Equity Premium Predictions with Adaptive Macro Indices

Fundamental economic conditions are crucial determinants of equity premia. However, commonly used predictors do not adequately capture the changing nature of economic conditions and hence have limited power in forecasting equity returns. To address the inadequacy, this paper constructs macro indices from large datasets and adaptively chooses optimal indices to predict stock returns. I find that adaptive macro indices explain a substantial fraction of the short-term variation in future stock returns, and have more forecasting power than both the historical average of stock returns and commonly used predictors. The forecasting power exhibits a cyclical pattern, implying the ability of adaptive macro indices in capturing time-varying economic conditions. This finding highlights the importance of using dynamically-measured economic conditions to investigates empirical linkages between equity premium and macroeconomic fundamentals.