Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We...

I build a price-ratio model based on the Campbell and Shiller (1988) decomposition to test which components of investor expectations best explains cross-sectional price differences. I evaluate the in- and out-of-sample performance of my model, which uses a higher-order expansion with an added...

Abstract We predict cumulative stock returns over horizons from 1 month to 10 years using a tree-based machine learning approach. Cumulative stock returns are significantly predictable in the cross-section over all horizons. A hedge portfolio generates 250 bp/month at a 1 year horizon and 110...

Current empirical asset pricing research on idiosyncratic volatility (IVOL), negatively related to cross-sectional expected returns, fails to take explicit account of risk that results from a shock to a network of economically related stocks. These stocks move together, and are therefore...

We examine in this paper the training and test set performance of several equity factor models with a dataset of 20 years of data, 1,200 stocks and 100 factors. First, we examine several models to forecast expected returns, which can be used as baselines for more complex models: linear...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations...

I propose to forecast the market returns through its constituents. In contrast to the voluminous literature that concentrates on the predictive power of aggregate cross-sectional or macroeconomic predictors, I analyze the return predictability of sub-portfolios that compose the market portfolio....

This paper demonstrates that the forecasted CAPM beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock level momentum, and 30% to 50% for industry level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior...