A crucial issue in asset pricing is to understand the relative importance of discount rate (DR) news and cash flow (CF) news in driving the time-series and cross-sectional variations of stock returns. Many studies directly estimate the DR news but back out the CF news as the residual. We argue that this approach has a serious limitation because the DR news cannot be accurately measured due to the small predictive power, and the CF news, as the residual, inherits the large misspecification error of the DR news. We apply this residual-based decomposition approach to Treasury bonds and equities and find results that are either counterintuitive or unrobust. Potential solutions, including modeling both DR news and CF news directly, the Bayesian model averaging approach, and the principal component analysis, are explored. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: [email protected], Oxford University Press.

The current economic climate makes understanding credit risk correlation particularly important. After allowing for a comprehensive set of observable firm-specific, industry, market, and macroeconomic factors, there is an economically significant co-movement in credit default swap spreads that remains to be explained. Including a time dummy completely accounts for the remaining co-movement, confirming the existence of a systematic component that has been previously unaccounted for. Our findings suggest that it may be important to consider unobservable risk factor(s) in credit risk models.

We compare six modeling methods for Loss Given Default (LGD). We find that non-parametric methods (regression tree and neural network) perform better than parametric methods both in and out of sample when over-fitting is properly controlled. Among the parametric methods, fractional response regression has a slight edge over OLS regression. Performance of the transformation methods (inverse Gaussian and beta transformation) is very sensitive to ε, a small adjustment made to LGDs of 0 or 1 prior to transformation. Model fit is poor when ε is too small or too large, although the fitted LGDs have strong bi-modal distribution with very small ε. Therefore, models that produce strong bi-model pattern do not necessarily have good model fit and accurate LGD predictions. Even with an optimal ε, the performance of the transformation methods can only match that of the OLS.

A central issue in finance is whether stock prices move because of revisions in expected cash flows or discount rates, and by how much of each. Using direct cash flow forecasts, we show that stock returns have a significant cash flow news component whose importance increases with the investment horizon. For horizons over two years, cash flow news is more important. These conclusions hold at both the firm and aggregate levels, and diversification plays a secondary role in affecting the relative importance of cash flow and discount rate news. Our findings highlight the importance of cash flows in asset pricing.

We conduct a thorough analysis on the role played by the unobserved systematic risk factor in default prediction. We find that this latent factor outweighs the observed systematic risk factors and can substantially improve the in-sample predictive accuracy at the firm, rating group, and aggregate levels. Thus it might be helpful to include the unobserved systematic risk factor when simulating portfolio credit losses. However, we also find that this factor only marginally improves out-of-sample model performance. Therefore, although the models we investigated all show reasonably good ability to rank order firms by default risk, accurate prediction of default rate remains challenging even when the unobserved systematic risk factor is considered.

Exploiting the setting of firms that are unable to disclose timely financial reports and thus must file with the U.S. Securities and Exchange Commission (SEC) the NT 10-K (Q) report, this study examines whether short sellers target firms with financial reporting weaknesses. We find that short interest increases in firms prior to the NT 10-K (Q) filing, suggesting that short sellers identify and target firms that cannot file their financial reports in a timely manner. Short selling is positively significantly related to subsequent late filing status, and is more pronounced in late filers with high newswire activity and with accelerated filing deadlines. Short selling of late filing firms is significantly negatively related to subsequent performance thereby suggesting that short sellers' trades pertinent to late filers are profitable. Overall, the results underscore a high information processing ability of short sellers in the setting of firms that exhibit financial reporting deficiencies.