Stock Return Forecasting: A Supervised PCA With Selecting and Scaling

This paper proposes a sparse scaled principal component analysis (PCA) to forecast stock returns. The sparse scaled PCA is a modification to the common PCA by first selecting the important predictors and then scaling the selected predictors according to their predictive power on the target to be forecasted. An advantage of the proposed sparse scaled PCA is that it takes the benefits of both scaled PCA and supervised PCA. An empirical study is conducted on the US stock market to evaluate its empirical performance, and the results confirm the superiority of sparse scaled PCA over a variety of dimension‐reduction techniques, including PCA, PLS, scaled PCA, and supervised PCA in both in‐sample and out‐of‐sample forecasting. Economic value analysis shows that the outperformance can yield economic utility gains at a reasonable transaction cost.