I’m still on my “blog lite” gig, so today I’m going to borrow shamelessly from the wisdom of Raymond M. Smullyan’s This Book Needs No Title: A Budget of Living Paradoxes (Prentice-Hall, 1980). Smullyan is a ninety-year-old mathematician, concert pianist, logician, Taoist philosopher, and magician who, when he wrote this book, was a professor of philosophy and mathematics at Indiana University.
In his short essay “Four Types of Life” Smullyan illustrates his point by looking at musicians, but it should be evident that the essay is equally applicable to traders. Like much of what Smullyan writes, it shifts the ground out from under us just enough to make us rethink some basic premises.
Type 1 is the person who complains that he is not “getting anywhere.” “He says that he does not learn fast enough, that his fingers are very bad that day, that his memory is poor, that he is not ‘with the music,’ that things are too ‘mechanical,’ that he does not practice enough, that he doesn’t have enough ‘self-discipline’ to force himself to go through all the gruesome, boring, painful details, that he knows that discipline and self-denial are necessary for the ‘perfection of an art,’ but that he doesn’t have enough of these qualities, etc. And so he complains and gripes and complains and gripes, but through all this restless uneasiness, he progresses anyhow and eventually becomes a first-rate musician.” (p. 82)
Type 2 is the opposite. He plays “for hours and hours a day, he is in a state of complete ecstasy, he just enjoys himself . . . , he does not distinguish between ‘playing’ and ‘practicing,’ he has no conscious idea of ‘improving,’ he does not ‘try to play well,’ he has no idea whether he is playing well or playing badly, and he couldn’t care less.” (p. 82) As a result he never progresses in a way commensurate with his talents; he never becomes first rate.
Type 3, who combines the negative qualities of the first two types, is the only real loser. He grumbles but also gets nowhere. After a few years he will probably give up his music altogether.
Type 4 combines the advantages of the first two types. He enjoys himself for hours and hours a day; he “has an enormous love of beauty and pursues it relentlessly. He may have a great critical faculty, but entirely on an unconscious level. He may, for example, play a particular passage over and over again, but he does not think of it as ‘practicing’ or ‘learning.’ He does indeed sift and sort, but he does not know that he is ‘sifting and sorting.’ He is much like a dog who is offered a dish containing a mixture of good foods and bad foods. He spits out the bad foods and eats up the good foods. The doggie does not complain nor criticize the bad foods; he is far too busy enjoying himself hunting out the good foods.” (p. 84)
Smullyan continues, and let me once again share his own words: “The behavior of this type often fools other people completely. People hearing him practicing may say: ‘This fellow is amazing! He has so much patience! He spends hours and hours on details! He has so much self-discipline! He does not balk at performing irksome tasks. He is obviously highly self-critical, and that’s why he improves! . . . He schools himself, he disciplines himself, he overcomes obstacles, and that’s why he succeeds.’ . . . Yes, that’s what people say of him, but they are totally wrong! In reality, this man no more ‘evaluates himself’ than the doggie selecting the good foods evaluates his own performance as a ‘food selector.’ . . . [O]ur artist is not trying to improve himself, but rather is automatically selecting those ways of playing which produce the most beautiful results. Of course, our fourth type ultimately becomes a first-rate musician just like our first type, but what a difference of approach! The first type has the ambition of becoming a ‘great food selector,’ whereas the fourth type simply loves good food.” (p. 85)
I think that Smullyan’s point is profound, maybe even the holy grail. Then again, I’m a music lover, a sucker for dogs, and a sometime epicure, so maybe I’ve just been seduced.
Next week I’ll be back to my usual fare, nothing so potentially seismic.
Wednesday, March 31, 2010
Tuesday, March 30, 2010
Practice as preparation for performance
We all recognize the importance of practice in acquiring skills. But, as I noted briefly in yesterday’s post, Clawson and Newburg in Powered by Feel add a dimension to practice that is sometimes overlooked. Practice that is merely working on a skill does not prepare people to perform in the real world. “They taught their minds and bodies to perform a skill over and over again. Yet they did not prepare themselves to do those same skills under pressure, when it mattered. The variation and energy isn’t there. They may even become bored.” (pp. 65-66)
I doubt that any trader could claim a record that matches the UConn women’s basketball team—75 consecutive double-digit wins. Trading the markets and playing basketball are, after all, very different games. Nonetheless, the trader can still learn from such a staggeringly successful basketball team. For one thing, although the UConn women’s basketball team practiced patterns until they executed them flawlessly and conditioned their bodies to go longer and harder than their opponents did, their preparation for performance wasn’t just more of the same. Among other things, their coach confronted them with stressful scenarios—two minutes to go and down ten, how do you win? He invented pressure situations even as the competition wilted. If it’s too easy to beat the competition, then beat the game. At every turn he would up the ante, making his players reach deeper into themselves to find something that would improve their performance against teams both weak and strong, conventional and unconventional.
The practice sessions of this team are the antithesis of the route to mediocrity that Clawson and Newburg describe. First of all, if a person doesn’t love what he’s doing he is destined for mediocrity; all the practice in the world won’t prepare him to be a top performer. He’ll just be going through the motions. As Clawson puts it, “Working without feel tends to create working without doing work and a loss of energy.” (p. 207) Second, and somewhat akin to the first, to avoid ending up with a mediocre performance record practice must include a very specific “feel good” component. The positive feeling that a person acquires in practice serves as a grounding for him when he becomes a performer; it’s a feeling the performer can revisit when he is under pressure. As Newburg writes, “World-class performers prepare to live their dream, to feel the way they wanted to feel when it mattered most by feeling it when it seemed to matter least.” (p. 65)
I doubt that any trader could claim a record that matches the UConn women’s basketball team—75 consecutive double-digit wins. Trading the markets and playing basketball are, after all, very different games. Nonetheless, the trader can still learn from such a staggeringly successful basketball team. For one thing, although the UConn women’s basketball team practiced patterns until they executed them flawlessly and conditioned their bodies to go longer and harder than their opponents did, their preparation for performance wasn’t just more of the same. Among other things, their coach confronted them with stressful scenarios—two minutes to go and down ten, how do you win? He invented pressure situations even as the competition wilted. If it’s too easy to beat the competition, then beat the game. At every turn he would up the ante, making his players reach deeper into themselves to find something that would improve their performance against teams both weak and strong, conventional and unconventional.
The practice sessions of this team are the antithesis of the route to mediocrity that Clawson and Newburg describe. First of all, if a person doesn’t love what he’s doing he is destined for mediocrity; all the practice in the world won’t prepare him to be a top performer. He’ll just be going through the motions. As Clawson puts it, “Working without feel tends to create working without doing work and a loss of energy.” (p. 207) Second, and somewhat akin to the first, to avoid ending up with a mediocre performance record practice must include a very specific “feel good” component. The positive feeling that a person acquires in practice serves as a grounding for him when he becomes a performer; it’s a feeling the performer can revisit when he is under pressure. As Newburg writes, “World-class performers prepare to live their dream, to feel the way they wanted to feel when it mattered most by feeling it when it seemed to matter least.” (p. 65)
Monday, March 29, 2010
How do you want to feel?
For the last couple of days I have been feeling heavy, as if I’m carrying too much weight (and I am decidedly not). I have also been sluggish and unfocused. Three investing books sit on my desk begging to be read and reviewed, but I don’t have the energy to do them justice. I suspect it’s just spring fever; this is not the first time that the advent of spring has brought on these annoyingly enervating symptoms. Rather than plow through books that require concentration I’ve decided to go “blog lite” this week and write two posts based on the book Powered by Feel: How Individuals, Teams and Companies Excel (World Scientific Publishing, 2009) by James G. S. Clawson and Douglas S. Newburg.
“How do you want to feel?” is the focal question of the book. It’s a strange question to try to answer, especially since the authors rule out such clichés as happy, awesome, and in the zone. They suggest that one way to go about finding an answer is to reflect on those times when you felt flow: how did it feel? Start with some nouns and adjectives, then refine your first draft. The answer Clawson came up with was “light, unhurried, and engaged”; Newburg’s was “elegance, as powerful as it is simple.” For an Olympic swimmer it was “easy speed”; for an academic “buoyant, connected mastery.”
What’s wrong with the “in the zone” answer in addition to being trite and “not enough to be useful”? Newburg suggests that people sometimes use the concept of the zone “to pressure themselves, to judge themselves when they cannot get into it. Then they wonder why they are not getting into it.” (p. 183) Of course, “easy speed” might be as difficult to accomplish as being in the zone; indeed, the former might be viewed as a more concrete version of the latter. On the other hand, we might argue that “in the zone” is a binary concept; we’re either in the zone or out of the zone. “Easy speed,” by contrast, admits of degrees; sometimes speed is a little easier, sometimes a little harder.
At any event, the proper kinds of phrases stress process over results. For instance, the swimmer is not describing how he feels standing on the medal stand but how he feels in the water when he is competing. The academic is describing how he feels in the classroom engaging with students.
It is not sufficient for people to describe how they want to feel; they have to nurture that feeling. This sometimes entails unlearning bad habits and acquiring new ones, as we all know not an easy undertaking. It definitely involves practice and, Newburg argues, “practice in an environment that increases their ability to feel how they want to feel and do what they want to do.” Do they practice to music? Do they practice alone? What does their practice environment look like? Their practice should be accompanied by, indeed infused with, the kinds of feelings they want to have. “This type of practice leads to preparation because it develops the skill to feel the way they want to feel when they perform. This is more than simply the muscle memory of repetition.” (p. 184)
(to be continued)
“How do you want to feel?” is the focal question of the book. It’s a strange question to try to answer, especially since the authors rule out such clichés as happy, awesome, and in the zone. They suggest that one way to go about finding an answer is to reflect on those times when you felt flow: how did it feel? Start with some nouns and adjectives, then refine your first draft. The answer Clawson came up with was “light, unhurried, and engaged”; Newburg’s was “elegance, as powerful as it is simple.” For an Olympic swimmer it was “easy speed”; for an academic “buoyant, connected mastery.”
What’s wrong with the “in the zone” answer in addition to being trite and “not enough to be useful”? Newburg suggests that people sometimes use the concept of the zone “to pressure themselves, to judge themselves when they cannot get into it. Then they wonder why they are not getting into it.” (p. 183) Of course, “easy speed” might be as difficult to accomplish as being in the zone; indeed, the former might be viewed as a more concrete version of the latter. On the other hand, we might argue that “in the zone” is a binary concept; we’re either in the zone or out of the zone. “Easy speed,” by contrast, admits of degrees; sometimes speed is a little easier, sometimes a little harder.
At any event, the proper kinds of phrases stress process over results. For instance, the swimmer is not describing how he feels standing on the medal stand but how he feels in the water when he is competing. The academic is describing how he feels in the classroom engaging with students.
It is not sufficient for people to describe how they want to feel; they have to nurture that feeling. This sometimes entails unlearning bad habits and acquiring new ones, as we all know not an easy undertaking. It definitely involves practice and, Newburg argues, “practice in an environment that increases their ability to feel how they want to feel and do what they want to do.” Do they practice to music? Do they practice alone? What does their practice environment look like? Their practice should be accompanied by, indeed infused with, the kinds of feelings they want to have. “This type of practice leads to preparation because it develops the skill to feel the way they want to feel when they perform. This is more than simply the muscle memory of repetition.” (p. 184)
(to be continued)
Friday, March 26, 2010
Frishberg, Investing without Borders
Daniel Frishberg is the founder of the Texas-based BizRadio Network where he broadcasts a daily two-hour program “The Money Man Report.” He is also a partner in a private equity fund and is the chief investment strategist for a management company. Investing without Borders: How 6 Billion Investors Can Find Profits in the Global Economy (Wiley, 2010) is his second book.
The book is not really about global investing although it starts off that way and comments on global investing do appear throughout the book. Rather, it’s a chatty hodgepodge of macroeconomic reflections (and predictions), the seemingly mandatory criticism of financial planners and stockbrokers, bond strategies, contrarian thinking, snippets from radio interviews, money management, investor psychology, and the occasional stock pick. Sometimes he writes for the rank novice, at other times for the professional or quasi-professional. For instance, he spends pages explaining the basics of bonds for the uninitiated; he also extols the virtues of structured notes. With respect to the latter, he says that he “can sometimes negotiate a deal with investment banks like Barclays Bank and Goldman Sachs” but that the individual investor without a few million to invest at a time can pick up the leftovers from a deal done by an institutional investor like the author. He admits that “you have to know what you’re doing, and you have to read THE FINE PRINT, because sometimes the way these notes are structured can be very deceptive.” (p. 34)
For the rest of this post I’m going to take off my critic’s hat and instead focus on two topics that interest me: intuitive trading and stop losses.
In the 1990s the Marines Leadership and Combat Development School was teaching standard business school decision-making techniques. In very tense combat situations, however, the men fell short. The head of the program talked with a psychologist who was studying firemen who have no time for rational analysis before making a decision. He then decided to bring a group of his men to the NYMEX trading pits because they “reminded him of a war room during combat.” To no one’s surprise, the Marines were decimated by the floor traders. The surprise came a month later when the floor traders went to Quantico to play war games against the Marines and “wiped the Marines out.” The traders were “just better gut thinkers. They were practiced at quickly evaluating risks, and they were willing to act decisively on imperfect and contradictory information.” (p. 68) Successful traders and investors, Frishberg contends, just get it. “The point here is,” he continues, “you can’t rely on any favorite technique except getting as much experience and information into your unconscious and learning to let that beautiful computer inside you do its job.” (p. 69)
As readers of this blog know, I have a love-hate relationship with initial stops; see my post “The case against stop losses” and the comments on it. Frishberg has his own take, distinguishing between two sets of entry criteria. If the entry is pattern-based, the trader must have a system of tight stops. If, however, “you’re investing your money with courage and commitment, helping people around the world get what they want, and you love the company at $20 a share, don’t you love it more at $17? . . . So how can you reconcile the strategy of applying tight stop-losses with a commitment to live your life with courage and conviction?” (p. 139) In this case Frishberg suggests splitting the money allocated to the trade into thirds. He commits the first third when he thinks we’ve reached “maximum fear and negativity.” If the stock continues to fall, he will commit the second third. And if the stock starts to turn up, he’ll add the final third. Of course, if the overarching thesis behind the trade falters, it’s time for a major reassessment.
The strategy of doubling down on a losing position has both advocates and critics and I’m not going to take sides here except to say that it’s definitely not my style. I would just comment that if an investor can’t reconcile the strategy of applying tight stop-losses with a commitment to live his life with courage and conviction (and note that this does not entail doubling down) the same should hold for the trader. It’s just that a trader can change his convictions much more quickly
The book is not really about global investing although it starts off that way and comments on global investing do appear throughout the book. Rather, it’s a chatty hodgepodge of macroeconomic reflections (and predictions), the seemingly mandatory criticism of financial planners and stockbrokers, bond strategies, contrarian thinking, snippets from radio interviews, money management, investor psychology, and the occasional stock pick. Sometimes he writes for the rank novice, at other times for the professional or quasi-professional. For instance, he spends pages explaining the basics of bonds for the uninitiated; he also extols the virtues of structured notes. With respect to the latter, he says that he “can sometimes negotiate a deal with investment banks like Barclays Bank and Goldman Sachs” but that the individual investor without a few million to invest at a time can pick up the leftovers from a deal done by an institutional investor like the author. He admits that “you have to know what you’re doing, and you have to read THE FINE PRINT, because sometimes the way these notes are structured can be very deceptive.” (p. 34)
For the rest of this post I’m going to take off my critic’s hat and instead focus on two topics that interest me: intuitive trading and stop losses.
In the 1990s the Marines Leadership and Combat Development School was teaching standard business school decision-making techniques. In very tense combat situations, however, the men fell short. The head of the program talked with a psychologist who was studying firemen who have no time for rational analysis before making a decision. He then decided to bring a group of his men to the NYMEX trading pits because they “reminded him of a war room during combat.” To no one’s surprise, the Marines were decimated by the floor traders. The surprise came a month later when the floor traders went to Quantico to play war games against the Marines and “wiped the Marines out.” The traders were “just better gut thinkers. They were practiced at quickly evaluating risks, and they were willing to act decisively on imperfect and contradictory information.” (p. 68) Successful traders and investors, Frishberg contends, just get it. “The point here is,” he continues, “you can’t rely on any favorite technique except getting as much experience and information into your unconscious and learning to let that beautiful computer inside you do its job.” (p. 69)
As readers of this blog know, I have a love-hate relationship with initial stops; see my post “The case against stop losses” and the comments on it. Frishberg has his own take, distinguishing between two sets of entry criteria. If the entry is pattern-based, the trader must have a system of tight stops. If, however, “you’re investing your money with courage and commitment, helping people around the world get what they want, and you love the company at $20 a share, don’t you love it more at $17? . . . So how can you reconcile the strategy of applying tight stop-losses with a commitment to live your life with courage and conviction?” (p. 139) In this case Frishberg suggests splitting the money allocated to the trade into thirds. He commits the first third when he thinks we’ve reached “maximum fear and negativity.” If the stock continues to fall, he will commit the second third. And if the stock starts to turn up, he’ll add the final third. Of course, if the overarching thesis behind the trade falters, it’s time for a major reassessment.
The strategy of doubling down on a losing position has both advocates and critics and I’m not going to take sides here except to say that it’s definitely not my style. I would just comment that if an investor can’t reconcile the strategy of applying tight stop-losses with a commitment to live his life with courage and conviction (and note that this does not entail doubling down) the same should hold for the trader. It’s just that a trader can change his convictions much more quickly
Thursday, March 25, 2010
Efficient training
Knowledge-Free and Learning-Based Methods in Intelligent Game Playing by Jacek Mandziuk (Springer, 2010) is not a book I intend to read from cover to cover. It explores computational intelligence-based methods (as opposed to AI methods) for playing such games as chess, checkers, backgammon, and Go. What caught my attention was a section entitled “Training with External Opponents.” In this section the author summarizes Susan Epstein’s paper “Toward an Ideal Trainer.”
The most important conclusion of her research is that training with one opponent, whether in self-play or against an expert player, is insufficient. If a player is trained by a single expert, that player will not necessarily be competent when confronting weaker or unconventional opponents. The trainee needs a diversity of trainers. “In order to develop a flexible and ‘intelligent’ player (i.e. the one capable of passing the Turing test in games) it is necessary to confront it with a large range of playing styles and various playing skills.”
She initially tried to improve upon single-opponent training by adding some noise into the decision-making process of the trainer. This strategy brought better results, but “it still does not guarantee that the training is sufficiently universal and that the part of a state space visited by the learner is representative enough to allow efficient play against intelligent, nonconventional opposition.” (p. 126)
Is it valid to extrapolate from the world of CI training when assessing the value of expert trading mentors? In one obvious respect the extrapolation breaks down: computers don’t have biases, they don’t carry psychological baggage, and presumably they all have the same willingness to learn. But if we overlook that “minor” problem, I think there is merit in the claim that a trainee needs a diversity of trainers. Assume that the mentor/trainer is truly skilled at a certain style of trading, that he is also a skilled teacher, and that his style of trading fits the personality of the trainee. Even then, at the end of the day (and I don’t mean that literally) I doubt that a flexible, intelligent trader emerges. The trainee has learned one style of trading; that makes him neither flexible nor intelligent. Indeed, if compared to a sophisticated algorithmic computer program he might seem decidedly unintelligent. When the market throws him a curve ball will he be prepared to deal with it? If the tone of the market starts to shift, will he be flexible enough to go with the flow?
I am not suggesting that the would-be trader spend many thousands of dollars to study under several mentors. With some very good blogs, webinars, and (as always) books available, the trainee can become familiar with a range of styles. Admittedly, this kind of learning suffers from being passive. A web surfer doesn’t have the advantage of a teacher who prods, corrects, and praises. If he needs this interaction he has a range of alternatives, from self-talk to structured chat rooms to full-blown educational programs.
It goes without saying that training needs to be complemented with practice, not only for would-be traders but for would-be game-playing computers as well. Epstein proposed “lesson and practice” learning that consists in “interleaving the periods of training with strong opponents (the lessons) with periods of knowledge consolidation and usage (the practice).” (p. 126)
In the meantime, an interesting exercise for the self-taught trader might be to imagine three or four trainers with very different styles together in the same room, trading the same underlying instrument. Would A be taking the other side of B’s trade? Would C be waiting for two or three setups a day while A was clicking away? Would B scale out while C held a full position to the end? Who would be squeezed and when? What would these trainers be saying to one another, expletives not necessarily deleted, during the course of the day? If you overlaid (and color coded) their trades on a single chart, would you know a little more about market dynamics than you do now?
The most important conclusion of her research is that training with one opponent, whether in self-play or against an expert player, is insufficient. If a player is trained by a single expert, that player will not necessarily be competent when confronting weaker or unconventional opponents. The trainee needs a diversity of trainers. “In order to develop a flexible and ‘intelligent’ player (i.e. the one capable of passing the Turing test in games) it is necessary to confront it with a large range of playing styles and various playing skills.”
She initially tried to improve upon single-opponent training by adding some noise into the decision-making process of the trainer. This strategy brought better results, but “it still does not guarantee that the training is sufficiently universal and that the part of a state space visited by the learner is representative enough to allow efficient play against intelligent, nonconventional opposition.” (p. 126)
Is it valid to extrapolate from the world of CI training when assessing the value of expert trading mentors? In one obvious respect the extrapolation breaks down: computers don’t have biases, they don’t carry psychological baggage, and presumably they all have the same willingness to learn. But if we overlook that “minor” problem, I think there is merit in the claim that a trainee needs a diversity of trainers. Assume that the mentor/trainer is truly skilled at a certain style of trading, that he is also a skilled teacher, and that his style of trading fits the personality of the trainee. Even then, at the end of the day (and I don’t mean that literally) I doubt that a flexible, intelligent trader emerges. The trainee has learned one style of trading; that makes him neither flexible nor intelligent. Indeed, if compared to a sophisticated algorithmic computer program he might seem decidedly unintelligent. When the market throws him a curve ball will he be prepared to deal with it? If the tone of the market starts to shift, will he be flexible enough to go with the flow?
I am not suggesting that the would-be trader spend many thousands of dollars to study under several mentors. With some very good blogs, webinars, and (as always) books available, the trainee can become familiar with a range of styles. Admittedly, this kind of learning suffers from being passive. A web surfer doesn’t have the advantage of a teacher who prods, corrects, and praises. If he needs this interaction he has a range of alternatives, from self-talk to structured chat rooms to full-blown educational programs.
It goes without saying that training needs to be complemented with practice, not only for would-be traders but for would-be game-playing computers as well. Epstein proposed “lesson and practice” learning that consists in “interleaving the periods of training with strong opponents (the lessons) with periods of knowledge consolidation and usage (the practice).” (p. 126)
In the meantime, an interesting exercise for the self-taught trader might be to imagine three or four trainers with very different styles together in the same room, trading the same underlying instrument. Would A be taking the other side of B’s trade? Would C be waiting for two or three setups a day while A was clicking away? Would B scale out while C held a full position to the end? Who would be squeezed and when? What would these trainers be saying to one another, expletives not necessarily deleted, during the course of the day? If you overlaid (and color coded) their trades on a single chart, would you know a little more about market dynamics than you do now?
Wednesday, March 24, 2010
Henning, The Value and Momentum Trader
Reading Grant Henning’s The Value and Momentum Trader: Dynamic Stock Selection Models to Beat the Market (Wiley, 2010) is like wandering through a botanical garden if you are a plant lover. You adopt a leisurely pace because there are so many things to look at. Most are familiar, but even among them there are some you want to examine more closely.
Henning considers himself a pragmatist, where “pragmatism can lead to appropriate solutions without the distractions of rigorous theoretical accommodation.” (p. 4) A trader doesn’t need to know why a particular trading system works well for him; as long as it works, the search is over. Henning set his own personal bar high: to develop a trading system that would generate earnings in excess of 10% per month. He created his trading profile over time. He would trade only stocks, he would be a swing trader holding positions for anywhere between three days and three months, he would use margin when conditions were extraordinarily favorable, and he would be a long-only trader in bull markets and either be out of the market entirely or be invested in short index funds when the market was “unfavorable.”
The two major tasks facing Henning were to develop a stock screen and to find a way to measure market conditions. He offers the reader the fruits of his labor. First, ideas for creating a hybrid screen that combines fundamental and technical parameters. Fundamental data are used to identify stock value; technical data are timing devices that highlight price momentum. The initial process is rather time-consuming, but once set up it spits out weighted buy and sell recommendations daily with only about an hour’s worth of work each evening.
The momentum screen in and of itself will serve as a barometer of market conditions. For instance, in a weak market few stocks are near their 52-week highs. Henning also considers the VIX and the put/call ratio to be reliable timing indicators.
Henning’s trading is labor intensive, not designed for the weekend warrior. Screening for potential buy candidates is only the first step. He then monitors these stocks pre-market, seeking out intel. Once the market opens he watches price action and decides whether to enter and, if so, the best entry point and best order method (limit or market). He culls out losers in his portfolio and determines when to take profits on winners. He manages his portfolio both in terms of diversification and proportionality of holdings. And, of course, he keeps records.
Both Henning’s stock screen and his overall trading methodology are hybrids. The screen uses fundamental as well as technical inputs. The methodology combines systematic and discretionary elements. Hybridism is, it seems, Henning’s pragmatic solution. And, unlike Toyota, he doesn’t have to worry about a runaway acceleration in trading revenues.
Henning considers himself a pragmatist, where “pragmatism can lead to appropriate solutions without the distractions of rigorous theoretical accommodation.” (p. 4) A trader doesn’t need to know why a particular trading system works well for him; as long as it works, the search is over. Henning set his own personal bar high: to develop a trading system that would generate earnings in excess of 10% per month. He created his trading profile over time. He would trade only stocks, he would be a swing trader holding positions for anywhere between three days and three months, he would use margin when conditions were extraordinarily favorable, and he would be a long-only trader in bull markets and either be out of the market entirely or be invested in short index funds when the market was “unfavorable.”
The two major tasks facing Henning were to develop a stock screen and to find a way to measure market conditions. He offers the reader the fruits of his labor. First, ideas for creating a hybrid screen that combines fundamental and technical parameters. Fundamental data are used to identify stock value; technical data are timing devices that highlight price momentum. The initial process is rather time-consuming, but once set up it spits out weighted buy and sell recommendations daily with only about an hour’s worth of work each evening.
The momentum screen in and of itself will serve as a barometer of market conditions. For instance, in a weak market few stocks are near their 52-week highs. Henning also considers the VIX and the put/call ratio to be reliable timing indicators.
Henning’s trading is labor intensive, not designed for the weekend warrior. Screening for potential buy candidates is only the first step. He then monitors these stocks pre-market, seeking out intel. Once the market opens he watches price action and decides whether to enter and, if so, the best entry point and best order method (limit or market). He culls out losers in his portfolio and determines when to take profits on winners. He manages his portfolio both in terms of diversification and proportionality of holdings. And, of course, he keeps records.
Both Henning’s stock screen and his overall trading methodology are hybrids. The screen uses fundamental as well as technical inputs. The methodology combines systematic and discretionary elements. Hybridism is, it seems, Henning’s pragmatic solution. And, unlike Toyota, he doesn’t have to worry about a runaway acceleration in trading revenues.
Tuesday, March 23, 2010
Our love of variety
This research by Dan Ariely and a team in Rome is off topic (or at least I hope it is), but fascinating nonetheless: capuchin monkeys, it seems, choose variety for variety’s sake. And so do we: people eat 43% more M&Ms when there are ten colors in the bowl instead of seven. The authors of the study may push the limits of credulity when they suggest that variety-seeking “contributed to the rise of bartering and then abstract money in human society.” But I’m the first to confess that this year when it comes to my vegetable garden I’m one of the most variety-seeking monkeys around. Gone are the green beans that, a hundred quart freezer bags later, I knew I couldn’t eat for at least another twenty years. And after last year’s disastrous growing season I decided it was time rethink the rationale for the garden. Why grow only the ordinary? Of course tomatoes will still with any luck be the premier crop. But in addition to such staples as lettuce and peas I opted to buy some seeds that would stretch both my gardening skills and either reacquaint my palate with some old-time favorites that aren’t so commonplace or, in one case, try something I’ve never eaten in my life. Here are the new additions: celeriac, kohlrabi, radicchio, Kamo eggplant, cardoon, Parisian pickling cucumbers, Belgian endives, broccoli raab, cannellini beans, and chickpeas. I now know why Belgian endives are so expensive; I’ll either have gorgeous salads come wintertime and many hours of work later or a big fat nothing. I really hope the cardoon prospers because I haven’t a clue what it tastes like. This is a clear case of variety for variety’s sake. And I consider exploration into the unknown tremendous fun!
Monday, March 22, 2010
Risk management and profit targets
In his book Strategic Risk Taking (Wharton School Publishing, 2008) Aswath Damodaran criticizes those theorists and practitioners who equate risk management with risk hedging. They have, he argues, “underplayed the fact that the most successful firms in any industry get there not by avoiding risk but by actively seeking it out and exploiting it to their own advantage.” The role of risk managers should not be simply the removal or reduction in exposure to risk; risk managers should also increase exposure to some risk. That is, risk management should encompass both risk hedging at one end of the spectrum and strategic risk taking at the other end of the spectrum.
Damodaran embraces the principle inherent in the Chinese symbol for risk: risk is a combination of danger and opportunity, and we should maintain a balance between the two. This doesn’t mean that risk is symmetric. For instance, some risks offer a small chance of an outsized reward and a high probability of a limited loss. Think, for instance, of options traders who buy very cheap out-of-the-money calls as a lottery ticket; if the stock price explodes they’ll have a staggering return on their investment, but odds are that their calls will expire worthless. Other risks present a very high probability of a small gain and a very low probability of a significant loss. Iron condor traders know this scenario all too well.
Individual traders and investors have to be their own risk managers, and this means that they have to strike a balance between believing that danger lurks around every corner and throwing caution to the wind. One way to strike this balance in the world of stocks and futures, according to common wisdom, is to put on only those trades that have a favorable reward/risk ratio—at least 2:1, preferably 3:1—although some scalping techniques, we are told, work better with a ratio of 1:2—that is, for every dollar in expected return the trader will assume a risk of two dollars. (If I recall correctly, Linda Raschke’s crew did some research and came up with this finding, but I don’t have the reference handy.)
Personally, I have never found these ratios particularly useful, though I have no doubt that the successful scalper has to be keenly aware of what ratio works best for his strategy especially in this era of high-frequency trading. For the non-scalper, however, setting a target through backtesting can go only so far before his crystal ball has to take over since a reward/risk ratio divides a future value by a present value (assuming an initial stop). Will price reach a median line, a Fibonacci retracement, a moving average, or an earlier swing high or low? Maybe, maybe not. Will it blow through those targets? Maybe, maybe not. So we have a defined risk, presumably in the form of an initial stop, and a projected reward that may turn out to be illusory (the target isn’t met) or too modest (the target is exceeded).
If risk is a combination of danger and opportunity, the trader should always be cognizant of the potential downside (which is not the same as minimizing it) but not cap the upside. How does the trader accomplish this balancing act? Trade management and position sizing are critical ingredients; initial stop placement, I believe, much less so. In his annual letter Warren Buffett wrote, “Big opportunities come infrequently. When it’s raining gold, reach for a bucket, not a thimble.” We may not have buckets as large as Buffett’s, but the sentiment is applicable to all but the one-lot trader. When a trade starts to run we want to extract as much as possible out of it. We just have to make certain that we’ve removed our gold from the bucket before it starts to rain aqua regia.
Damodaran embraces the principle inherent in the Chinese symbol for risk: risk is a combination of danger and opportunity, and we should maintain a balance between the two. This doesn’t mean that risk is symmetric. For instance, some risks offer a small chance of an outsized reward and a high probability of a limited loss. Think, for instance, of options traders who buy very cheap out-of-the-money calls as a lottery ticket; if the stock price explodes they’ll have a staggering return on their investment, but odds are that their calls will expire worthless. Other risks present a very high probability of a small gain and a very low probability of a significant loss. Iron condor traders know this scenario all too well.
Individual traders and investors have to be their own risk managers, and this means that they have to strike a balance between believing that danger lurks around every corner and throwing caution to the wind. One way to strike this balance in the world of stocks and futures, according to common wisdom, is to put on only those trades that have a favorable reward/risk ratio—at least 2:1, preferably 3:1—although some scalping techniques, we are told, work better with a ratio of 1:2—that is, for every dollar in expected return the trader will assume a risk of two dollars. (If I recall correctly, Linda Raschke’s crew did some research and came up with this finding, but I don’t have the reference handy.)
Personally, I have never found these ratios particularly useful, though I have no doubt that the successful scalper has to be keenly aware of what ratio works best for his strategy especially in this era of high-frequency trading. For the non-scalper, however, setting a target through backtesting can go only so far before his crystal ball has to take over since a reward/risk ratio divides a future value by a present value (assuming an initial stop). Will price reach a median line, a Fibonacci retracement, a moving average, or an earlier swing high or low? Maybe, maybe not. Will it blow through those targets? Maybe, maybe not. So we have a defined risk, presumably in the form of an initial stop, and a projected reward that may turn out to be illusory (the target isn’t met) or too modest (the target is exceeded).
If risk is a combination of danger and opportunity, the trader should always be cognizant of the potential downside (which is not the same as minimizing it) but not cap the upside. How does the trader accomplish this balancing act? Trade management and position sizing are critical ingredients; initial stop placement, I believe, much less so. In his annual letter Warren Buffett wrote, “Big opportunities come infrequently. When it’s raining gold, reach for a bucket, not a thimble.” We may not have buckets as large as Buffett’s, but the sentiment is applicable to all but the one-lot trader. When a trade starts to run we want to extract as much as possible out of it. We just have to make certain that we’ve removed our gold from the bucket before it starts to rain aqua regia.
Saturday, March 20, 2010
An anti-creativity checklist
From the Harvard Business Review comes a five-minute video by Youngme Moon, a professor at the B school, entitled “An anti-creativity checklist: 14 things to remember to do.” Although it’s directed at business organizations, it should resonate with individual traders and investors who have stifling inner voices.
Friday, March 19, 2010
Caplan, The New Option Secret: Volatility
Unlike so many authors of trading books who take a single mediocre idea, stretch it and then garnish it with boilerplate ideas, David L. Caplan is a man of few words. He says what he has to say, illustrates his trading ideas with charts, and then stops. In the case of The New Option Secret: Volatility (1996, available through Traders Press) he compromises to produce a full-length book. His text makes up the first half of the book. He then turns the stage over to other experienced option traders, reprinting articles and excerpts from books on volatility. (Well, yes, he does include some of his own work in this section.)
Caplan trades options on futures, so not all of Caplan’s ideas translate perfectly into the stock options market. That caveat aside, Caplan offers six trades for the off-floor trader. (1) A neutral option position, best executed when option volatilities are high to medium and the market is trading in a range. Here the trader would sell an OTM put and an OTM call in the same expiration month. (2) The free trade, appropriate for a low option volatility environment in a trending market. The strategy: buy a close-to-the-money call or put; if the market moves in the intended direction, later sell a much farther OTM call or put at the same price. (3) A ratio spread, designed to take advantage of a premium disparity between option strike prices. This trade, which consists of buying a close-to-the-money option and selling two or more options farther OTM, benefits from a mildly trending market and high volatility in OTM options. (4) A calendar spread, appropriate when there is a premium disparity between the option months and high volatility in the front month. Here trend is not so important as long as the trader believes that the front-month option that he sells will not be ITM at expiration. (5) An ITM debit spread, buying an ITM or ATM option and selling a farther OTM option. The strategy here is to take advantage of the premium disparity between strike prices in a trending market. (6) A no-cost option where the trader buys a near-the-money option and sells a higher-volatility OTM put and call. This trade attempts to exploit strong technical support and resistance levels.
Caplan’s general game plan in all of these trading strategies is to “combine the comparative volatility level with the technical pattern of the underlying market to determine whether a ‘special circumstance’ or ‘favorable situation’ exists.” (p. 48) He explores each of these trading strategies in turn, illustrating them with ample, easily readable charts. He concludes his text with an overview of types of volatility, a section on the Greeks, and what he describes as trading gems and tidbits.
Caplan’s book is neither theoretical nor technical and it’s decidedly nonmathematical. What it offers are practical suggestions for a person setting out to trade options on futures.
Caplan trades options on futures, so not all of Caplan’s ideas translate perfectly into the stock options market. That caveat aside, Caplan offers six trades for the off-floor trader. (1) A neutral option position, best executed when option volatilities are high to medium and the market is trading in a range. Here the trader would sell an OTM put and an OTM call in the same expiration month. (2) The free trade, appropriate for a low option volatility environment in a trending market. The strategy: buy a close-to-the-money call or put; if the market moves in the intended direction, later sell a much farther OTM call or put at the same price. (3) A ratio spread, designed to take advantage of a premium disparity between option strike prices. This trade, which consists of buying a close-to-the-money option and selling two or more options farther OTM, benefits from a mildly trending market and high volatility in OTM options. (4) A calendar spread, appropriate when there is a premium disparity between the option months and high volatility in the front month. Here trend is not so important as long as the trader believes that the front-month option that he sells will not be ITM at expiration. (5) An ITM debit spread, buying an ITM or ATM option and selling a farther OTM option. The strategy here is to take advantage of the premium disparity between strike prices in a trending market. (6) A no-cost option where the trader buys a near-the-money option and sells a higher-volatility OTM put and call. This trade attempts to exploit strong technical support and resistance levels.
Caplan’s general game plan in all of these trading strategies is to “combine the comparative volatility level with the technical pattern of the underlying market to determine whether a ‘special circumstance’ or ‘favorable situation’ exists.” (p. 48) He explores each of these trading strategies in turn, illustrating them with ample, easily readable charts. He concludes his text with an overview of types of volatility, a section on the Greeks, and what he describes as trading gems and tidbits.
Caplan’s book is neither theoretical nor technical and it’s decidedly nonmathematical. What it offers are practical suggestions for a person setting out to trade options on futures.
Wednesday, March 17, 2010
“Short-term” reversals
In a publicly available paper “Diversification Across Characteristics” Erik Hjalmarsson extends the 2009 work of Asness, Moskowitz, and Pedersen. He studies the performance of long-short strategies based on seven reversal/momentum and value characteristics: short-term reversals, medium-term momentum, long-term reversals, book-to-market value, cashflow-price ratio, earnings-price ratio, and size. He builds seven single-characteristic portfolios and then compares their performance to that of an equal-weighted portfolio of the single-characteristic ones. The equal-weighted diversified portfolio, he finds, “almost always deliver[s] substantially better Sharpe ratios than any of the single-characteristic portfolios or the two-characteristic momentum-value portfolio considered by” Asness, Moskowitz, and Pedersen.
The three technical characteristics Hjalmarsson uses are short-term reversals (the prior month’s [t-1] return), medium-term momentum (returns from month t-12 to t-2), and long-term reversals (returns from month t-60 to t-13). For the medium-term momentum portfolio returns are calculated by taking the difference between the returns on the top decile of the universe of stocks and the bottom decile; for the short-term and long-term reversal portfolios returns are calculated by taking the difference between the returns on the bottom decile and the top decile.
Although Hjalmarsson touts the risk-adjusted strength of the portfolio of portfolios, I want to look at the short-term reversal single-characteristic portfolio since it delivered the best non-risk-adjusted returns. Throughout the entire period of the study, July 1951 through December 2008, it had an annualized mean return of 23.267% whereas the market returned only 6.104%. By comparison, medium-term momentum returned 14.956%, long-term reversals 9.314%, book-to-market value 11.651%, cashflow-price ratio 9.766%, earnings-price ratio 8.839%, firm size 5.145%, and the equal-weighted portfolio 11.849%. Its Sharpe ratio for the entire period (1.446) was also substantially higher than that of any other single-characteristic portfolio though lower than that of the equal-weighted portfolio (1.666). When the entire study period was halved, the short-term reversal portfolio once again trumped all the other individual strategies both in mean return and in Sharpe ratio. During the period July 1951 through December 1979 its Sharpe ratio was even higher than that of the equal-weighted portfolio. And during the difficult period of January 1999 through December 2008 its mean return (13.634% as opposed to a negative market return of 2.149%) beat all others, though no longer nearly so decisively. Its Sharpe ratio, however, dropped from first place among the seven strategies to sixth place; only the medium-term momentum portfolio had a lower Sharpe ratio. From a high of 2.013 during the 1951-1979 period it fell to 0.499 during 1999-2008. By contrast, the Sharpe ratio of the equal-weighted portfolio, which had an overall value of 1.666 and values of 1.658 and 1.672 when the study period was halved, fell to only 1.316 during the January 199 through December 2008 period.
The findings of Hjalmarsson’s paper are important for those concerned with portfolio management as well as those engaged in active trading. He clearly documents that over time the “short-term” mean reversion strategy has outperformed the “medium-term” momentum strategy. Of course, when developing a trading system for an account without the capital to buy and sell one-fifth of all the stocks on the NYSE, AMEX, and NASDAQ much more work has to be done—for starters, selecting trading vehicles, determining optimal timeframes, and developing a switching mechanism to adjust to changing market conditions.
By the way, Hjalmarsson makes use of the monthly return data from Kenneth French’s (of Fama and French fame) Dartmouth website. His data library, downloadable as text files, is definitely worth a look.
The three technical characteristics Hjalmarsson uses are short-term reversals (the prior month’s [t-1] return), medium-term momentum (returns from month t-12 to t-2), and long-term reversals (returns from month t-60 to t-13). For the medium-term momentum portfolio returns are calculated by taking the difference between the returns on the top decile of the universe of stocks and the bottom decile; for the short-term and long-term reversal portfolios returns are calculated by taking the difference between the returns on the bottom decile and the top decile.
Although Hjalmarsson touts the risk-adjusted strength of the portfolio of portfolios, I want to look at the short-term reversal single-characteristic portfolio since it delivered the best non-risk-adjusted returns. Throughout the entire period of the study, July 1951 through December 2008, it had an annualized mean return of 23.267% whereas the market returned only 6.104%. By comparison, medium-term momentum returned 14.956%, long-term reversals 9.314%, book-to-market value 11.651%, cashflow-price ratio 9.766%, earnings-price ratio 8.839%, firm size 5.145%, and the equal-weighted portfolio 11.849%. Its Sharpe ratio for the entire period (1.446) was also substantially higher than that of any other single-characteristic portfolio though lower than that of the equal-weighted portfolio (1.666). When the entire study period was halved, the short-term reversal portfolio once again trumped all the other individual strategies both in mean return and in Sharpe ratio. During the period July 1951 through December 1979 its Sharpe ratio was even higher than that of the equal-weighted portfolio. And during the difficult period of January 1999 through December 2008 its mean return (13.634% as opposed to a negative market return of 2.149%) beat all others, though no longer nearly so decisively. Its Sharpe ratio, however, dropped from first place among the seven strategies to sixth place; only the medium-term momentum portfolio had a lower Sharpe ratio. From a high of 2.013 during the 1951-1979 period it fell to 0.499 during 1999-2008. By contrast, the Sharpe ratio of the equal-weighted portfolio, which had an overall value of 1.666 and values of 1.658 and 1.672 when the study period was halved, fell to only 1.316 during the January 199 through December 2008 period.
The findings of Hjalmarsson’s paper are important for those concerned with portfolio management as well as those engaged in active trading. He clearly documents that over time the “short-term” mean reversion strategy has outperformed the “medium-term” momentum strategy. Of course, when developing a trading system for an account without the capital to buy and sell one-fifth of all the stocks on the NYSE, AMEX, and NASDAQ much more work has to be done—for starters, selecting trading vehicles, determining optimal timeframes, and developing a switching mechanism to adjust to changing market conditions.
By the way, Hjalmarsson makes use of the monthly return data from Kenneth French’s (of Fama and French fame) Dartmouth website. His data library, downloadable as text files, is definitely worth a look.
Tuesday, March 16, 2010
An excerpt from Poker, Sex and Dying
David L. Caplan in The New Option Secret: Volatility (review on the way) described Poker, Sex and Dying as “one of the most influential of all the works” he had read. He quoted its closing paragraphs. I want to share one of them as something of a follow-up to yesterday’s post.“When we are dealing with a subject as explosive or difficult as human personality traits caution becomes necessary. There are no absolutes. All individuals are unique. Every opponent is particularly dangerous. We can, with a certain degree of accuracy, predict actions and reactions, however we can not definitively define how any human being will react 100% of the time. In many respects, insight, knowledge and understanding creates a pandora’s box. The more knowledge we acquire, and the more understanding we gain often only serves to highlight the knowledge and understanding that we don’t have. The more we learn simply teaches us that we are always on the tip of the iceberg and ironically, may never be able to move beyond that point. Expertise, be it given, or solicited, is like a jagged piece of glass in that it is best handled with care.”
Monday, March 15, 2010
Smackdown over expertise
I have no idea whether David Varadi was challenging Brett Steenbarger’s webinar. I respect both bloggers enormously, but of course there’s no way to avoid taking sides. So here’s my very personal response to what might be an invented confrontation.
Back when I decided I needed a single-word trading mantra I chose “competence.” A friend who is a lot snarlier than I saw the mantra and, in typical ironic fashion, said that I had really high ambitions. (Dr. Brett would obviously agree.) Well, yes I did, and yes I do. And here’s my lexical reason why. “Competence” means being adequately or well qualified; it defines a specific range of skill, knowledge, or ability. Importantly, it is also used to describe a “sufficient means for a comfortable existence.” To my mind, competence is a worthy goal since it gives the trader both a monetary reward and about a “B” for skill. If the trader is getting a grade that shows she is not truly exceptional, it means she is still learning or at least should be. Competence both defines a level of accomplishment and, for me at least, provides a prod to move higher.
I have a personal aversion to much that goes under the rubric of expertise. Perhaps because so many experts are self-styled. They promote themselves more effectively than do people who consider themselves merely competent. Who are these so-called experts? Mark Twain described an expert as “an ordinary fellow from another town” and Will Rogers said that he was “a man fifty miles from home with a briefcase.” Whom would you rather have on a panel—a self-styled expert or someone who’s just plain vanilla competent?
I realize that true expertise is laudable; it is an appropriate developmental goal. We all know the drill: study, practice, measure, here and there become creative. After a long process (10,000 hours is now the accepted norm) an expert may emerge. Since expertise entails mastery and a high level of skill we would be foolhardy to opt for a competent surgeon when we could have an expert surgeon. Similarly, we would undoubtedly hire an expert trader over a competent trader.
But what is the best way to view oneself after having put in the requisite 10,000 hours of rigorous training and having acquired both knowledge and skill—as a competent trader or an expert trader? I know that no matter how good a trader I might be there will always be countless others who are better. This horde of faceless traders makes me paranoid. I also know that no matter how well I read a particular market today a paradigm shift will occur that will transform me into an illiterate. Another reason for paranoia. Famously, Andy Grove didn’t say “Only the experts survive” but “Only the paranoid survive.”
If I am only competent I must keep improving to compete successfully against the exceptional. I also know that I’m not “expert” enough to withstand a financial tsunami so I must keep up my defenses. And there are always the wannabes nipping at my heels. In my own mind I’ll never be an expert and that doesn’t bother me in the slightest. I’ll just keep trying to get better, a process that will cease only when I stop trading. In brief, I remain happy with my mantra. (And if I ever set myself up as a trading expert, this is an open invitation to knock me down.)
Back when I decided I needed a single-word trading mantra I chose “competence.” A friend who is a lot snarlier than I saw the mantra and, in typical ironic fashion, said that I had really high ambitions. (Dr. Brett would obviously agree.) Well, yes I did, and yes I do. And here’s my lexical reason why. “Competence” means being adequately or well qualified; it defines a specific range of skill, knowledge, or ability. Importantly, it is also used to describe a “sufficient means for a comfortable existence.” To my mind, competence is a worthy goal since it gives the trader both a monetary reward and about a “B” for skill. If the trader is getting a grade that shows she is not truly exceptional, it means she is still learning or at least should be. Competence both defines a level of accomplishment and, for me at least, provides a prod to move higher.
I have a personal aversion to much that goes under the rubric of expertise. Perhaps because so many experts are self-styled. They promote themselves more effectively than do people who consider themselves merely competent. Who are these so-called experts? Mark Twain described an expert as “an ordinary fellow from another town” and Will Rogers said that he was “a man fifty miles from home with a briefcase.” Whom would you rather have on a panel—a self-styled expert or someone who’s just plain vanilla competent?
I realize that true expertise is laudable; it is an appropriate developmental goal. We all know the drill: study, practice, measure, here and there become creative. After a long process (10,000 hours is now the accepted norm) an expert may emerge. Since expertise entails mastery and a high level of skill we would be foolhardy to opt for a competent surgeon when we could have an expert surgeon. Similarly, we would undoubtedly hire an expert trader over a competent trader.
But what is the best way to view oneself after having put in the requisite 10,000 hours of rigorous training and having acquired both knowledge and skill—as a competent trader or an expert trader? I know that no matter how good a trader I might be there will always be countless others who are better. This horde of faceless traders makes me paranoid. I also know that no matter how well I read a particular market today a paradigm shift will occur that will transform me into an illiterate. Another reason for paranoia. Famously, Andy Grove didn’t say “Only the experts survive” but “Only the paranoid survive.”
If I am only competent I must keep improving to compete successfully against the exceptional. I also know that I’m not “expert” enough to withstand a financial tsunami so I must keep up my defenses. And there are always the wannabes nipping at my heels. In my own mind I’ll never be an expert and that doesn’t bother me in the slightest. I’ll just keep trying to get better, a process that will cease only when I stop trading. In brief, I remain happy with my mantra. (And if I ever set myself up as a trading expert, this is an open invitation to knock me down.)
Sunday, March 14, 2010
Classic trading article links
When PBS is fundraising (and, yes, it’s that time again) it often appeals to the nostalgia of baby boomers, hoping to rake in the big bucks from a presumably affluent demographic. I admit I’m the first to change the channel. But here I am sending you back in time, though not so far and without any expectation of financial gain. The link is to a website that served me well via a library of basic trading articles. If you haven’t been there, go; if you have, perhaps it’s time for a refresher course.
Saturday, March 13, 2010
Bookplates
I know that not everyone who reads my blog is a book lover. But if you are, here is a sampling of Yale's collection of nearly one million bookplates. Fun to look at on a dark, rainy Saturday morning in Connecticut.
Friday, March 12, 2010
RSI and Hayden’s hidden divergences
In my last post about John Hayden’s RSI: The Complete Guide (Traders Press, 2004) I noted that he takes a contrarian view of simple (or regular) divergence. “The appearance of a simple divergence [bullish/bearish] is a very strong indication that the trend is opposite of what the name implies." (p. 72) For Hayden an uptrend is in place when RSI values remain between 40 and 80 and there is a simple bearish divergence. The continuation of a downtrend is indicated when RSI values are between 60 and 20 and there is a simple bullish divergence. In brief, in most trends a regular divergence points to trend continuation, not trend reversal.
Hidden divergences, by contrast, do presage at least a short-term trend reversal. A hidden bullish divergence in a downtrend is a strong signal that the trend is about to reverse, even if only temporarily; a hidden bearish divergence in an uptrend points to a reversal down. Hayden’s hidden divergences are not, let me be quick to point out, what we commonly think of as hidden divergences. That is, they are not higher price lows and lower indicator lows in an uptrend or lower price highs and higher indicator highs in a downtrend that point to trend continuation. (See, for instance, Barbara Star’s "Hidden Divergence.") Hayden’s hidden divergences look like "their more common cousins” except they “do not occur at the bottom or top of the RSI chart. . . . They appear after the RSI has either rallied (bullish hidden divergence) or after the RSI has dropped from its high (bearish hidden divergence). Hidden divergence typically occurs in the 40 to 60 ranges. When hidden divergence occurs, it is classified as the strongest divergence possible. The market will do exactly as the name indicates.” (p. 70)
Hidden divergences are not major trend reversal signals; they are useful for traders who want to play countertrends. For instance, a rally presaged by a bullish hidden divergence in a downtrend that occurs at the 40 level is often stopped in its tracks when it reaches the RSI resistance level of 60. The downtrend may then reassert itself. A bearish hidden divergence in an uptrend is a similarly short-term play, with the pullback often ending when RSI reaches the support level of 40.
I have not tested the efficacy of Hayden’s hidden divergence signal. For savvy programmers it might be worth a few hours’ time.
Hidden divergences, by contrast, do presage at least a short-term trend reversal. A hidden bullish divergence in a downtrend is a strong signal that the trend is about to reverse, even if only temporarily; a hidden bearish divergence in an uptrend points to a reversal down. Hayden’s hidden divergences are not, let me be quick to point out, what we commonly think of as hidden divergences. That is, they are not higher price lows and lower indicator lows in an uptrend or lower price highs and higher indicator highs in a downtrend that point to trend continuation. (See, for instance, Barbara Star’s "Hidden Divergence.") Hayden’s hidden divergences look like "their more common cousins” except they “do not occur at the bottom or top of the RSI chart. . . . They appear after the RSI has either rallied (bullish hidden divergence) or after the RSI has dropped from its high (bearish hidden divergence). Hidden divergence typically occurs in the 40 to 60 ranges. When hidden divergence occurs, it is classified as the strongest divergence possible. The market will do exactly as the name indicates.” (p. 70)
Hidden divergences are not major trend reversal signals; they are useful for traders who want to play countertrends. For instance, a rally presaged by a bullish hidden divergence in a downtrend that occurs at the 40 level is often stopped in its tracks when it reaches the RSI resistance level of 60. The downtrend may then reassert itself. A bearish hidden divergence in an uptrend is a similarly short-term play, with the pullback often ending when RSI reaches the support level of 40.
I have not tested the efficacy of Hayden’s hidden divergence signal. For savvy programmers it might be worth a few hours’ time.
Wednesday, March 10, 2010
Patterson, The Quants
It’s all too easy to nitpick over Scott Patterson’s The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (Crown Business, 2010). But why bother? It’s a thoroughly enjoyable book, and how many of those have you read lately? We get to know “the players,” as Patterson dubs them, warts and all: Peter Muller, Ken Griffin, Cliff Asness, Boaz Weinstein, Jim Simons, Ed Thorp, Aaron Brown, Paul Wilmott, and Benoit Mandelbrot. Some had skin in the game, others were Cassandras. We’re also introduced to some of their strategies: delta hedging, stat arb, value/momentum, high frequency trading, and now and then sheer moxie.
The book is a cautionary tale. Grossly simplified, it argues that although quantitative strategies have the deceptive allure of science, financial markets are unlike the physical world and cannot be accurately modeled mathematically. Moreover, quantitative strategies are easily replicable, at least in outline, leading to overcrowded trades. When volatility spikes and liquidity dries up, every quantitative strategy will get crushed. In August 2007 even Renaissance’s Medallion fund took a drubbing.
Quants are not going away any time soon, nor should they. But they (and, more important, their risk managers and, most important, those who manage the risk managers) need to recognize the limitations of their models and strategies. It’s not just those fat tails that wag the financial markets. One of the most obvious problems, stemming in part from the very nature of quantitative analysis, is the copycat phenomenon. That is, it’s easier to copy or reverse engineer a math formula or a piece of computer code than a judgment about the quality of a firm’s management. Just consider the recent rush to reap the rewards of high frequency trading. The problem is that as more and more players enter the same business profit margins tend to get squeezed. And then entrepreneurs, not necessarily quants, are inclined to ramp up risk, whether by increasing leverage or some other strategy, to keep the profits flowing. They try to hit home runs instead of singles and doubles, generally a recipe for failure.
What keeps good quant funds going, continuing to make money even in difficult times? Intelligence, originality, hard work, and luck would probably head the list. Paranoia might also help. For instance, Jim Simons and his merry band of math and science Ph.D.s allegedly devoted the first forty hours of their work week to assigned tasks; during the “second forty hours” they could experiment, with full access to all of the fund’s data. Their intelligence, originality, hard work and, as Simons has always said, luck have kept the Medallion money machine working in overdrive throughout a range of conditions (except in August 2007 when they were the only buyers in town, a very lonely and expensive scenario). In terms of strategy, what sets Medallion apart? We learn only (but here “only” might be worth a fortune) that it adjusts for changing market conditions far more frequently than its competitors.
In general Scott Patterson’s book gives up few trade secrets, but that’s just as well. Riding the coattails of the super successful might be profitable much of the time, but who wants to be there for the multi-trader pileup? What The Quants offers the wannabe alpha trader is a series of portraits of very bright, passionate men, most with oversized egos, who are driven to win. We see them both on the way up and when they’re teetering on the brink. We may not like them all, but I for one was transfixed reading about them.
The book is a cautionary tale. Grossly simplified, it argues that although quantitative strategies have the deceptive allure of science, financial markets are unlike the physical world and cannot be accurately modeled mathematically. Moreover, quantitative strategies are easily replicable, at least in outline, leading to overcrowded trades. When volatility spikes and liquidity dries up, every quantitative strategy will get crushed. In August 2007 even Renaissance’s Medallion fund took a drubbing.
Quants are not going away any time soon, nor should they. But they (and, more important, their risk managers and, most important, those who manage the risk managers) need to recognize the limitations of their models and strategies. It’s not just those fat tails that wag the financial markets. One of the most obvious problems, stemming in part from the very nature of quantitative analysis, is the copycat phenomenon. That is, it’s easier to copy or reverse engineer a math formula or a piece of computer code than a judgment about the quality of a firm’s management. Just consider the recent rush to reap the rewards of high frequency trading. The problem is that as more and more players enter the same business profit margins tend to get squeezed. And then entrepreneurs, not necessarily quants, are inclined to ramp up risk, whether by increasing leverage or some other strategy, to keep the profits flowing. They try to hit home runs instead of singles and doubles, generally a recipe for failure.
What keeps good quant funds going, continuing to make money even in difficult times? Intelligence, originality, hard work, and luck would probably head the list. Paranoia might also help. For instance, Jim Simons and his merry band of math and science Ph.D.s allegedly devoted the first forty hours of their work week to assigned tasks; during the “second forty hours” they could experiment, with full access to all of the fund’s data. Their intelligence, originality, hard work and, as Simons has always said, luck have kept the Medallion money machine working in overdrive throughout a range of conditions (except in August 2007 when they were the only buyers in town, a very lonely and expensive scenario). In terms of strategy, what sets Medallion apart? We learn only (but here “only” might be worth a fortune) that it adjusts for changing market conditions far more frequently than its competitors.
In general Scott Patterson’s book gives up few trade secrets, but that’s just as well. Riding the coattails of the super successful might be profitable much of the time, but who wants to be there for the multi-trader pileup? What The Quants offers the wannabe alpha trader is a series of portraits of very bright, passionate men, most with oversized egos, who are driven to win. We see them both on the way up and when they’re teetering on the brink. We may not like them all, but I for one was transfixed reading about them.
Tuesday, March 9, 2010
RSI redux à la John Hayden
Sometimes it’s wise to revisit stock-in-trade technical indicators. RSI is a prime candidate. It is seemingly omnipresent and has been massaged in virtually every way imaginable. Its lookback period has been shortened and lengthened, its results have been smoothed (often many times over), for closing prices tinkerers have substituted highs and lows and weighted closes, and other indicators have been overlaid on it. But before we get too fancy we should understand how the basic indicator works.
John Hayden published RSI: The Complete Guide (Traders Press) in 2004. No guide, of course, is ever complete, but Hayden’s book is a good start. In dissecting the RSI indicator Hayden uses the Morris modified calculation. It is not cumulative like Wilder’s original version and hence does not have the element of exponential smoothing. It simply measures the ratio of the average increase in price over N days to the average decrease in price over N days and then normalizes that ratio so that it is bound on the upside by 100 and on the downside by 0.
The Morris modified RSI behaves logarithmically. For instance, if both the up average and the down average equal 1, RSI is 50. If the up average is 2 and the down average is 1, RSI is 66.67; if the up average is 1 and the down average is 2, it is 33.33. If the up average is 3 and the down average is 1, RSI is 75; if the averages are reversed, RSI is 25. If the up average is 4 and the down average is 1, RSI is 80; reversing the averages we get an RSI of 20. From these numbers we see that “the largest increase or decrease in the RSI value occurs when the ratio changes from 1:1 to the next whole number (2:1 or 1:2)” and that “the RSI value experiences its largest changes in value as it oscillates between the index values of 40 and 60. In other words the RSI is most sensitive to price change when the RSI is oscillating between 40 and 60,” (p. 11) or, more accurately, between 33.33 and 66.67. When Hayden is touting Fibonacci levels he uses the latter pair of numbers, when he’s using a quick and dirty system he goes with the former. For instance, he claims that “in an uptrend, the RSI finds resistance at 80 and support at 40” while “in a downtrend, the RSI finds resistance at 60 and support at 20.” (p. 63) A few pages later he describes resistance in a downtrend as “the 66.7 level or more generally the 60 level.” (p. 67)
Hayden is a harsh critic of those who use divergence as a trend reversal indicator. By the very nature of its mathematical calculation RSI will offer up many bullish divergences in downtrends and bearish divergences in uptrends. What do they signify? Hayden contends, in opposition to much of the literature, that “a bullish divergence signifies that the existing trend is down and the Bears are exhausted. We should be expecting a rally to sell into. . . . Inversely, when we see a bearish divergence, the trend is up and we should probably expect a retracement to lower prices because the Bulls are exhausted. It is time that we should be looking for a reason to buy.” (p. 67)
(to be continued)
John Hayden published RSI: The Complete Guide (Traders Press) in 2004. No guide, of course, is ever complete, but Hayden’s book is a good start. In dissecting the RSI indicator Hayden uses the Morris modified calculation. It is not cumulative like Wilder’s original version and hence does not have the element of exponential smoothing. It simply measures the ratio of the average increase in price over N days to the average decrease in price over N days and then normalizes that ratio so that it is bound on the upside by 100 and on the downside by 0.
The Morris modified RSI behaves logarithmically. For instance, if both the up average and the down average equal 1, RSI is 50. If the up average is 2 and the down average is 1, RSI is 66.67; if the up average is 1 and the down average is 2, it is 33.33. If the up average is 3 and the down average is 1, RSI is 75; if the averages are reversed, RSI is 25. If the up average is 4 and the down average is 1, RSI is 80; reversing the averages we get an RSI of 20. From these numbers we see that “the largest increase or decrease in the RSI value occurs when the ratio changes from 1:1 to the next whole number (2:1 or 1:2)” and that “the RSI value experiences its largest changes in value as it oscillates between the index values of 40 and 60. In other words the RSI is most sensitive to price change when the RSI is oscillating between 40 and 60,” (p. 11) or, more accurately, between 33.33 and 66.67. When Hayden is touting Fibonacci levels he uses the latter pair of numbers, when he’s using a quick and dirty system he goes with the former. For instance, he claims that “in an uptrend, the RSI finds resistance at 80 and support at 40” while “in a downtrend, the RSI finds resistance at 60 and support at 20.” (p. 63) A few pages later he describes resistance in a downtrend as “the 66.7 level or more generally the 60 level.” (p. 67)
Hayden is a harsh critic of those who use divergence as a trend reversal indicator. By the very nature of its mathematical calculation RSI will offer up many bullish divergences in downtrends and bearish divergences in uptrends. What do they signify? Hayden contends, in opposition to much of the literature, that “a bullish divergence signifies that the existing trend is down and the Bears are exhausted. We should be expecting a rally to sell into. . . . Inversely, when we see a bearish divergence, the trend is up and we should probably expect a retracement to lower prices because the Bulls are exhausted. It is time that we should be looking for a reason to buy.” (p. 67)
(to be continued)
Monday, March 8, 2010
Historical volatility charts
Options traders are fixated on volatility, both historical and implied. Equities and futures traders pay lip service to volatility but seldom make it part of their models, save for the occasional Bollinger bands overlaid on charts.
Jeff Augen is an options volatility geek. His book The Volatility Edge in Options Trading (FT Press, 2008) is a thoughtful work for the experienced option trader. Today I’m going to look at the charts Augen uses in his options analysis because I think they might provide a new perspective for developing stock and futures strategies.
We normally look at charts of closing prices. Augen, however, prefers to look at historical volatility charts. Historical volatility is easily programmed in most charting software packages. For instance, in MetaStock the AAPL 20-day historical volatility that I illustrate below is captured by the formula Std(Log(C/Ref(C,-1)),20)*Sqrt(252)*100. Here, like Augen, I’m assuming a 252-day trading year and am calculating volatility for 20 price changes (21 trading days).
(Click on the image to enlarge it.)
Note the difference between volatility as measured by Bollinger bands and historical volatility as used in option calculations. Bollinger bands measure the standard deviation of the stock price; historical volatility measures the standard deviation of the log of the price change.
Okay, you ask, so what’s the point of looking at historical volatility? Augen admits that “volatility alone has limited predictive power.” But, he continues, “certain trends emerge if you view a large number of volatility charts. Most notable is the relationship between high volatility and poor stock performance.” (p. 56)
Larry Connors’ research supports this view. He looked at 11,282 stocks from January 1, 1995 through May 31, 2007. Each day he ranked the 100-day historical volatility of these stocks. He then compared the 252-day performance of the top 20% in terms of volatility versus the 20% with the lowest volatility. The latter outperformed the former by about 2:1 (14.7% vs. 7.3%). And only 42.2% of the high volatility stocks closed higher one year later, as opposed to 74.5% of the low volatility stocks.
Historical volatility might be a worthy filter in developing a trading system. By the way, it’s one of the metrics available on MarketRewind. See, for starters, the free weekly ETF Rewind.
One cautionary note about focusing on historical volatility to the exclusion of implied volatility. There are situations in which there is a major disconnect between historical volatility and implied volatility, usually indicating a significant upcoming event. Take the recent case of Medivation whose late stage Alzheimer’s drug study did not meet its goals. On March 2 the stock closed at 40.25 with a 20-day historical volatility of 41%, admittedly a high figure. (For instance, GOOG’s HV was 17% and WMT’s was 14%.) But options at the 40 strike had an implied volatility of about 269%! And what happened on March 3? The stock cratered, down some 68% to about 13 when I checked during the day.
Jeff Augen is an options volatility geek. His book The Volatility Edge in Options Trading (FT Press, 2008) is a thoughtful work for the experienced option trader. Today I’m going to look at the charts Augen uses in his options analysis because I think they might provide a new perspective for developing stock and futures strategies.
We normally look at charts of closing prices. Augen, however, prefers to look at historical volatility charts. Historical volatility is easily programmed in most charting software packages. For instance, in MetaStock the AAPL 20-day historical volatility that I illustrate below is captured by the formula Std(Log(C/Ref(C,-1)),20)*Sqrt(252)*100. Here, like Augen, I’m assuming a 252-day trading year and am calculating volatility for 20 price changes (21 trading days).
(Click on the image to enlarge it.)
Note the difference between volatility as measured by Bollinger bands and historical volatility as used in option calculations. Bollinger bands measure the standard deviation of the stock price; historical volatility measures the standard deviation of the log of the price change.
Okay, you ask, so what’s the point of looking at historical volatility? Augen admits that “volatility alone has limited predictive power.” But, he continues, “certain trends emerge if you view a large number of volatility charts. Most notable is the relationship between high volatility and poor stock performance.” (p. 56)
Larry Connors’ research supports this view. He looked at 11,282 stocks from January 1, 1995 through May 31, 2007. Each day he ranked the 100-day historical volatility of these stocks. He then compared the 252-day performance of the top 20% in terms of volatility versus the 20% with the lowest volatility. The latter outperformed the former by about 2:1 (14.7% vs. 7.3%). And only 42.2% of the high volatility stocks closed higher one year later, as opposed to 74.5% of the low volatility stocks.
Historical volatility might be a worthy filter in developing a trading system. By the way, it’s one of the metrics available on MarketRewind. See, for starters, the free weekly ETF Rewind.
One cautionary note about focusing on historical volatility to the exclusion of implied volatility. There are situations in which there is a major disconnect between historical volatility and implied volatility, usually indicating a significant upcoming event. Take the recent case of Medivation whose late stage Alzheimer’s drug study did not meet its goals. On March 2 the stock closed at 40.25 with a 20-day historical volatility of 41%, admittedly a high figure. (For instance, GOOG’s HV was 17% and WMT’s was 14%.) But options at the 40 strike had an implied volatility of about 269%! And what happened on March 3? The stock cratered, down some 68% to about 13 when I checked during the day.
Saturday, March 6, 2010
YTD stock performance by type
From The Reformed Broker comes a breakdown of the best performing stock types, five-day and year to date. He uses the Morningstar designations (listed here in alphabetical order so as not to disclose the results) Aggressive Growth, Classic Growth, Cyclical, Distressed, Hard Asset, High Yield, Slow Growth, and Speculative Growth.
Friday, March 5, 2010
Pulling the plug
An early epigraph in Dean Shepherd’s book From Lemons to Lemonade: Squeeze Every Last Drop of Success Out of Your Mistakes (Wharton School Publishing, 2009) comes from Bill Gates: “Success is a lousy teacher. It seduces smart people into thinking they can’t lose.” On the other hand, as I wrote in an earlier post, “Success breeds success; with failure it’s just try, try again,” it can be very difficult to learn from our failures.
The first, sometimes seemingly insurmountable problem, is admitting failure and pulling the plug on a project, a trading system, or an individual trade gone wrong. We have been steeped in “Vince Lombardi-style slogans . . . that winners never quit. That quitters never win. That, in the oft-quoted line from the movie Apollo 13, ‘Failure is not an option.’” (p. 53) We are taught that persistence and determination are laudable personal qualities, that we should never give up.
So what are we inclined to do when things are going against us? We may procrastinate, avoiding the negative emotional reaction to pulling the plug. Perhaps things will turn around and we won’t have to take the loss, admit we were wrong, and feel bad.
If we believe that success is within our control, we may try to throw more resources at the problem. “Normally, when you are doing good business, you try and hit singles and doubles, but now that things aren’t working, you are trying to hit triples and home runs. And then you are really in bad trouble.” (pp. 54-55) Nick Leeson is the poster child for making a very bad situation disastrous by scaling up. Placing an unlimited risk short straddle in the Singapore and Tokyo exchanges the day before the Kobe earthquake was a desperate and of course failed attempt to hit a home run. (By the way, if you ever doubt the wisdom of pulling the plug watch Rogue Trader.)
It’s easy to argue that taking a loss on a trade is not failure but simply the cost of doing business. It is equally easy to point out that we aren’t trading our egos; we aren’t failures simply because we bail on a trade or a series of trades. But the fact remains that it takes emotional work to replace the voice of Vince Lombardi with that of the risk manager, especially when we are our own risk managers.
In trading organizations there are usually risk management procedures in place to constrain individual traders. For instance, SAC Capital has “down-and-out” clauses for its portfolio managers. After a 5% drawdown SAC can cut the manager’s trading account in half; after a 10% drawdown, it might be sayonara for the manager. (See the recent Bloomberg article link.
Individual traders need to have well-defined procedures in place for dealing with the inevitable failures, whether they take the form of stop losses, quitting for the day after x number of failed trades, quitting for a period of time after an x% drawdown, or reducing position size. (And this list is certainly not exhaustive.) Making risk management discretionary is courting disaster.
The first, sometimes seemingly insurmountable problem, is admitting failure and pulling the plug on a project, a trading system, or an individual trade gone wrong. We have been steeped in “Vince Lombardi-style slogans . . . that winners never quit. That quitters never win. That, in the oft-quoted line from the movie Apollo 13, ‘Failure is not an option.’” (p. 53) We are taught that persistence and determination are laudable personal qualities, that we should never give up.
So what are we inclined to do when things are going against us? We may procrastinate, avoiding the negative emotional reaction to pulling the plug. Perhaps things will turn around and we won’t have to take the loss, admit we were wrong, and feel bad.
If we believe that success is within our control, we may try to throw more resources at the problem. “Normally, when you are doing good business, you try and hit singles and doubles, but now that things aren’t working, you are trying to hit triples and home runs. And then you are really in bad trouble.” (pp. 54-55) Nick Leeson is the poster child for making a very bad situation disastrous by scaling up. Placing an unlimited risk short straddle in the Singapore and Tokyo exchanges the day before the Kobe earthquake was a desperate and of course failed attempt to hit a home run. (By the way, if you ever doubt the wisdom of pulling the plug watch Rogue Trader.)
It’s easy to argue that taking a loss on a trade is not failure but simply the cost of doing business. It is equally easy to point out that we aren’t trading our egos; we aren’t failures simply because we bail on a trade or a series of trades. But the fact remains that it takes emotional work to replace the voice of Vince Lombardi with that of the risk manager, especially when we are our own risk managers.
In trading organizations there are usually risk management procedures in place to constrain individual traders. For instance, SAC Capital has “down-and-out” clauses for its portfolio managers. After a 5% drawdown SAC can cut the manager’s trading account in half; after a 10% drawdown, it might be sayonara for the manager. (See the recent Bloomberg article link.
Individual traders need to have well-defined procedures in place for dealing with the inevitable failures, whether they take the form of stop losses, quitting for the day after x number of failed trades, quitting for a period of time after an x% drawdown, or reducing position size. (And this list is certainly not exhaustive.) Making risk management discretionary is courting disaster.
Thursday, March 4, 2010
Mental performance when physically exhausted
Dan Ariely of Predictably Irrational fame wrote a very brief piece entitled “Squash and Short-Term Thinking.” He extrapolated from his own experience playing squash when out of shape to suggest that perhaps being exhausted “makes people focus on the short-term and ignore the long-term—and this way become more susceptible to making mistakes.”
Well, if Ariely can draw such an overarching hypothesis from a single anecdote, perhaps I can propose something more mundane based on my experience. I have a long, treacherous driveway infamous for trapping FedEx drivers. If the snow is heavy the driveway is plowed. If it is under 3-4 inches I normally shovel the driveway myself. It’s a daunting task that takes about four hours to complete. I usually do it in two or three installments.
When I come in from a stint of shoveling I sit down at the computer, sip some tea, and pull up an arithmetical puzzle that under normal circumstances is a no-brainer. I can go through it as quickly as I can click my mouse. After shoveling, however, I stumble on it, make mistakes and have to start over. It’s a humbling experience.
I assume that part of the oxygen that fuels my brain (it requires about 25% of the body’s oxygen supply) was rerouted to my skeletal muscles during shoveling and that it takes time to get the oxygen flowing full force to my brain again. But whatever the physiological case, I can attest to the fact that I am decidedly mentally subpar after serious physical exertion.
There are, of course, many other circumstances such as stress and emotional upset that have similar or even more serious ramifications. See, for instance, my post on the long-term effects of making decisions when you’re upset.
I’m not going to use the single snow shoveling example to ramble on about the allocation of our physical, ideational, and psychological resources. But it’s important for each of us to know what sorts of things interfere with our peak mental performance.
Well, if Ariely can draw such an overarching hypothesis from a single anecdote, perhaps I can propose something more mundane based on my experience. I have a long, treacherous driveway infamous for trapping FedEx drivers. If the snow is heavy the driveway is plowed. If it is under 3-4 inches I normally shovel the driveway myself. It’s a daunting task that takes about four hours to complete. I usually do it in two or three installments.
When I come in from a stint of shoveling I sit down at the computer, sip some tea, and pull up an arithmetical puzzle that under normal circumstances is a no-brainer. I can go through it as quickly as I can click my mouse. After shoveling, however, I stumble on it, make mistakes and have to start over. It’s a humbling experience.
I assume that part of the oxygen that fuels my brain (it requires about 25% of the body’s oxygen supply) was rerouted to my skeletal muscles during shoveling and that it takes time to get the oxygen flowing full force to my brain again. But whatever the physiological case, I can attest to the fact that I am decidedly mentally subpar after serious physical exertion.
There are, of course, many other circumstances such as stress and emotional upset that have similar or even more serious ramifications. See, for instance, my post on the long-term effects of making decisions when you’re upset.
I’m not going to use the single snow shoveling example to ramble on about the allocation of our physical, ideational, and psychological resources. But it’s important for each of us to know what sorts of things interfere with our peak mental performance.
Wednesday, March 3, 2010
Ideas, vision, data
A couple of links from Harvard Business Review blogs.
"Having Ideas Versus Having a Vision" This one resonated with me. I'm long on ideas and short on that "vision thing."
"Do You Need All That Data?"
"Having Ideas Versus Having a Vision" This one resonated with me. I'm long on ideas and short on that "vision thing."
"Do You Need All That Data?"
Price discovery as a chaotic process
Jeff Augen’s book The Volatility Edge in Options Trading: New Technical Strategies for Investing in Unstable Markets (FT Press, 2008) is a treasure trove of thought-provoking ideas, and not just for the option trader. Today I want to share his thesis that “price discovery cannot operate properly unless the market is chaotic” (p. 7) and, its corollary, that the lack of market chaos can cause a small drawdown to become a crash.
Augen uses the word “chaos” in “the true mathematical sense—a system that appears random but behaves according to a well-defined set of rules.” (p. 8) Or, as others have described a chaotic system, it exhibits three main characteristics. (Here I’m relying on good old Wikipedia although I’ve written more than once about chaotic systems on this blog.) First, it is “highly sensitive to initial conditions,” what is known as the butterfly effect. Second, it is deterministic. And third, despite its deterministic nature, it is not predictable.
According to Augen the market is chaotic because it is “characterized by large numbers of investors pursuing divergent strategies based on different goals and views of the market.” He gives a “microscopic” example which is worth quoting in full, mainly because there’s no efficient way to summarize it. “Investor #1, on hearing a piece of bad news, decides to sell a stock. The stock falls slightly and triggers another investor’s (#2) stop-sell limit order. This new sell order causes the price to fall further. However, investor #3, who has a longer-term view of the company and believes that the stock is undervalued, has been waiting for a dip in the price. He aggressively buys a large number of shares, momentarily stabilizing the price. However, a large institutional investor with a computer program that tracks this particular stock, looking for such behavior, suddenly receives notice that a sell-short trigger has been activated. The large institutional sell order causes the stock to fall rapidly. It also triggers stop-sell limit orders from other investors who are protecting their profits. The sell-off accelerates as investors aggressively run from their positions in the stock. However, a small group of speculators who previously anticipated the bad news and sold short now begin buying the stock to cover their short sales and lock in a profit. They are using automated systems with triggers that generate a buying decision as soon as a certain profit level has been reached. The stock begins to climb again as aggressive buy-to-cover orders accumulate. As the stock climbs, short sellers begin to see their profits evaporate. They become increasingly aggressive about buying back the stock. The trend begins to slow as short sellers take themselves out of the market by unwinding positions. The price does not stabilize, however, because other investors witnessing the sudden rise and looking at particular chart patterns interpret the emerging rally as a buying opportunity and flock to purchase the stock before it runs up too much. The process continues indefinitely because price discovery is a dynamic and never-ending process.” (pp. 7-8)
By contrast, consider what would have happened if, as the scenario began to unfold, every investor had made the same sell decision as investor #1. In this case the stock would have plummeted. “A new fair-market value would not have been discovered until a very low point had been reached. In this scenario the lack of market chaos would have caused a small drawdown to become a crash.” (p. 8)
Augen claims that “the size of the resulting decline is closely related to the lack of chaos exhibited just prior to the sell-off. . . . The initial days of the ’29, ’87, and ’00 crashes all had a distinctly nonchaotic character.” (p. 8)
On one level, I suppose, these points are obvious to anyone who has followed markets, especially intraday. We know that market participants have a range of time horizons that affect their views of the market. At one extreme are the high frequency traders who provide liquidity and some very short-term directionality. (And here, parenthetically, let me quote Augen once again: “If liquidity is the fuel that powers the price discovery engine, chaos is certainly the principal ingredient in that fuel.”) At the other extreme are the buy and hold--until it becomes excruciatingly painful--investors. We also know that traders have different market biases, some long and others short. And we know that some are discretionary traders and others follow systems, in many cases triggered by algorithms.
But what appeals to me is the notion that chaos is such a positive force. Remove it and things get sucked into a black hole. As regulators rightfully try to rein in Wall Street excesses they would do well to keep this principle in mind. It’s important to keep a wide range of players at the table; we just don’t want them to be playing with marked cards.
Augen uses the word “chaos” in “the true mathematical sense—a system that appears random but behaves according to a well-defined set of rules.” (p. 8) Or, as others have described a chaotic system, it exhibits three main characteristics. (Here I’m relying on good old Wikipedia although I’ve written more than once about chaotic systems on this blog.) First, it is “highly sensitive to initial conditions,” what is known as the butterfly effect. Second, it is deterministic. And third, despite its deterministic nature, it is not predictable.
According to Augen the market is chaotic because it is “characterized by large numbers of investors pursuing divergent strategies based on different goals and views of the market.” He gives a “microscopic” example which is worth quoting in full, mainly because there’s no efficient way to summarize it. “Investor #1, on hearing a piece of bad news, decides to sell a stock. The stock falls slightly and triggers another investor’s (#2) stop-sell limit order. This new sell order causes the price to fall further. However, investor #3, who has a longer-term view of the company and believes that the stock is undervalued, has been waiting for a dip in the price. He aggressively buys a large number of shares, momentarily stabilizing the price. However, a large institutional investor with a computer program that tracks this particular stock, looking for such behavior, suddenly receives notice that a sell-short trigger has been activated. The large institutional sell order causes the stock to fall rapidly. It also triggers stop-sell limit orders from other investors who are protecting their profits. The sell-off accelerates as investors aggressively run from their positions in the stock. However, a small group of speculators who previously anticipated the bad news and sold short now begin buying the stock to cover their short sales and lock in a profit. They are using automated systems with triggers that generate a buying decision as soon as a certain profit level has been reached. The stock begins to climb again as aggressive buy-to-cover orders accumulate. As the stock climbs, short sellers begin to see their profits evaporate. They become increasingly aggressive about buying back the stock. The trend begins to slow as short sellers take themselves out of the market by unwinding positions. The price does not stabilize, however, because other investors witnessing the sudden rise and looking at particular chart patterns interpret the emerging rally as a buying opportunity and flock to purchase the stock before it runs up too much. The process continues indefinitely because price discovery is a dynamic and never-ending process.” (pp. 7-8)
By contrast, consider what would have happened if, as the scenario began to unfold, every investor had made the same sell decision as investor #1. In this case the stock would have plummeted. “A new fair-market value would not have been discovered until a very low point had been reached. In this scenario the lack of market chaos would have caused a small drawdown to become a crash.” (p. 8)
Augen claims that “the size of the resulting decline is closely related to the lack of chaos exhibited just prior to the sell-off. . . . The initial days of the ’29, ’87, and ’00 crashes all had a distinctly nonchaotic character.” (p. 8)
On one level, I suppose, these points are obvious to anyone who has followed markets, especially intraday. We know that market participants have a range of time horizons that affect their views of the market. At one extreme are the high frequency traders who provide liquidity and some very short-term directionality. (And here, parenthetically, let me quote Augen once again: “If liquidity is the fuel that powers the price discovery engine, chaos is certainly the principal ingredient in that fuel.”) At the other extreme are the buy and hold--until it becomes excruciatingly painful--investors. We also know that traders have different market biases, some long and others short. And we know that some are discretionary traders and others follow systems, in many cases triggered by algorithms.
But what appeals to me is the notion that chaos is such a positive force. Remove it and things get sucked into a black hole. As regulators rightfully try to rein in Wall Street excesses they would do well to keep this principle in mind. It’s important to keep a wide range of players at the table; we just don’t want them to be playing with marked cards.
Tuesday, March 2, 2010
Analysis paralysis
A good piece entitled "How to Kill Innovation: Keep Asking Questions." It addresses the problem of business innovation but applies equally well to trading and investing.
Elko, Touchdown!
From FT Press (2010) comes a book by business performance coach Kevin Elko: Touchdown! Achieving Your Greatness on the Playing Field of Business (and Life). As the title indicates, many of the book’s lessons are illustrated with football stories. Here I’m going to share a few of Elko’s pearls of wisdom abstracted for the most part from their sports context and from their place in a major theme of the book, the “seasons of life.”
“People sometimes make mistakes in two different ways when assessing their personal gifts: (1) they think they have no gifts, or (2) they think they have many. The fact is we all have at least one, and sometimes maybe two. But we must quit wasting time developing too many gifts—or worse, wasting time developing none.” (p. 58)
“[D]o not settle for safe. Listen, risk, match your gifts with what makes you feel alive, and then win. . . .” (p. 62)
Re preparation: “. . . a game is won or lost during the week before it is played. . . . By the time the players get to the field, their play should be all ‘muscle memory,’ what they have encoded through repetition during the week, so that the win is basically subliminal.” (p. 68)
“[A]lways measure your progress. If you don’t, you won’t.” (p. 71)
“[L]earn your move until it is second nature to you. You do not need many moves; instead, perfect your move, the one that best matches you, and then keep making it. If it works for you, it is your plan for your season. You don’t need to be fancy or complicated when you have a winning plan.” (p. 104)
“Words such as winning and goal—although good concepts indeed—are too abstract when trying to get to the doing—to taking effective action. Many times, athletes try to command their bodies to win, but their bodies are confused by the abstract terminology. Winning is not an action. Winning is an outcome of sustained effective action.” (p. 108)
Some things are “too hard to play hard. All you have to do is get your process in front of you and let it happen, not force it to happen.” (p. 109)
Others have developed these points more eloquently, but eloquence isn’t necessary to inspire a football team or a business group. One of my favorite pieces of advice is in fact Elko’s own focus phrase. Confronted with a less than perfect lawn, he got a tip from a farmer: “Keep planting grass; don’t pull weeds.” (p. 110)
“People sometimes make mistakes in two different ways when assessing their personal gifts: (1) they think they have no gifts, or (2) they think they have many. The fact is we all have at least one, and sometimes maybe two. But we must quit wasting time developing too many gifts—or worse, wasting time developing none.” (p. 58)
“[D]o not settle for safe. Listen, risk, match your gifts with what makes you feel alive, and then win. . . .” (p. 62)
Re preparation: “. . . a game is won or lost during the week before it is played. . . . By the time the players get to the field, their play should be all ‘muscle memory,’ what they have encoded through repetition during the week, so that the win is basically subliminal.” (p. 68)
“[A]lways measure your progress. If you don’t, you won’t.” (p. 71)
“[L]earn your move until it is second nature to you. You do not need many moves; instead, perfect your move, the one that best matches you, and then keep making it. If it works for you, it is your plan for your season. You don’t need to be fancy or complicated when you have a winning plan.” (p. 104)
“Words such as winning and goal—although good concepts indeed—are too abstract when trying to get to the doing—to taking effective action. Many times, athletes try to command their bodies to win, but their bodies are confused by the abstract terminology. Winning is not an action. Winning is an outcome of sustained effective action.” (p. 108)
Some things are “too hard to play hard. All you have to do is get your process in front of you and let it happen, not force it to happen.” (p. 109)
Others have developed these points more eloquently, but eloquence isn’t necessary to inspire a football team or a business group. One of my favorite pieces of advice is in fact Elko’s own focus phrase. Confronted with a less than perfect lawn, he got a tip from a farmer: “Keep planting grass; don’t pull weeds.” (p. 110)
Monday, March 1, 2010
Goldman's 2009 daily trading revenues
For everyone who fixates on Goldman Sachs' trading profits, here's the latest data.
Rules of thumb and black swans
Earlier I wrote about two views of probability that Riccardo Rebonato outlined in his book Plight of the Fortune Tellers. Today I’m turning my attention to the System I and System II modes of probabilistic assessment, presumably named by someone utterly lacking in imagination. “The former provides fast, approximate, but not very accurate responses. The latter is more deliberative, much slower, and correspondingly more accurate. Some neurophysiologists believe that the distinction is ‘real,’ i.e., that different parts of the brain are actually engaged in System I and System II cognitive operations.” (p. 28)
Rebonato opts to focus on System I responses to uncertainties. He starts with our ancestor in the jungle where a bush suddenly starts to rustle. “It does not matter greatly if the probability of the rustling being due to a crouching leopard poised to spring is 42.8% or 61.7%: quickly running away is an appropriate response in either case.” (pp. 28-29) Our ancestors had to develop quick-and-dirty rules—run quickly but don’t be a nervous Nellie, because “there [was] no safe way to err.” (p. 29)
Heuristics, or rules of thumb, that inform our probability assessments are well documented in behavioral finance literature. They often do a surprisingly good job. System I allows us to deal “(imperfectly, but adequately enough to survive) with probabilities in the range, say, 10-90%. But the more remote the risk, the more difficult it is for the evolutionary advantage of being able to assess this risk efficiently to establish itself." (p. 35)
For instance, in studies looking at how much people are willing to pay for insurance certain irrationalities arise. If the perceived probability of a risk is very low people do one of two things. Either they mentally set the probability to zero and therefore refuse to buy insurance at all. Or they set the probability above a certain threshold and are willing to overpay for insurance. (Think, for instance, of the willingness of investors to buy protective puts against a market decline of, say, 50%. They either assume a “What, me worry?” attitude or they often pay a hefty premium for their “portfolio insurance,” especially if the market has had a few rough days.)
The upshot is that the rules of thumb that we’ve developed to get along in the world assume a world without black swans. Black swans addle the System I brain. And System II analytical methods not only are deprived of the use of System I heuristics; they “have to fight against our evolutionarily honed probabilistic rules of thumb, i.e., against voices whispered from deep inside our psyche.” (p. 39) No wonder we keep getting ourselves into such big trouble.
Rebonato opts to focus on System I responses to uncertainties. He starts with our ancestor in the jungle where a bush suddenly starts to rustle. “It does not matter greatly if the probability of the rustling being due to a crouching leopard poised to spring is 42.8% or 61.7%: quickly running away is an appropriate response in either case.” (pp. 28-29) Our ancestors had to develop quick-and-dirty rules—run quickly but don’t be a nervous Nellie, because “there [was] no safe way to err.” (p. 29)
Heuristics, or rules of thumb, that inform our probability assessments are well documented in behavioral finance literature. They often do a surprisingly good job. System I allows us to deal “(imperfectly, but adequately enough to survive) with probabilities in the range, say, 10-90%. But the more remote the risk, the more difficult it is for the evolutionary advantage of being able to assess this risk efficiently to establish itself." (p. 35)
For instance, in studies looking at how much people are willing to pay for insurance certain irrationalities arise. If the perceived probability of a risk is very low people do one of two things. Either they mentally set the probability to zero and therefore refuse to buy insurance at all. Or they set the probability above a certain threshold and are willing to overpay for insurance. (Think, for instance, of the willingness of investors to buy protective puts against a market decline of, say, 50%. They either assume a “What, me worry?” attitude or they often pay a hefty premium for their “portfolio insurance,” especially if the market has had a few rough days.)
The upshot is that the rules of thumb that we’ve developed to get along in the world assume a world without black swans. Black swans addle the System I brain. And System II analytical methods not only are deprived of the use of System I heuristics; they “have to fight against our evolutionarily honed probabilistic rules of thumb, i.e., against voices whispered from deep inside our psyche.” (p. 39) No wonder we keep getting ourselves into such big trouble.
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