Arbitrage Portfolios using Large Panel Data
Abstract We propose new methodology to estimate arbitrage portfolios by utilizing information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristic predictive power before any attribution to abnormal returns. We apply the methodology in simulated factor economies and on a large panel of U.S. stock returns from 1965-2014. The methodology works well in simulation and in out-of-sample portfolios of U.S. stocks. Empirically, we find the arbitrage portfolio has significant (statistically and economically) alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 0.67 to 1.12. Data mining driven alphas imply that performance of the strategy should decline after the discovery of pricing anomalies. However, we find that the abnormal returns on the arbitrage portfolio do not decrease significantly over time.
Soohun Kim, Robert Korajczyk, Andreas Neuhierl
Kim, Soohun, Robert Korajczyk, and Andreas Neuhierl. 2018. Arbitrage Portfolios using Large Panel Data.