We develop a measure of how information events impact investors' perceptions of risk that is broadly applicable and simple to implement. We derive this measure from an option-pricing model where investors anticipate an announcement that simultaneously conveys information on the announcer's...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the...

We present evidence of investors underreacting to the absence of events in financial markets. Routine-based insiders strategically choose to be silent when they possess private information not yet reflected in stock prices. Consistent with our hypothesis, insider silence following routine sell...

This paper investigates the informativeness and value relevance of analyst target prices in the context of mergers and acquisitions (M&A). Our results indicate that firms with high 12-month ahead target prices relative to current stock prices are more likely to become a takeover target and offer...