Don’t quit your day job. Instead, Ryan Mallory suggests, join the world of The Part-Time Trader (Wiley, 2014). Mallory himself started as a part-time trader while working in corporate America and then transitioned to being a full-time trader. He also cofounded SharePlanner, a finance trading site that gives real-time trade setups, ideas, and analysis to day traders, swing traders, and position traders—most of them part-timers.
In this book Mallory recounts his time as a dissatisfied “company man” who “added a heap of stress” to his life by trading at work. He himself proceeded by trial and error but eventually came up with a number of “best practices.” He describes how to balance job and trading, how to set up a “workplace trading desk,” how to fly beneath the radar and (critically) maintain monitor privacy. He explains why your best friend is the person who works in IT and how to trade when traveling. He admonishes part-time traders not to be too social.
I assume his advice is sound, although I was never in his position. For most of my working life my Simon Legree boss (me) would never have tolerated such distracting behavior.
In fairness to Mallory, he emphasizes that “you are being paid to perform a set of responsibilities and you should do them well. That goes without saying. … Essentially, what is true about the job that you hold should be the opposite of how you trade. Time-consuming job responsibilities means a trading strategy that requires you to be more hands off with your trading. Less time sensitivity and a job where you have the ability to work at your own pace without daily deadlines or a micromanaging boss allows you to customize a trading strategy that allows for higher-frequency trading.” (pp. 62-63)
Those who are trying to juggle the demands of a full-time job and part-time trading will undoubtedly find useful tips in this book. Mallory is writing from experience.
Wednesday, November 27, 2013
Monday, November 25, 2013
Ehlers, Cycle Analytics for Traders
John F. Ehlers is probably best known for his MESA (maximum entropy spectral analysis) technical indicators, developed over thirty years ago. He has continued his research in this field and brings traders up to date with his latest book, Cycle Analytics for Traders: Advanced Technical Trading Concepts (Wiley, 2013).
Two types of traders should read this book: those who want to know why things work and those who are looking for new and improved indicators. Since I belong to the former category, I’ll quickly dispense with what probably interests most technical traders—indicators. The book comes with a PIN code to access and copy the EasyLanguage computer code found in the book, some of which is quite lengthy and would be exceedingly tedious to retype. Among the indicators whose code is provided are the decycler, decycler oscillator, band-pass filter, Hurst coefficient, roofing filter, modified (and adaptive) stochastic, modified (and adaptive) RSI, autocorrelation, autocorrelation periodogram, spectral estimate, even better sinewave indicator, convolution, and Hilbert transformer. There is code to compute the dominant cycle using the dual differentiator method, the phase accumulation method, and the homodyne method. There are also indicators for SwamiCharts. (If you don’t know what SwamiCharts are, a quick Google search will fill you in.)
As for the why. Ehlers is careful to explain the principles and the math behind these indicators. But he does more. He reflects on the very nature of the markets themselves. I was particularly struck by his thoughts on the drunkard’s walk hypothesis.
Ehlers begins with the claim embraced by proponents of the efficient markets model that price fully reflects available information. This claim “has been assumed to imply that successive price changes are independent of each other” and that “successive changes are identically distributed. Together, these two hypotheses constitute the random walk model. This model says that the conditional and marginal probability distributions of an independent random variable are identical. In addition, it says that the probability density function must be the same for all time. This model is clearly flawed. If the mean return is constant over time then the return is independent of any information available at a given time.” (p. 70)
In its stead Ehlers proposes a constrained random walk model. I can’t summarize it properly here, but let me highlight a few points that may serve as guideposts. First, “the equation governing the distribution of the displacement of the random walker from his starting point” is the partial differential equation known as the diffusion equation. It can be illustrated by a smoke plume leaving a smokestack, which is akin to the way a trend carries itself through the market. Second, Ehlers modifies the random walk model to allow the coin toss (which determines whether the drunkard takes one step to the left or the right) “to determine the persistence of motion. In other words, with probability p the drunkard makes his next step in the same direction as the last one, and with probability 1-p he makes a move in the opposite direction. … The interesting feature of the modified drunkard’s walk is that as the distance between the point and the time between steps decreases, one no longer obtains the diffusion equation,” but rather a different partial differential equation, the telegrapher’s equation.
The drunkard’s walk solution can thus describe two market conditions. “The first condition, where the probability is evenly divided between stepping to the right or the left, results in the trend mode, described by the diffusion equation. The second condition, where the probability of motion direction is skewed, results in the cycle mode, described by the telegrapher’s equation.” (p. 72)
I trust that this review, however sketchy, indicates that Ehlers’ book would be a very valuable addition to any trader’s library. It encapsulates decades of thoughtful work.
Two types of traders should read this book: those who want to know why things work and those who are looking for new and improved indicators. Since I belong to the former category, I’ll quickly dispense with what probably interests most technical traders—indicators. The book comes with a PIN code to access and copy the EasyLanguage computer code found in the book, some of which is quite lengthy and would be exceedingly tedious to retype. Among the indicators whose code is provided are the decycler, decycler oscillator, band-pass filter, Hurst coefficient, roofing filter, modified (and adaptive) stochastic, modified (and adaptive) RSI, autocorrelation, autocorrelation periodogram, spectral estimate, even better sinewave indicator, convolution, and Hilbert transformer. There is code to compute the dominant cycle using the dual differentiator method, the phase accumulation method, and the homodyne method. There are also indicators for SwamiCharts. (If you don’t know what SwamiCharts are, a quick Google search will fill you in.)
As for the why. Ehlers is careful to explain the principles and the math behind these indicators. But he does more. He reflects on the very nature of the markets themselves. I was particularly struck by his thoughts on the drunkard’s walk hypothesis.
Ehlers begins with the claim embraced by proponents of the efficient markets model that price fully reflects available information. This claim “has been assumed to imply that successive price changes are independent of each other” and that “successive changes are identically distributed. Together, these two hypotheses constitute the random walk model. This model says that the conditional and marginal probability distributions of an independent random variable are identical. In addition, it says that the probability density function must be the same for all time. This model is clearly flawed. If the mean return is constant over time then the return is independent of any information available at a given time.” (p. 70)
In its stead Ehlers proposes a constrained random walk model. I can’t summarize it properly here, but let me highlight a few points that may serve as guideposts. First, “the equation governing the distribution of the displacement of the random walker from his starting point” is the partial differential equation known as the diffusion equation. It can be illustrated by a smoke plume leaving a smokestack, which is akin to the way a trend carries itself through the market. Second, Ehlers modifies the random walk model to allow the coin toss (which determines whether the drunkard takes one step to the left or the right) “to determine the persistence of motion. In other words, with probability p the drunkard makes his next step in the same direction as the last one, and with probability 1-p he makes a move in the opposite direction. … The interesting feature of the modified drunkard’s walk is that as the distance between the point and the time between steps decreases, one no longer obtains the diffusion equation,” but rather a different partial differential equation, the telegrapher’s equation.
The drunkard’s walk solution can thus describe two market conditions. “The first condition, where the probability is evenly divided between stepping to the right or the left, results in the trend mode, described by the diffusion equation. The second condition, where the probability of motion direction is skewed, results in the cycle mode, described by the telegrapher’s equation.” (p. 72)
I trust that this review, however sketchy, indicates that Ehlers’ book would be a very valuable addition to any trader’s library. It encapsulates decades of thoughtful work.
Wednesday, November 20, 2013
Wilcox & Fabozzi, Financial Advice and Investment Decisions
Financial Advice and Investment Decisions: A Manifesto for Change by Jarrod W. Wilcox and Frank J. Fabozzi (Wiley, 2013) is meant as a wake-up call for individual investors (and, presumably, their financial advisers as well). The book is comprised of thirteen chapters and four appendices. The chapters cover such topics as the extended balance sheet approach to financial planning, properties of mostly efficient markets, growing discretionary wealth, coping with uncertain knowledge, controlling investing behavioral biases, tax efficient investing, matching investment vehicles to needs, active vs. passive strategies, performance measurement, and organizational investment. The final chapter looks at causal feedback loops in society that are affected by financial decision making.
The authors believe that investors have to up their game. First, they should prepare for economic dislocations. Structural factors contributing to our current high unemployment, for example, are “likely to get worse over the longer term”; moreover, “the most important processes involved are strongly nonlinear in their progress.” (p. 255) So people should save to compensate for possible future unemployment or underemployment. Second, odds are that they will live longer, which again means they will need more savings. As the authors write, “[T]echnology is rapidly improving and will likely substitute for not only traditional healthcare but insurance practices. What happens when moderate amounts of life extension become an optional consumption good rather than insurable events? Better start saving.”(p. 258)
Some of the themes of this book have been covered elsewhere (although usually not within a single volume), but other material is new—at least to this reader. Wilcox, for instance, offers a surplus growth model (akin to a neglected model devised by Mark Rubinstein in 1976) which applies the Kelly optimal growth model “not to the investment portfolio” but to “the surplus wealth that could be lost without failing to meet financial obligations.” (p. 85) This intriguing model is described briefly but with some mathematical precision. It maximizes the expected log return of discretionary wealth. “Using only two terms of a Taylor series, and approximating, this amount[s] to maximizing LE – LE2V/2 where L is leverage, E is the expected portfolio return, and V is portfolio return variance.” (pp. 102-103)
On another front, trying to capture the essence of “mostly efficient markets,” the authors write: “If market cycles operated as smooth outcomes of simple feedback loops, they would be subject to anticipation by intelligent speculators, thereby losing their force. In practice, however, the behavior of multiple linked feedback loops is not only complex, but its character may be disguised by frictions—thresholds that must be exceeded before action is taken—that make their operation spasmodic. … Because of the resulting lumpy nature of the actual purchases, the sources of system risk that develop across multiple investors and investments are often obscured. This kind of emergent behavior, whether in terms of small movements of a single security or cataclysms over most of the world’s financial system, can be better understood if we think of its operation through a network of investors.” (p. 51) The authors illustrate properties of investor networks—and network contagion--with simple grid diagrams.
This book is written for the serious retail investor who wants more than the usual financial advice pap. It doesn’t offer stock tips or even the standard fundamental/technical words of wisdom. Instead, it provides a framework within which to make intelligent investment decisions. And a good framework is worth a thousand stock tips.
The authors believe that investors have to up their game. First, they should prepare for economic dislocations. Structural factors contributing to our current high unemployment, for example, are “likely to get worse over the longer term”; moreover, “the most important processes involved are strongly nonlinear in their progress.” (p. 255) So people should save to compensate for possible future unemployment or underemployment. Second, odds are that they will live longer, which again means they will need more savings. As the authors write, “[T]echnology is rapidly improving and will likely substitute for not only traditional healthcare but insurance practices. What happens when moderate amounts of life extension become an optional consumption good rather than insurable events? Better start saving.”(p. 258)
Some of the themes of this book have been covered elsewhere (although usually not within a single volume), but other material is new—at least to this reader. Wilcox, for instance, offers a surplus growth model (akin to a neglected model devised by Mark Rubinstein in 1976) which applies the Kelly optimal growth model “not to the investment portfolio” but to “the surplus wealth that could be lost without failing to meet financial obligations.” (p. 85) This intriguing model is described briefly but with some mathematical precision. It maximizes the expected log return of discretionary wealth. “Using only two terms of a Taylor series, and approximating, this amount[s] to maximizing LE – LE2V/2 where L is leverage, E is the expected portfolio return, and V is portfolio return variance.” (pp. 102-103)
On another front, trying to capture the essence of “mostly efficient markets,” the authors write: “If market cycles operated as smooth outcomes of simple feedback loops, they would be subject to anticipation by intelligent speculators, thereby losing their force. In practice, however, the behavior of multiple linked feedback loops is not only complex, but its character may be disguised by frictions—thresholds that must be exceeded before action is taken—that make their operation spasmodic. … Because of the resulting lumpy nature of the actual purchases, the sources of system risk that develop across multiple investors and investments are often obscured. This kind of emergent behavior, whether in terms of small movements of a single security or cataclysms over most of the world’s financial system, can be better understood if we think of its operation through a network of investors.” (p. 51) The authors illustrate properties of investor networks—and network contagion--with simple grid diagrams.
This book is written for the serious retail investor who wants more than the usual financial advice pap. It doesn’t offer stock tips or even the standard fundamental/technical words of wisdom. Instead, it provides a framework within which to make intelligent investment decisions. And a good framework is worth a thousand stock tips.
Monday, November 18, 2013
Howard, The Mortgage Wars
Just when you thought you knew everything there was to know about the meltdown of the mortgage market, along comes The Mortgage Wars: Inside Fannie Mae, Big-Money Politics, and the Collapse of the American Dream (McGraw-Hill, 2014). Timothy Howard, former CFO of Fannie Mae, was in the trenches until the end of 2004. At that time he left Fannie Mae, “along with Fannie Mae’s chairman and CEO Frank Raines, in the wake of allegations by the company’s regulator that [they] had deliberately falsified its financial reports.” A civil case lasting over eight years ensued, with Raines and the author named as individual defendants. The defendants filed motions for summary judgment in their favor, which was granted in the fall of 2012. Thus vindicated, Howard was finally free to tell his side of the story. And a fascinating story it is. Here’s some background.
Fannie Mae was set up in 1938 as a government-owned national mortgage association. In 1954 it became a mixed-ownership corporation, with the U.S. Treasury holding nonvoting preferred stock that was meant to be gradually retired so that Fannie Mae would become a wholly privately owned company. In 1968, as the national debt was approaching $100 billion, “a threshold President Lyndon Johnson desperately did not wish to exceed,” and as “pressures were growing to include Fannie Mae’s then $2.5 billion in borrowings in the debt totals,” it was rechartered. Fannie Mae was split in two—a stockholder-owned company and a new agency, Ginnie Mae. Two years later Congress created the Federal Home Loan Mortgage Corporation (FHLMC), owned and regulated by the Federal Home Loan Banks. “The act did have one noticeable shortcoming: it did not produce a pronounceable acronym for its new offspring. The closest phonetic equivalent to FHLMC, ‘Flummox,’ was out of the question as a nickname. It became known as ‘Freddie Mac’ instead.” (pp. 21-22)
Fannie Mae may have become a shareholder-owned company, but it enjoyed benefits that both created the perception of a special relationship with the U.S. government and lowered the cost or increased the marketability of the company’s securities. As such, it “faced criticism and pressures from three main sources: free-market advocates, actual and potential competitors, and the two principal bank regulators, the Federal Reserve and the Treasury.” (p. 32) But, despite calls for Fannie Mae to sever all ties with the government and become a stand-alone entity, internal studies concluded that such a step would be suicidal. Fannie Mae remained a GSE.
Fannie Mae’s real challenge was interest rate risk. In 1985, when its credit losses were $170 million, it tightened its underwriting standards; by 1993 management “could credibly claim in Congress and elsewhere that [its] management of mortgage credit risk was second to none.” (p. 46)
Criticism, however, was unrelenting—and from a host of powerful adversaries. For instance, “Greenspan and Summers both viewed the GSEs’ federal charters as the antithesis of the free-market principles they cherished. Their shared ideology led them to advocate tighter restrictions on the government-sponsored enterprises while simultaneously seeking to relax regulations on banks—which they considered to be free market entities in spite of the fact that they benefited from federal deposit insurance and a regulator, the Fed, willing to lower their cost of funds by dropping market interest rates whenever they got into difficulty (as banks periodically did).” (p. 105)
The attacks on Fannie Mae only intensified. Howard describes in vivid detail the campaign against the GSEs, as “seemingly credible sources, including the Wall Street Journal,” argued “with … frequency and fervor that the risky GSE’s must be replaced by far safer private-market alternatives.” (p. 148) The private-label mortgage-backed securities market began to flourish. We know where that took us.
Here I’ve merely offered a glimpse into some of the early events and players (there were many) that prompted the mortgage wars and eventually the housing market meltdown and the nationalization of a Fannie Mae that had lost its way. Howard takes the story through to its end, detailing the chain of events with the passion of an aggrieved insider and the precision of a number cruncher. He adds considerably to our understanding of what went wrong and offers suggestions about how we can prevent a reoccurrence. The Mortgage Wars should be required reading for politicians, regulators, and bankers—and all of us who are tasked with keeping them in check.
Fannie Mae was set up in 1938 as a government-owned national mortgage association. In 1954 it became a mixed-ownership corporation, with the U.S. Treasury holding nonvoting preferred stock that was meant to be gradually retired so that Fannie Mae would become a wholly privately owned company. In 1968, as the national debt was approaching $100 billion, “a threshold President Lyndon Johnson desperately did not wish to exceed,” and as “pressures were growing to include Fannie Mae’s then $2.5 billion in borrowings in the debt totals,” it was rechartered. Fannie Mae was split in two—a stockholder-owned company and a new agency, Ginnie Mae. Two years later Congress created the Federal Home Loan Mortgage Corporation (FHLMC), owned and regulated by the Federal Home Loan Banks. “The act did have one noticeable shortcoming: it did not produce a pronounceable acronym for its new offspring. The closest phonetic equivalent to FHLMC, ‘Flummox,’ was out of the question as a nickname. It became known as ‘Freddie Mac’ instead.” (pp. 21-22)
Fannie Mae may have become a shareholder-owned company, but it enjoyed benefits that both created the perception of a special relationship with the U.S. government and lowered the cost or increased the marketability of the company’s securities. As such, it “faced criticism and pressures from three main sources: free-market advocates, actual and potential competitors, and the two principal bank regulators, the Federal Reserve and the Treasury.” (p. 32) But, despite calls for Fannie Mae to sever all ties with the government and become a stand-alone entity, internal studies concluded that such a step would be suicidal. Fannie Mae remained a GSE.
Fannie Mae’s real challenge was interest rate risk. In 1985, when its credit losses were $170 million, it tightened its underwriting standards; by 1993 management “could credibly claim in Congress and elsewhere that [its] management of mortgage credit risk was second to none.” (p. 46)
Criticism, however, was unrelenting—and from a host of powerful adversaries. For instance, “Greenspan and Summers both viewed the GSEs’ federal charters as the antithesis of the free-market principles they cherished. Their shared ideology led them to advocate tighter restrictions on the government-sponsored enterprises while simultaneously seeking to relax regulations on banks—which they considered to be free market entities in spite of the fact that they benefited from federal deposit insurance and a regulator, the Fed, willing to lower their cost of funds by dropping market interest rates whenever they got into difficulty (as banks periodically did).” (p. 105)
The attacks on Fannie Mae only intensified. Howard describes in vivid detail the campaign against the GSEs, as “seemingly credible sources, including the Wall Street Journal,” argued “with … frequency and fervor that the risky GSE’s must be replaced by far safer private-market alternatives.” (p. 148) The private-label mortgage-backed securities market began to flourish. We know where that took us.
Here I’ve merely offered a glimpse into some of the early events and players (there were many) that prompted the mortgage wars and eventually the housing market meltdown and the nationalization of a Fannie Mae that had lost its way. Howard takes the story through to its end, detailing the chain of events with the passion of an aggrieved insider and the precision of a number cruncher. He adds considerably to our understanding of what went wrong and offers suggestions about how we can prevent a reoccurrence. The Mortgage Wars should be required reading for politicians, regulators, and bankers—and all of us who are tasked with keeping them in check.
Wednesday, November 13, 2013
Durenard, Professional Automated Trading
I’m in over my head with Eugene A. Durenard’s Professional Automated Trading: Theory and Practice (Wiley, 2013), so consider this post more of a notice than a review.
Durenard describes how to set up a framework to research and select trading models and to implement them in a real-time low-latency environment. The book “requires readers to have some knowledge of certain mathematical techniques (calculus, statistics, optimization, transition graphs, and basic operations research), certain functional and object-oriented programming techniques (mostly LISP and Java), and certain programming design patterns (mostly dealing with concurrency and multithreading).”
Durenard focuses on the design of trading strategies as trading agents, the goal being to build “robust trading systems that can gracefully withstand changes of regime.” He introduces swarm systems, which are “aggregate agents that embed various types of switching mechanisms.”
The assumption underlying Durenard’s framework is that markets are complex adaptive systems best understood, and exploited, by an aggregate adaptive agent. This agent has a collection of nonadaptive strategies at his disposal. The agent “is endowed with criteria to choose a subset of behaviors that is expected to produce a positive performance over the next foreseeable future. This is the behavior that the agent implements in real trading. As time unfolds, the agent learns from experience to choose its behavior more effectively. Effectiveness means that as the market goes through various cycles of regime changes, the performance during those change periods does not degrade.”
Durenard draws on concepts from evolutionary theory and learning to endow trading systems with opportunism, robustness, and flexibility. Learning is important because a swarm system needs not only behaviors that have proved effective in the past but also a degree of innovation. The innovation problem is “an active area” of Durenard’s current research.
This book is divided into four parts: strategy design and testing, evolving strategies, optimizing execution, and practical implementation. It offers its fair share of code to help the reader along—unfortunately, not this grossly under-qualified reader.
Durenard describes how to set up a framework to research and select trading models and to implement them in a real-time low-latency environment. The book “requires readers to have some knowledge of certain mathematical techniques (calculus, statistics, optimization, transition graphs, and basic operations research), certain functional and object-oriented programming techniques (mostly LISP and Java), and certain programming design patterns (mostly dealing with concurrency and multithreading).”
Durenard focuses on the design of trading strategies as trading agents, the goal being to build “robust trading systems that can gracefully withstand changes of regime.” He introduces swarm systems, which are “aggregate agents that embed various types of switching mechanisms.”
The assumption underlying Durenard’s framework is that markets are complex adaptive systems best understood, and exploited, by an aggregate adaptive agent. This agent has a collection of nonadaptive strategies at his disposal. The agent “is endowed with criteria to choose a subset of behaviors that is expected to produce a positive performance over the next foreseeable future. This is the behavior that the agent implements in real trading. As time unfolds, the agent learns from experience to choose its behavior more effectively. Effectiveness means that as the market goes through various cycles of regime changes, the performance during those change periods does not degrade.”
Durenard draws on concepts from evolutionary theory and learning to endow trading systems with opportunism, robustness, and flexibility. Learning is important because a swarm system needs not only behaviors that have proved effective in the past but also a degree of innovation. The innovation problem is “an active area” of Durenard’s current research.
This book is divided into four parts: strategy design and testing, evolving strategies, optimizing execution, and practical implementation. It offers its fair share of code to help the reader along—unfortunately, not this grossly under-qualified reader.
Monday, November 11, 2013
Birinyi, The Master Trader
Even though he is sometimes derided for being a perma-bull (or in his words, “a redundant bull” [p. 62]), Laszlo Birinyi has a long, proven track record which has earned him the title “a legend.” So the publication of The Master Trader: Birinyi’s Secrets to Understanding the Market (Wiley, 2013) is something of an event.
Birinyi’s investing style is difficult to categorize. He is no fan of technical analysis: “it is not predictive, it is not consistent, and it is not analysis.” (p. 1) And yet his money flows indicator is often included in technical analysis packages. No, no, he argues, money flows are not a technical indicator. “They are the ultimate fundamental input.”(p. 72)
In addition to money flows analysis, which looks at every trade (and, most importantly, the size of every trade) in every stock, Birinyi also uses anecdotal data to inform his trading. Magazines and newspapers, he contends, are “databases in disguise.” (p. 80) He also keeps track of the attitudes of commentators and economists.
And this is just the beginning. It quickly becomes clear that what he’s advocating involves a lot of work. Birinyi concedes the point but counters: “consider a portfolio of $100,000 which hopes to make 10 percent or $10,000. Working as a teacher or administrator or chef, how long would it take to accumulate $10,000? Three months, half a year? Why should you make it on Wall Street in only three days or six weeks?” (p. 196)
Birinyi’s firm crunches numbers relentlessly to analyze, among other things, market cycles, sector rotation, small vs. large stocks, growth vs. value, market sentiment, and the impact of the Fed. As he writes (though in connection with a suggested reading list), “you can never know too much about too many things on Wall Street.” (p. 281) Of course, what you know is more important than how much you know. Birinyi quotes Roland Grimm, former manager of the Yale Endowment, who said, “You have to be careful regarding the railroad analyst who knows how many ties there are between New York and Washington and not when to sell Penn Central.” (p. 244) Moreover, “sometimes too much data is actually a handicap as it incorporates different circumstances. Risk measures, as one example, before the advent of options were a totally different environment.” (p. 248)
In recent years Birinyi’s firm has “found portfolio enhancing opportunities in short-term trading by ignoring or omitting historical data.” And yet, “despite our efforts, we have not been able to develop metrics for shorter periods and have no confidence in others’ efforts to do so either. There is one exception—the next day—and even then only in certain circumstances.” (p. 250)
As for gaps, remember the old rule that large gaps have to be closed? Well, the new rule says that these gaps close only about 25% of the time. Within this number, however, there are tendencies even if no definite answers. “[I]n a world of computerized trading, models, and other mechanized inputs, gaps may provide a significant opportunity for human judgment.” (p. 275)
Times change, markets change, traders and market analysts come and go. But some things remain constant. Over the long haul careful, extensive analytic research combined with keen human judgment will triumph. Laszlo Birinyi’s career illustrates this constancy. The Master Trader details the principles, the studies, and the grunt work that contributed to his investing success over the decades.
Birinyi’s investing style is difficult to categorize. He is no fan of technical analysis: “it is not predictive, it is not consistent, and it is not analysis.” (p. 1) And yet his money flows indicator is often included in technical analysis packages. No, no, he argues, money flows are not a technical indicator. “They are the ultimate fundamental input.”(p. 72)
In addition to money flows analysis, which looks at every trade (and, most importantly, the size of every trade) in every stock, Birinyi also uses anecdotal data to inform his trading. Magazines and newspapers, he contends, are “databases in disguise.” (p. 80) He also keeps track of the attitudes of commentators and economists.
And this is just the beginning. It quickly becomes clear that what he’s advocating involves a lot of work. Birinyi concedes the point but counters: “consider a portfolio of $100,000 which hopes to make 10 percent or $10,000. Working as a teacher or administrator or chef, how long would it take to accumulate $10,000? Three months, half a year? Why should you make it on Wall Street in only three days or six weeks?” (p. 196)
Birinyi’s firm crunches numbers relentlessly to analyze, among other things, market cycles, sector rotation, small vs. large stocks, growth vs. value, market sentiment, and the impact of the Fed. As he writes (though in connection with a suggested reading list), “you can never know too much about too many things on Wall Street.” (p. 281) Of course, what you know is more important than how much you know. Birinyi quotes Roland Grimm, former manager of the Yale Endowment, who said, “You have to be careful regarding the railroad analyst who knows how many ties there are between New York and Washington and not when to sell Penn Central.” (p. 244) Moreover, “sometimes too much data is actually a handicap as it incorporates different circumstances. Risk measures, as one example, before the advent of options were a totally different environment.” (p. 248)
In recent years Birinyi’s firm has “found portfolio enhancing opportunities in short-term trading by ignoring or omitting historical data.” And yet, “despite our efforts, we have not been able to develop metrics for shorter periods and have no confidence in others’ efforts to do so either. There is one exception—the next day—and even then only in certain circumstances.” (p. 250)
As for gaps, remember the old rule that large gaps have to be closed? Well, the new rule says that these gaps close only about 25% of the time. Within this number, however, there are tendencies even if no definite answers. “[I]n a world of computerized trading, models, and other mechanized inputs, gaps may provide a significant opportunity for human judgment.” (p. 275)
Times change, markets change, traders and market analysts come and go. But some things remain constant. Over the long haul careful, extensive analytic research combined with keen human judgment will triumph. Laszlo Birinyi’s career illustrates this constancy. The Master Trader details the principles, the studies, and the grunt work that contributed to his investing success over the decades.
Wednesday, November 6, 2013
Atkeson & Houghton, Win By Not Losing
Nicholas Atkeson and Andrew Houghton, founding partners of Delta Investment Management, have written what, in the words of the lengthy subtitle, is a disciplined approach to building and protecting your wealth in the stock market by managing your risk. Win By Not Losing (McGraw-Hill, 2013) is a mix of stories about some not-so-famous investors (in fact, a few are identified simply by their first names) and an introduction to tactical investing.
The authors contend that “stock prices are influenced by oddities in human behavior that often cause security pricing to be predictable.” (p. 120) They support their contention by sharing some of their observations from the trading floor of an investment bank. Earnings momentum, for instance, can be both predictable and profitable: “the cycle of exceeding analysts’ estimates is often predictable in light of the pressures on analysts to be overly conservative.” (p. 121) And one study found that “over the 60 trading days after an earnings announcement, a long position in stocks with unexpected earnings in the highest decile, combined with a short position in stocks in the lowest decile, yields an annualized ‘abnormal’ return of about 25 percent before transaction costs.” (p. 122)
It’s all very well and good to analyze individual stocks, but the overall market environment should be of paramount concern to the investor. The authors suggest that a person should be a tactical equity investor if he believes that “there is a reasonable probability the stock market will experience a period of severe depreciation during your investment horizon” and/or “there is a reasonable probability the stock market will not experience sufficient appreciation during your investment horizon to meet your investment objectives.” (p. 177)
The authors note that the risk-conscious tactical investor has a fairly narrow window in which to make decisions because “evaluations of market risk levels tend to be most accurate over a week to a month.” (p. 180) They recommend entering the stock market when “the perceived market risk is moderate and declining.” (p. 196) Their own favorite indicator is the 75-day simple moving average applied to a group of roughly 3,600 stocks. “When the majority of stocks in the market are trading above their 75-day moving average, the market is bullish. When the majority of stocks are trading under this level, the market is bearish.” (p. 199) (They publish a free weekly Market Sentiment Indicator report on their website; it also appears in Barron’s.)
One of the authors’ recommendations is that an investor should be aggressive when participating in up markets. One way to accomplish this is to boost the beta of the portfolio. If investors “were willing to accept portfolio volatility equal to the market, they could then increase their expected volatility during times they are invested in equities, as the higher in-the-market volatility would be offset by the lower out-of-the equity market volatility. These investors could raise the in-the-market portfolio beta to a level at which the average of in-the-market and out-of-the-market volatility is equal to the market volatility on its own.” (p. 206)
Winning By Not Losing is not for the rank novice, but anyone with some experience in the stock market, especially the person who wants to move beyond a buy and hold strategy, can find useful tidbits in this book.
The authors contend that “stock prices are influenced by oddities in human behavior that often cause security pricing to be predictable.” (p. 120) They support their contention by sharing some of their observations from the trading floor of an investment bank. Earnings momentum, for instance, can be both predictable and profitable: “the cycle of exceeding analysts’ estimates is often predictable in light of the pressures on analysts to be overly conservative.” (p. 121) And one study found that “over the 60 trading days after an earnings announcement, a long position in stocks with unexpected earnings in the highest decile, combined with a short position in stocks in the lowest decile, yields an annualized ‘abnormal’ return of about 25 percent before transaction costs.” (p. 122)
It’s all very well and good to analyze individual stocks, but the overall market environment should be of paramount concern to the investor. The authors suggest that a person should be a tactical equity investor if he believes that “there is a reasonable probability the stock market will experience a period of severe depreciation during your investment horizon” and/or “there is a reasonable probability the stock market will not experience sufficient appreciation during your investment horizon to meet your investment objectives.” (p. 177)
The authors note that the risk-conscious tactical investor has a fairly narrow window in which to make decisions because “evaluations of market risk levels tend to be most accurate over a week to a month.” (p. 180) They recommend entering the stock market when “the perceived market risk is moderate and declining.” (p. 196) Their own favorite indicator is the 75-day simple moving average applied to a group of roughly 3,600 stocks. “When the majority of stocks in the market are trading above their 75-day moving average, the market is bullish. When the majority of stocks are trading under this level, the market is bearish.” (p. 199) (They publish a free weekly Market Sentiment Indicator report on their website; it also appears in Barron’s.)
One of the authors’ recommendations is that an investor should be aggressive when participating in up markets. One way to accomplish this is to boost the beta of the portfolio. If investors “were willing to accept portfolio volatility equal to the market, they could then increase their expected volatility during times they are invested in equities, as the higher in-the-market volatility would be offset by the lower out-of-the equity market volatility. These investors could raise the in-the-market portfolio beta to a level at which the average of in-the-market and out-of-the-market volatility is equal to the market volatility on its own.” (p. 206)
Winning By Not Losing is not for the rank novice, but anyone with some experience in the stock market, especially the person who wants to move beyond a buy and hold strategy, can find useful tidbits in this book.
Monday, November 4, 2013
Li, Tiger Woman on Wall Street
Junheng Li has written a smart, compelling book. Tiger Woman on Wall Street: Winning Business Strategies from Shanghai to New York and Back (McGraw-Hill, 2014) moves seamlessly between autobiography and analysis to create a finely chiseled portrait of the often shadowy Chinese business world. It’s an important read for anyone interested in investing in China—or in companies that have a Chinese presence.
Li grew up in Shanghai under the early tutelage of a harsh (what Westerners would call abusive) tiger dad. He forced her, for instance, to kneel on a washer board for more than an hour while he drilled her on the multiplication tables and slapped her when she gave the wrong answer or was slow to reply. She was three years old at the time. But, as she writes, “His high standards for me were just part of his language of love that got lost in translation.” (p. 10)
Li left Shanghai in 1996 to attend Middlebury College and subsequently to pursue a career as a Wall Street analyst. She now runs the independent equity research firm JL Warren Capital, aimed at plugging the gap between the business reality in China and American investors. If this book is any indication, the firm is doing a first-rate job.
The depth of analysis that Li offers is something that no individual investor could possibly match. She has both keen analytical skills and a familiarity with the Chinese business environment (as well as many useful contacts in China). So investors should take her caveats to heart. Consider, for example, the fact that most private Chinese companies “spend a far lower portion of their revenue on R&D than American peers in the same sector.” The reason? “In industries where innovation drives growth and market share, such as technology and healthcare, China’s culture of lawlessness hinders innovation. If you create something commercially compelling, it is nearly guaranteed that others will copy it and undercut your pricing.” (p. 148)
Trying to assess the value of a business in China is a major challenge. Sometimes companies fudge their numbers. Even when they are honest in their reports (and most publicly traded companies by now are), normal valuational methods often don’t apply. “Most value investors depend on what’s called a ‘mid-cycle analysis’ to assess the normalized earnings power before ascribing a value to a business. … To get that estimate, analysts look at the successive peaks and troughs in a company’s earnings and adjust them to a moving average. But for both Chinese companies and China’s economy, mid-cycle references do not exist. Since the introduction of the market reform in 1979, the Chinese economy has only gone up, never down. Whenever the economy showed signs of slowing down, the government stepped in with fiscal stimulus and expansionary monetary policy.” (pp. 188-89)
Chinese corruption is a well-known fact. Li believes that “a big portion of government-led infrastructure spending in 2008 trickled out in the form of bribery, embezzlement, and kickbacks, all of which went to the connected and enfranchised. Interestingly, shortly after Beijing released its massive stimulus package, Macau casino stocks began to soar, led by those companies with the most exposure to VIP gamblers from the mainland.” (p. 155)
I’ve just scratched the surface of the material that Li covers in this book. Tiger Woman on Wall Street offers carefully honed analysis even as it tugs at your heartstrings. I would say that it’s one of the best investing books of 2013 (except that it has a 2014 copyright).
Li grew up in Shanghai under the early tutelage of a harsh (what Westerners would call abusive) tiger dad. He forced her, for instance, to kneel on a washer board for more than an hour while he drilled her on the multiplication tables and slapped her when she gave the wrong answer or was slow to reply. She was three years old at the time. But, as she writes, “His high standards for me were just part of his language of love that got lost in translation.” (p. 10)
Li left Shanghai in 1996 to attend Middlebury College and subsequently to pursue a career as a Wall Street analyst. She now runs the independent equity research firm JL Warren Capital, aimed at plugging the gap between the business reality in China and American investors. If this book is any indication, the firm is doing a first-rate job.
The depth of analysis that Li offers is something that no individual investor could possibly match. She has both keen analytical skills and a familiarity with the Chinese business environment (as well as many useful contacts in China). So investors should take her caveats to heart. Consider, for example, the fact that most private Chinese companies “spend a far lower portion of their revenue on R&D than American peers in the same sector.” The reason? “In industries where innovation drives growth and market share, such as technology and healthcare, China’s culture of lawlessness hinders innovation. If you create something commercially compelling, it is nearly guaranteed that others will copy it and undercut your pricing.” (p. 148)
Trying to assess the value of a business in China is a major challenge. Sometimes companies fudge their numbers. Even when they are honest in their reports (and most publicly traded companies by now are), normal valuational methods often don’t apply. “Most value investors depend on what’s called a ‘mid-cycle analysis’ to assess the normalized earnings power before ascribing a value to a business. … To get that estimate, analysts look at the successive peaks and troughs in a company’s earnings and adjust them to a moving average. But for both Chinese companies and China’s economy, mid-cycle references do not exist. Since the introduction of the market reform in 1979, the Chinese economy has only gone up, never down. Whenever the economy showed signs of slowing down, the government stepped in with fiscal stimulus and expansionary monetary policy.” (pp. 188-89)
Chinese corruption is a well-known fact. Li believes that “a big portion of government-led infrastructure spending in 2008 trickled out in the form of bribery, embezzlement, and kickbacks, all of which went to the connected and enfranchised. Interestingly, shortly after Beijing released its massive stimulus package, Macau casino stocks began to soar, led by those companies with the most exposure to VIP gamblers from the mainland.” (p. 155)
I’ve just scratched the surface of the material that Li covers in this book. Tiger Woman on Wall Street offers carefully honed analysis even as it tugs at your heartstrings. I would say that it’s one of the best investing books of 2013 (except that it has a 2014 copyright).
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