This year’s Stock Trader’s Almanac, its forty-seventh annual edition, has a new look, at least on the outside. Although it is still spiral bound, it now sports a maroon soft cover with gold embossed lettering and golden spirals.
The inside remains pretty much the same, with a calendar section, a directory of trading patterns and databank, and a strategy planning and (somewhat abbreviated) record keeping section. The calendar section has on facing pages historical data on market performance (verso) and a week’s worth of calendar entries (recto). January’s verso pages, for example, give the month’s vital statistics, January’s first five days as an early warning system, the January barometer (which has had only seven significant errors in 63 years), and the January barometer since 1950 in graphic form. Each trading day’s entry on the recto pages includes the probability, based on a 21-year lookback period, that the Dow, S&P, and Nasdaq will rise. Particularly favorable days (based on the performance of the S&P) are flagged with a bull icon; particularly unfavorable trading days get a bear icon. A witch icon appears on options expiration days. At the bottom of each entry is an apt quotation. There’s about a five-square-inch space in which to write.
As we all know, 2014 is a midterm election year, but what does this mean for investors? Jeffrey A. Hirsch, co-editor of the Almanac, writes that “midterm elections have a history of being a bottom picker’s paradise. In the last 13 quadrennial cycles since 1961, 9 of the 16 bear markets bottomed in the midterm year. … [T]his has provided excellent buying opportunities. … From the midterm low to the pre-election year high, the Dow has gained nearly 50% on average since 1914.” Hirsch anticipates “a good hunk of the next major downturn to transpire in 2014” with a low in the DJIA 12,000 range likely.
We are now in the market’s “magical” quarter where gains over the years have been the greatest and the most consistent. If we take the S&P 500 as our benchmark, it has delivered an average return of 4.0% in the fourth quarter (1949-2012). Compare that to 2.3% for the first quarter, 1.6% for the second, and 0.6% for the third. Looking at the fourth quarter in the context of the presidential cycle, post-election results have averaged 3.1%, midterm a whopping 8.0%, pre-election 3.0%, and election a meager 1.9%.
For those traders who are interested in intra-day trends, the Almanac divides the trading day into half-hour segments and tracks the percentage of time the Dow closed higher in a given half hour than it did in the previous half hour. The positive half-hour segments between 1987 and May 2013 began at 10, 11, 11:30, 12:30, 1, 1:30, 3, 3:30, and the close, with the strongest performances coming from the close, 3 p.m., and 10 a.m. On a daily basis, since 1990 Tuesday has produced the strongest gains although, since the March 2009 bottom, Thursday has taken over the honors.
Investors who appreciate the power of seasonals and who believe that past is prologue will, as usual, relish this Almanac.
Wednesday, October 30, 2013
Monday, October 28, 2013
Runciman, The Confidence Trap
As we seem to lurch from one self-inflicted crisis in Washington to the next some people begin to despair. Is American democracy itself on the brink? Is democracy inherently flawed? In The Confidence Trap: A History of Democracy in Crisis from World War I to the Present (Princeton University Press, 2013) David Runciman highlights the tension between “the onward march of democracy” and the “constant drumbeat of intellectual anxiety.” Today, as the established democracies face four fundamental challenges—war, public finance, environmental threat, and the existence of a plausible competitor—“it is not clear,” Runciman writes, that they “are doing well in meeting any of them.”
Democracies have certain advantages over their rivals. For instance, they are better at surviving crises. At the same time, they seem unable to learn how to avoid crises. “It is some consolation in a democracy to know that nothing bad lasts for long,” but “consolation can produce its own kind of complacency. Knowing that they are safe from the worst effects of hubris can make democracies reckless—what’s the worst that could happen?—as well as sluggish—why not wait for the system to correct itself? That is why the crises keep coming.”
Starting with Tocqueville as “the indispensable guide to the ongoing relationship between democracy and crisis,” Runciman examines seven crises: 1918 (false dawn), 1933 (fear itself), 1947 (trying again), 1962 (on the brink), 1974 (crisis of confidence), 1989 (the end of history), and 2008 (back to the future).
Throughout Runciman illustrates versions of what he calls the confidence trap. For instance, it is too soon to act and it is never too soon to act. This tension was manifest in the opposing views of Sarkozy and Merkel during the Euro crisis: Sarkozy urged immediate action, Merkel believed that it was important not to be rushed.
Politicians can point to history as a rationalization for their bad behavior. “American democracy had survived its near-death experience in 1933. It had survived everything that had been thrown at it since. It had proved its adaptability and its resilience. There is less incentive for politicians to compromise if they believe the system can withstand most forms of confrontation.” (p. 284)
“This,” Runciman writes, “is the confidence trap. Democracies are adaptable. Because they are adaptable, they build up long-term problems, comforted by the knowledge that they will adapt to meet them. Debt accumulates; retrenchment is deferred. Democracies are also competitive, which means that politicians will blame each other for their failure to tackle the long-term problems. However, they do it in a way that gives the lie to the urgency, because if it were truly urgent, then they would compromise to fix it. Instead they squabble. … So democracy becomes a game of chicken. When things get really bad, we will adapt. Until they get really bad, we need not adapt, because democracies are ultimately adaptable. … Games of chicken are harmless, until they go wrong, at which point they become lethal.” (p. 285)
Scaremongers argue that profligate spending is pushing the U.S. to the point of a full-scale default. Not so, Runciman replies. “The institutional constraints on sustained fiscal irresponsibility would kick in before then. The real problem is not that the United States will knowingly walk off a cliff. It is that no one knows where the edge of the cliff is, or which of the intermediate ridges along the way—the lesser ‘fiscal cliffs’—pose real danger.” … The U.S. may “get itself into more trouble than it realizes because it will be unable to tell apart the point when deferring retrenchment keeps its options open from the point when deferring retrenchment closes them down. Credible systems, like credible banks, can find they lose credibility quickly and unexpectedly.” (pp. 312-13)
Runciman uses dialectical tension to strike an appropriate balance between optimism and pessimism. He opts not to assume that we will eventually encounter the crisis that overwhelms us. He refuses to see politics as inherently tragic; democracy “is too inadvertently comical for that.” (p. 324) At the same time, he believes that democracy is a series of mismatches, that it displays a repeated pattern of crisis and recovery. “The long-term strength of democracy comes from its short-term restlessness; it is also at risk of being undermined by its short-term restlessness.” Put in the most general of terms, “democracies succeed because they fail and they fail because they succeed. There is no way around this.” (p. 304)
Democracies have certain advantages over their rivals. For instance, they are better at surviving crises. At the same time, they seem unable to learn how to avoid crises. “It is some consolation in a democracy to know that nothing bad lasts for long,” but “consolation can produce its own kind of complacency. Knowing that they are safe from the worst effects of hubris can make democracies reckless—what’s the worst that could happen?—as well as sluggish—why not wait for the system to correct itself? That is why the crises keep coming.”
Starting with Tocqueville as “the indispensable guide to the ongoing relationship between democracy and crisis,” Runciman examines seven crises: 1918 (false dawn), 1933 (fear itself), 1947 (trying again), 1962 (on the brink), 1974 (crisis of confidence), 1989 (the end of history), and 2008 (back to the future).
Throughout Runciman illustrates versions of what he calls the confidence trap. For instance, it is too soon to act and it is never too soon to act. This tension was manifest in the opposing views of Sarkozy and Merkel during the Euro crisis: Sarkozy urged immediate action, Merkel believed that it was important not to be rushed.
Politicians can point to history as a rationalization for their bad behavior. “American democracy had survived its near-death experience in 1933. It had survived everything that had been thrown at it since. It had proved its adaptability and its resilience. There is less incentive for politicians to compromise if they believe the system can withstand most forms of confrontation.” (p. 284)
“This,” Runciman writes, “is the confidence trap. Democracies are adaptable. Because they are adaptable, they build up long-term problems, comforted by the knowledge that they will adapt to meet them. Debt accumulates; retrenchment is deferred. Democracies are also competitive, which means that politicians will blame each other for their failure to tackle the long-term problems. However, they do it in a way that gives the lie to the urgency, because if it were truly urgent, then they would compromise to fix it. Instead they squabble. … So democracy becomes a game of chicken. When things get really bad, we will adapt. Until they get really bad, we need not adapt, because democracies are ultimately adaptable. … Games of chicken are harmless, until they go wrong, at which point they become lethal.” (p. 285)
Scaremongers argue that profligate spending is pushing the U.S. to the point of a full-scale default. Not so, Runciman replies. “The institutional constraints on sustained fiscal irresponsibility would kick in before then. The real problem is not that the United States will knowingly walk off a cliff. It is that no one knows where the edge of the cliff is, or which of the intermediate ridges along the way—the lesser ‘fiscal cliffs’—pose real danger.” … The U.S. may “get itself into more trouble than it realizes because it will be unable to tell apart the point when deferring retrenchment keeps its options open from the point when deferring retrenchment closes them down. Credible systems, like credible banks, can find they lose credibility quickly and unexpectedly.” (pp. 312-13)
Runciman uses dialectical tension to strike an appropriate balance between optimism and pessimism. He opts not to assume that we will eventually encounter the crisis that overwhelms us. He refuses to see politics as inherently tragic; democracy “is too inadvertently comical for that.” (p. 324) At the same time, he believes that democracy is a series of mismatches, that it displays a repeated pattern of crisis and recovery. “The long-term strength of democracy comes from its short-term restlessness; it is also at risk of being undermined by its short-term restlessness.” Put in the most general of terms, “democracies succeed because they fail and they fail because they succeed. There is no way around this.” (p. 304)
Wednesday, October 23, 2013
Katsman, Retirement GPS
Aaron Katsman’s Retirement GPS: How to Navigate Your Way to a Secure Financial Future with Global Investing (McGraw-Hill, 2013) offers a look at how to invest in foreign stocks and bonds. Even if you don’t agree with the author’s recommendation that 50% of a retirement portfolio be allocated to international stocks and bonds (being more tactically inclined, I personally am uneasy with such fixed allocations), you can still profit from some of his tips.
The GPS portfolio is based on macro events and macro trends. In deciding whether to invest in a particular country, Katsman asks such questions as whether it has a stable political climate, economic freedom, a young population and a growing middle class, a projected GDP growth with lower inflation for the following year, and a diversified economy. The countries that shine right now in these respects are the Asian Tigers (Hong Kong, South Korea, Taiwan, and Singapore), Australia, ASEAN (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam), Colombia, Peru, Israel, and Turkey.
As to investing in global bonds, Katsman points out that over 15 years (ending in 2011) a hedged global bond portfolio, one which tries to neutralize the impact of currency swings, had returns very similar to those on U.S. bonds but with less volatility. Mind you, the cost of currency hedging can be quite steep.
Most investors are probably reasonably familiar with global stocks and how to access them. International bonds are more of a mystery. The retail global bond investor has three basic alternatives: individual bonds, bond funds, and ETFs. Unless an investor has a very large portfolio, the first alternative is not practical. So on to alternative two. The investor who opts for a mutual fund will be subject to fees that can eat into returns. Moreover, unlike individual bonds, there is no guarantee of principal in a bond fund. And, of course, it’s important to monitor the fund manager. As for ETFs, passive emerging market or global bond funds are flawed because passive investment simply does not work in fixed income, especially in emerging markets; “most of the bonds in the index are not available to be bought.” (p. 128)
Katsman’s book meanders into some unexpected byways, such as crowd funding, that probably wouldn’t figure in most portfolios. And he veers away from the main theme of the book when he advocates for financial advisors (yes, he is one of them). But even if this book lacks the rigor of most mainstream investing books and leaves many questions unanswered, it offers some useful advice. The reader can fill in the gaps elsewhere.
The GPS portfolio is based on macro events and macro trends. In deciding whether to invest in a particular country, Katsman asks such questions as whether it has a stable political climate, economic freedom, a young population and a growing middle class, a projected GDP growth with lower inflation for the following year, and a diversified economy. The countries that shine right now in these respects are the Asian Tigers (Hong Kong, South Korea, Taiwan, and Singapore), Australia, ASEAN (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam), Colombia, Peru, Israel, and Turkey.
As to investing in global bonds, Katsman points out that over 15 years (ending in 2011) a hedged global bond portfolio, one which tries to neutralize the impact of currency swings, had returns very similar to those on U.S. bonds but with less volatility. Mind you, the cost of currency hedging can be quite steep.
Most investors are probably reasonably familiar with global stocks and how to access them. International bonds are more of a mystery. The retail global bond investor has three basic alternatives: individual bonds, bond funds, and ETFs. Unless an investor has a very large portfolio, the first alternative is not practical. So on to alternative two. The investor who opts for a mutual fund will be subject to fees that can eat into returns. Moreover, unlike individual bonds, there is no guarantee of principal in a bond fund. And, of course, it’s important to monitor the fund manager. As for ETFs, passive emerging market or global bond funds are flawed because passive investment simply does not work in fixed income, especially in emerging markets; “most of the bonds in the index are not available to be bought.” (p. 128)
Katsman’s book meanders into some unexpected byways, such as crowd funding, that probably wouldn’t figure in most portfolios. And he veers away from the main theme of the book when he advocates for financial advisors (yes, he is one of them). But even if this book lacks the rigor of most mainstream investing books and leaves many questions unanswered, it offers some useful advice. The reader can fill in the gaps elsewhere.
Monday, October 21, 2013
Yanofsky, The Outer Limits of Reason
If you’re looking for a refreshing break from the usual investing/trading fare, let me recommend Noson S. Yanofsky’s The Outer Limits of Reason: What Science, Mathematics, and Logic Cannot Tell Us (MIT Press, 2013). Written for the layman, it explores the realm of the unsolvable, unprovable, and unknowable. Some (perhaps even most) of the material will be familiar, but Yanofsky offers a compelling synthesis of various “outer limits” problems.
Some computing challenges are staggering—that is, practically unsolvable, even if not theoretically impossible. For instance, Euler may have solved the seven bridges of Königsberg problem with sheer brain power, but solving the traveling salesman problem for 100 cities—assuming that our computer can check a million routes in a second—would take 2.9 * 10142 centuries! Splitting a hundred numbers into two sets to see if the sum of one part equals half the sum of all the elements would take a mere 401,969,368,413,314 centuries. And trying to predict with any degree of precision an event in a chaotic system, or a complex adaptive system, is a pretty hopeless undertaking; it’s not even a question of time or computer power. (Just ask any trader.)
Many problems, such as the halting problem or the tiling problem, are undecidable—at least by computer logic. Often these problems suffer from some form of self-referential limitation. Think of time-travel paradoxes, Russell’s paradox, Gödel’s first incompleteness theorem or my personal stroll-down-memory-lane favorite (I guess because, when I was first introduced to logic, I learned a new word in the process) the heterological paradox. In fact, Yanofsky writes, “the universe is the ultimate self-referential system; the universe uses scientists to study itself.” (p. 343)
Other problems stem from the chasm between the describable and the indescribable—the former countably infinite, the latter (presumably) uncountably infinite. That is, “there is no longest word or longest novel, because there is no limit to the longest formula, and so on. This makes language infinite. However, it can be alphabetized or counted, which makes language countably infinite. … It is plausible to say that there is an uncountably infinite number of phenemona that can occur. This is stated without proof because I cannot quantify all phenomena. To quantify them, I would have to describe them and I cannot do that without language.” (p. 175)
Yanofsky doesn’t break new ground in this book, but he offers a “one-stop” emporium for those who enjoy pondering the limits of reason. I had a grand time reading it.
Some computing challenges are staggering—that is, practically unsolvable, even if not theoretically impossible. For instance, Euler may have solved the seven bridges of Königsberg problem with sheer brain power, but solving the traveling salesman problem for 100 cities—assuming that our computer can check a million routes in a second—would take 2.9 * 10142 centuries! Splitting a hundred numbers into two sets to see if the sum of one part equals half the sum of all the elements would take a mere 401,969,368,413,314 centuries. And trying to predict with any degree of precision an event in a chaotic system, or a complex adaptive system, is a pretty hopeless undertaking; it’s not even a question of time or computer power. (Just ask any trader.)
Many problems, such as the halting problem or the tiling problem, are undecidable—at least by computer logic. Often these problems suffer from some form of self-referential limitation. Think of time-travel paradoxes, Russell’s paradox, Gödel’s first incompleteness theorem or my personal stroll-down-memory-lane favorite (I guess because, when I was first introduced to logic, I learned a new word in the process) the heterological paradox. In fact, Yanofsky writes, “the universe is the ultimate self-referential system; the universe uses scientists to study itself.” (p. 343)
Other problems stem from the chasm between the describable and the indescribable—the former countably infinite, the latter (presumably) uncountably infinite. That is, “there is no longest word or longest novel, because there is no limit to the longest formula, and so on. This makes language infinite. However, it can be alphabetized or counted, which makes language countably infinite. … It is plausible to say that there is an uncountably infinite number of phenemona that can occur. This is stated without proof because I cannot quantify all phenomena. To quantify them, I would have to describe them and I cannot do that without language.” (p. 175)
Yanofsky doesn’t break new ground in this book, but he offers a “one-stop” emporium for those who enjoy pondering the limits of reason. I had a grand time reading it.
Monday, October 14, 2013
Mihaljevic, The Manual of Ideas
John Mihaljevic is a managing editor of the prestigious monthly journal for value-oriented investors The Manual of Ideas. Hence the title of as well as a major resource for this book, The Manual of Ideas: The Proven Framework for Finding the Best Value Investments (Wiley, 2013).
Even if you yawn at the very thought of yet another book on value investing you owe it to yourself to give this manual a try. It is a serious, thoroughly researched work that draws on interviews with top value investors, quantitative screens, and good old-fashioned qualitative analysis. It offers not only principles but actionable advice. And it reads well. In brief, its value far exceeds its price ($25.99 hardcover on Amazon).
The book covers a range of value investing styles and opportunities: deep value, sum-of-the-parts value, Greenblatt’s approach, jockey stocks (i.e., investing in companies with great management), following superinvestors, small- and micro-cap stocks, special situations, equity stubs, and international.
Let’s say that you want to track superinvestors. Mihaljevic offers some caveats when it comes to choosing whom to follow. These caveats should be fairly obvious, but investors often overlook them when looking for investment ideas, so they’re worth repeating. For instance, you should take into account portfolio turnover. Since a fund has to file a 13F-HR only quarterly and there is on top of that a 45-day filing delay, “the higher the turnover of a superinvestor’s portfolio the higher the chance that an investor is considering selling an equity by the time we consider buying it.” And if a superinvestor engages in a lot of short selling, “we cannot be sure whether a long position represents a high-conviction call on the merits of a specific investment or whether it simply represents the long side of a pair trade.” (pp. 161-62)
Mihaljevic lists some of his favorite superinvestors along with their CIK (Central Index Key) numbers, used when searching SEC filings. He also gives tips on how to track a handful of top investors who do not file 13F-HR forms with the SEC.
Those who are interested in investing in small cap stocks will find useful screens and, more importantly, ways to go beyond these “crude tools for finding interesting ideas.” (p. 197) The book offers, for instance, methods for deciding whether a fallen angel will fly again and for uncovering hidden inflection points. It encourages investors to ask whether a company passed the right screen for the wrong reason, whether the financial statements raise any red flags, who has been buying and selling the shares, what management’s attitude is toward outside shareholders, and where the shares are relative to their historical range.
For those with nerves of steel, an appreciation of the often destructive power of leverage, and a keen sense of risk management, equity stubs “can be one of the most rewarding pieces of a portfolio.” (p. 239) Successful investing in equity stubs requires sound judgment and experience. Even then, “due to the lopsided payoff in leveraged equities, the probability of winning on any one investment may be well under 50 percent.” (p. 264) For those determined to pursue this area of value investing, Mihaljevic suggests that “industry-wide sell-offs represent better hunting grounds for potential opportunities than do company-specific crises.” (p. 265)
An “intra-text” note from this reviewer who watched, and thoroughly enjoyed, the documentary “Note By Note: The Making of Steinway L1037”: As readers probably know, John Paulson recently bought Steinway, which Mihaljevic describes as a classic case of a company that “saw no urgency to unlock shareholder value.” Although “the renowned piano maker … also owns a band instruments business and valuable real estate in New York, … investors who bought Steinway shares based on a sum-of-the-parts appraisal would have witnessed years of inaction on monetizing real estate.” (p. 61) Well, if it takes about a year to make a piano, perhaps we can understand the frustratingly snail-like pace of Steinway’s board.
The Manual of Ideas is a book I can highly recommend to retail and institutional investors alike or, to slice the investing world another way, to beginners as well as old-hands. It has something to say to every investor, even those who don’t consider themselves true value investors.
Even if you yawn at the very thought of yet another book on value investing you owe it to yourself to give this manual a try. It is a serious, thoroughly researched work that draws on interviews with top value investors, quantitative screens, and good old-fashioned qualitative analysis. It offers not only principles but actionable advice. And it reads well. In brief, its value far exceeds its price ($25.99 hardcover on Amazon).
The book covers a range of value investing styles and opportunities: deep value, sum-of-the-parts value, Greenblatt’s approach, jockey stocks (i.e., investing in companies with great management), following superinvestors, small- and micro-cap stocks, special situations, equity stubs, and international.
Let’s say that you want to track superinvestors. Mihaljevic offers some caveats when it comes to choosing whom to follow. These caveats should be fairly obvious, but investors often overlook them when looking for investment ideas, so they’re worth repeating. For instance, you should take into account portfolio turnover. Since a fund has to file a 13F-HR only quarterly and there is on top of that a 45-day filing delay, “the higher the turnover of a superinvestor’s portfolio the higher the chance that an investor is considering selling an equity by the time we consider buying it.” And if a superinvestor engages in a lot of short selling, “we cannot be sure whether a long position represents a high-conviction call on the merits of a specific investment or whether it simply represents the long side of a pair trade.” (pp. 161-62)
Mihaljevic lists some of his favorite superinvestors along with their CIK (Central Index Key) numbers, used when searching SEC filings. He also gives tips on how to track a handful of top investors who do not file 13F-HR forms with the SEC.
Those who are interested in investing in small cap stocks will find useful screens and, more importantly, ways to go beyond these “crude tools for finding interesting ideas.” (p. 197) The book offers, for instance, methods for deciding whether a fallen angel will fly again and for uncovering hidden inflection points. It encourages investors to ask whether a company passed the right screen for the wrong reason, whether the financial statements raise any red flags, who has been buying and selling the shares, what management’s attitude is toward outside shareholders, and where the shares are relative to their historical range.
For those with nerves of steel, an appreciation of the often destructive power of leverage, and a keen sense of risk management, equity stubs “can be one of the most rewarding pieces of a portfolio.” (p. 239) Successful investing in equity stubs requires sound judgment and experience. Even then, “due to the lopsided payoff in leveraged equities, the probability of winning on any one investment may be well under 50 percent.” (p. 264) For those determined to pursue this area of value investing, Mihaljevic suggests that “industry-wide sell-offs represent better hunting grounds for potential opportunities than do company-specific crises.” (p. 265)
An “intra-text” note from this reviewer who watched, and thoroughly enjoyed, the documentary “Note By Note: The Making of Steinway L1037”: As readers probably know, John Paulson recently bought Steinway, which Mihaljevic describes as a classic case of a company that “saw no urgency to unlock shareholder value.” Although “the renowned piano maker … also owns a band instruments business and valuable real estate in New York, … investors who bought Steinway shares based on a sum-of-the-parts appraisal would have witnessed years of inaction on monetizing real estate.” (p. 61) Well, if it takes about a year to make a piano, perhaps we can understand the frustratingly snail-like pace of Steinway’s board.
The Manual of Ideas is a book I can highly recommend to retail and institutional investors alike or, to slice the investing world another way, to beginners as well as old-hands. It has something to say to every investor, even those who don’t consider themselves true value investors.
Wednesday, October 9, 2013
Focardi, Fabozzi, and Bali, Mathematical Methods for Finance
Mathematical Methods for Finance: Tools for Asset and Risk Management by Sergio M. Focardi, Frank J. Fabozzi, and Turan G. Bali (Wiley, 2013) may have chapter titles that sound like a math department’s course offerings, but this book is no substitute for actually taking the courses. The reader is not about to learn differential calculus in 30 pages or matrix algebra in 26 pages. And the two concluding chapters on stochastic calculus are definitely not for those who opted to round out their college schedules with the history of art instead of math. The authors may claim that “there is no prerequisite mathematical knowledge for reading this book,” but without some pretty extensive math background (or an extraordinary ability to grasp mathematical concepts intuitively) the reader’s understanding of the text will be minimal. Prospective readers looking for a crash course should understand that they won’t go from 0 to 60 in 300 pages.
Since the authors are trying to cast a wide net they explain key concepts in each field. For instance, the differential calculus chapter discusses the notion of limit, the essentials of limit theorems, the common definitions linking relevant conditions to limits of functions and sequences, the concept of continuity and total variation, differentiation and commonly used rules for computing first-order derivatives, computing second-order and higher-order derivatives, the chain rule, and the Taylor series expansion. The reader whose grasp of calculus is firm, or the reader who just needs a quick refresher course, will then learn a couple of applications of derivatives in bond portfolio management.
For many who would like to understand the quantitative side of modern finance this book serves as a daunting to-do list. Probability in finance, for example, is not the kind of probability used by poker players (and taught in beginning probability courses). Poker players don’t have to worry about fat tails and Hermite polynomials, quants do. Poker players can win big without ever having seen an integral, quants can lose millions/billions for their firms if they don’t understand integrals inside out.
Even though the book spends far more time on mathematical concepts than on applications, Mathematical Methods for Finance is still a good reference book for those who want to move from the math of the academy to the math of Wall Street. A primer, however, it’s not.
Since the authors are trying to cast a wide net they explain key concepts in each field. For instance, the differential calculus chapter discusses the notion of limit, the essentials of limit theorems, the common definitions linking relevant conditions to limits of functions and sequences, the concept of continuity and total variation, differentiation and commonly used rules for computing first-order derivatives, computing second-order and higher-order derivatives, the chain rule, and the Taylor series expansion. The reader whose grasp of calculus is firm, or the reader who just needs a quick refresher course, will then learn a couple of applications of derivatives in bond portfolio management.
For many who would like to understand the quantitative side of modern finance this book serves as a daunting to-do list. Probability in finance, for example, is not the kind of probability used by poker players (and taught in beginning probability courses). Poker players don’t have to worry about fat tails and Hermite polynomials, quants do. Poker players can win big without ever having seen an integral, quants can lose millions/billions for their firms if they don’t understand integrals inside out.
Even though the book spends far more time on mathematical concepts than on applications, Mathematical Methods for Finance is still a good reference book for those who want to move from the math of the academy to the math of Wall Street. A primer, however, it’s not.
Monday, October 7, 2013
Greenblatt, Breakthrough Strategies for Predicting Any Market,2d ed.
The first edition of Jeff Greenblatt’s Breakthrough Strategies for Predicting Any Market: Charting Elliott Wave, Lucas, Fibonacci, Gann, and Time for Profit appeared in 2007. Since that time Greenblatt came to embrace the work of W. D. Gann. He also found that Alan Andrews’ median lines served as “an excellent GPS system in order to understand how trends evolve.” The second edition (Wiley, 2013) thus includes new chapters on Gann, Andrews, and median channels. It also addresses market sentiment/psychology.
Greenblatt is the director of Lucas Wave International and editor of The Fibonacci Forecaster. And who was Lucas, you might ask. (Well, at least I did.) Edouard Lucas (1842-1891) was a French mathematician who invented the famous (some might say infamous) Tower of Hanoi puzzle. He also studied number sequences, most notably the Fibonacci sequence. The closely related number sequence (2, 1, 3, 4, 7, 11, 18, 29, …) is named after Lucas.
But back to the book.
Breakthrough Strategies focuses on chart reading, the idea being to wean traders away from a reliance on lagging indicators and to introduce them instead to the kind of pattern analysis/recognition that can serve as a forecasting tool. By pattern recognition Greenblatt doesn’t mean simply finding double tops or ascending triangles. He is looking for quantifiable patterns. He counts bars in search of cycles, squares time and price, calculates Fibonacci retracements and extensions.
He takes the reader chart by chart (some of them pretty difficult to decipher amid all the lines and numbers) to illustrate the various techniques he uses. The idea is to build up a repertoire of methods that are objective, not subjective. Of course, in trading nothing is purely objective. Just because a Gann calculation agrees with some Fibonacci level doesn’t mean that the trader has an objective signal. An Andrews channel in conjunction with another Fibonacci level might offer up a competing signal. Moreover, even when all the stars align the results may be disappointing, as Greenblatt readily admits.
So who can profit from this book? Certainly not a beginning trader; it’s far too complex for someone just getting his feet wet. Not the minimalist who likes clean charts. And, of course, not the person who thinks that all this old stuff is so much voodoo. Greenblatt is unlikely to win converts to his cause with this book. But for the trader who is using any of these strategies and is looking for a way to complement them (especially for those who might contemplate adding Gann to their quiver), Greenblatt’s book is a helpful guide.
Greenblatt is the director of Lucas Wave International and editor of The Fibonacci Forecaster. And who was Lucas, you might ask. (Well, at least I did.) Edouard Lucas (1842-1891) was a French mathematician who invented the famous (some might say infamous) Tower of Hanoi puzzle. He also studied number sequences, most notably the Fibonacci sequence. The closely related number sequence (2, 1, 3, 4, 7, 11, 18, 29, …) is named after Lucas.
But back to the book.
Breakthrough Strategies focuses on chart reading, the idea being to wean traders away from a reliance on lagging indicators and to introduce them instead to the kind of pattern analysis/recognition that can serve as a forecasting tool. By pattern recognition Greenblatt doesn’t mean simply finding double tops or ascending triangles. He is looking for quantifiable patterns. He counts bars in search of cycles, squares time and price, calculates Fibonacci retracements and extensions.
He takes the reader chart by chart (some of them pretty difficult to decipher amid all the lines and numbers) to illustrate the various techniques he uses. The idea is to build up a repertoire of methods that are objective, not subjective. Of course, in trading nothing is purely objective. Just because a Gann calculation agrees with some Fibonacci level doesn’t mean that the trader has an objective signal. An Andrews channel in conjunction with another Fibonacci level might offer up a competing signal. Moreover, even when all the stars align the results may be disappointing, as Greenblatt readily admits.
So who can profit from this book? Certainly not a beginning trader; it’s far too complex for someone just getting his feet wet. Not the minimalist who likes clean charts. And, of course, not the person who thinks that all this old stuff is so much voodoo. Greenblatt is unlikely to win converts to his cause with this book. But for the trader who is using any of these strategies and is looking for a way to complement them (especially for those who might contemplate adding Gann to their quiver), Greenblatt’s book is a helpful guide.
Wednesday, October 2, 2013
Markowitz, Risk-Return Analysis, vol. 1
Harry M. Markowitz, now in his mid-80s, has embarked on a four-volume series entitled Risk-Return Analysis, of which this volume, The Theory and Practice of Rational Investing (McGraw-Hill, 2013), is the first.
Most of us grew up with modern portfolio theory, first enunciated in a 1952 journal article and later proclaimed to be the beginning of modern finance. Brokerage firms provided clients with graphs showing optimal portfolios that offered the highest expected return for a given level of risk, achieved largely through diversification, and investors who wanted to sound knowledgeable threw around the phrase “the efficient frontier”. Modern portfolio theory seemed to work pretty well for decades. But then came the financial crisis, and critic after critic began sounding the death knell for MPT.
Markowitz’s response is masterful. He doesn’t launch a frontal attack on his critics. (Well, here and there he does let loose. For instance, on p. 72 he writes that “the persistence of the Great Confusion—that MV [mean-variance] analysis is applicable in practice only when return distributions are Gaussian or utility functions quadratic—is as if geography textbooks of 1550 still described the Earth as flat.”) Instead, for the most part he invites them to understand just what it is he is saying (which is different from what they are criticizing). This exercise involves a lot more than catch phrases and graphs. Moreover, he explains where and how his own thinking has evolved over the decades.
The result is a book that every finance professional should own. Serious retail investors who are not afraid of a little statistically related math will also be rewarded intellectually, perhaps financially as well. But, casual reader beware, this book is not one that I would describe as a good read.
The book is divided into five chapters: the expected utility maxim, mean-variance approximations to expected utility, mean-variance approximations to the geometric mean, alternative measures of risk, and the likelihood of various return distributions.
Perhaps the most fundamental mistaken notion about MPT that Markowitz has sought to dispel over the years, the one that he addressed in the passage I quoted above, is that mean-variance analysis should not be used if distributions are not normal. Markowitz explains that this criticism is misguided because he bases his “support for mean-variance analysis on mean-variance approximations to expected utility.” (p. 34)
Why, some might ask, “if one believes that action should be in accordance with the maximize EU rule, … seek to approximately maximize it via a mean-variance analysis? Why not just maximize expected utility?” (p. 41) One very important reason is that “the only inputs required for a mean-variance analysis are the means, variances, and covariances of the securities or asset classes of the analysis.” The formula for the expected return of a portfolio, according to this analysis, “is true whether or not returns are normally distributed, or even symmetrical, and whether or not the return distributions have ‘fat tails’….” (pp. 42-43)
In this book Markowitz also assesses competing risk measures (variance, mean absolute deviation, semivariance, value at risk, and conditional value at risk). His findings are provocative and, I’m sure, will elicit fervent rebuttals from proponents of these risk measures.
The Theory and Practice of Rational Investing is an important work, inviting academics and finance professionals to take a second look (or perhaps a first genuine look) at modern portfolio theory and thereby more fully understand its foundations, tenets, and consequences.
Most of us grew up with modern portfolio theory, first enunciated in a 1952 journal article and later proclaimed to be the beginning of modern finance. Brokerage firms provided clients with graphs showing optimal portfolios that offered the highest expected return for a given level of risk, achieved largely through diversification, and investors who wanted to sound knowledgeable threw around the phrase “the efficient frontier”. Modern portfolio theory seemed to work pretty well for decades. But then came the financial crisis, and critic after critic began sounding the death knell for MPT.
Markowitz’s response is masterful. He doesn’t launch a frontal attack on his critics. (Well, here and there he does let loose. For instance, on p. 72 he writes that “the persistence of the Great Confusion—that MV [mean-variance] analysis is applicable in practice only when return distributions are Gaussian or utility functions quadratic—is as if geography textbooks of 1550 still described the Earth as flat.”) Instead, for the most part he invites them to understand just what it is he is saying (which is different from what they are criticizing). This exercise involves a lot more than catch phrases and graphs. Moreover, he explains where and how his own thinking has evolved over the decades.
The result is a book that every finance professional should own. Serious retail investors who are not afraid of a little statistically related math will also be rewarded intellectually, perhaps financially as well. But, casual reader beware, this book is not one that I would describe as a good read.
The book is divided into five chapters: the expected utility maxim, mean-variance approximations to expected utility, mean-variance approximations to the geometric mean, alternative measures of risk, and the likelihood of various return distributions.
Perhaps the most fundamental mistaken notion about MPT that Markowitz has sought to dispel over the years, the one that he addressed in the passage I quoted above, is that mean-variance analysis should not be used if distributions are not normal. Markowitz explains that this criticism is misguided because he bases his “support for mean-variance analysis on mean-variance approximations to expected utility.” (p. 34)
Why, some might ask, “if one believes that action should be in accordance with the maximize EU rule, … seek to approximately maximize it via a mean-variance analysis? Why not just maximize expected utility?” (p. 41) One very important reason is that “the only inputs required for a mean-variance analysis are the means, variances, and covariances of the securities or asset classes of the analysis.” The formula for the expected return of a portfolio, according to this analysis, “is true whether or not returns are normally distributed, or even symmetrical, and whether or not the return distributions have ‘fat tails’….” (pp. 42-43)
In this book Markowitz also assesses competing risk measures (variance, mean absolute deviation, semivariance, value at risk, and conditional value at risk). His findings are provocative and, I’m sure, will elicit fervent rebuttals from proponents of these risk measures.
The Theory and Practice of Rational Investing is an important work, inviting academics and finance professionals to take a second look (or perhaps a first genuine look) at modern portfolio theory and thereby more fully understand its foundations, tenets, and consequences.
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