Information Aggregation for Stock Return Predictability

35 Pages Posted: 24 Sep 2016

Date Written: September 19, 2016

Abstract

The literature on stock return predictability has identified macroeconomic and technical predictors that when combined, leads to out-of-sample outperformance relative to the historical mean null. This paper investigates a new method for aggregating information beyond using forecast combination or principal components. By sequentially layering groups of information, the predictive performance of this new approach outperforms that of prior methods. Applying layering to volatility forecasting yields more mixed results. In all, a mean-variance investor investing in monthly stock returns gains from this new method as much as 4.5% per year.

Keywords: Return Predictability, Forecast combination, Principal Components

JEL Classification: G11, G14, G17

Suggested Citation

Chang, Wayne, Information Aggregation for Stock Return Predictability (September 19, 2016). Available at SSRN: https://ssrn.com/abstract=2841389 or http://dx.doi.org/10.2139/ssrn.2841389

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