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SUMMARY:Identifying and exploiting alpha in linear asset pricing models wi
 th many potential risk factors - Mohammad Hashem Pesaran (UCS) 
DTSTART:20250522T120000Z
DTEND:20250522T130000Z
UID:TALK218392@talks.cam.ac.uk
CONTACT:Cerf Admin
DESCRIPTION:We consider a decomposition of the risk premia of traded facto
 rs as the sum of factor means and a parameter vector we denote by phi\, wh
 ich we identify from the cross-section regression of alpha on the vector o
 f factor loadings\, betas. If phi is non-zero\, then alpha must also be no
 n-zero and one can construct ”phi-portfolios” which exploit the system
 atic 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-v
 ariance portfolios. The paper then proposes a two-step bias corrected esti
 mator of phi and derives its asymptotic distribution allowing for idiosync
 ratic pricing errors\, weak missing factors\, and weak error cross-section
 al dependence. Small sample results from extensive Monte Carlo experiments
  show that the proposed estimator has the correct size with good power pro
 perties. The paper then provides an empirical application to a large numbe
 r of U.S. securities with risk factors selected from a large number of pot
 ential risk factors according to their strength and constructs phi-portfol
 ios and compares their Sharpe ratios to mean variance and S&P portfolios.\
 n
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