Italian Abstract: L’identificazione di potenziali casi di abuso di mercato è un’attività complessa e impegnativa a causa dell’enorme volume di dati da trattare e della molteplicità di fattori da prendere in considerazione nella valutazione della condotta operativa di ciascun...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

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...

The paper aims at the identification and analysis of major issues facing financial sector supervision resulting from the digitalization of financial markets and the digitalization of supervisory architecture with the tool box applied. It is based on the desk research using foremostly industrial...

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...

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...

This paper documents a significantly negative cross-sectional relation between machine forecast disagreement (MFD) and future stock returns. MFD measures divergence of opinion among investors about stock value based on the dispersion in machines' expected return forecasts and provides an...

Traditionally, a predefined surprise proxy (such as the consensus errors of analyst forecasts) is used to estimate the market impact of public announcements. We instead use the post-event price movements to tease out what the market consensus must have been, and to estimate the event-day...

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...

Shares trading in the Bolsa mexicana de Valores do not seem to react to company news. Using a sample of Mexican corporate news announcements from the period July 1994 through June 1996, this paper finds that there is nothing unusual about returns, volatility of returns, volume of trade or...