Big Data, Market Efficiency and Conditional Asset Pricing
Professor Guofu Zhou
Frederick Bierman & James E. Spears Professor of Finance
Olin Business School
Washington University in St. Louis
In the era of big data, numerous economic forces influence the stock market. We propose the Optimal Linear Factor (OLF) for predicting the market risk premium. The OLF captures the effects of a large set of predictors through a linear combination and possesses the oracle property, performing as if the true combination coefficients were known. Theoretically superior to existing methods and empirically consistent in delivering strong performance, the OLF identifies key drivers of market predictability—nominal GDP growth, inflation, fiscal policy uncertainty, dividend payout, and the earnings-to-price ratio. We also introduce a novel multi-signal integration approach, underscoring the need for further research on conditional asset pricing models to replace traditional unconditional ones. In addition, we examine a range of anomalies in the option market.