This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct...

We construct mean-variance portfolios using a factor model approach. We show the importance of portfolio allocation for large unbalanced equity data sets using the full CRSP database. We compare the performance of our portfolio construction methodology to the 1/N naive diversification strategy,...

"A three-factor model using the standardized-unexpected-earnings and cashflow-to-price factors explains 15 well-known asset pricing anomalies." Our data-mining experiment provides a backdrop against which such claims can be evaluated. We construct three-factor linear pricing models that match...

In a novel model mining experiment, we data mine hundreds of randomly constructed three-factor models and find that many outperform well-known models from the literature, including those with four and five factors. The results provide compelling evidence that the threshold of factor model...

We propose a model to forecast very large realized covariance matrices of returns, applying it to the constituents of the S&P 500 on a daily basis. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value and...