Since Markowitz [15, pp. 98–101] and Sharpe [19] developed the single-index market model (SIMM hereafter) it has received considerable research attention. Empirical tests have established the model's econometric significance [3, 12, 13] in partial equilibrim analysis. However, research into the relationship between the SIMM and its macroeconomic environment has been meager. It has been shown that the market factor changes intertemporally [13, 16, 18, 20]. However, whether these changes in the market factor and, more basically, changes in the macroeconomic situation affect the SIMM is unknown.

After Markowitz [14, p. 100] and Sharpe [19, 20] suggested estimating the beta systematic risk coefficient for market assets, finance professors, stock brokers, investment managers, and others began expending large quantities of resources each year on estimating betas. Unfortunately however, it appears that the ordinary least-squares (OLS) regressions used in nearly every instance may be inappropriate. This paper suggests that many stocks' beta coefficients move randomly through time rather than remain stable as the OLS model presumes.

Based on the theory of the pricing of capital assets developed by Sharpe [12], Lintner [9] and Mossin [11], Professor Jensen formulated a return-generating model to measure portfolio performance [5]. In a subsequent paper, Professor Jensen [6] investigated the impact of the investment horizon on the functional form of the model. Lee [8] has proposed a generalized specification of the model to resolve this problem. Alternative estimation methods for testing the linearity of the model in terms of time-series data have also been suggested by Lee. Moreover, the stability of the beta coefficient over time and the impact of the market's condition on both the alpha (or, Jensen's measure of performance [5]) and beta of the model have come under scrutiny in financial research.

Using a novel dataset of leveraged loan trades executed by managers of collateralized loan obligations (CLOs), we document the importance of “active loan trades” – trades executed at a manager’s discretion. More active trading increases the returns to CLO equity investors, lowers collateral portfolio default rates, and increases the manager’s chances of closing a new deal. Examining the observed loan trades, we find that more active CLOs trade at better prices than less active CLOs, selling leveraged loans earlier and before they get downgraded. Our findings suggest that more active CLOs are better at anticipating deteriorations in loan credit quality.

This paper presents an empirical examination of the selectivity and market timing performance of a sample of U.S. equity pension fund managers. Regardless of the choice of benchmark portfolio or estimation model, the average selectivity measure is positive and the average timing measure is negative. However both selectivity and timing appear to be somewhat sensitive to the choice of a benchmark when managers are classified by investment style. Meta‐analysis revealed some real variation around the mean values for each measure. The 80 percent probability intervals for selectivity revealed that the best managers produced substantial risk‐adjusted excess returns. We also found a negative correlation between selectivity and timing, but we argue that the observed negative correlation in our data is largely an artifact of negatively correlated sampling errors for the two estimates.