Tuesday, November 9, 2010
Quant equation archive
I just found this site, sitmo.com, and thought I would pass along my discovery even though it might be old hat for some of my readers. It has a collection of option calculators as well as a quant equation archive. Lots of fascinating stuff here.
Monday, November 8, 2010
Baker & Nofsinger, eds., Behavioral Finance
Behavioral Finance: Investors, Corporations, and Markets, edited by H. Kent Baker and John R. Nofsinger (Wiley, 2010) is a must-have book for anyone who wants a comprehensive review of the literature on behavioral finance. In thirty-six chapters academics from around the world write about the key concepts of behavioral finance, behavioral biases, behavioral aspects of asset pricing, behavioral corporate finance, investor behavior, and social influences. The book is hefty (757 pages of typographically dense text), and each contribution includes an extensive bibliography. But this is not simply a reference book; it reads surprisingly well.
Why should we study behavioral finance? “Anyone with a spouse, child, boss, or modicum of self-insight knows that the assumption of Homo economicus is false.” (p. 23) In our investing and trading—indeed, in all the financial decisions we make, we are prone to behavioral biases; we are often inconsistent in our choices. Only if we understand the kinds of emotional pulls that negatively affect our financial decisions can we begin to address them as problems. Some of the authors offer suggestions for overcoming these problems.
Here are a few takeaways from the book that give a sense of its tone and breadth.
First, I am happy to report that the literature shows that “high-IQ investors have better stock-picking abilities” than low-IQ investors and they “also appear more skillful because they incur lower transaction costs.” (p. 571) I figure that everyone reading this review falls into the Lake Wobegon category.
Second, individual investors can form powerful herds. “[T]rading by individuals is highly correlated and surprisingly persistent. …[I]ndividual investors tend to commit the same kind of behavioral biases at or around the same time [and hence] have the potential of aggregating. If this is the case, individual investors cannot be treated merely as noise traders but more like a giant institution in terms of their potential impact on the markets.” (p. 531)
Third, what are some of the behavioral factors affecting perceived risk? Although the author lists eleven factors, I’ll share just two. “Benefit: The more individuals perceive a benefit from a potential risky activity, the more accepting and less anxiety (fear) they feel…. Controllability: People undertake more risk when they perceive they are personally in control because they are more likely to trust their own abilities and skills….” (p. 139)
And finally, investors’ attitude toward risk is not fixed. They care about fluctuations in their wealth, not simply the total level. “[T]hey are much more sensitive to reductions in their wealth than to increases,” and “people are less risk averse after prior gains and more risk averse after prior losses.” (p. 355) Interestingly, CBOT traders tend to exhibit a different pattern, reducing risk in the afternoon if they’ve had a profitable morning.
As should be expected in this kind of volume, there is a fair amount of repetition. The same studies are quoted by several authors. We read about such topics as overconfidence and the disposition effect multiple times. The context is different, the principles are the same. But through repetition we come to appreciate the scope of behavioral finance (and often its limitations as well).
Although this book is certainly no primer, the reader needs only a passing familiarity with behavioral finance to profit from it. And for those who are better acquainted with the field, it is a useful compendium and an excellent research tool. It has earned a place in my library.
Why should we study behavioral finance? “Anyone with a spouse, child, boss, or modicum of self-insight knows that the assumption of Homo economicus is false.” (p. 23) In our investing and trading—indeed, in all the financial decisions we make, we are prone to behavioral biases; we are often inconsistent in our choices. Only if we understand the kinds of emotional pulls that negatively affect our financial decisions can we begin to address them as problems. Some of the authors offer suggestions for overcoming these problems.
Here are a few takeaways from the book that give a sense of its tone and breadth.
First, I am happy to report that the literature shows that “high-IQ investors have better stock-picking abilities” than low-IQ investors and they “also appear more skillful because they incur lower transaction costs.” (p. 571) I figure that everyone reading this review falls into the Lake Wobegon category.
Second, individual investors can form powerful herds. “[T]rading by individuals is highly correlated and surprisingly persistent. …[I]ndividual investors tend to commit the same kind of behavioral biases at or around the same time [and hence] have the potential of aggregating. If this is the case, individual investors cannot be treated merely as noise traders but more like a giant institution in terms of their potential impact on the markets.” (p. 531)
Third, what are some of the behavioral factors affecting perceived risk? Although the author lists eleven factors, I’ll share just two. “Benefit: The more individuals perceive a benefit from a potential risky activity, the more accepting and less anxiety (fear) they feel…. Controllability: People undertake more risk when they perceive they are personally in control because they are more likely to trust their own abilities and skills….” (p. 139)
And finally, investors’ attitude toward risk is not fixed. They care about fluctuations in their wealth, not simply the total level. “[T]hey are much more sensitive to reductions in their wealth than to increases,” and “people are less risk averse after prior gains and more risk averse after prior losses.” (p. 355) Interestingly, CBOT traders tend to exhibit a different pattern, reducing risk in the afternoon if they’ve had a profitable morning.
As should be expected in this kind of volume, there is a fair amount of repetition. The same studies are quoted by several authors. We read about such topics as overconfidence and the disposition effect multiple times. The context is different, the principles are the same. But through repetition we come to appreciate the scope of behavioral finance (and often its limitations as well).
Although this book is certainly no primer, the reader needs only a passing familiarity with behavioral finance to profit from it. And for those who are better acquainted with the field, it is a useful compendium and an excellent research tool. It has earned a place in my library.
Friday, November 5, 2010
Nyaradi, Super Sectors
John Nyaradi’s Super Sectors: How to Outsmart the Market Using Sector Rotation and ETFs (Wiley, 2010) is for the most part a book for the novice investor who wants to be a bit more active in the market. The general plan is to use ETFs combined with straightforward signals to find winners and manage losses.
After introducing ETFs and the classic S&P sector rotation model, Nyaradi recommends expanding one’s horizon beyond the nine basic sectors in the search for ETFs that will outperform. The “new science of sector rotation” has a much larger palette from which to select, including international offerings, currencies, and commodities. Moreover, given government intervention in the economy after the “Great Recession,” a trading plan that uses sector rotation can no longer rely exclusively on the traditional economic cycle. Instead, Nyaradi suggests a more technical approach.
He offers several trading systems, all mechanical and easy to implement. The first is “almost like buy and hold” and relies on a long-term moving average for buy and sell signals. The second uses support and resistance lines on point and figure charts. The third system invokes the familiar golden crossover.
Nyaradi’s own system for trading ETFs is a bit more complicated because it looks for confirmation of the likelihood of a profitable trade. He therefore uses five signals to determine if and when to enter a trade, two based on point and figure charts, two on technical indicators, and the last on relative strength. He then describes how to score these five signals. The ETFs with the highest scores have the highest probability for profit.
As we all know, getting into a trade is less than half the battle. The hard part is managing the trade and knowing when and how to get out. Nyaradi discusses position sizing, stop placement, and exit strategies.
After a chapter on the psychology of trading, he gets to what the title promised—five super sectors. No nail biters here, though room for debate. They are Asia, energy, health care, technology, and financials.
The most interesting part of the book for any reader who is not a rank novice is the chapter entitled “Ask the Experts.” Nyaradi has gathered a cast of eighteen top investors, traders, and managers to pick their brains about what they view as potential super sectors. The interviewees are Larry Connors, Marc Faber, Keith Fitz-Gerald, Todd Harrison, Gene Inger, Carl Larry, Timothy Lutts, Tom Lydon, John Mauldin, Lawrence G. McMillan, Paul Merriman, Robert Prechter, Jim Rogers, Matthew Simmons, Sam Stovall, Cliff Wachtel, and Gabriel Wisdom and Michael Moore. The interviews average two and a half pages each.
After introducing ETFs and the classic S&P sector rotation model, Nyaradi recommends expanding one’s horizon beyond the nine basic sectors in the search for ETFs that will outperform. The “new science of sector rotation” has a much larger palette from which to select, including international offerings, currencies, and commodities. Moreover, given government intervention in the economy after the “Great Recession,” a trading plan that uses sector rotation can no longer rely exclusively on the traditional economic cycle. Instead, Nyaradi suggests a more technical approach.
He offers several trading systems, all mechanical and easy to implement. The first is “almost like buy and hold” and relies on a long-term moving average for buy and sell signals. The second uses support and resistance lines on point and figure charts. The third system invokes the familiar golden crossover.
Nyaradi’s own system for trading ETFs is a bit more complicated because it looks for confirmation of the likelihood of a profitable trade. He therefore uses five signals to determine if and when to enter a trade, two based on point and figure charts, two on technical indicators, and the last on relative strength. He then describes how to score these five signals. The ETFs with the highest scores have the highest probability for profit.
As we all know, getting into a trade is less than half the battle. The hard part is managing the trade and knowing when and how to get out. Nyaradi discusses position sizing, stop placement, and exit strategies.
After a chapter on the psychology of trading, he gets to what the title promised—five super sectors. No nail biters here, though room for debate. They are Asia, energy, health care, technology, and financials.
The most interesting part of the book for any reader who is not a rank novice is the chapter entitled “Ask the Experts.” Nyaradi has gathered a cast of eighteen top investors, traders, and managers to pick their brains about what they view as potential super sectors. The interviewees are Larry Connors, Marc Faber, Keith Fitz-Gerald, Todd Harrison, Gene Inger, Carl Larry, Timothy Lutts, Tom Lydon, John Mauldin, Lawrence G. McMillan, Paul Merriman, Robert Prechter, Jim Rogers, Matthew Simmons, Sam Stovall, Cliff Wachtel, and Gabriel Wisdom and Michael Moore. The interviews average two and a half pages each.
Thursday, November 4, 2010
Beck, The Gartley Trading Method
It’s odd that just as buy and hold is pronounced dead a spate of books arrives advocating a more patient approach to trading. Ross L. Beck’s The Gartley Trading Method: New Techniques to Profit from the Market’s Most Powerful Formation (Wiley, 2010) is the latest. Beck invokes Jesse Livermore’s classic quotation, “It never was my thinking that made the big money for me. It always was my sitting.”
Although there are several modern renditions of the Gartley pattern (the most notable coming from Larry Pesavento and Scott Carney), Beck returns to the original pattern to begin building his own version. In Profits in the Stock Market (1935), H. M. Gartley discussed this pattern under the heading “One of the Best Trading Opportunities.” It is a reversal pattern after a substantial trend move (A-B). There is a corrective B-C leg with expanding volume typical of what happens when traders cover their shorts in the first instance or close out their longs in the second case. Focusing on the first case to keep things simple, the B-C rally exceeds the previous rallies in the A-B downtrend in both price and time. “And when a minor decline, after canceling a third to a half of the preceding minor advance (B-C) comes to a halt,” Gartley writes, “with volume drying up again, a real opportunity is presented to buy stocks, with a stop under the previous low.” (p. 44)
In its general outlines this pattern should be familiar to traders, though not under the Gartley trademark. Think, for instance, of the Trader Vic 1-2-3.
It’s hard to leave well enough alone. Beck re-labels the pattern and suggests that “in addition to conforming to Elliott Wave, … the real key to making this pattern work has to do with angles and the geometry of W. D. Gann.” (p. 73) He also agrees with Scott Carney that “the best Gartleys are the ones that complete at .786.” So overlaid on Gartley’s simple pattern are Fibonacci ratios, Elliott waves, and Gann geometry.
Beck also introduces trade-continuation Gartleys. Here there is no need for volume analysis, and Gartley’s A-B leg is absent. Instead, “when there appears to be a corrective phase, such as an Elliott Wave 4 taking place during an impulsive phase, then look for the following: 1. AB = CD is apparent in the corrective phase. 2. AB = CD price projection clusters appear with one of the [Fibonacci] ratios.” (p. 75)
Beck then outlines entry and exit strategies. Entry strategies include the Fibonacci entry method, the 1-bar reversal entry method, the candlestick entry method, and the technical indicator entry method. Exit strategies are more difficult; Beck advocates a single in/scale out approach. He demonstrates his technique with several case studies.
In the appendixes he looks at Elliott Wave theory, Gann’s mysterious emblem, and the Wolfe wave.
Fibonacci and Elliott wave traders will find a lot to like in this book. The basic Gartley pattern lends itself to all sorts of emendations, and Beck’s is one of the more clearly articulated.
Although there are several modern renditions of the Gartley pattern (the most notable coming from Larry Pesavento and Scott Carney), Beck returns to the original pattern to begin building his own version. In Profits in the Stock Market (1935), H. M. Gartley discussed this pattern under the heading “One of the Best Trading Opportunities.” It is a reversal pattern after a substantial trend move (A-B). There is a corrective B-C leg with expanding volume typical of what happens when traders cover their shorts in the first instance or close out their longs in the second case. Focusing on the first case to keep things simple, the B-C rally exceeds the previous rallies in the A-B downtrend in both price and time. “And when a minor decline, after canceling a third to a half of the preceding minor advance (B-C) comes to a halt,” Gartley writes, “with volume drying up again, a real opportunity is presented to buy stocks, with a stop under the previous low.” (p. 44)
It’s hard to leave well enough alone. Beck re-labels the pattern and suggests that “in addition to conforming to Elliott Wave, … the real key to making this pattern work has to do with angles and the geometry of W. D. Gann.” (p. 73) He also agrees with Scott Carney that “the best Gartleys are the ones that complete at .786.” So overlaid on Gartley’s simple pattern are Fibonacci ratios, Elliott waves, and Gann geometry.
Beck also introduces trade-continuation Gartleys. Here there is no need for volume analysis, and Gartley’s A-B leg is absent. Instead, “when there appears to be a corrective phase, such as an Elliott Wave 4 taking place during an impulsive phase, then look for the following: 1. AB = CD is apparent in the corrective phase. 2. AB = CD price projection clusters appear with one of the [Fibonacci] ratios.” (p. 75)
Beck then outlines entry and exit strategies. Entry strategies include the Fibonacci entry method, the 1-bar reversal entry method, the candlestick entry method, and the technical indicator entry method. Exit strategies are more difficult; Beck advocates a single in/scale out approach. He demonstrates his technique with several case studies.
In the appendixes he looks at Elliott Wave theory, Gann’s mysterious emblem, and the Wolfe wave.
Fibonacci and Elliott wave traders will find a lot to like in this book. The basic Gartley pattern lends itself to all sorts of emendations, and Beck’s is one of the more clearly articulated.
Wednesday, November 3, 2010
“The Grieving Owl”—the analyst and the trader
To those who have wondered both silently and in writing whether I ever read anything that isn’t financially related, the short answer is “yes.” Rarely, however, can I mine any of these books for blog post material. For instance, I recently finished John Le Carré’s Our Kind of Traitor. Since it had a Russian money laundering backdrop, I thought it might prove fruitful. Wrong. But then, to make a long story short, and a short story very short, came David Sedaris’ Squirrel Seeks Chipmunk: A Modest Bestiary (Little, Brown, 2010) and “The Grieving Owl.” I’ve taken the liberty of subtitling this story “the analyst and the trader,” even though the book carries the usual disclaimer: “Any similarity to real persons, living or dead, is coincidental and not intended by the author.” I should add my own disclaimer: The portrait of the “trader” may be unduly harsh, but don’t forget which owl is painting it. Take it in the whimsical spirit in which it is offered.
Here are the salient passages.
“It’s not just that they’re stupid, my family—that, I could forgive. It’s that they’re actively against knowledge—opposed to it the way that cats, say, are opposed to swimming, or turtles have taken a stand against mountain climbing. All they talk about is food, food, food, which can be interesting but usually isn’t.”
. . .
“One of the things an owl learns early is never engage with the prey. It’s good advice if you want to eat and continue to feel good about yourself. Catch the thing and kill it immediately, and you can believe that it wanted to die, that the life it led—this mean little exercise in scratching the earth or collecting seeds from pods—was not a real life but just some pale imitation of it. The drawback is that you learn nothing new.”
The narrator owl hasn’t learned this survivalist lesson well; he wants to learn new things. And so he bargains with his potential prey, the rat: “Teach me something new, and I’ll let you go.” The rat obliges and is duly freed to take off across the parking lot. But “just as he reached the restaurant’s back door,” the narrator owl continues, “my pill of a brother swooped down and carried him away. It seemed he had been following me, just as, a week earlier, I’d been trailed by my older sister, who ate the kitten I had just interrogated, the one who taught me the difference between regular yarn and angora, which is reportedly just that much softer.
“'Who’s the smart one now?’ my brother hooted as he flew off over the steak house. I might have given chase, but the rat was already dead—done in, surely, by my brother’s talons the second he snatched him up. This has become a game for certain members of my family. Rather than hunt their own prey, they trail behind me and eat whoever it was I’d just been talking to. ‘It saves me time,’ my sister explained after last week’s kitten episode.
“With the few hours she saved, I imagine she sat on a branch and blinked, not a thought in her empty head.” (pp. 74-75)
Here are the salient passages.
“It’s not just that they’re stupid, my family—that, I could forgive. It’s that they’re actively against knowledge—opposed to it the way that cats, say, are opposed to swimming, or turtles have taken a stand against mountain climbing. All they talk about is food, food, food, which can be interesting but usually isn’t.”
. . .
“One of the things an owl learns early is never engage with the prey. It’s good advice if you want to eat and continue to feel good about yourself. Catch the thing and kill it immediately, and you can believe that it wanted to die, that the life it led—this mean little exercise in scratching the earth or collecting seeds from pods—was not a real life but just some pale imitation of it. The drawback is that you learn nothing new.”
The narrator owl hasn’t learned this survivalist lesson well; he wants to learn new things. And so he bargains with his potential prey, the rat: “Teach me something new, and I’ll let you go.” The rat obliges and is duly freed to take off across the parking lot. But “just as he reached the restaurant’s back door,” the narrator owl continues, “my pill of a brother swooped down and carried him away. It seemed he had been following me, just as, a week earlier, I’d been trailed by my older sister, who ate the kitten I had just interrogated, the one who taught me the difference between regular yarn and angora, which is reportedly just that much softer.
“'Who’s the smart one now?’ my brother hooted as he flew off over the steak house. I might have given chase, but the rat was already dead—done in, surely, by my brother’s talons the second he snatched him up. This has become a game for certain members of my family. Rather than hunt their own prey, they trail behind me and eat whoever it was I’d just been talking to. ‘It saves me time,’ my sister explained after last week’s kitten episode.
“With the few hours she saved, I imagine she sat on a branch and blinked, not a thought in her empty head.” (pp. 74-75)
Tuesday, November 2, 2010
Models
Emanuel Derman, author of the popular My Life as a Quant, is always worth reading. His piece in Haslett’s Risk Management (Wiley, 2010), “Models” (pp. 681-88), contrasts the models of hobbyists, scientific models, and financial models. Hobbyists are satisfied, sometimes delighted, with resemblance: a model airplane resembles the real thing. Scientists with their models aim to foretell the future and control it. These models can be either fundamental (laws of the universe) or phenomenological. Phenomenological models “make pragmatic analogies between things one would like to understand and things one already understands from fundamental models.” They are approximations and “often have a toylike quality.”
Financial models “are used less for divination than for interpolation or extrapolation from the known dollar prices of liquid securities to the unknown dollar values of illiquid securities.” For example, the Black-Scholes model “proceeds from a known stock price and a riskless bond price to the unknown price of a hybrid security—an option—much in the same way one estimates the value of fruit salad from its constituent fruits or, inversely, the way one estimates the price of one fruit from the prices of the other fruits in the salad. None of these metrics is strictly accurate, but they all provide immensely helpful ways to begin to estimate value.”
Financial models transform intuitive linear quantities into nonlinear dollar values. We can transform price per square foot into the dollar value of an apartment; this is intuitively easy because price per square foot “captures much of the variability of apartment prices. Similarly, P/E describes much of the variability of share prices. Developing intuition about yield to maturity, option-adjusted spread, default probability, or return volatility is harder than thinking about price per square foot. Nevertheless, all of these parameters are clearly related to value and easier to think about than dollar value itself. They are intuitively graspable, and the more sophisticated one becomes, the richer one’s intuition becomes. Models are developed by leapfrogging from a simple, intuitive mental concept (e.g., volatility) to the mathematics that describes it (e.g., geometric Brownian motion, the Black-Scholes model), to a richer mental concept (e.g., the volatility smile), to experienced-based intuition about it, and, finally, to a model (e.g., a stochastic volatility model) that incorporates the new concept.”
Alas, “the gap between a successful financial model and the correct value is nearly indefinable because fair value is finance’s fata morgana, undefined by prices, which themselves are not stationary. So, model success is temporary at best. If fair value were precisely calculable, markets would not exist.”
The essence of financial modeling is to use the known price of a security that is as similar as possible to the security whose value you want to know. “The law of one price [that any two securities with identical estimated future payoffs, no matter how the future turns out, should have identical current prices]—this valuation by analogy—is the only genuine law in quantitative finance, and it is not a law of nature. It is a general reflection on the practices of human beings—who, when they have enough time and enough information, will grab a bargain when they see one.” The modeler’s job is to show that “the target and the replicating portfolio have identical future payoffs under all circumstances.” That’s tricky, of course. The Black-Scholes model, for example, sees a future that is not real since stock returns are not normally distributed nor do stock prices move continuously.
Financial models change over time to reflect changing economic conditions and increasing financial sophistication, but their correctness is always uncertain and this uncertainty is much vaguer than probabilistic risk. In the final analysis “models are best regarded as a collection of parallel, inanimate ‘thought universes’ to explore. Each universe should be internally consistent, but the financial/human world, unlike the world of matter, is vastly more complex and vivacious than any model we could ever make of it.”
* * *
A footnote to this summary of Derman’s paper. In The Business of Options (Wiley, 2001) Martin O’Connell recalls a 1985 seminar on interest rate options where he was one of four speakers. The best known was Myron Scholes. “One of the participants was quite persistent in hassling Dr. Scholes about perceived imperfections in his model. Finally, things came to a head when the guy said: ‘Your model is just wrong.’ Dr. Scholes, who so far had not said anything funny or ironic, came back with: ‘Of course, it’s wrong. That’s why we call it a model.’” (p. 37)
Financial models “are used less for divination than for interpolation or extrapolation from the known dollar prices of liquid securities to the unknown dollar values of illiquid securities.” For example, the Black-Scholes model “proceeds from a known stock price and a riskless bond price to the unknown price of a hybrid security—an option—much in the same way one estimates the value of fruit salad from its constituent fruits or, inversely, the way one estimates the price of one fruit from the prices of the other fruits in the salad. None of these metrics is strictly accurate, but they all provide immensely helpful ways to begin to estimate value.”
Financial models transform intuitive linear quantities into nonlinear dollar values. We can transform price per square foot into the dollar value of an apartment; this is intuitively easy because price per square foot “captures much of the variability of apartment prices. Similarly, P/E describes much of the variability of share prices. Developing intuition about yield to maturity, option-adjusted spread, default probability, or return volatility is harder than thinking about price per square foot. Nevertheless, all of these parameters are clearly related to value and easier to think about than dollar value itself. They are intuitively graspable, and the more sophisticated one becomes, the richer one’s intuition becomes. Models are developed by leapfrogging from a simple, intuitive mental concept (e.g., volatility) to the mathematics that describes it (e.g., geometric Brownian motion, the Black-Scholes model), to a richer mental concept (e.g., the volatility smile), to experienced-based intuition about it, and, finally, to a model (e.g., a stochastic volatility model) that incorporates the new concept.”
Alas, “the gap between a successful financial model and the correct value is nearly indefinable because fair value is finance’s fata morgana, undefined by prices, which themselves are not stationary. So, model success is temporary at best. If fair value were precisely calculable, markets would not exist.”
The essence of financial modeling is to use the known price of a security that is as similar as possible to the security whose value you want to know. “The law of one price [that any two securities with identical estimated future payoffs, no matter how the future turns out, should have identical current prices]—this valuation by analogy—is the only genuine law in quantitative finance, and it is not a law of nature. It is a general reflection on the practices of human beings—who, when they have enough time and enough information, will grab a bargain when they see one.” The modeler’s job is to show that “the target and the replicating portfolio have identical future payoffs under all circumstances.” That’s tricky, of course. The Black-Scholes model, for example, sees a future that is not real since stock returns are not normally distributed nor do stock prices move continuously.
Financial models change over time to reflect changing economic conditions and increasing financial sophistication, but their correctness is always uncertain and this uncertainty is much vaguer than probabilistic risk. In the final analysis “models are best regarded as a collection of parallel, inanimate ‘thought universes’ to explore. Each universe should be internally consistent, but the financial/human world, unlike the world of matter, is vastly more complex and vivacious than any model we could ever make of it.”
* * *
A footnote to this summary of Derman’s paper. In The Business of Options (Wiley, 2001) Martin O’Connell recalls a 1985 seminar on interest rate options where he was one of four speakers. The best known was Myron Scholes. “One of the participants was quite persistent in hassling Dr. Scholes about perceived imperfections in his model. Finally, things came to a head when the guy said: ‘Your model is just wrong.’ Dr. Scholes, who so far had not said anything funny or ironic, came back with: ‘Of course, it’s wrong. That’s why we call it a model.’” (p. 37)
Monday, November 1, 2010
Tatro, Trade the Trader
The key argument of Quint Tatro’s Trade the Trader: Know Your Competition and Find Your Edge for Profitable Trading (FT Press, 2010) is that since average investors have flocked to technical analysis “you must be willing to trade on the failure of these patterns to exploit the crowd’s movement for your own benefit.” (p. 32)
Tatro simplifies patterns, reducing them to lateral trends and angular trends (though he does reference such well-known patterns as the head and shoulders). Trend lines provide a guide for the trader to either go with the trend or trade a trend break. Often these basics will work. But, Tatro writes, “The problem for most investors is that their belief about what technical analysis is ends with the basics when it should, in fact, just begin there. If at its core technical analysis is the graphical representation of traders’ emotions and now most of those traders are seeking to capitalize by using basic technical analysis, your goal is no longer to assume the basics will always work. Instead, you must sometimes be able to alter your strategy to trade the traders who are trading the basics.” (p. 81)
This book devotes a lot of space to such fundamentals as pattern recognition, picking your time frame, developing your plan, using and controlling risk, and dealing with emotions. It explains a way to determine entry points and how to set stops. Only then does Tatro embark on describing how to trade the trader. We know the saying that from failed moves come fast moves. The challenge, however, is to distinguish between situations that will lead to expected moves and those that will result in failed moves.
Tatro admits that trading pattern failures “can be a dangerous game,” especially for those who try to anticipate failures. He says that it has been his experience that “trading pattern failures is best done only after the traditional pattern has in fact failed, thereby giving you a clear level from which to place your stop.” (p. 144)
Trade the Trader is a well-crafted book from which beginning traders can obviously profit. But even those with more experience can learn from Tatro’s ability to simplify—and in the process clarify—trading patterns and market sentiment.
Tatro simplifies patterns, reducing them to lateral trends and angular trends (though he does reference such well-known patterns as the head and shoulders). Trend lines provide a guide for the trader to either go with the trend or trade a trend break. Often these basics will work. But, Tatro writes, “The problem for most investors is that their belief about what technical analysis is ends with the basics when it should, in fact, just begin there. If at its core technical analysis is the graphical representation of traders’ emotions and now most of those traders are seeking to capitalize by using basic technical analysis, your goal is no longer to assume the basics will always work. Instead, you must sometimes be able to alter your strategy to trade the traders who are trading the basics.” (p. 81)
This book devotes a lot of space to such fundamentals as pattern recognition, picking your time frame, developing your plan, using and controlling risk, and dealing with emotions. It explains a way to determine entry points and how to set stops. Only then does Tatro embark on describing how to trade the trader. We know the saying that from failed moves come fast moves. The challenge, however, is to distinguish between situations that will lead to expected moves and those that will result in failed moves.
Tatro admits that trading pattern failures “can be a dangerous game,” especially for those who try to anticipate failures. He says that it has been his experience that “trading pattern failures is best done only after the traditional pattern has in fact failed, thereby giving you a clear level from which to place your stop.” (p. 144)
Trade the Trader is a well-crafted book from which beginning traders can obviously profit. But even those with more experience can learn from Tatro’s ability to simplify—and in the process clarify—trading patterns and market sentiment.
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