We develop a data‐generated tool for distinguishing between fraudulent and truthful reports based on the language used in the management discussion and analysis section of annual and interim reports. Using this method, we are able to assign a probability of truth to each report which is then shown to be an effective indicator of fraud. Our work goes beyond the development of a tool alone, however, by conducting an extensive comparison of our probability‐of‐truth measure with eight alternative detection tools representing both quantitative and language‐based approaches. Comparisons are made across a variety of samples and show that our language‐based approach can be effective in both cross‐sectional and time‐series settings. It is useful both in distinguishing between fraudulent and truthful firms and in identifying fraudulent reports from a series of reports issued by a single firm. This second setting is one in which accounting‐based detection tools have frequently struggled. We establish that, not only is our probability‐of‐truth measure significantly associated with fraud, so too is the change in this measure from a firm's previous reports. Prior reports may serve an important benchmarking role in using language‐based tools to identify fraud.

I examine whether bond rating changes can be anticipated by investors and test whether the stock price reaction to the eventual change varies as a result. All else equal, the market reaction to changes that could have been easily predicted should be significantly smaller than the reaction to changes that are largely a surprise. Although rating upgrades prove difficult to predict, approximately 20% of downgrades can be correctly predicted using a relatively small number of publicly available variables. There is no significant difference between the stock price reaction to anticipated versus unanticipated rating changes. 2007 The Southern Finance Association and the Southwestern Finance Association.

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