No abstract

We investigate how both the ownership structure and explicit contractual structure of syndicated loan deals are shaped by the debt‐contracting value (DCV) of borrowers' accounting information. DCV captures the inherent ability of firms' accounting numbers to capture credit quality deterioration in a timely fashion. We hypothesize and document that when a borrower's accounting information possesses higher DCV, information asymmetry between the lead arranger and other syndicate participants is lower, allowing lead arrangers to hold a smaller proportion of new loan deals. Further, we document that the influence of DCV on the proportion of the loan retained is conditional on the lead arranger's reputation, the existence of a credit rating, and the lead arranger's previous relationships with the same borrower. Finally, we find that when loans include performance pricing provisions, the likelihood that the single performance measure used is an accounting ratio, rather than a credit rating, is increasing in DCV.

Prior studies attribute analysts’ forecast superiority over time-series forecasting models to their access to a large set of firm, industry, and macroeconomic information (an information advantage), which they use to update their forecasts on a daily, weekly or monthly basis (a timing advantage). This study leverages recently developed mixed data sampling (MIDAS) regression methods to synthesize a broad spectrum of high frequency data to construct forecasts of firm-level earnings. We compare the accuracy of these forecasts to those of analysts at short horizons of one quarter or less. We find that our MIDAS forecasts are more accurate and have forecast errors that are smaller than analysts’ when forecast dispersion is high and when the firm size is smaller. In addition, we find that combining our MIDAS forecasts with analysts’ forecasts systematically outperforms analysts alone, which indicates that our MIDAS models provide information orthogonal to analysts. Our results provide preliminary support for the potential to automate the process of forecasting firm-level earnings, or other accounting performance measures, on a high-frequency basis. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2864 . This paper was accepted by Mary Barth, accounting.