Traders take positions in pairs of similar stocks, longing the “undervalued” one and shorting the “overvalued one”, thereby placing a bet on the ultimate outperformance of the long position.
We now turn to equity trading strategies that attempt to decouple market movements from portfolio returns. Traders take positions in pairs of similar stocks, longing the “undervalued” one and shorting the “overvalued one”, thereby placing a bet on the ultimate outperformance of the long position. By using equal dollar investments on each member of the trading pair to effect an overall neutral long/short position, the trader is looking to remove beta as a factor affecting portfolio performance. The pair of stocks is usually composed of close competitors in the same market, and therefore presents the same country risk, currency risk, market capitalization risk, etc. By attempting to limit risk exposure to the relative merits of the two stocks, hedged equity market neutral strategy (HEMNS) can be equally attractive in up and down markets.
HEMNS is usually considered a long-term strategy, and may utilize a mix of fundamental and technical analysis. An alternate form of HEMNS is statistical arbitrage (stat arb), in which the pair of positions is determined through the use of technical and quantitative analysis. The goal is to take positions in which a traders proprietary algorithms show a pair of stock to be temporarily mispriced (one too high, one too low); the trader is betting that the stocks will shortly revert to their mean price, at which point the trade is liquidated.
In this example, the trader opens the position with equal dollar amounts of a pair of closely-related auto parts stocks. During the period, each stock pays a dividend. The position is put on for 90 days. Each stock pays a dividend during the period. After accounting for transaction costs, financing costs on the short position, and dividend payments, the strategy provided an annualized return of over 42%. (This is for purposes of illustration only).
For HEMNS to work, there must be a short-term suspension of the efficient market hypothesis (EMH), since a profit opportunity only arises if there is a difference between a stock’s theoretical fair market value and its current price. However, there can be multiple factors at work in HEMNS besides “incorrect” pricing. For instance, style risk arises from choosing assets according to value, capitalization, or momentum. There are also risks due to liquidity, leverage, interest rates, etc. Since each of these risks are rewarded by specific premia, it may be difficult to say for certain that HEMNS returns are due solely or even largely to an asset manager’s ability to differentiate overvalued and undervalued stocks. Such ability, if consistently manifest over time, could be termed alpha, as it may signify returns due to a manager’s skill, not just risk exposure.
However, studies of several paired-equity styles, such as high-value/low value, winning momentum/losing momentum, and small cap/large cap, have shown systematic risk premia available that are not captured by the standard CAPM, as we have discussed in previous articles. Some argue that these risk premia are due to factors like recession risk, bankruptcy risk, and liquidity risk. Others favor behavioral finance, which claims that market inefficiencies are due to suboptimal investor decisions, as an explanation. Investors may not really care, as long as returns are good.
Later on in this series, it will be our task to see if we can replicate HEMNS results without any special stock-picking skill. But we have many more strategies to examine first. We will continue our tour of hedge fund strategies next time with equity hedged short selling.
We have devoted a lot of blog space in the past examining the pros and cons of the Capital Asset Pricing Model (CAPM). The model predicts the amount of excess return (return above the risk-free rate) of an arbitrary portfolio that can be ascribed to a relationship (called beta) to the excess returns on the underlying market portfolio.
We have devoted a lot of blog space in the past examining the pros and cons of the Capital Asset Pricing Model (CAPM). The model predicts the amount of excess return (return above the risk-free rate) of an arbitrary portfolio that can be ascribed to a relationship (called beta) to the excess returns on the underlying market portfolio. It assumes that investors seek maximum utility from their portfolios, and that investors expect to be compensated for taking on additional risk. In CAPM, no portfolio can outperform a mix of the risk-free asset and the market portfolio on a risk-adjusted basis.
Risk and return are the only two variables of importance in CAPM. Risk is measured by beta, which is rooted in the variance (or more properly, its square root, standard deviation) of returns, and is an indication of volatility.
Recall that CAPM makes a number of simplifying (and criticized) assumptions to work. A couple of the assumptions that have been the subject to criticism are:
1) Portfolio returns are distributed symmetrically around a mean.
2) Portfolio returns are assumed to have no outliers (or “fat tails”).
Empirical evidence suggests otherwise. Researchers have thus sought alternatives to a variance-based beta that relaxes both of these assumptions, and one popular candidate is called semivariance – a measure of the dispersion of those values in a distribution that fall below the mean or target value of a data set. In short, semivariance-based modifications to CAPM concentrate on downside-risk only. Semivariance is a better statistic when dealing with asymmetric distributions, as it automatically incorporates the notion of skewness. Ignoring skewness, by assuming that variables are symmetrically distributed when they are not, will cause any model to understate the risk of variables with high skewness
One popular semivariant CAPM alternative is called D-CAPM (Downside-CAPM). Regular old beta is replaced by downside-beta (βD). Different researchers have supplied different technical definitions for βD; here we will use the one provided by Javier Estrada:
βD = downside covariance between asset and market portfolio / downside variance of market portfolio.
There are several ways to calculate βD, but I will spare you the details. The important point is that empirical studies indicate D-CAPM gives better predictions compared to CAPM, especially for emerging markets. This may be due to the hypothesis that returns from emerging markets are less normal and more skewed than returns from developed markets.
D-CAPM is generally well-regarded because of its plausibility, supporting evidence, and the widespread use of D-CAPM. For instance, a study by Mamoghli and Daboussi concluded “It comes out from these results that the D-CAPM makes it possible to overcome the drawbacks of the traditional CAPM concerning the asymmetrical nature of returns and the risk perception…”
However, D-CAPM is not without its critics. Let me quote from one, in which downside correlation is taken to task: “This measure ignores the ability of upside returns of one asset to hedge the downside returns of another asset in a portfolio. Within the scope of D-CAPM the standard equation to calculate portfolio’s downside semideviation cannot be correct, since the Estrada’s downside correlation equates upside returns to zeros and does not represent the true downside correlations. Specifically, the downside correlation cannot be measured, because the portfolio’s semideviation depends on the weights of assets, their standard deviations and correlation between them, rather than on the semivariance. This formula is specified for normally distributed and symmetrical returns and there is no formula invented to calculate the portfolio’s semideviation yet”.
This criticism, if true, is especially relevant to hedge fund traders, who use long-short strategies as a matter of course.
Nevertheless, Professor Estrada responded as follows: “First, it’s true that the downside beta I suggest in my paper does not account for something that would be legitimately called risk, which is the market going up and the asset going down. The way I suggest to calculate downside beta only accounts for down-down states. But there is a reason for this: It’s the only way to achieve a symmetric semivariance-semicovariance matrix, necessary for a solution of the problem. For example, the only way to apply standard techniques to solve an optimization problem with downside risk is to have a symmetric matrix. Second, and importantly, my several papers on downside beta, D-CAPM, and downside risk in general have been published in several journals and therefore peer-reviewed. I believe Mr. Cheremushkin’s comment is an unpublished piece.”
This controversy will no doubt be resolved over the fullness of time. We welcome your views.
 Technically, beta is equal to the covariance of an asset with the market portfolio divided by the variance of the market portfolio.
 Estrada, Javier (2003). “Mean-Semivariance Behavior (II): The D-CAPM.” University of Navarra.
 Mamoghli, Chokri and Daboussi, Sami (2008). Valuation of Hedge Funds Portfolios in a Downside Risk Framework.
 Cheremushkin, Sergei V. (2009). “Why D-CAPM is a big mistake? The incorrectness of the cosemivariance statistics.” Mordovian State University. Also private communication 9-Aug-10.
 Private communication 9-Aug-10.
ur survey of portfolio theories continues; we have already evaluated Modern Portfolio Theory, the Capital Asset Pricing Model and the Arbitrage Pricing Theory in earlier blogs, and now turn to a series of articles on Behavioral Portfolio Theory (BPT).
Our survey of portfolio theories continues; we have already evaluated Modern Portfolio Theory, the Capital Asset Pricing Model and the Arbitrage Pricing Theory in earlier blogs, and now turn to a series of articles on Behavioral Portfolio Theory (BPT). Recall that as we continue to lay out a financial framework, our ultimate goal is to figure out whether hedge funds actually provide alpha returns to investors.
Whereas both CAPM and ABT assume that investors are proceeding from a single unified motivation – to maximize the values of their portfolios – BPT instead suggests that investors have a variety of motivations and structure their investments accordingly. Think of a layered pyramid, in which the base is set up to protect against losses and the top designed for high reward. This has been characterized as the “government bond and lottery ticket” portfolio because of the extreme divergence of goals.
One of the earliest attempts at BPT was Safety-First Portfolio Theory (SFPT, Roy 1952). The theory posits that investors want to minimize the probability of ruin, which is defined as when an investor’s wealth W falls below his subsistence level s:
min Pr(W < s).
SFPT, like the other portfolio theories, makes a few assumptions:
- a portfolio has a return mean μP and return standard deviation σP;
- the risk-free asset RF is not available;
- investors trade off assets according to the expected utility of the assets and the law of diminishing returns;
- the subsistence level s is low; and
- the distribution of portfolio returns is normal (bell-shaped).
In short, an investor wants to own a portfolio for which
(s – μP) / σP
Later refinements of the theory included:
- relaxing the fixed value s;
- constraining the probability of ruin such that it never exceeds a predetermined ruin probability
- expecting investors to maximize wealth within the safety-first constraint
As an early behavioral portfolio theory, SFPT has a somewhat simplified vision of investor as primarily risk-adverse. Also, it postulates that investors follow utility theory in their decision-making, an assumption widely discredited by empirical studies.
We progress next time to a theory that had its roots in SFPT: SP/A Theory, which deals with security, potential, and aspiration. Don’t miss it!