Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the...

In this paper, we construct a sample of news co-occurrences using big data technologies. We show that stocks that co-occur in news articles are less risky, bigger, and more covered by financial analysts, and economically-connected stocks are mentioned more often in the same news articles. We...

This paper documents novel evidence that private debt contains value-relevant nonpublic information with significant economic value. We extract banks' private information from term loan spreads. Abnormal loan spreads significantly predict firms' future operating performance and uncertainty...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...

Which market has leading informational advantage: stocks or options? Using large set of stock and option characteristics, and machine learning, we provide a comprehensive analysis of which characteristics are the first order importance predictors of options and stock returns. First, we find that...

We investigate the cross-sectional return predictability of delta-hedged equity options using machine learning and big data. Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample...

We analyze 7.7 million patents via textual analysis to develop a novel patent-to-patent measure of innovation similarity among firms. With this measure, we investigate whether stock price movements reveal information about firms' technological connectedness. We find that investors impound...

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency...

This paper examines insider trading around first-time debt covenant violation disclosures in SEC filings, and is interesting from a research and regulatory standpoint for three reasons – delay and infrequency of a first-time disclosure, lack of attention to covenant disclosures by regulators,...

The Shenzhen Stock Exchange (SZSE) in China is unique worldwide in requiring disclosure of the timing, participants and selected content of private in-house meetings between firm managers and outside investors. We investigate whether these private meetings benefit hosting-firms and their major...