Robust-H-infinity Forecasting and Asset Pricing Anomalies

We examine five important asset pricing anomalies, namely, size, value, momentum, profitability, and investment rate to evaluate their efficacy in major West European economies, that is, France, Germany, Italy, and Spain. We employ four prominent asset pricing models, namely Capital Asset Pricing Model (CAPM), Fama–French three-factor (FF3) model, Carhart model and Fama–French five-factor (FF5) model to evaluate whether portfolio managers can create trading strategies to generate risk-adjusted extra normal returns for their investors. We also examine the prominent anomalies which pass the test of asset pricing in our sample countries and evaluate the best performing asset pricing model in explaining returns in each of these countries. We find that in spite of being matured markets, these countries provide portfolio managers with opportunities to exploit these strategies to generate extra normal returns for their investors. Momentum anomaly for Germany and profitability anomaly for Italy can be exploited by fund managers for generating risk-adjusted returns. For France, except for net investment rate anomaly, all the other anomalies remained unexplained by asset pricing models. We also find CAPM to be the better model in explaining returns of Italy and Spain. While FF3 factor and FF5 factor models explain returns in Germany, our sample asset pricing models failed to work for France. Our study has implications for portfolio managers, academia, and policymakers.

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Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

Quarterly Journal of Finance ◽

10.1142/s201013921350016x ◽

2013 ◽

Vol 03 (03n04) ◽

pp. 1350016 ◽

Cited By ~ 3

Author(s):

Jing-Zhi Huang ◽

Zhijian Huang

Keyword(s):

Asset Pricing ◽

Transaction Costs ◽

Real Time ◽

Empirical Evidence ◽

Equity Market ◽

Data Snooping ◽

Asset Pricing Anomalies ◽

Pricing Anomalies ◽

Out Of Sample ◽

Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

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