Identifying and exploiting alpha in linear asset pricing models with strong, semi-strong, and latent factors
arxiv(2024)
摘要
The risk premia of traded factors are the sum of factor means and a parameter
vector we denote by ϕ 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.
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