Recent studies suggest that the correlation of stock returns increases with decreasing geographical distance. However, there is some debate on the appropriate methodology for measuring the effects of distance on correlation. We modify a regression approach suggested in the literature and complement it with an approach from spatial statistics, the mark correlation function. For the stocks contained in the S&P 500 that we examine, both approaches lead to similar results: correlation increases with decreasing distance. Contrary to previous studies, however, we find that differences in distance do not matter much once the firms' headquarters are more than 40 miles apart, or separated through a federal border. Finally, we show through simulations that distance can significantly affect portfolio risk. Investors wishing to exploit local information should be aware that local portfolios are relatively risky.

A firm's current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in the set of default risk drivers. The analysis is done with a discrete duration model. Out-of-sample analysis of default events two to five years ahead reveals that the discriminating power of the duration model increases substantially when leverage forecasts are included. We further document that credit ratings contain information beyond the one contained in standard variables but that this information is unrelated to forecasts of leverage ratios.

A firm's current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in the set of default risk drivers. The analysis is done with a discrete duration model. Out-of-sample analysis of default events two to five years ahead reveals that the discriminating power of the duration model increases substantially when leverage forecasts are included. We further document that credit ratings contain information beyond the one contained in standard variables but that this information is unrelated to forecasts of leverage ratios.