Abstract
Statistical risk models have been estimated since the mid-1960s. One can argue how many factors are necessary, but there appears to be substantial evidence that statistical models outperform fundamental models for several expected returns models, such as we test in this analysis. In this paper, we show that tracking portfolios constructed with expected return rankings based on earnings forecasting and price momentum composite alpha strategies produce statistically significant excess returns and increased Sharpe Ratios when optimized with 3-factor statistical risk model.
Highlights
In this paper, we study the construction of US mean-variance efficient portfolios during the period 1999–2017
We show that tracking portfolios constructed with expected return rankings based on earnings forecasting and price momentum composite alpha strategies produce statistically significant excess returns and increased Sharpe Ratios when optimized with 3-factor statistical risk model
We study the construction of US mean-variance efficient portfolios during the period 1999–2017
Summary
We study the construction of US mean-variance efficient portfolios during the period 1999–2017. BP = [book value per share]/[price per share] = book − price ratio; CP = [cash flow per share]/[price per share] = cash flow − price ratio; SP = [net sales per share]/[price per share] = sales − price ratio; REP = [current EP ratio]/[average EP ratio over the past 5 years]; RBP = [current BP ratio]/[average BP ratio over the past 5 years]; RCP = [current CP ratio]/[average CP ratio over the past 5 years]; RSP = [current SP ratio]/[average SP ratio over the past 5 years]; CTEF = consensus earnings − per − share I/B/E/S forecast, revisions, and breadth, PM = Price Momentum; e = randomly distributed error term Given concerns about both outlier distortion and multicollinearity, Bloch et al [29] tested the relative explanatory and predictive merits of alternative regression estimation procedures: OLS, robust regression using the Beaton and Tukey [42] bi-square criterion to mitigate the impact of outliers, latent root to address the issue of multicollinearity [see [43]], and weighted latent root, denoted WLRR, a combination of robust and latent root.
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