Liquid Betting against Beta in Dow Jones Industrial Average Stocks

Benjamin R. Auer and Frank Schuhmacher

Financial Analysts Journal, 2015, vol. 71, issue 6, 30-43

Abstract: The authors considered liquidity and transaction costs in the practical implementation of betting against beta (BAB) strategies. Using the 30 highly liquid stocks of the Dow Jones Industrial Average over 1926–2013, they analyzed whether the beta anomaly exists and, if so, whether it can be exploited within that universe. With respect to its existence, they found strong evidence of an inverse risk–return relationship. With respect to its exploitability, they found that pure BAB trading portfolios and mixed portfolios (combinations of the pure portfolios and the S&P 500 Index) generate significant abnormal returns that cannot be explained by standard asset-pricing factors. Their results hold both before and after transaction costs and are robust in various settings.The beta anomaly can be considered a persistent anomaly in finance because it has been documented worldwide and is based on solid theory. A recent study, however, argues that the trading efficacy of betting against beta (BAB) strategies may be limited because of illiquidity barriers. To investigate this issue, we explicitly considered liquidity and the role of transaction costs in the practical implementation of BAB strategies. Using the 30 highly liquid stocks of the Dow Jones Industrial Average over 1926–2013, we analyzed whether the anomaly exists and, if so, whether it can be exploited within that universe. With regard to its existence, we found strong evidence of an inverse risk–return relationship. Our results concerning its exploitability reveal that BAB trading portfolios generate significant abnormal returns that cannot be explained by the standard asset-pricing factors of size, book-to-market, and momentum. For example, a portfolio that is long in the 15 stocks with the lowest betas and short in the 15 stocks with the highest betas has historically had a monthly Sharpe ratio of 0.086 after transaction costs. Also, combining this portfolio with the S&P 500 Index increases the monthly Sharpe ratio from 0.089 (for the S&P 500 alone) to 0.146 (for the optimal mix) after transaction costs.Editor’s note: The authors may have a commercial interest in the topics discussed in this article.Editor’s note: This article was reviewed and accepted by Executive Editor Robert Litterman.

Date: 2015
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DOI: 10.2469/faj.v71.n6.4

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