The use of financial ratio models to help investors predict and interpret significant corporate events created by B Korcan Ak, Patricia M Dechow, Yuan Sun and Annika Yu Wang

A firm in a steady state generates predictable income and investors can generally agree on its valuation. However, when a significant corporate event occurs this creates greater uncertainty and disagreement about firm valuation, and investors could prefer to avoid holding such a stock. We examine research that has developed financial ratio models to: (a) predict significant corporate events; and (b) predict future performance after significant corporate events. The events we analyze include financial distress and bankruptcy, downsizing, raising equity capital, and material earnings misstatements. We find that financial ratio models generally help investors avoid stocks that are likely to have significant corporate events. We also find that, conditional on a significant event occurring, financial ratio models help investors distinguish good firms from bad. However, we find that research design choices often make it difficult to determine model predictive accuracy. We discuss the role of accounting rule changes and their impact over time on the predictive power of models, and provide suggestions for improving models based on our cross-event analysis.