Stoken, Survival of the Fittest for Investors

Over the past few years, as academics have come to view markets as complex adaptive systems, Darwin’s ideas have worked their way into the mainstream financial literature. In Survival of the Fittest for Investors: Using Darwin’s Laws of Evolution to Build a Winning Portfolio (McGraw-Hill, 2012) Dick Stoken explores both the theory and practice of Darwinism as it applies to the individual investor.

Free markets, Stoken explains, use the same algorithm as evolution—search through potential designs, select the few that are good enough, then replicate or amplify them. Free markets are “littered with errors,”, but “each error, along the way, provides feedback so as to formulate a new trial until a solution is found.” (pp. 29-30) Moreover, similar to prey/predator models, “free markets fluctuate…. During the exuberant phase of the cycle, errors become embedded into the system and, at some point, interfere with its ability to self-regulate. Reversals are necessary to flush enough of the errors out so that the system can regain its former vitality.” (p. 31)

Stoken analyzes bubbles at some length and argues that the errors in the most recent financial crisis were not man-made. “If only X had done Y” is a misplaced criticism. The errors “were of the kind that living systems, operating via a trial-and-error process, typically make. Therefore, they could not, in an ordinary sense, be man-fixed.” The real error, he contends, was “in not allowing for error. … Our wealth-generating machine lowered its margin of safety drastically, leaving little room for anything to go wrong.” Stoken continues: “The more complex a system becomes, the greater the number of errors. Bubble time is also peak complexity time. The particular error doesn’t matter so much, as all roads lead to system failure. Much like Hercules in his battle with the nine-headed serpent Hydra, when Hercules cut off one head, two more would sprout; if we fixed one error, the problem shifted and another and more potent accident soon popped up.” (pp. 106-107)

What is an investor who accepts this view of the market to do? Of course, he has to adapt. Stoken offers several concrete options, from a passive diversified portfolio of alternative investments to a levered actively managed “combined assets” portfolio, from annual rebalancing to using a breakout system for buy and sell signals. He includes basic backtesting results for each strategy.

Survival of the Fittest for Investors is one of the “fittest,” best written investment books I’ve read in some time. The investor who is searching for a way to boost returns would do well to add it to his must-read list.

A daily chart advent calendar

As usual, I'm late to the game. On November 30 The Economist published a daily chart advent calendar, a collection of the 24 most popular maps, charts, data visualisations and interactive features on their site this year plus a new chart for Christmas day. Not just the routine stuff. The first graph is the street price of cocaine in rich nations and adult usage in these countries. We're now on day ten; you're not allowed to peek ahead.

Schmitt, 401(k) Day Trading

Over the past couple of weeks I have reviewed several books that meticulously explore the viability of various investing and trading strategies. Richard Schmitt’s 401(k) Day Trading: The Art of Cashing in on a Shaky Market in Minutes a Day (Wiley, 2011) does not fall into this category.

Schmitt argues that the investor with unconsolidated 401(k) accounts can outperform buy-and-hold returns by trading part of his retirement portfolio once every day. How, you may ask, can he do this given the restrictions on frequent trading imposed by most mutual funds? It’s simple. He has two commission-free stock fund accounts, one for buying stock and the other for selling stock, and a cash fund. And here’s the strategy. “You use one account for transfers just before the market close from a stock fund to a cash fund whenever the stock market is advancing for the day. In the other account, you transfer from a cash fund to a stock fund when the market is declining for the day.” (p. 220)

The investor doesn’t swap out his total buying or selling account each day. Instead, he determines how much to exchange based on the S&P 500’s daily change (not the daily range) multiplied by a constant calibration factor. “Under normal conditions, you could use a calibration factor of one thousandth of the initial amount of your retirement savings portfolio you decide to subject to day trading.” (p. 194) So, if the investor had a $100,000 portfolio, the S&P moved 10 points, and his calibration factor was $100, he would exchange $1,000.

Schmitt provides very little statistical information about his backtesting of this method. He compares only the differences in returns between the $100,000 day trading portfolio and the $100,000 S&P 500 portfolio. To “accommodate the market volatility experienced during the decade ended December 31, 2010,” the backtested day trading portfolio used a calibration factor of $75 for each daily one-point change in the S&P 500 index. (I assume this figure was optimized in hindsight, warning flag #1.) The day trading portfolio outperformed the index portfolio over the ten-year period (120 vs. 95.3) and a five-year timeframe (115.1 vs. 100.7). In 2010 itself, however, the index portfolio outperformed 112.8 vs. 107.3. We have no data on portfolio drawdowns.

The author acknowledges that “a comparison of alternative asset management strategies relies heavily on the measurement period selected for use in computing the strategies’ investment returns” (warning flag #2). “A 20-year comparison of alternative strategies becomes a bit more interesting.” (p. 207) We don’t know how badly Schmitt’s strategy would have performed over that time frame. Suffice it to say that the author acknowledges that “day trading is a strategy of the times, but not for all times. It may perform well in a volatile stock market with little or no net change over time but does not fare as well by comparison in a rapidly increasing or decreasing stock market.” (p. 208) So, warning flag #3 is that the investor has to be able to read the tea leaves of the market environment, present and near future.

Schmitt’s book provides a lot more information about 401(k) plans than I have shared in this review, and to that extent I have been unfair. But before investors with 401(k) plans get all excited about a way to earn excess returns with very little work they should step back a bit and think it through.

Hassett, The Risk Premium Factor

In The Risk Premium Factor: A New Model for Understanding the Volatile Forces That Drive Stock Prices (Wiley, 2011) Stephen D. Hassett sets out to provide a model for estimating the equity risk premium (and hence the cost of capital) and for solving the equity premium puzzle.

Hassett begins with the CAPM equation for the cost of equity: risk-free rate + beta x equity risk premium (ERP). For the market as a whole, the cost of equity is the risk-free rate + ERP. The problem is how to calculate ERP. Enter Hassett’s risk premium factor (RPF) model, which proposes that “the equity risk premium (ERP) is a simple function of the risk-free rate.”

“Conventional theory would hold that if the equity risk premium (ERP) were 6.0 percent and 10-year Treasury yield were 4.0 percent, then investors would expect equities to yield 10 percent, but if the 10-year Treasury were 10 percent, then investors would require a 16 percent return—a proportionately smaller premium.” By contrast, Hassett argues that ERP is not fixed but “varies directly with the level of the risk-free rate in accordance with a risk premium factor (RPF).” For example, “with an RPF of 1.48, equities are expected to yield 9.9 percent when Treasury yields are 4.0 percent and 24.8 percent (10 + 1.48 x 10 = 24.8) when they are at 10 percent to provide investors with the same proportional compensation for risk.” (p. 19)

To calculate the RPF Hassett ran regressions on annual data between 1960 and 2008 and quarterly data from Q4 1986 to Q4 2008. He found two shifts in the RPF—in 1981 and September 2002. (The causes of these shifts, the author admits, are still not fully explained.) The RPF values for the annual data sets were 1.24 between 1960 and 1980, 0.90 from 1981 through 2001, and 1.51 for 2002 through 2008.

Hassett acknowledges that the RPF can be discerned only in hindsight and cannot be projected, but he still considers his method superior to other methods for determining risk premiums. For instance, “if the RPF changed just two times over 50 years, one might argue that in any year there is a 96 percent chance … that the RPF will remain constant over the next year.” (p. 28)

The RPF model is also brought to bear on the equity premium puzzle, the inability to reconcile the observed ERP with financial models. The authors of a 1985 paper found that on average short-term Treasuries produced a real return of about 1% over the long term and equities yielded 7%. This, the authors maintained, would require a puzzling coefficient of risk aversion on the order of 40 or 50 to justify the 7% ERP. Haslett invokes his model in conjunction with the notion of loss aversion to tackle the puzzle.

Hassett uses his model to explain major market gyrations. He also explains how it can be used to value an acquisition or project.

For most investors the model is most applicable in valuing the overall stock market. Hassett argues that when trying to decide whether the market is over- or undervalued the analyst should focus on the two drivers of valuation—earnings and interest rates (interpreted through the lens of the RPF model).

I am not equipped to pass judgment on Hassett’s model. It’s certainly a simple model, not one of those complex quant models that have come under attack of late. It also seems plausible. Is it useful? Perhaps.

Triana, The Number That Killed Us

Pablo Triana, a professor at ESADE Business School (Spain), is a man on a mission: to rid modern finance of complex mathematical models. In The Number That Killed Us: A Story of Modern Banking, Flawed Mathematics, and a Big Financial Crisis (Wiley, 2012) his target is one of the most widely used models, VaR (Value at Risk).

Triana’s thesis is fairly straightforward, although he spends over 200 pages fleshing it out. VaR is fundamentally flawed because it relies on historical data, viewing the past as prologue; it assumes a normal probability distribution; and it doesn’t differentiate among kinds of assets. Moreover, since VaR is the key metric invoked to determine leverage, traders can use it to game the system. They can put together portfolios with ostensibly low risk profiles and hence be eligible to use more leverage, an exercise that sometimes masks reckless behavior.

VaR, Triana argues, was the “top miscreant” in the financial crisis. “Without those unrealistically insignificant risk estimates, the securities that sank the banks and unleashed the crisis would most likely not have been accumulated in such a vicious fashion, as the gambles would not have been internally authorized and, most critically, would have been impossibly expensive capital-wise.” (p. 3)

Triana wants to get rid of VaR. What should replace it? “Going forward, let’s do less mathematical financial risk analysis, please. Softer sapience based on traders’ war scars, experience-honed intuition, historical lessons, and networking with other players will not only typically beat quant sapience when it comes to understanding and deciphering exposures (we humans can’t be that bad!), but most crucially should be far more effective in preventing obviously lethal, chaos-igniting practices.” (pp. 44-45)

Financial risk, he contends, “is a simple discipline. Or rather, a discipline that ought to be based on fairly simple tenets: Financial risk is not measurable or forecastable, the past is not prologue, battle-scarred experience-honed intuitive wisdom should be accorded utmost notoriety, certain assets are intrinsically riskier than others, too much leverage should be avoided, and too much toxic leverage should be banned.” (p. 213)

The book closes with a brief Q&A with Nassim Taleb and an essay by Aaron Brown that presents a more balanced view of VaR.

I’m certainly no expert on VaR or on the risk management practices of investment banks, but from the little I know Triana does justice to neither. Considering that he doesn’t want to reform quantitative risk management but either to abandon it or to keep a simplified version of it on a tight leash, I suppose fine points are irrelevant. Triana paints with broad, impassioned brushstrokes.

In my opinion supplementing VaR in particular and quantitative models in general with a large dose of human wisdom is a laudable goal. Maintaining a healthy margin of safety is important even if it means diminished profits during good times. Keeping models as simple as possible is undoubtedly good practice. On the other hand, chucking math and replacing it with so-called “battle-scarred experience-honed intuitive wisdom” is not. For one thing, behavioral finance has taught us that, despite our best intentions, we can be terrible bunglers. We humans really are that bad! For another, what masquerades as wisdom often turns out to be an oversized ego. Just think of …. Well, I’m sure you can easily fill in the blanks.

Twomey, Inside the Currency Market

For those who are intellectually curious and who want to know more about the currency market than they’ll ever need to become a halfway decent trader Brian Twomey’s Inside the Currency Market: Mechanics, Valuation, and Strategies (Bloomberg/Wiley, 2012) is a fascinating if sometimes overwhelming book.

Twomey begins not with the definition of a pip but with an analysis of Bank of International Settlements (BIS) reports (which, by the way, are available online). The second chapter, entitled “Currency Trading Beyond the Basics,” lives up to its billing. It deals with such topics as margin in various countries, rollover rates and LIBOR, swap points, and purchasing power parity.

Twomey supplies formulas where necessary, charts where helpful as he takes the reader on a journey through trade weight indices, short-term interest rates and money market instruments, LIBOR, government bonds and yield curves, swaps and forwards, stock and bond markets, currency cycles and volatility. The journey is also geographical, encompassing markets in all the major crosses. And it includes a series of recommended trade strategies.

The final chapter is on technical analysis but, once again, not just the run-of-the-mill fare. He describes volume and open interest, COT reports, Bollinger bands, simple moving averages, Ichimoku, the Baltic dry index, the IMF and special drawing rights, pivot points, and currency correlations and trend lines.

This post is beginning to sound like a laundry list, but it’s the only way I can convey the breadth and the sometimes unexpected content of Twomey’s book. The book is definitely not for those who want a “dummies” introduction or who are looking for instructions on how to get rich quick in the forex market. It’s also not for those in search of some light reading for a rainy afternoon. Inside the Currency Market is for the serious student who wants to go beyond simple buy and sell signals to understand the range of market factors that influence currency prices.

Derman, Models.Behaving.Badly

Models.Behaving.Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life (Free Press, 2011) is a wise book by a man who has thought deeply about his life, and not just as a quant on Wall Street. Emanuel Derman reminisces about his youth as a member of Habonim (a coeducational Zionist movement) in apartheid South Africa, his ordeal with myopic ophthalmological specialists, and his immersion in theoretical physics. He ventures into an unlikely corner of philosophy—not epistemology but Spinoza’s theory of emotions. He describes “the crooked paths that culminate in theories,” in particular classical and quantum electromagnetic theory.

Although these discussions have a life of their own, they serve to illustrate three fundamental ways of understanding the world: models, theories, and intuition. Intuition is “a merging of the understander with the understood”; “it emerges only from intimate knowledge acquired after careful observation and painstaking effort.” (pp. 96-97) Theory bears a close relationship to intuition. “[W]hen it is successful … it describes the object of its focus so accurately that the theory becomes virtually indistinguishable from the object itself. Maxwell’s equations are electricity and magnetism; the Dirac equation is the electron….” (p. 61)

Of the three, models are the most common and potentially the most troubling ways of understanding. “A model is a metaphor of limited applicability, not the thing itself.” (p. 54) It is an analogy which, although not unfounded, is partial and flawed. “Models project multidimensional reality onto smaller, more manageable spaces where regularities appear and then, in that smaller space, allow us to extrapolate and interpolate from the observed to the unknown.” (p. 58)

Since the financial markets do not obey the laws of science but are subject to the vagaries of human behavior, there can be no financial theories, only financial models. And, as Derman writes graphically, “When we make a model involving human beings, we are trying to force the ugly stepsister’s foot into Cinderella’s pretty glass slipper. It doesn’t fit without cutting off some of the essential parts.”

Models are not useless; indeed, they can be “immensely helpful in calculating initial estimates of value.” (p. 194) The best model in all of economics, Derman argues, is Black-Scholes. Although it is imperfect, it stands head and shoulders above CAPM: “an unkind way to look at CAPM is to say that it’s not very good.” (p. 182)

Reading Derman’s book is both an intellectually rewarding and a thoroughly pleasurable way to spend an afternoon. I recommend it unequivocally.