I’ve never started a book review by quoting the acknowledgments, which are typically laundry lists of people who assisted the author in some way or another. But Jared A. Levy has written something totally different, which I think bears sharing: “Nothing truly meaningful in life is created or even possible if not for the trials and tribulations that shape us as humans. I’m most gracious to life’s mountains that have stood before me, challenged me, and ultimately given me a chance to get a better view of the beautiful existence I have been fortunate enough to live. Thank you.”
And now to the book proper. Retail investors and traders are flocking to options and, according to a recent article in The New York Times, not exactly producing bonanza results. SigFig, a company that tracks 200,000 retail investors, reported that “people who traded options last year received only about one-fifth the returns of people who did not trade options: 1.1 percent compared to 5.1 percent.” That pathetic figure would indicate that most option traders have very little idea what they are doing. They are gambling instead of speculating.
Visual Guide to Options (Bloomberg/Wiley, 2013) joins a host of books on option trading that, I suspect, few people who are willing—nay, eager—to throw money to the wind on poorly conceived option trades and probably nonexistent trade management strategies have ever bothered to read. Admittedly, reading an overview of option trading does not guarantee that future trades will be well constructed and intelligently managed, but it should at least provide a glimpse of the mountains that challenge the serious option trader.
Levy’s book touches on all the points that a novice option trader should know and some, such as volatility skew, that are a bit more advanced. Unlike many books on options that tend to be purely descriptive, it emphasizes the kind of strategic thinking and hypothesis testing that are essential for successful option trading.
But what really differentiates it from its many competitors in the field is its visual component. In the past I have criticized the Bloomberg “visual” series for its gratuitous use of colored boxes that did little to complement the text. But here the color overload actually works.
First of all, there is the Bloomberg screen eye candy. Using a Bloomberg terminal may compromise your privacy and blow out your checking account, but the available data—both raw and statistically analyzed—and graphics are mind-numbing. The TOS platform, although impressive, doesn’t quite measure up. Even though the retail trader is unlikely to have access to a Bloomberg terminal, I still think it’s important for him to understand what the big boys look at. They are, after all, the competition. Levy’s many screen shots offer the reader a tantalizing entry to this world.
Second, even the more seasoned trader can occasionally profit from reading some of the colored boxes. I would especially call attention to those labeled “Smart Investor Tip!” and “Step by Step.”
Levy’s book might be a bit of a stretch (albeit a doable stretch) for the rank novice, but it should be a useful read for anybody with modest options experience and/or meager trading profitability.
Thursday, May 30, 2013
Tuesday, May 28, 2013
Chan, Algorithmic Trading
Non-quants need not apply. Although sometimes even mathematically challenged readers can profit from books describing quantitative strategies, I would not recommend Ernest P. Chan’s Algorithmic Trading: Winning Strategies and Their Rationale (Wiley, 2013) to them. The book is very good, much better than his 2009 effort, Quantitative Trading. But it’s not for the casual reader or the trader in search of a couple of strategies he can mindlessly co-opt. Its target audience is traders who are comfortable navigating formulas and MATLAB code.
So now that 90% of people have stopped reading this post, let me continue for the remaining 10%.
Chan divides his book into mean reversion and momentum strategies. It is critical to understand what a mean-reverting price series is. “The mathematical description of a mean-reverting price series is that the change of the price series in the next period is proportional to the difference between the mean price and the current price.” An equivalent way of looking at the price series is stationarity, where “the variance of the log of the prices increases slower than that of a geometric random walk.” A second kind of mean reversion that bears mentioning is cross-sectional mean reversion, where “the cumulative returns of the instruments in a basket will revert to the cumulative return of the basket.” (p. 41)
Unfortunately, most financial price series are not mean reverting. This fact is not, however, a strategy killer. Chan contends that “we don’t necessarily need true stationarity or cointegration in order to implement a successful mean reversion strategy: If we are clever, we can capture short-term or seasonal mean reversion, and liquidate our positions before the prices go to their next equilibrium level.” (p. 63) Moreover, one can go beyond a single-stock strategy to a basket of “cointegrating stocks and ETFs to create our own stationary, mean-reverting portfolio.” (p. 60) In its simplest form, the trader can opt for a mean-reverting pair, although pair trading of stocks is no longer very profitable in the efficient U.S. markets. A better option is trading ETF pairs and triplets. An example of a cointegrating triplet is GLD-GDX-USO. Along the same line, traders can look for opportunities for cointegration in commodity currencies.
What about trading VX futures? “Every student of finance knows that volatility is mean reverting; more precisely, we know that the VIX index is mean reverting. In fact, an augmented Dickey-Fuller (ADF) test will show that it is stationary with 99 percent certainty. You might think, then, that trading VX futures would be a great mean-reverting strategy.” And you would be wrong: “a look at the back-adjusted front-month futures prices over time indicates that the mean reversion in VX only happens after volatility peaked around November 20, 2008 (the credit crisis), May 20, 210 (aftermath of flash crash), and then again on October 3, 2011. At other times, it just inexorably declines.” (p. 122) In fact, since VX has been in contango around three fourths of the time, we’re better off using a momentum strategy with VX.
Like mean reversion, momentum can be classified as either time series momentum or cross-sectional momentum. In time series momentum “past returns of a price series are positively correlated with future returns. Cross-sectional momentum refers to the relative performance of a price series in relation to other price series: a price series with returns that outperformed other price series will likely keep doing so in the future and vice versa.” (pp. 133-34)
Chan spends less time on momentum than on mean reversion, in part because both the concept and its implementation (especially as regards risk management) are much simpler. He also admits that he’s found it “harder to create profitable momentum strategies, and those that are profitable tend to have lower Sharpe ratios than mean-reversal strategies.” (p. 151) However, it would be unwise to focus solely on mean reversion strategies. “[A]s most futures and currencies exhibit momentum, momentum strategies allow us to truly diversify our risks across different asset classes and countries. Adding momentum strategies to a portfolio of mean-reverting strategies allow us to achieve higher Sharpe ratios and smaller drawdowns than either type of strategy alone.” (p. 154)
I’ve steered clear of math in this post, but, in doing so, I’ve only touched the surface of Chan’s book. Do you want to know whether scaling-in and scaling-out work? How to use the Kalman filter? Or do you just want to crib some Matlab code? Algorithmic Trading is an excellent source for quants and would-be quants.
So now that 90% of people have stopped reading this post, let me continue for the remaining 10%.
Chan divides his book into mean reversion and momentum strategies. It is critical to understand what a mean-reverting price series is. “The mathematical description of a mean-reverting price series is that the change of the price series in the next period is proportional to the difference between the mean price and the current price.” An equivalent way of looking at the price series is stationarity, where “the variance of the log of the prices increases slower than that of a geometric random walk.” A second kind of mean reversion that bears mentioning is cross-sectional mean reversion, where “the cumulative returns of the instruments in a basket will revert to the cumulative return of the basket.” (p. 41)
Unfortunately, most financial price series are not mean reverting. This fact is not, however, a strategy killer. Chan contends that “we don’t necessarily need true stationarity or cointegration in order to implement a successful mean reversion strategy: If we are clever, we can capture short-term or seasonal mean reversion, and liquidate our positions before the prices go to their next equilibrium level.” (p. 63) Moreover, one can go beyond a single-stock strategy to a basket of “cointegrating stocks and ETFs to create our own stationary, mean-reverting portfolio.” (p. 60) In its simplest form, the trader can opt for a mean-reverting pair, although pair trading of stocks is no longer very profitable in the efficient U.S. markets. A better option is trading ETF pairs and triplets. An example of a cointegrating triplet is GLD-GDX-USO. Along the same line, traders can look for opportunities for cointegration in commodity currencies.
What about trading VX futures? “Every student of finance knows that volatility is mean reverting; more precisely, we know that the VIX index is mean reverting. In fact, an augmented Dickey-Fuller (ADF) test will show that it is stationary with 99 percent certainty. You might think, then, that trading VX futures would be a great mean-reverting strategy.” And you would be wrong: “a look at the back-adjusted front-month futures prices over time indicates that the mean reversion in VX only happens after volatility peaked around November 20, 2008 (the credit crisis), May 20, 210 (aftermath of flash crash), and then again on October 3, 2011. At other times, it just inexorably declines.” (p. 122) In fact, since VX has been in contango around three fourths of the time, we’re better off using a momentum strategy with VX.
Like mean reversion, momentum can be classified as either time series momentum or cross-sectional momentum. In time series momentum “past returns of a price series are positively correlated with future returns. Cross-sectional momentum refers to the relative performance of a price series in relation to other price series: a price series with returns that outperformed other price series will likely keep doing so in the future and vice versa.” (pp. 133-34)
Chan spends less time on momentum than on mean reversion, in part because both the concept and its implementation (especially as regards risk management) are much simpler. He also admits that he’s found it “harder to create profitable momentum strategies, and those that are profitable tend to have lower Sharpe ratios than mean-reversal strategies.” (p. 151) However, it would be unwise to focus solely on mean reversion strategies. “[A]s most futures and currencies exhibit momentum, momentum strategies allow us to truly diversify our risks across different asset classes and countries. Adding momentum strategies to a portfolio of mean-reverting strategies allow us to achieve higher Sharpe ratios and smaller drawdowns than either type of strategy alone.” (p. 154)
I’ve steered clear of math in this post, but, in doing so, I’ve only touched the surface of Chan’s book. Do you want to know whether scaling-in and scaling-out work? How to use the Kalman filter? Or do you just want to crib some Matlab code? Algorithmic Trading is an excellent source for quants and would-be quants.
Friday, May 24, 2013
Scott Page, Model Thinking
If you have a few spare hours next month, join me in taking Scott Page's free online course, Model Thinking. Those who have been reading this blog from its early days know that I'm a fan of Page's work. I reviewed his book The Difference in two posts.
Wednesday, May 22, 2013
Robinson, Finding Your Element
A side trip today from the standard fare, thanks to NetGalley. Are you where you want to be in life, both professionally and personally, or—as with most people—is something missing? Ken Robinson’s Finding Your Element: Living a Life of Passion and Purpose (Viking, 2013) challenges the reader with a series of questions, each a chapter title. What are you good at? How do you know? What do you love? What makes you happy? What’s your attitude? Where are you now? Where’s your tribe? What’s next?
First, what are you good at? “Being in your Element takes both aptitude and ability. It involves finding your natural talents and honing them in practice.” (p. 36)
How do you know? “To find your Element you may have to challenge your own beliefs about yourself. Whatever age you are, you’ve almost certainly developed an inner story about what you can do and what you can’t do; what you’re good at and what you’re not good at. You may be right, of course. But … you may be misleading yourself.” (p. 76)
What do you love? This “raises a whole series of other questions,” such as, What if I have no passions? What if I love something I’m not good at? (p. 78) Well, you’d better have passion. “You do need some aptitude for what you want to do, but passion is what makes the real difference. After all, … ‘There are plenty of extremely talented people who never do anything, really.’ … Mastering any discipline takes time and effort. If you’re on the right path, much of the pleasure is in the process.” (p. 102)
What makes you happy? “One of the paradoxes of our times is that on the whole, people seem less happy than twenty or thirty years ago in spite of rising levels of affluence over the same period.” (p. 113) Recent Gallup research underlines the importance of career well-being. “At a fundamental level … we all need something to do and ideally something to look forward to when we wake up every day. Yet only twenty percent of people in the Gallup study can give a strong yes in response.” (p. 125)
What’s your attitude? “As the political activist Antonio Gramsci once said, ‘The man who does not want to act says that he cannot.’ But if you are inclined to act, self-belief and determination are a match for the most unpromising beginnings and the most challenging circumstances.” (p. 165)
Where are you now? “As you look around and take stock of where you are now and where you might like to move next: How easily can you take a risk? What are the biggest hurdles? What would it take to get over them? What would happen if you did? What would happen if you didn’t? Will your loved ones support you or oppose you? How do you know? Are you ready?” (pp. 186-87)
Where’s your tribe? “There’s something encouraging about how people who share the same passion will help each other, even if they’re potentially vying for the same customers. This is one of the most valuable traits of a tribe: the love for the pursuit tends to outweigh the instinct to protect one’s turf.” (pp. 201-202)
What’s next? And, a related question, “If you couldn’t fail, what would you most like to achieve?” (p. 236)
In his final chapter Robinson quotes Anais Nin’s short poem, “Risk” in which she “uses a powerful, organic metaphor to contrast the risks of suppressing your potential with the rewards of releasing it” (p. 242):
And then the day came,
when the risk
to remain tight
in a bud
was more painful
than the risk
it took
to blossom.
First, what are you good at? “Being in your Element takes both aptitude and ability. It involves finding your natural talents and honing them in practice.” (p. 36)
How do you know? “To find your Element you may have to challenge your own beliefs about yourself. Whatever age you are, you’ve almost certainly developed an inner story about what you can do and what you can’t do; what you’re good at and what you’re not good at. You may be right, of course. But … you may be misleading yourself.” (p. 76)
What do you love? This “raises a whole series of other questions,” such as, What if I have no passions? What if I love something I’m not good at? (p. 78) Well, you’d better have passion. “You do need some aptitude for what you want to do, but passion is what makes the real difference. After all, … ‘There are plenty of extremely talented people who never do anything, really.’ … Mastering any discipline takes time and effort. If you’re on the right path, much of the pleasure is in the process.” (p. 102)
What makes you happy? “One of the paradoxes of our times is that on the whole, people seem less happy than twenty or thirty years ago in spite of rising levels of affluence over the same period.” (p. 113) Recent Gallup research underlines the importance of career well-being. “At a fundamental level … we all need something to do and ideally something to look forward to when we wake up every day. Yet only twenty percent of people in the Gallup study can give a strong yes in response.” (p. 125)
What’s your attitude? “As the political activist Antonio Gramsci once said, ‘The man who does not want to act says that he cannot.’ But if you are inclined to act, self-belief and determination are a match for the most unpromising beginnings and the most challenging circumstances.” (p. 165)
Where are you now? “As you look around and take stock of where you are now and where you might like to move next: How easily can you take a risk? What are the biggest hurdles? What would it take to get over them? What would happen if you did? What would happen if you didn’t? Will your loved ones support you or oppose you? How do you know? Are you ready?” (pp. 186-87)
Where’s your tribe? “There’s something encouraging about how people who share the same passion will help each other, even if they’re potentially vying for the same customers. This is one of the most valuable traits of a tribe: the love for the pursuit tends to outweigh the instinct to protect one’s turf.” (pp. 201-202)
What’s next? And, a related question, “If you couldn’t fail, what would you most like to achieve?” (p. 236)
In his final chapter Robinson quotes Anais Nin’s short poem, “Risk” in which she “uses a powerful, organic metaphor to contrast the risks of suppressing your potential with the rewards of releasing it” (p. 242):
And then the day came,
when the risk
to remain tight
in a bud
was more painful
than the risk
it took
to blossom.
Monday, May 20, 2013
Fitschen, Building Reliable Trading Systems
Keith Fitschen, best known for his Aberration commodities trading system, has done every would-be mechanical trader a tremendous service by writing Building Reliable Trading Systems: Tradable Strategies That Perform as They Backtest and Meet Your Risk-Reward Goals (Wiley, 2013). It is clear, thorough, and, perhaps best of all, provocative. With it also comes access to the TradeStation Easy Language code and daily signals for the systems developed in the book.
Fitschen has been developing trading systems for more than 25 years and is fully aware of their potential pitfalls. Among other things, they can perform brilliantly in back-tests only to fail because they are overly curve-fit, although Fitschen is quick to point out that “there’s always a certain element of curve-fitting because we don’t have an infinite data set and we’re developing a solution based on historical data.” (p. 23) They can work well in one market and bomb in another. In fact, in this book the author uses a Donchian baseline entry signal for both stocks and commodities—but “a counter-trend Donchian entry for stocks and a trend-following Donchian entry for commodities.” (p. 65) Moreover, trading systems normally view the world in limiting, self-constraining black and white: “you can only enter when the entry logic conditions are fully met, and only exit when an exit logic condition is fully met. There are no shades of gray like: this is a weak entry; this is a very strong entry; or this is a good entry even though one logic condition isn’t met.” (p. 119)
To help counter the curve-fitting problem, the author developed a method called “Build, Rebuild, and Compare,” or BRAC, where the second step uses part of the initial historical database. Why not out-of-sample testing? “The main problem with out-of-sample testing is there is no performance reference for the out-of-sample results.” (p. 24)
To address the black-or-white problem, Fitschen introduces the idea of bar-scoring as an adjunct to fairly good entries and exits. For instance, he uses one standard deviation below the 10-day average as baseline entry criteria for stocks. This yields 595,806 trades, which averaged 0.288 percent per trade. To this baseline he adds five criteria—two price measures, a volume measure, a volatility measure, and an eight-bin breakdown of bar type. He uses 20 bins for his bar-scoring criteria. Without going into any of the details, the upshot is that bar-scoring allows the trader to significantly outperform the basic strategy. Of course, this outperformance must be verified using BRAC.
Fitschen tests (and thereby challenges) some widely accepted claims, such as “always look to fade the gap,” “always trade in the direction of the trend,” “a divergence is a strong signal,” and “trade Fibonacci retracements.” He shows, for instance, that “divergence trading doesn’t have a significant edge in either stock or commodity trading; in fact, for commodities you’d be much better off trading in the opposite direction on each divergence signal.” (p. 137) To Fibonacci buffs he says, “I think we can conclude that with very noisy data, like stock market data, numbers are just numbers; there are no magic ones.” (p. 147)
The money management section of this book is particularly strong. The author distinguishes between large ($100,000+) and small ($20,000-$100,000) accounts and develops money management techniques for each—both commodity and stock strategies. As for the distinction between large and small accounts, he explains: “A small account is … defined as one that cannot be traded risking a small fixed percentage of equity on each trade. … Small-account traders are forced to take greater relative risk than large-account traders. Small-account traders are always a small number of adverse trades away from a margin call, while large-account traders who risk a small percentage of equity on each trade are always a very large number of adverse trades away from a margin call. … It should be the aim of every small-account trader to grow his equity to large-account status so he can enjoy the benefits of large-account trading. Besides walking in from the edge, the large-account trader enjoys money management strategies not available to the small-account trader.” (p. 183)
Fitschen gives the small-account trader ammunition to help increase the size of his account and offers the large-account trader systematic skills to avoid lapsing back into small-account trader status. There are no magic bullets, just as there are no magic numbers. What is required is a lot of hypothesis creation and careful testing to devise systems that match your risk/reward profile. Building Reliable Trading Systems is an excellent place to start.
Fitschen has been developing trading systems for more than 25 years and is fully aware of their potential pitfalls. Among other things, they can perform brilliantly in back-tests only to fail because they are overly curve-fit, although Fitschen is quick to point out that “there’s always a certain element of curve-fitting because we don’t have an infinite data set and we’re developing a solution based on historical data.” (p. 23) They can work well in one market and bomb in another. In fact, in this book the author uses a Donchian baseline entry signal for both stocks and commodities—but “a counter-trend Donchian entry for stocks and a trend-following Donchian entry for commodities.” (p. 65) Moreover, trading systems normally view the world in limiting, self-constraining black and white: “you can only enter when the entry logic conditions are fully met, and only exit when an exit logic condition is fully met. There are no shades of gray like: this is a weak entry; this is a very strong entry; or this is a good entry even though one logic condition isn’t met.” (p. 119)
To help counter the curve-fitting problem, the author developed a method called “Build, Rebuild, and Compare,” or BRAC, where the second step uses part of the initial historical database. Why not out-of-sample testing? “The main problem with out-of-sample testing is there is no performance reference for the out-of-sample results.” (p. 24)
To address the black-or-white problem, Fitschen introduces the idea of bar-scoring as an adjunct to fairly good entries and exits. For instance, he uses one standard deviation below the 10-day average as baseline entry criteria for stocks. This yields 595,806 trades, which averaged 0.288 percent per trade. To this baseline he adds five criteria—two price measures, a volume measure, a volatility measure, and an eight-bin breakdown of bar type. He uses 20 bins for his bar-scoring criteria. Without going into any of the details, the upshot is that bar-scoring allows the trader to significantly outperform the basic strategy. Of course, this outperformance must be verified using BRAC.
Fitschen tests (and thereby challenges) some widely accepted claims, such as “always look to fade the gap,” “always trade in the direction of the trend,” “a divergence is a strong signal,” and “trade Fibonacci retracements.” He shows, for instance, that “divergence trading doesn’t have a significant edge in either stock or commodity trading; in fact, for commodities you’d be much better off trading in the opposite direction on each divergence signal.” (p. 137) To Fibonacci buffs he says, “I think we can conclude that with very noisy data, like stock market data, numbers are just numbers; there are no magic ones.” (p. 147)
The money management section of this book is particularly strong. The author distinguishes between large ($100,000+) and small ($20,000-$100,000) accounts and develops money management techniques for each—both commodity and stock strategies. As for the distinction between large and small accounts, he explains: “A small account is … defined as one that cannot be traded risking a small fixed percentage of equity on each trade. … Small-account traders are forced to take greater relative risk than large-account traders. Small-account traders are always a small number of adverse trades away from a margin call, while large-account traders who risk a small percentage of equity on each trade are always a very large number of adverse trades away from a margin call. … It should be the aim of every small-account trader to grow his equity to large-account status so he can enjoy the benefits of large-account trading. Besides walking in from the edge, the large-account trader enjoys money management strategies not available to the small-account trader.” (p. 183)
Fitschen gives the small-account trader ammunition to help increase the size of his account and offers the large-account trader systematic skills to avoid lapsing back into small-account trader status. There are no magic bullets, just as there are no magic numbers. What is required is a lot of hypothesis creation and careful testing to devise systems that match your risk/reward profile. Building Reliable Trading Systems is an excellent place to start.
Monday, May 13, 2013
Heins & Tilson, The Art of Value Investing
Since 2005 John Heins and Whitney Tilson have published the newsletter Value Investor Insight, which features two in-depth interviews with professional money managers each month. An annual subscription (with access to all the back issues) is $349. Or, for about a tenth the price ($45 retail, $28.58 on Amazon) you can get several years’ worth of insights that appeared in the newsletter from a who’s who of value investors.
When a foreign political figure was accused of plagiarizing his dissertation, he was mocked with the meme Ph+D = (Ctrl+C) + (Ctrl+V). The Art of Value Investing (Wiley, 2013) is also the product of—in this case, totally legitimate—copying and pasting. Although it may not warrant a Ph.D. itself, its readers will get a broad, non-technical education from over a hundred successful practitioners.
The authors divided the book into twelve chapters. They then took snippets from published interviews, occasionally from publicly available sources as well, and arranged them by topic, some four or five to a page. Not every snippet is mind-numbingly brilliant, but taken together they offer a collective portrait—to the extent that such a thing is possible—of the value investor.
Here I’ll share six quotations, five from value investors and the final one from Dan Ariely, the popular author and professor of behavioral economics.
“Wall Street sometimes gets confused between risk and uncertainty, and you can profit handsomely from that confusion. The low-risk, high-uncertainty [situation] gives us our most sought after coin-toss odds. Heads, I win; tails, I don’t lose much.” (Mohnish Pabrai, p. 69)
“There’s the perception that having specific catalysts for all your positions mitigates risk, when in fact we believe the opposite is true. If there’s an obvious catalyst, there’s an excellent chance that it’s at least partially priced into the stock, which increases your risk in the event it never shows up. As long as the potential return in an investment is significant enough, and the potential downside is limited, we’re okay with dead money.” (Tucker Golden, p. 168)
“I’m still more back-of-the-envelope when it comes to valuation. To me it all comes down to the assumptions you’re making. If they’re correct, a back-of-the-envelope calculation works perfectly well. If they’re not, sophisticated modeling isn’t going to help.” (Robert Kleinschmidt, p. 198)
“I’ve never considered it a legitimate goal to say you’re going to invest at the bottom. There is no price other than zero that can’t be exceeded on the downside, so you can’t really know where the bottom is, other than in retrospect. That means you have to invest at other times. If you wait until the bottom has passed, when the dust has settled and uncertainty has been resolved, demand starts to outstrip supply and you end up competing with too many other buyers. So if you can’t expect to buy at the bottom and it’s hard to buy on the way up after the bottom, that means you have to be willing to buy on the way down. It’s our job as value investors, whatever the asset class, to try to catch falling knives as skillfully as possible.” (Howard Marks, p. 208)
“We practice the Taoist wei wu wei, the ‘doing not doing’ as regards our portfolio. We are mostly inert when it comes to shuffling the portfolio around.… We believe successful investing involves anticipating change, not reacting to it.” (Bill Miller, 235)
“With investing, focusing on what’s already happened is generally a bad strategy. The decision at any point should be only about looking forward. Just adjusting how you set up your spreadsheets and what you track on reports could help in this regard.” (p. 245)
When a foreign political figure was accused of plagiarizing his dissertation, he was mocked with the meme Ph+D = (Ctrl+C) + (Ctrl+V). The Art of Value Investing (Wiley, 2013) is also the product of—in this case, totally legitimate—copying and pasting. Although it may not warrant a Ph.D. itself, its readers will get a broad, non-technical education from over a hundred successful practitioners.
The authors divided the book into twelve chapters. They then took snippets from published interviews, occasionally from publicly available sources as well, and arranged them by topic, some four or five to a page. Not every snippet is mind-numbingly brilliant, but taken together they offer a collective portrait—to the extent that such a thing is possible—of the value investor.
Here I’ll share six quotations, five from value investors and the final one from Dan Ariely, the popular author and professor of behavioral economics.
“Wall Street sometimes gets confused between risk and uncertainty, and you can profit handsomely from that confusion. The low-risk, high-uncertainty [situation] gives us our most sought after coin-toss odds. Heads, I win; tails, I don’t lose much.” (Mohnish Pabrai, p. 69)
“There’s the perception that having specific catalysts for all your positions mitigates risk, when in fact we believe the opposite is true. If there’s an obvious catalyst, there’s an excellent chance that it’s at least partially priced into the stock, which increases your risk in the event it never shows up. As long as the potential return in an investment is significant enough, and the potential downside is limited, we’re okay with dead money.” (Tucker Golden, p. 168)
“I’m still more back-of-the-envelope when it comes to valuation. To me it all comes down to the assumptions you’re making. If they’re correct, a back-of-the-envelope calculation works perfectly well. If they’re not, sophisticated modeling isn’t going to help.” (Robert Kleinschmidt, p. 198)
“I’ve never considered it a legitimate goal to say you’re going to invest at the bottom. There is no price other than zero that can’t be exceeded on the downside, so you can’t really know where the bottom is, other than in retrospect. That means you have to invest at other times. If you wait until the bottom has passed, when the dust has settled and uncertainty has been resolved, demand starts to outstrip supply and you end up competing with too many other buyers. So if you can’t expect to buy at the bottom and it’s hard to buy on the way up after the bottom, that means you have to be willing to buy on the way down. It’s our job as value investors, whatever the asset class, to try to catch falling knives as skillfully as possible.” (Howard Marks, p. 208)
“We practice the Taoist wei wu wei, the ‘doing not doing’ as regards our portfolio. We are mostly inert when it comes to shuffling the portfolio around.… We believe successful investing involves anticipating change, not reacting to it.” (Bill Miller, 235)
“With investing, focusing on what’s already happened is generally a bad strategy. The decision at any point should be only about looking forward. Just adjusting how you set up your spreadsheets and what you track on reports could help in this regard.” (p. 245)
Wednesday, May 8, 2013
Minervini, Trade Like a Stock Market Wizard
Mark Minervini, U.S. investing champion in 1997, averaged a 220% return per year from 1994 to 2000 for a compounded total return of 33,500%. Yes, we all know that these astonishing figures coincided with a major bull market, but how many traders came anywhere close to his record during this period?
In Trade Like a Stock Market Wizard: How to Achieve Superperformance in Stocks in Any Market (McGraw-Hill, 2013) Minervini shares his SEPA (Specific Entry Point Analysis) trading strategy. It’s essentially a trend following/breakout strategy that screens for such variables as earnings surprises and relative strength and that looks for catalysts driving institutional interest. It relies on both fundamentals and technicals. Its focus is on youthful small- to mid-cap stocks.
There are strong echoes of Bill O’Neil, Ben and Mitch Zacks, Richard Donchian, even Jesse Livermore in Minervini’s work. That he borrows from such luminaries is not surprising. Having dropped out of school at the age of 15, he subsequently became “a fanatical student of the stock market. … Over the years,” he writes, “I’ve read an incredible number of investment books, including more than 1,000 titles in my personal library alone.” (p. 3)
Minervini’s approach is not for the lazy—unless, of course, you want to subscribe to his real-time service. Although some of the screens can be done with the push of a button, the investor will have to follow up with his own fundamental research and chart analysis. For instance, what is the quality of a company’s earnings? How powerful is the competition? As an example, Minervini writes that “It’s no coincidence that within only 15 trading days of Netflix going public, Blockbuster Video’s stock permanently topped out. … From the point at which Netflix went public [to its high in 2011], the stock increased more than 3,400 percent. During the same period Blockbuster’s stock price lost 99 percent of its value.” (pp. 102-104)
For those who have the guts to buy 52-week highs, for those who are looking for the next mega-performer, and for those who are willing to put in the time to become a skilled analyst and trader, Minervini’s book will resonate. Even those who are disinclined to chase performance (which often simply means buying after a trend has been established) will discover that the book has a lot of merit.
Instead of delving deeper into SEPA, however worthy a task, let me share three thoughts from his section on risk management.
“To achieve consistent profitability, you must protect your profits and principal. As a matter of fact, I don’t differentiate between the two. A big mistake I see many traders make is to consider trading profits as house money…. Let’s say I make $5,000 on Monday. I don’t consider myself $5,000 ‘ahead of the game,’ free to risk that amount shooting for the moon. My account simply has a new starting balance, subject to the same set of rules as before.” (p. 273)
And, a subhead: “If you’re not feeling stupid, you’re not managing risk.” He continues: “Making you feel stupid is the market’s way of pressuring you to act foolish. Don’t succumb. Remain disciplined and cut your losses. The alternative to managing risk is not managing risk, and that never turns out well.” (p. 289)
Finally, perhaps with a nod to Warren Buffett, “You will never achieve superperformance if you overly diversify and rely on diversification for protection. … If you are strict with your selection criteria and demand the best for your portfolio, it should be difficult to find a lot of names that are worthy to be included among your elite group.” (pp. 312-13)
In Trade Like a Stock Market Wizard: How to Achieve Superperformance in Stocks in Any Market (McGraw-Hill, 2013) Minervini shares his SEPA (Specific Entry Point Analysis) trading strategy. It’s essentially a trend following/breakout strategy that screens for such variables as earnings surprises and relative strength and that looks for catalysts driving institutional interest. It relies on both fundamentals and technicals. Its focus is on youthful small- to mid-cap stocks.
There are strong echoes of Bill O’Neil, Ben and Mitch Zacks, Richard Donchian, even Jesse Livermore in Minervini’s work. That he borrows from such luminaries is not surprising. Having dropped out of school at the age of 15, he subsequently became “a fanatical student of the stock market. … Over the years,” he writes, “I’ve read an incredible number of investment books, including more than 1,000 titles in my personal library alone.” (p. 3)
Minervini’s approach is not for the lazy—unless, of course, you want to subscribe to his real-time service. Although some of the screens can be done with the push of a button, the investor will have to follow up with his own fundamental research and chart analysis. For instance, what is the quality of a company’s earnings? How powerful is the competition? As an example, Minervini writes that “It’s no coincidence that within only 15 trading days of Netflix going public, Blockbuster Video’s stock permanently topped out. … From the point at which Netflix went public [to its high in 2011], the stock increased more than 3,400 percent. During the same period Blockbuster’s stock price lost 99 percent of its value.” (pp. 102-104)
For those who have the guts to buy 52-week highs, for those who are looking for the next mega-performer, and for those who are willing to put in the time to become a skilled analyst and trader, Minervini’s book will resonate. Even those who are disinclined to chase performance (which often simply means buying after a trend has been established) will discover that the book has a lot of merit.
Instead of delving deeper into SEPA, however worthy a task, let me share three thoughts from his section on risk management.
“To achieve consistent profitability, you must protect your profits and principal. As a matter of fact, I don’t differentiate between the two. A big mistake I see many traders make is to consider trading profits as house money…. Let’s say I make $5,000 on Monday. I don’t consider myself $5,000 ‘ahead of the game,’ free to risk that amount shooting for the moon. My account simply has a new starting balance, subject to the same set of rules as before.” (p. 273)
And, a subhead: “If you’re not feeling stupid, you’re not managing risk.” He continues: “Making you feel stupid is the market’s way of pressuring you to act foolish. Don’t succumb. Remain disciplined and cut your losses. The alternative to managing risk is not managing risk, and that never turns out well.” (p. 289)
Finally, perhaps with a nod to Warren Buffett, “You will never achieve superperformance if you overly diversify and rely on diversification for protection. … If you are strict with your selection criteria and demand the best for your portfolio, it should be difficult to find a lot of names that are worthy to be included among your elite group.” (pp. 312-13)
Monday, May 6, 2013
Farber, Everybody Ought to Be Rich
No, this is not some witless self-help book. Rather, Everybody Ought to Be Rich (Oxford University Press, 2013) is, as the subtitle says, an account of The Life and Times of John J. Raskob, Capitalist. Never heard of him? Perhaps that’s because “he never ran a major corporation. He never invented a noteworthy product. Even when he started up a new enterprise he almost never took public credit for his accomplishment. When he built the tallest skyscraper in the world—the Empire State Building—he did not name that building after himself, as his close friend and rival Walter Chrysler had done with his own venture.” Nevertheless, he was a key player in the rise of American capitalism. “In partnership with the great industrialists and financiers of his time he put his knowledge to work: buying up companies, leveraging investments, creating new pools of credit for both the rich investor and the middle class consumer, reorganizing corporations, plotting hostile takeovers, financing skyscrapers, and channeling money into the political system.” (p. 4)
David Farber’s biography traces Raskob’s life from his birth in Lockport, New York, in 1879 to his death in 1950. Although Farber devotes a large chunk of the book to Raskob’s chairmanship of the Democratic Party during Al Smith’s unsuccessful bid for the presidency (Roosevelt summarily fired him after he got the party’s nod in 1932) and his generous support for Catholic charities, Raskob’s contributions to American capitalism are the real story here.
Raskob began his career as an assistant to Pierre du Pont and eventually became treasurer of the DuPont Company. He realized soon enough, however, that he couldn’t advance further at DuPont; the top spots were reserved for family members. “By 1915, Raskob had come to believe that an unstable, upstart auto company, the General Motors Corporation, offered the best opportunity.” (p. 100) In that year, in the wake of a fight for control of GM, du Pont was named chairman of the board and Raskob became a member of the board. But Raskob would move only in stages from DuPont to GM. There was still work to be done at DuPont, including a major restructuring of the company’s finances, among other things replacing old bonded debt with stock.
By the mid-1910s, as a consequence of his leveraged purchases of both DuPont and GM stock, Raskob was “a capitalist of the first order, no longer beholden to salary or bonuses.” (p. 120) He continued to engineer financial deals. In one of the most notable, he established and financed a holding company, owned by DuPont, that would buy over $25 million in GM and Chevrolet stock. DuPont owned 23.83% of GM-Chevrolet. Raskob subsequently became head of the GM Finance Committee, “ruled by DuPont Company officials.”
In early 1918 Raskob proposed that GM enter the auto installment financing business; at the beginning of 1919 the General Motors Acceptance Corporation launched. By April 1922 GMAC had financed 146,937 consumer sales.
Raskob was an early advocate of profit-sharing plans in the form of stock ownership. The plan that eventually emerged at GM included only top executives; it aimed “at registering effectively in the minds of the members of the exclusive club that what was good for General Motors was good for them.” (p. 208) Raskob benefited handsomely from this program; “in 1927 alone he would receive bonus shares of Class A stock worth $612,660 (or about $7.6 million when corrected for inflation).” (p. 209)
In 1929 Raskob devised a plan to expand stock ownership in the form of a workers’ investment trust so that “a ‘factory mechanic in Detroit with $200’ to invest would be able to secure a strong financial future for his family.” (p. 266) His plan became the talk of the nation and was generally lauded. Newspapers raved: “This is the greatest vision of Wall Street’s greatest mind.” It “might prove to be the most modern and most useful of all the numerous humanitarian enterprises founded at various times by rich Americans.” He wrote an article for the August 1929 issue of the Ladies Home Journal that was given the infamous title “Everybody Ought to be Rich.” In it he argued that too many Americans lacked economic security, “but that this inequality could be remedied by providing far more Americans with investment opportunities. “ He said that he expected to create a company to provide “millions of Americans the opportunity to invest in the stock market the same way they bought their autos, through an installment plan.” (p. 270)
Despite his idea for an investment trust, Raskob was not himself a market bull. In fact, throughout much of 1928 and all of 1929 he shorted GM stock to hedge his long position. In the weeks immediately after the Crash, he was more manipulative. He made a well-publicized purchase of 50,000 shares of GM valued at over $2 million. “After his buy order had the desired effect on GM’s share price, John quietly dumped the lot the very next day, making a tidy sum on the transaction.” (p. 279)
Even so, Raskob’s market timing ultimately proved to be off. He thought the bear market was bottoming out by the end of 1929 and began reinvesting. He lost tens of millions. Still and all, “his wounds were relatively minor—he was very, very rich before the Crash and very rich after it.” (p. 280)
David Farber’s biography traces Raskob’s life from his birth in Lockport, New York, in 1879 to his death in 1950. Although Farber devotes a large chunk of the book to Raskob’s chairmanship of the Democratic Party during Al Smith’s unsuccessful bid for the presidency (Roosevelt summarily fired him after he got the party’s nod in 1932) and his generous support for Catholic charities, Raskob’s contributions to American capitalism are the real story here.
Raskob began his career as an assistant to Pierre du Pont and eventually became treasurer of the DuPont Company. He realized soon enough, however, that he couldn’t advance further at DuPont; the top spots were reserved for family members. “By 1915, Raskob had come to believe that an unstable, upstart auto company, the General Motors Corporation, offered the best opportunity.” (p. 100) In that year, in the wake of a fight for control of GM, du Pont was named chairman of the board and Raskob became a member of the board. But Raskob would move only in stages from DuPont to GM. There was still work to be done at DuPont, including a major restructuring of the company’s finances, among other things replacing old bonded debt with stock.
By the mid-1910s, as a consequence of his leveraged purchases of both DuPont and GM stock, Raskob was “a capitalist of the first order, no longer beholden to salary or bonuses.” (p. 120) He continued to engineer financial deals. In one of the most notable, he established and financed a holding company, owned by DuPont, that would buy over $25 million in GM and Chevrolet stock. DuPont owned 23.83% of GM-Chevrolet. Raskob subsequently became head of the GM Finance Committee, “ruled by DuPont Company officials.”
In early 1918 Raskob proposed that GM enter the auto installment financing business; at the beginning of 1919 the General Motors Acceptance Corporation launched. By April 1922 GMAC had financed 146,937 consumer sales.
Raskob was an early advocate of profit-sharing plans in the form of stock ownership. The plan that eventually emerged at GM included only top executives; it aimed “at registering effectively in the minds of the members of the exclusive club that what was good for General Motors was good for them.” (p. 208) Raskob benefited handsomely from this program; “in 1927 alone he would receive bonus shares of Class A stock worth $612,660 (or about $7.6 million when corrected for inflation).” (p. 209)
In 1929 Raskob devised a plan to expand stock ownership in the form of a workers’ investment trust so that “a ‘factory mechanic in Detroit with $200’ to invest would be able to secure a strong financial future for his family.” (p. 266) His plan became the talk of the nation and was generally lauded. Newspapers raved: “This is the greatest vision of Wall Street’s greatest mind.” It “might prove to be the most modern and most useful of all the numerous humanitarian enterprises founded at various times by rich Americans.” He wrote an article for the August 1929 issue of the Ladies Home Journal that was given the infamous title “Everybody Ought to be Rich.” In it he argued that too many Americans lacked economic security, “but that this inequality could be remedied by providing far more Americans with investment opportunities. “ He said that he expected to create a company to provide “millions of Americans the opportunity to invest in the stock market the same way they bought their autos, through an installment plan.” (p. 270)
Despite his idea for an investment trust, Raskob was not himself a market bull. In fact, throughout much of 1928 and all of 1929 he shorted GM stock to hedge his long position. In the weeks immediately after the Crash, he was more manipulative. He made a well-publicized purchase of 50,000 shares of GM valued at over $2 million. “After his buy order had the desired effect on GM’s share price, John quietly dumped the lot the very next day, making a tidy sum on the transaction.” (p. 279)
Even so, Raskob’s market timing ultimately proved to be off. He thought the bear market was bottoming out by the end of 1929 and began reinvesting. He lost tens of millions. Still and all, “his wounds were relatively minor—he was very, very rich before the Crash and very rich after it.” (p. 280)
Wednesday, May 1, 2013
Bourquin & Mango, Traders at Work
As long-time readers of this blog undoubtedly know, I’m a sucker for interview-style books. Traders at Work: How the World’s Most Successful Traders Make Their Living in the Markets by Tim Bourquin and Nicholas Mango (Apress, 2013) may not be the best of the batch, but it’s an engaging read nonetheless.
The authors interviewed sixteen traders: Todd Gordon, Linda Raschke, Serge Berger, Alex Foster, Derek Schimming, Peter Brandt, Rob Wilson, John Carter, Anne-Marie Baiynd, Jeff White, Patrick Hemminger, Don Miller, Charles German, Andrew Menaker, Brian Lund, and Michael Toma. They trade a range of products, including forex, fixed income, equity futures, commodities, options, even just plain old stocks.
They tell war stories (for instance, how an options account quickly went from $150,000 down to $8,000) and share strategies. By and large they are technical traders, but naturally that encompasses many different approaches. They describe their typical trading day, the kind of research they do, the reports they write, their trade management style.
The authors have refrained from those cookie-cutter interviews that can transform people into dead wood. They’ve let the traders take the lead, and the result is a text that actually sounds like a series of (almost) impromptu conversations.
The final chapter of the book describes twenty habits of wealthy traders, including “Wealthy traders read about mobs, riots, and human psychology” and “Wealthy traders judge their trading success on anything but money.”
If you want insights into technical trading without reading a whole library of books, Traders at Work is a good shortcut.
The authors interviewed sixteen traders: Todd Gordon, Linda Raschke, Serge Berger, Alex Foster, Derek Schimming, Peter Brandt, Rob Wilson, John Carter, Anne-Marie Baiynd, Jeff White, Patrick Hemminger, Don Miller, Charles German, Andrew Menaker, Brian Lund, and Michael Toma. They trade a range of products, including forex, fixed income, equity futures, commodities, options, even just plain old stocks.
They tell war stories (for instance, how an options account quickly went from $150,000 down to $8,000) and share strategies. By and large they are technical traders, but naturally that encompasses many different approaches. They describe their typical trading day, the kind of research they do, the reports they write, their trade management style.
The authors have refrained from those cookie-cutter interviews that can transform people into dead wood. They’ve let the traders take the lead, and the result is a text that actually sounds like a series of (almost) impromptu conversations.
The final chapter of the book describes twenty habits of wealthy traders, including “Wealthy traders read about mobs, riots, and human psychology” and “Wealthy traders judge their trading success on anything but money.”
If you want insights into technical trading without reading a whole library of books, Traders at Work is a good shortcut.
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