This paper uses transaction-level fund trading data from the United States to study the information advantage of institutional investors. Our research design follows a two-step procedure. In the first step, we identify funds that sell shares in firms before their unexpected revelation of stock...

This paper uses a unique transaction-level fund trading dataset to evaluate institutional investors' trading performance. Our research design follows a two-step procedure. In the first stage, we identify funds that heavily sold shares in firms before their public revelation of stock option...

Extant studies provide two additional explanations other than backdating for the abnormal stock returns around CEO option grants – timing of option grants and timing of corporate disclosures. We examine the effect of the Sarbanes-Oxley Act of 2002 (SOX), the stock option backdating scandal,...

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of...

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of...

We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that...