Estimating Latent Asset-Pricing FactorsÂ
Abstract
We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors with high Sharpe-ratios, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data, we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with significantly smaller pricing errors.
Bio
Markus Pelger is an Assistant Professor at the Management Science & Engineering Department at Stanford University and a Reid and Polly Anderson Faculty Fellow at Stanford University. His research interests are in machine-learning, statistics, financial econometrics, asset pricing, and risk management.
