Using machine learning methods, we develop a new measure of aggregate analyst sentiment. We first train analyst-specific neural network (NN) models that capture each analyst's common biases across firms. Using NN model outputs, we decompose the forecast errors of individual analysts into...

Growing AI readership, proxied by expected machine downloads, motivates firms to prepare filings that are friendlier to machine parsing and processing. Firms avoid words that are perceived as negative by computational algorithms, as compared to those deemed negative only by dictionaries meant...

We use a deep learning model to extract syntax and context information from mutual fund managers' narrative discussions and measure their risk assessment. We validate the forward-looking nature of the risk measure by showing that more negative (positive) risk assessment in managers' narratives...

This paper implements natural language processing (NLP) models and neural networks to predict mutual fund performance using the textual information disclosed in mutual fund shareholder letters. Informed funds identified by the prediction model deliver superior abnormal returns and are more...

This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare...

This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare...