Hunter’s bête noir, John Arnold, was one of Enron’s most profitable traders in its heyday; his VAR was one-fifth to one-third of Enron’s entire limit. After Enron imploded he set up his own hedge fund, Centaurus. In 2006 Centaurus earned a 317% return and Arnold’s personal income surpassed that of T. Boone Pickens, Steve Cohen, and Paul Tudor Jones.
Dreyfuss’s story also has a supporting cast, starting with Nicholas Maounis, who founded Amaranth and who rewarded outsize returns with outsize trading capital. In 2005, for instance, Hunter’s trades turned around what would have been a losing year for the fund. And so “capital allocations were cut for some portfolio managers and traders in order to funnel more money to Hunter.” Maounis turned a blind eye to the potential risks in Hunter’s huge, concentrated positions.
And regulators placed few limits on Hunter’s activities. ICE was exempt from government regulation and had no trading limits. NYMEX set only loose guidelines. “The only firm limit set by NYMEX, under CFTC guidance, was on how many expiring contracts a trader could hold during its last three trading days: one thousand. But traders did not have much trouble getting permission to exceed these limits. Indeed, in September 2005, Amaranth’s limit for such trading was raised to twenty-five hundred.”
Hedge Hogs is a cautionary tale but not a finger-wagging one. Since the plot is so compelling, Dreyfuss doesn’t have to be preachy. Of course, one knows in advance how it ends, but that doesn’t interfere with the trading drama. The book’s a page turner.
Barbara T. Dreyfuss investigates the “high stakes battle” that led to the fund’s demise in Hedge Hogs: The Cowboy Traders Behind Wall Street’s Largest Hedge Fund Disaster (Random House, 2013). It’s a gripping tale of two natural gas traders whose activities roiled energy markets, “costing utilities, small companies, schools, hospitals, and homes millions of dollars,” and whose “contest ended when one collapsed a multibillion-dollar firm and the other became a billionaire.”
Prior to joining Amaranth Brian Hunter had had a rocky career at Deutsche Bank where, after losing $53 million in two weeks, he was demoted from head of natural gas trading to research analyst and “due to his lack of integrity and immaturity” got no bonus. But Amaranth, eager to expand its commodity business, scooped him up nonetheless. Two and a half years later, when news of Amaranth’s demise became public, executives at Deutsche Bank felt vindicated. As one said, using a reference to cricket players practicing before a game, “he was merely playing in the nets when he was here.”
Hunter’s bête noir, John Arnold, was one of Enron’s most profitable traders in its heyday; his VAR was one-fifth to one-third of Enron’s entire limit. After Enron imploded he set up his own hedge fund, Centaurus. In 2006 Centaurus earned a 317% return and Arnold’s personal income surpassed that of T. Boone Pickens, Steve Cohen, and Paul Tudor Jones.
Dreyfuss’s story also has a supporting cast, starting with Nicholas Maounis, who founded Amaranth and who rewarded outsize returns with outsize trading capital. In 2005, for instance, Hunter’s trades turned around what would have been a losing year for the fund. And so “capital allocations were cut for some portfolio managers and traders in order to funnel more money to Hunter.” Maounis turned a blind eye to the potential risks in Hunter’s huge, concentrated positions.
And regulators placed few limits on Hunter’s activities. ICE was exempt from government regulation and had no trading limits. NYMEX set only loose guidelines. “The only firm limit set by NYMEX, under CFTC guidance, was on how many expiring contracts a trader could hold during its last three trading days: one thousand. But traders did not have much trouble getting permission to exceed these limits. Indeed, in September 2005, Amaranth’s limit for such trading was raised to twenty-five hundred.”
Hedge Hogs is a cautionary tale but not a finger-wagging one. Since the plot is so compelling, Dreyfuss doesn’t have to be preachy. Of course, one knows in advance how it ends, but that doesn’t interfere with the trading drama. The book’s a page turner.
Wednesday, June 19, 2013
Gorman & Kennedy, Visual Guide to Elliott Wave Trading
First, what Visual Guide to Elliott Wave Trading by Wayne Gorman and Jeffrey Kennedy (Bloomberg/Wiley, 2013) is not. It is not an Elliott wave primer. The authors direct the reader who knows nothing about wave patterns to the classic presentation by Frost and Prechter, available free online.
Instead, this visual guide shows how to actually use Elliott waves in trading, both as a stand-alone tool and, more perfunctorily, in combination with technical indicators. It also includes two chapters on incorporating Elliott waves into options trading strategies
Many of the Elliott waves the author illustrate (and naturally the illustrations are abundant) are of the “real world” vs. the “textbook” variety. That is, they are tricky to decipher even in hindsight. This difficulty has led many critics to claim that Elliott wave theory is useless in real time. In fact, the authors admit that “under the Elliott wave model, there is usually more than one valid wave count at any particular time” and that “sometimes these wave counts point in opposite directions.” (p. 195)
For the trader in doubt (who is not pursuing an option strategy that can profit under more than one scenario), Gorman and Kennedy provide visual cues—usually familiar patterns such as channels and wedges, sometimes Fibonacci levels—that help the trader make sense of the waves. The chapter titles in Part II (“Trading Examples”) point to some of these cues: “How Zigzags and Flats Set Up a Trade for the Next Impulse Wave,” “How a Triangle Positions You for the Next Move,” “Riding Wave C in a Zigzag,” and “Using Ending Diagonals to Trade Swift and Sharp Reversals.”
The authors draw the majority of their examples from the futures and FX markets, which tend to trade more technically than do stocks. They describe sample trades as these trades progress over time, explaining how they set stops and targets, what they were thinking (and feeling) during the trades, and what they learned from them.
Visual Guide to Elliott Wave Trading will help the trader who is interested in wave theory as a practical tool for increasing his bottom line—even if he can’t properly label all the wave levels on his charts.
Instead, this visual guide shows how to actually use Elliott waves in trading, both as a stand-alone tool and, more perfunctorily, in combination with technical indicators. It also includes two chapters on incorporating Elliott waves into options trading strategies
Many of the Elliott waves the author illustrate (and naturally the illustrations are abundant) are of the “real world” vs. the “textbook” variety. That is, they are tricky to decipher even in hindsight. This difficulty has led many critics to claim that Elliott wave theory is useless in real time. In fact, the authors admit that “under the Elliott wave model, there is usually more than one valid wave count at any particular time” and that “sometimes these wave counts point in opposite directions.” (p. 195)
For the trader in doubt (who is not pursuing an option strategy that can profit under more than one scenario), Gorman and Kennedy provide visual cues—usually familiar patterns such as channels and wedges, sometimes Fibonacci levels—that help the trader make sense of the waves. The chapter titles in Part II (“Trading Examples”) point to some of these cues: “How Zigzags and Flats Set Up a Trade for the Next Impulse Wave,” “How a Triangle Positions You for the Next Move,” “Riding Wave C in a Zigzag,” and “Using Ending Diagonals to Trade Swift and Sharp Reversals.”
The authors draw the majority of their examples from the futures and FX markets, which tend to trade more technically than do stocks. They describe sample trades as these trades progress over time, explaining how they set stops and targets, what they were thinking (and feeling) during the trades, and what they learned from them.
Visual Guide to Elliott Wave Trading will help the trader who is interested in wave theory as a practical tool for increasing his bottom line—even if he can’t properly label all the wave levels on his charts.
Monday, June 17, 2013
Hagstrom, Investing: The Last Liberal Art, 2d ed.
Robert G. Hagstrom, chief investment strategist and managing director for Legg Mason Investment Counsel and author of eight investment books, found inspiration for this book in Charlie Munger’s notion of a latticework of mental models. This second edition of Investing: The Last Liberal Art (Columbia Business School Publishing, 2013) explores “big ideas” from seven academic disciplines—physics, biology, sociology, psychology, philosophy, literature, and mathematics—to build a latticework that will help us better understand financial markets.
Hagstrom’s latticework of mental models is not, of course, definitive. Each person who is willing to invest the intellectual effort required will come up with his own set of models—a set that will undoubtedly change over time as he incorporates new ideas (some, I would suggest, seemingly minor or peripheral but nonetheless potentially fertile) and shifts others around. Moreover, the application of these models, which involves metaphoric thinking, will vary from person to person.
But Hagstrom provides a good model of what a latticework of mental models could look like as well as a thoroughly enjoyable romp through fields of ideas (admittedly most of them familiar) that might serve the investor well. To name but a few: complex adaptive systems that throw the classic theories of equilibrium into serious question, evolution, the psychology of misjudgment, and Bayesian analysis.
The very nature of Hagstrom’s task implies a level of superficiality. If the investor really wants to know about pragmatism or the wisdom of crowds, he should read James (or, a much tougher nut to crack, Peirce) and Surowiecki. He shouldn’t rely on Hagstrom’s summaries. But by the same token he shouldn’t get bogged down in an unending quest to become a polymath. He is, after all, supposed to be using mental models to invest better, not simply striving to know a lot about a lot.
Charlie Munger makes the process sound easy, but then he’s been at it for a long time and is a voracious reader. “Worldly wisdom,” he said, “is mostly very, very simple. There are a relatively small number of disciplines and a relatively small number of truly big ideas. And it’s a lot of fun to figure out. Even better, the fun never stops. Furthermore, there’s a lot of money in it, as I can testify from my own personal experience.” (p. 11) Despite this advice to Stanford students, he himself was obviously never satisfied populating his mental models with only a few truly big ideas. Otherwise, why would he imagine that “Paradise will be a kind of library”?
Hagstrom’s latticework of mental models is not, of course, definitive. Each person who is willing to invest the intellectual effort required will come up with his own set of models—a set that will undoubtedly change over time as he incorporates new ideas (some, I would suggest, seemingly minor or peripheral but nonetheless potentially fertile) and shifts others around. Moreover, the application of these models, which involves metaphoric thinking, will vary from person to person.
But Hagstrom provides a good model of what a latticework of mental models could look like as well as a thoroughly enjoyable romp through fields of ideas (admittedly most of them familiar) that might serve the investor well. To name but a few: complex adaptive systems that throw the classic theories of equilibrium into serious question, evolution, the psychology of misjudgment, and Bayesian analysis.
The very nature of Hagstrom’s task implies a level of superficiality. If the investor really wants to know about pragmatism or the wisdom of crowds, he should read James (or, a much tougher nut to crack, Peirce) and Surowiecki. He shouldn’t rely on Hagstrom’s summaries. But by the same token he shouldn’t get bogged down in an unending quest to become a polymath. He is, after all, supposed to be using mental models to invest better, not simply striving to know a lot about a lot.
Charlie Munger makes the process sound easy, but then he’s been at it for a long time and is a voracious reader. “Worldly wisdom,” he said, “is mostly very, very simple. There are a relatively small number of disciplines and a relatively small number of truly big ideas. And it’s a lot of fun to figure out. Even better, the fun never stops. Furthermore, there’s a lot of money in it, as I can testify from my own personal experience.” (p. 11) Despite this advice to Stanford students, he himself was obviously never satisfied populating his mental models with only a few truly big ideas. Otherwise, why would he imagine that “Paradise will be a kind of library”?
Friday, June 14, 2013
Boillot, I Killed a Rabid Fox with a Croquet Mallet
How could I pass up a book with such a bizarre title? And that, of course, is precisely the point of the book itself. I Killed a Rabid Fox with a Croquet Mallet: Making Your Business Stories Compelling and Memorable (HB Books/HB Agency, 2013) was the brain child of Nicolas Boillot, CEO of the marketing firm that published the book. The title story has since become something of an annoying earworm for me, but I guess that means it did its job.
I could explain how, according to the author, businesses should leverage their stories to reach and influence customers. But since that would take me far afield of this blog’s raison d’être, let me give a single example. Think of all the stories about people abusing L.L. Bean’s return policy. “We believe,” the author writes, “L.L. Bean banked on the fact that most people who heard these stories had one reaction: That person’s abusing the system. But what an amazing company to have such a return policy!” I myself had that reaction when a friend explained how she had returned a dog bed after her dear sweet basset hound destroyed its zipper.
And now to the rabid fox. Here are the ingredients of the story (it’s too long to recount in full, so you’ll have to use your imagination to fill in the blanks). Summertime, six five-year-olds playing in backyard pool, mangy-looking fox following family dog along pool’s edge, wife atop picnic table shouting at fox, fox along with dog and wife disappearing into bushes, fox trotting threateningly close toward author, author bludgeoning it to death with croquet mallet, kids screaming.
“Years later,” the author writes, “I’m still known in my town for killing a rabid fox with a croquet mallet…. It’s a compelling and memorable story that quickly infiltrated my business life.”
Perhaps because I had my own run-in with a rabid fox some years back, but without such a compelling tale to accompany it, I can’t get rid of Boillot’s story. Maybe if I share it?
I could explain how, according to the author, businesses should leverage their stories to reach and influence customers. But since that would take me far afield of this blog’s raison d’être, let me give a single example. Think of all the stories about people abusing L.L. Bean’s return policy. “We believe,” the author writes, “L.L. Bean banked on the fact that most people who heard these stories had one reaction: That person’s abusing the system. But what an amazing company to have such a return policy!” I myself had that reaction when a friend explained how she had returned a dog bed after her dear sweet basset hound destroyed its zipper.
And now to the rabid fox. Here are the ingredients of the story (it’s too long to recount in full, so you’ll have to use your imagination to fill in the blanks). Summertime, six five-year-olds playing in backyard pool, mangy-looking fox following family dog along pool’s edge, wife atop picnic table shouting at fox, fox along with dog and wife disappearing into bushes, fox trotting threateningly close toward author, author bludgeoning it to death with croquet mallet, kids screaming.
“Years later,” the author writes, “I’m still known in my town for killing a rabid fox with a croquet mallet…. It’s a compelling and memorable story that quickly infiltrated my business life.”
Perhaps because I had my own run-in with a rabid fox some years back, but without such a compelling tale to accompany it, I can’t get rid of Boillot’s story. Maybe if I share it?
Wednesday, June 12, 2013
Davenport & Kim, Keeping Up with the Quants
It may be an overstatement to describe data analysis as “the sexiest job of the 21st century” (CNBC headline, 6/5/13), but data analysts are definitely in demand. An overwhelming amount of data has been and continues to be collected. These mountains of raw data often seem akin to all that stuff hoarders can’t bear to part with, taking up space (though of course not as much as physical junk does) and having no apparent function. Amazon is the ultimate data hoarder, claiming never to throw anything away. After all, you never know when with a new kind of pick and shovel or a better map you just might find gold in them thar hills.
Keeping Up with the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport and Jinho Kim (Harvard Business Review Press, 2013) is a terrific book for those who aspire to be data-savvy consumers or managers, even for those who might become quants one day themselves. In clear, non-technical prose the authors explain the ingredients common to all types of quantitative analysis: framing the problem (problem recognition, review of previous findings), solving the problem (modeling and variable selection, data collection, and data analysis), and communicating and acting on results. They draw examples from a range of fields, including finance, and in the process expand the reader’s horizons.
Take Florence Nightingale, for instance. Did you know that she was an early adopter of quantitative methods? She was appalled at the conditions and high mortality rates in a makeshift British military hospital in Turkey during the Crimean War. She began collecting data. “Nightingale’s greatest innovation, however, was in presentation of the results.” She developed innovative diagrams, “a kind of pie chart with displays in the shape of wedge cuts. Nightingale printed them in several colors to clearly show how the mortality from each cause changed from month to month.” Eventually, death rates in the hospital fell dramatically and, when she returned to England, Nightingale “found herself a celebrity and praised as a heroine. Nightingale became a Fellow of the Royal Statistical Society in 1859—the first woman to become a member—and an honorary member of the American Statistical Association in 1874.” (pp. 103-104)
Keeping Up with the Quants is at its core a how-to book in practical problem solving using scientific (analytic) methods coupled with sound business practice. But that description makes it seem boring. And the book is anything but boring. It takes a page from Xiao-Li Meng’s Harvard undergraduate course called Real-Life Statistics: Your Chance for Happiness (or Misery) which includes modules on such topics as romance, wine and chocolate, finance, medicine, and the stock market. The course aims, in Meng’s words, to make statistics “not just palatable, but delicious.” (p. 96) Well, I wouldn’t call Keeping Up with the Quants a delicious book, mainly because I have problems analogizing from food to the written text, but it’s a book I plan to read a second time. (And, by contrast, my delicious dinner from last night is long gone.)
Those readers with a narrow financial focus will learn how the Australian authorities solved the Simon Hannes insider trading case and why being able to adjust models frequently has helped make James Simons’s flagship Medallion fund so phenomenally successful. Anyone contemplating cooking the books should heed the first-digit (or Benford’s) law. “By just looking at the first digit of each data entry and comparing the actual frequency of occurrence with the predicted frequency, one can easily finger concocted data. In general, faked or fraudulent data appear to have far fewer numbers starting with 1, and many more starting with 6, than do true data.” (p. 146)
I would recommend this book to anyone who is using data, big or small, to make decisions—actually, that pretty well encompasses everyone. Whether you’re picking stocks or deciding if you should get a pet, you will learn something from Davenport and Kim.
Keeping Up with the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport and Jinho Kim (Harvard Business Review Press, 2013) is a terrific book for those who aspire to be data-savvy consumers or managers, even for those who might become quants one day themselves. In clear, non-technical prose the authors explain the ingredients common to all types of quantitative analysis: framing the problem (problem recognition, review of previous findings), solving the problem (modeling and variable selection, data collection, and data analysis), and communicating and acting on results. They draw examples from a range of fields, including finance, and in the process expand the reader’s horizons.
Take Florence Nightingale, for instance. Did you know that she was an early adopter of quantitative methods? She was appalled at the conditions and high mortality rates in a makeshift British military hospital in Turkey during the Crimean War. She began collecting data. “Nightingale’s greatest innovation, however, was in presentation of the results.” She developed innovative diagrams, “a kind of pie chart with displays in the shape of wedge cuts. Nightingale printed them in several colors to clearly show how the mortality from each cause changed from month to month.” Eventually, death rates in the hospital fell dramatically and, when she returned to England, Nightingale “found herself a celebrity and praised as a heroine. Nightingale became a Fellow of the Royal Statistical Society in 1859—the first woman to become a member—and an honorary member of the American Statistical Association in 1874.” (pp. 103-104)
Keeping Up with the Quants is at its core a how-to book in practical problem solving using scientific (analytic) methods coupled with sound business practice. But that description makes it seem boring. And the book is anything but boring. It takes a page from Xiao-Li Meng’s Harvard undergraduate course called Real-Life Statistics: Your Chance for Happiness (or Misery) which includes modules on such topics as romance, wine and chocolate, finance, medicine, and the stock market. The course aims, in Meng’s words, to make statistics “not just palatable, but delicious.” (p. 96) Well, I wouldn’t call Keeping Up with the Quants a delicious book, mainly because I have problems analogizing from food to the written text, but it’s a book I plan to read a second time. (And, by contrast, my delicious dinner from last night is long gone.)
Those readers with a narrow financial focus will learn how the Australian authorities solved the Simon Hannes insider trading case and why being able to adjust models frequently has helped make James Simons’s flagship Medallion fund so phenomenally successful. Anyone contemplating cooking the books should heed the first-digit (or Benford’s) law. “By just looking at the first digit of each data entry and comparing the actual frequency of occurrence with the predicted frequency, one can easily finger concocted data. In general, faked or fraudulent data appear to have far fewer numbers starting with 1, and many more starting with 6, than do true data.” (p. 146)
I would recommend this book to anyone who is using data, big or small, to make decisions—actually, that pretty well encompasses everyone. Whether you’re picking stocks or deciding if you should get a pet, you will learn something from Davenport and Kim.
Monday, June 10, 2013
Paul & Moynihan, What I Learned Losing a Million Dollars
If you weren’t active in the financial markets when this book was first published nearly twenty years ago, you owe it to yourself to read the new Columbia University Press edition of What I Learned Losing a Million Dollars by the late Jim Paul (1943-2001) and Brendan Moynihan. The basic tenet is that trying to figure out how to make money in the markets by imitating the most successful pros is a waste of time: “What one guy said not to do, another guy said you should do. … Think about it this way; if one guy did what another said not to do, how come the first guy didn’t lose his money? And if the first guy hadn’t lost, why didn’t the second guy?” (p. 63) Well, maybe he did. What the pros had in common was not a strategy for making money but an ability to control their losses. “Learning how not to lose money,” Paul reasoned, “is more important than learning how to make money.” (pp. 64-65)
Jim Paul was good at making money—and losing it—as he recounts in vivid detail in the first part of the book. But although he was nearly broke, he didn’t intend to give up on trading and get a “real job.” He therefore set out to examine “the mental processes, behavioral characteristics, and emotions of people who lose money in the markets” (p. 70) and how to modify them.
The first important distinction Paul draws is between external and internal losses. An external loss is objective; it’s a fact. “[W]hen Kentucky loses a basketball game, it is no more of a loss for a member of the losing team than for a spectator in terms of it being an external, objective fact.” But the player and spectator “could personalize this external loss if they equated their self-esteem with the success or failure of the team.” (pp. 75-76)
Traders are prone to internalize their losses; they tend to equate losing money in the market with being stupid or being wrong. That is, they personalize the objective loss of money and turn it into a loss of self-worth. Which, of course, accounts for people’s unwillingness to sell losing positions.
Traders may also fall victim to conflating discrete events and continuous processes. For instance, sports games and political contests are discrete events; the game ends, the contest finishes. A market position, however, is a continuous process with no predetermined end. “Betting and gambling are suitable for discrete events but not for continuous processes. … In betting and gambling games if you stop acting and do nothing, the losses will stop. But when investing, trading, or speculating, if you’re losing and stop acting, the losses don’t stop; they can continue to grow almost indefinitely.” (p. 94)
Many traders also succumb to fallacies in popularly held beliefs about probability. “Perhaps the most common fallacy to which market participants are susceptible,” Paul writes, “is money odds vs. probability odds. Many market participants express the probability of success in terms of a risk-reward ratio. For example, if I bought my famous takeover stock … at twenty-six dollars and placed a sell stop below the market at twenty-three dollars with an upside objective of thirty-six dollars, my risk-reward ratio would be three to ten. Risk three dollars to make ten dollars. It is clear that I don’t understand probability. Couching my rationalizations in arithmetic terms does not automatically lend credibility to my position. The three-to-ten ratio has nothing to do with the probability that the stock” will move a certain amount. “All the ratio does is compare the dollar amount of what I think I might lose to the dollar amount of what I think I might make. But it doesn’t say anything about the probability of either event occurring.” (pp. 96-97)
A side note here in case you're not familiar with this graphic Charlie Munger quotation: "If you don’t get elementary probability into your repertoire you go through a long life a one-legged man in an ass-kicking contest."
Paul stresses the need for a trading plan but recognizes that people, being human, will sometimes deviate from their plan. And so he gives a final piece of advice: “Speculating (and this includes investing and trading) is the only human endeavor in which what feels good is the right thing to do.” (p. 150) If the market starts going against you, and looking at prices going down is not fun, do what feels good: get out. If you have a long position on and prices are going up, keep feeling good; leave it alone.
What I Learned Losing a Million Dollars is a fast but enlightening read, worth much more than most “What I Learned Making a Million Dollars” books. I thoroughly enjoyed it.
Jim Paul was good at making money—and losing it—as he recounts in vivid detail in the first part of the book. But although he was nearly broke, he didn’t intend to give up on trading and get a “real job.” He therefore set out to examine “the mental processes, behavioral characteristics, and emotions of people who lose money in the markets” (p. 70) and how to modify them.
The first important distinction Paul draws is between external and internal losses. An external loss is objective; it’s a fact. “[W]hen Kentucky loses a basketball game, it is no more of a loss for a member of the losing team than for a spectator in terms of it being an external, objective fact.” But the player and spectator “could personalize this external loss if they equated their self-esteem with the success or failure of the team.” (pp. 75-76)
Traders are prone to internalize their losses; they tend to equate losing money in the market with being stupid or being wrong. That is, they personalize the objective loss of money and turn it into a loss of self-worth. Which, of course, accounts for people’s unwillingness to sell losing positions.
Traders may also fall victim to conflating discrete events and continuous processes. For instance, sports games and political contests are discrete events; the game ends, the contest finishes. A market position, however, is a continuous process with no predetermined end. “Betting and gambling are suitable for discrete events but not for continuous processes. … In betting and gambling games if you stop acting and do nothing, the losses will stop. But when investing, trading, or speculating, if you’re losing and stop acting, the losses don’t stop; they can continue to grow almost indefinitely.” (p. 94)
Many traders also succumb to fallacies in popularly held beliefs about probability. “Perhaps the most common fallacy to which market participants are susceptible,” Paul writes, “is money odds vs. probability odds. Many market participants express the probability of success in terms of a risk-reward ratio. For example, if I bought my famous takeover stock … at twenty-six dollars and placed a sell stop below the market at twenty-three dollars with an upside objective of thirty-six dollars, my risk-reward ratio would be three to ten. Risk three dollars to make ten dollars. It is clear that I don’t understand probability. Couching my rationalizations in arithmetic terms does not automatically lend credibility to my position. The three-to-ten ratio has nothing to do with the probability that the stock” will move a certain amount. “All the ratio does is compare the dollar amount of what I think I might lose to the dollar amount of what I think I might make. But it doesn’t say anything about the probability of either event occurring.” (pp. 96-97)
A side note here in case you're not familiar with this graphic Charlie Munger quotation: "If you don’t get elementary probability into your repertoire you go through a long life a one-legged man in an ass-kicking contest."
Paul stresses the need for a trading plan but recognizes that people, being human, will sometimes deviate from their plan. And so he gives a final piece of advice: “Speculating (and this includes investing and trading) is the only human endeavor in which what feels good is the right thing to do.” (p. 150) If the market starts going against you, and looking at prices going down is not fun, do what feels good: get out. If you have a long position on and prices are going up, keep feeling good; leave it alone.
What I Learned Losing a Million Dollars is a fast but enlightening read, worth much more than most “What I Learned Making a Million Dollars” books. I thoroughly enjoyed it.
Wednesday, June 5, 2013
McGuire, The Silver Bull Market
I suppose the first obvious question to ask the author is: What silver bull market? All year the price of silver has been in an inexorable decline and is less than half its $48 2011 peak; it is now at 2010 levels, although admittedly way above its 2001 low of roughly $4 an ounce. Shayne McGuire, a managing director and head of global research at Teacher Retirement System of Texas, may have mistimed The Silver Bull Market: Investing in the Other Gold (Wiley, 2013). But that doesn’t mean the book’s not worth reading. In fact, now just might be a prime time to read it. Silver prices are notoriously volatile; they can rebound as easily as they can crater.
What is the logic behind owning silver? First, if you like gold, you should like silver as well; silver’s performance is highly correlated to gold’s although its price swings tend to be more intense. Silver, like gold, is an inflation hedge—and a lot cheaper for the investor with a small portfolio to own. Silver is also an industrial play on global technological advancement, although it is less industrial than, say, copper. Moreover, unlike gold, much of it gets used up in industrial production.
McGuire also points out that the gold-silver ratio is out of historical balance. “While gold is 8 times scarcer than silver (in terms of total ounces produced annually), its price is more than 50 times higher than silver’s. For 3,000 years in which the exchange rate could be observed” [now that’s one long chart!] “gold was 9 to 16 times more expensive, making today’s level historically extreme.” (p. 12)
Investors are understandably leery about investing in silver given its rocky history and continuing volatility. Many remember silver’s collapse in 1980, when it lost half its value on a single day. The author quotes a commodity expert as saying that “Silver rushes up the stairs to jump out the window.” (p. 84) But those who can catch it on its way up can reap large profits.
McGuire offers a brief (about 50-page) history of silver in the United States and what it means for the metal’s future and closes his book with advice on how to invest in silver, including coins and silver mining stocks.
What is the logic behind owning silver? First, if you like gold, you should like silver as well; silver’s performance is highly correlated to gold’s although its price swings tend to be more intense. Silver, like gold, is an inflation hedge—and a lot cheaper for the investor with a small portfolio to own. Silver is also an industrial play on global technological advancement, although it is less industrial than, say, copper. Moreover, unlike gold, much of it gets used up in industrial production.
McGuire also points out that the gold-silver ratio is out of historical balance. “While gold is 8 times scarcer than silver (in terms of total ounces produced annually), its price is more than 50 times higher than silver’s. For 3,000 years in which the exchange rate could be observed” [now that’s one long chart!] “gold was 9 to 16 times more expensive, making today’s level historically extreme.” (p. 12)
Investors are understandably leery about investing in silver given its rocky history and continuing volatility. Many remember silver’s collapse in 1980, when it lost half its value on a single day. The author quotes a commodity expert as saying that “Silver rushes up the stairs to jump out the window.” (p. 84) But those who can catch it on its way up can reap large profits.
McGuire offers a brief (about 50-page) history of silver in the United States and what it means for the metal’s future and closes his book with advice on how to invest in silver, including coins and silver mining stocks.
Monday, June 3, 2013
Sinclair, Volatility Trading, 2d ed.
Euan Sinclair has a Ph.D. in theoretical physics, which means he knows his way around the world of mathematics. Any reader who has difficulty deciphering (mainly statistical) formulas may find parts of this second edition of Volatility Trading (Wiley, 2013) challenging. But, even for him, all is not lost. Most of the book is written in clear English prose.
Volatility is a slippery concept. First of all, it encompasses three separate “time-stamped” notions: historical volatility, implied volatility, and future realized volatility. “[To] find an edge in option trading, we need an estimate of future realized volatility to trade against that implied by the options. But before we can forecast future volatility we need to be able to measure what it has been in the past.” (p. 13) And measuring historical volatility is no easy task. Sinclair analyzes five estimators: close/close volatility, Parkinson volatility, Rogers/Satchell volatility, Garman/Klass volatility, and Yang/Zhang volatility—each with its own strengths and weaknesses. He concludes that “there really is no indication that any one estimator is best. All measures contain information. … For example, if the Parkinson volatility is 40 percent and the close-to-close volatility is 20 percent, we can reasonably conclude that much of the true volatility is being driven by large intraday ranges and that the closing prices underrepresent the true volatility of the process.” (p. 28) In the end, Sinclair concludes, “volatility measurement is something of an art.” (p. 33)
Qualitative considerations are also prominent in stylized facts (features that are consistent enough to be generally accepted as truth) about returns and volatility. Sinclair lists the following:
The author offers outlines of some trading strategies, all requiring additional work on the part of the reader. As he admits, “Edge estimation and capture are difficult and both involve subjective judgment.” (p. 131)
In the second half of the book Sinclair moves on to other essential ingredients of trading success, starting with money management. He first analyzes the Kelly criterion and alternatives to Kelly. A second ingredient is trade evaluation, better known as record keeping. “Without accurate and comprehensive records it is easy to fall victim to selective memory biases. We all tend to remember the few big wins and big losses. But these are unlikely to be typical of our trading, and basing decisions on how these trades alone worked out would be unwise. Trading is largely about making solid, unspectacular plays—precisely the ones that we tend to forget.” (p. 163) In summarizing the chapter on trade evaluation, Sinclair writes: “The easiest way to improve is to keep detailed records and then review them. Not only is this necessary to improve but merely doing it almost guarantees improvement. It forces us to take ownership of our successes and failures and helps us to see exactly what we can and cannot do.” (p. 184)
Re-read those last three sentences. They may be the most important thing you take away from this post even though they are not the cornerstone of Sinclair’s analysis.
I, of course, was also attracted to the brief chapter on resources, both books and websites, and found a couple of real gems there.
Volatility Trading is not for the novice options trader. But neither is it simply for the quant. It addresses a range of issues—including some, such as behavioral finance, the VIX, and leveraged ETFs, that I have not touched on here. Odds are that the intermediate to advanced options trader will experience some “aha” moment reading this book.
Volatility is a slippery concept. First of all, it encompasses three separate “time-stamped” notions: historical volatility, implied volatility, and future realized volatility. “[To] find an edge in option trading, we need an estimate of future realized volatility to trade against that implied by the options. But before we can forecast future volatility we need to be able to measure what it has been in the past.” (p. 13) And measuring historical volatility is no easy task. Sinclair analyzes five estimators: close/close volatility, Parkinson volatility, Rogers/Satchell volatility, Garman/Klass volatility, and Yang/Zhang volatility—each with its own strengths and weaknesses. He concludes that “there really is no indication that any one estimator is best. All measures contain information. … For example, if the Parkinson volatility is 40 percent and the close-to-close volatility is 20 percent, we can reasonably conclude that much of the true volatility is being driven by large intraday ranges and that the closing prices underrepresent the true volatility of the process.” (p. 28) In the end, Sinclair concludes, “volatility measurement is something of an art.” (p. 33)
Qualitative considerations are also prominent in stylized facts (features that are consistent enough to be generally accepted as truth) about returns and volatility. Sinclair lists the following:
- Volatility is not constant. It mean-reverts, clusters, and possesses long memory.
- Large returns occur relatively frequently. These large moves have subsequent aftershocks.
- In most markets, volatility and returns have a negative correlation. This effect is asymmetric: negative returns cause volatility to rise sharply while positive returns lead to a smaller drop in volatility.
- Volatility and volume have a strong positive relationship.
- The distribution of volatility is close to log-normal. (p. 36)
The author offers outlines of some trading strategies, all requiring additional work on the part of the reader. As he admits, “Edge estimation and capture are difficult and both involve subjective judgment.” (p. 131)
In the second half of the book Sinclair moves on to other essential ingredients of trading success, starting with money management. He first analyzes the Kelly criterion and alternatives to Kelly. A second ingredient is trade evaluation, better known as record keeping. “Without accurate and comprehensive records it is easy to fall victim to selective memory biases. We all tend to remember the few big wins and big losses. But these are unlikely to be typical of our trading, and basing decisions on how these trades alone worked out would be unwise. Trading is largely about making solid, unspectacular plays—precisely the ones that we tend to forget.” (p. 163) In summarizing the chapter on trade evaluation, Sinclair writes: “The easiest way to improve is to keep detailed records and then review them. Not only is this necessary to improve but merely doing it almost guarantees improvement. It forces us to take ownership of our successes and failures and helps us to see exactly what we can and cannot do.” (p. 184)
Re-read those last three sentences. They may be the most important thing you take away from this post even though they are not the cornerstone of Sinclair’s analysis.
I, of course, was also attracted to the brief chapter on resources, both books and websites, and found a couple of real gems there.
Volatility Trading is not for the novice options trader. But neither is it simply for the quant. It addresses a range of issues—including some, such as behavioral finance, the VIX, and leveraged ETFs, that I have not touched on here. Odds are that the intermediate to advanced options trader will experience some “aha” moment reading this book.
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