Conditional alphas and realized betas

Statistics and Modeling for Complex Data

This paper proposes a two-step procedure to back out the conditional alpha of a given stock from high-frequency returns. We rst estimate the realized factor loadings of the stock, and then retrieve the conditional alpha by estimating the conditional expectation of the stock return in excess over the realized risk premia. The estimation method is fully nonparametric in stark contrast with the literature on conditional alphas and betas. Apart from the methodological contribution, we employ NYSE data to determine the main drivers of conditional alphas as well as to track mispricing over time. In addition, we assess economic relevance of our conditional alpha estimates by means of a market-neutral trading strategy that longs stocks with positive alphas and shorts stocks with negative alphas. The preliminary results are very promising.