Risk Aversion and Skewness Preference: a comment

Empirically, co-skewness of asset returns seems to explain a substantial part of the cross-sectional variation of mean return not explained by beta. Thisfinding is typically interpreted in terms of a risk averse representativeinvestor with a cubic utility function. This comment questions thisinterpretation. We show that the empirical tests fail to impose risk aversionand the implied utility function takes an inverse S-shape. Unfortunately, thefirst-order conditions are not sufficient to guarantee that the market portfoliois the global maximum for an inverse S-shaped utility function, and ourresults suggest that the market portfolio is more likely to represent theglobal minimum than the global maximum. In addition, if we impose riskaversion, then co-skewness has minimal explanatory power

Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes

In this paper we examine global tactical asset allocation (GTAA) strategies across a broad range of asset classes. Contrary to market timing for single asset classes and tactical allocation across similar assets, this topic has received little attention in the existing literature. Our main finding is that momentum and value strategies applied to GTAA across twelve asset classes deliver statistically and economically significant abnormal returns. For a long top-quartile and short bottom-quartile portfolio based on a combination of momentum and value signals we find a return of 12% per annum over the 1986-2007 period. Performance is stable over time, also present in an out-of-sample period and sufficiently high to overcome transaction costs in practice. The return cannot be explained by potential structural biases towards asset classes with high risk premiums, nor the Fama French and Carhart hedge factors. We argue that financial markets may be macro inefficient due to insufficient ‘smart money’ being available to arbitrage mispricing effects away

The Volatility Effect: Lower Risk without Lower Return

We present empirical evidence that stocks with low volatility earn high risk-adjusted returns. The annual alpha spread of global low versus high volatility decile portfolios amounts to 12% over the 1986-2006 period. We also observe this volatility effect within the US, European and Japanese markets in isolation. Furthermore, we find that the volatility effect cannot be explained by other well-known effects such as value and size. Our results indicate that equity investors overpay for risky stocks. Possible explanations for this phenomenon include (i) leverage restrictions, (ii) inefficient two-step investment processes, and (iii) behavioral biases of private investors. In order to exploit the volatility effect in practice we argue that investors should include low risk stocks as a separate asset class in the strategic asset allocation phase of their investment process

Conditional Downside Risk and the CAPM

The mean-semivariance CAPM strongly outperforms the traditional mean-variance CAPM in terms of its ability to explain the cross-section of US stock returns. If regular beta is replaced by downside beta, the traditional risk-return relationship is restored. The downside betas of low-beta stocks are substantially higher than the regular betas, while high-beta stocks involve less systematic downside risk than suggested by their regular betas. This pattern is especially pronounced during bad states-of-the-world, when the market risk premium is high. In sum, our results provide evidence in favor of market portfolio efficiency, provided we account for conditional downside risk

Downside Risk and Asset Pricing

We analyze if the value-weighted stock market portfolio is second-order stochastic dominance (SSD) efficient relative to benchmark portfolios formed on size, value, and momentum. In the process, we also develop several methodological improvements to the existing tests for SSD efficiency. Interestingly, the market portfolio is SSD efficient relative to all benchmark sets. By contrast, the market portfolio is inefficient if we replace the SSD criterion with the traditional mean-variance criterion. Combined these results suggests that the mean-variance inefficiency of the market portfolio is caused by the omission of return moments other than variance. Especially downside risk seems to be important for rationalizing asset pricing puzzles in the 1970s and the early 1980s

Downside Risk And Empirical Asset Pricing

Currently, the Nobel prize winning Capital Asset Pricing Model (CAPM) celebrates its 40th birthday. Although widely applied in financial management, this model does not fully capture the empirical riskreturn relation of stocks; witness the beta, size, value and momentum effects. These problems may be caused by the use of variance as the relevant risk measure. This study analyzes if asset pricing models that use alternative risk measures better describe the empirical riskreturn trade-off. The results suggest that downside risk helps to better understand the cross-section of stock returns, especially during economic recessions.Pim van Vliet (1977, Bodegraven, The Netherlands) obtained his Master’s degree in Economics with honors from the Erasmus University Rotterdam in 2000. In October 2000, he joined ERIM to carry out his doctoral research. Parts of his work have been published and presented at international conferences. His current research interest includes investor preferences, empirical asset pricing and investment strategies

Sorting out Downside Beta

Downside risk, when properly defined and estimated, helps to explain the cross-section of US stock returns. Sorting stocks by a proper estimate of downside market beta leads to a substantially larger cross-sectional spread in average returns than sorting on regular market beta. This result arises despite the fact that downside beta is based on fewer return observations and therefore is more difficult to estimate and predict. The explanatory power of downside risk remains after controlling for other stock characteristics, including firm-level size, value and momentum

Portfolio Return Characteristics of Different Industries

Over the last decade we have witnessed the rise and fall of the so-called new economy stocks. One central question is to what extent these new firms differ from traditional firms. Empirical evidence suggests that stock returns are not normally distributed. In this article we investigate whether this also holds for portfolios of stocks from a growth industry. Furthermore, we will compare this type of portfolios with portfolios of stocks from a more traditional industry. Usually, only value weighted and equally weighted portfolios are used to describe and compare portfolio return characteristics. Instead, in our analysis, we use a novel approach in which we use an infinite number of portfolios that together represent the set of all feasible portfolio opportunities

When Equity Factors Drop Their Shorts

Although factor premiums originate in both long and short legs of factor portfolios, we found that (1) most added value comes from the long legs, (2) the long legs offer more diversification than the short legs, and (3) the performance of the short legs is generally subsumed by that of the long legs. These results are robust over size, time, and markets and cannot be attributed to differences in tail risk. We also found that the claim that the value and low-risk factors are subsumed by the new (post-2015) Fama–French factors does not hold for the long legs of these factors.Disclosure: The authors disclose that they are employed by Robeco, a firm that offers various investment products. The construction of these products may, at times, draw on insights related to this research. No other person or party at Robeco except the authors had the right to review this article prior to its circulation. The views and results presented in this article were not driven by the views o

Media attention and the volatility effect

Stocks with low return volatility have high risk-adjusted returns, which might be driven by low media attention for such stocks. Using news coverage data we formally test whether the „attention-grabbing‟ hypothesis can explain the volatility effect for a sample of international stocks over the period 2001 to 2018. A low-volatility effect is still pre