Anomaly Investing: Out-of-Sample Performance and Intertemporal Considerations

37 Pages Posted: 5 Aug 2019

See all articles by James Tengyu Guo

James Tengyu Guo

London School of Economics & Political Science, Department of Finance

Date Written: August 1, 2019

Abstract

I first show that the naïve equal-weighted 1/N investing in the set of 34 stock market anomalies is a robust implementation for out-of-sample diversification. Two types of popular portfolio optimization methods, including Sharpe-Ratio-optimizing with weight constraints and Dimension-Reduction with machine learning techniques, do not achieve robustly higher out-of-sample performance. Further to explore the gains and risks in investing stock market anomalies, I take this equal-weighted anomaly portfolio to an intertemporal CAPM framework with stochastic volatility to understand the investment considerations of a specific anomaly investor. Based on my estimation, only the correlation-induced volatility news carries a significant risk premium, which highlights the economic importance of the comovement in anomaly asset prices.

Keywords: Portfolio Strategy, Diversification, Machine Learning, Hedging Demand of Sophisticated Investors

JEL Classification: G11, G23.

Suggested Citation

Guo, James Tengyu, Anomaly Investing: Out-of-Sample Performance and Intertemporal Considerations (August 1, 2019). Available at SSRN: https://ssrn.com/abstract=3430641 or http://dx.doi.org/10.2139/ssrn.3430641

James Tengyu Guo (Contact Author)

London School of Economics & Political Science, Department of Finance ( email )

London
Great Britain

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
64
Abstract Views
351
rank
386,472
PlumX Metrics