Identifying and exploiting alpha in linear asset pricing models with strong, semi-strong, and latent factors

TL;DR

This paper develops a new estimator for systematic risk premia in asset pricing models, enabling the construction of portfolios that exploit alpha and outperform traditional mean-variance portfolios, supported by theoretical and empirical analysis.

Contribution

It introduces a bias-corrected estimator for the systematic component of alpha in linear asset pricing models, accounting for weak factors and cross-sectional dependence.

Findings

phi-portfolios outperform mean-variance portfolios when phi is non-zero

The estimator has correct size and good power in small samples

Empirical application shows phi-portfolios achieve higher Sharpe ratios

Abstract

The risk premia of traded factors are the sum of factor means and a parameter vector we denote by {\phi} which is identified from the cross section regression of alpha of individual securities on the vector of factor loadings. If phi is non-zero one can construct "phi-portfolios" which exploit the systematic components of non-zero alpha. We show that for known values of betas and when phi is non-zero there exist phi-portfolios that dominate mean-variance portfolios. The paper then proposes a two-step bias corrected estimator of phi and derives its asymptotic distribution allowing for idiosyncratic pricing errors, weak missing factors, and weak error cross-sectional dependence. Small sample results from extensive Monte Carlo experiments show that the proposed estimator has the correct size with good power properties. The paper also provides an empirical application to a large number of U.S. securities with risk factors selected from a large number of potential risk factors according to their strength and constructs phi-portfolios and compares their Sharpe ratios to mean variance and S&P 500 portfolio.