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...

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

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...

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...

There exists significant hype regarding how much machine learning and incorporating social media data can improve forecast accuracy in commercial applications. To assess if the hype is warranted, we use data from the film industry in simulation experiments that contrast econometric approaches...

Life in modern society is increasingly connected by networks that link the world around us and create many new opportunities, services and benefits for humanity. But at the same time, the underlying networks have created pathways through which potentially hazardous and damaging incidents can...

In this paper, we explore machine learning (ML) methods to improve inflation forecasting in Brazil. An extensive out-of-sample forecasting exercise is designed with multiple horizons, a large database of 501 series, and 50 forecasting methods, including new ML techniques proposed here,...

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...