We develop a comprehensive measure of the quality of corporate financial communication. Net bad flows (NBF) captures bad news withholding and is calculable for all listed companies using daily stock return data. NBF indicates the relative lumpiness of bad news flows during the fiscal period. We...

We compare the performance of machine learning algorithms and wordlists at replicating manual coding of sentence-level tone and attribution in earnings press releases. We train learning algorithms on a sample of manually annotated performance sentences and assess accuracy using a separate...

We investigate the credibility of forward-looking performance disclosures (FLPDs) in the narrative sections of annual reports, as perceived by investors. Our proxy for these disclosures is an index of statements about future performance. We find that companies issue more FLPDs when raising debt...

We measure annual report commentary articulating entities' business model and strategy, and then examine the capital market effects of enhancing such disclosure. Our empirical disclosure proxy is based on n-grams drawn from popular strategy textbooks and the academic strategy literature....

We examine whether, and to what extent, investors focus on salient and easy-to-process features in responding to analyst forecasts. We focus on rounding as arguably the most salient forecast feature. We find that while rounding is only marginally associated with forecast accuracy, investors...