Most studies on equity markets using text data focus on English-based specified sentiment dictionaries or topic modeling. However, can we predict the impact of news directly from the text data? How much can we learn from such a direct approach? We present here a new framework for learning text...

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of...

Measuring timely high-resolution socioeconomic outcomes is critical for policy making and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity....