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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.

Capital assets pricing model. --- Machine learning --- Finance --- Economic aspects. --- Mathematical models. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Capital asset pricing model --- CAPM (Capital assets pricing model) --- Pricing model, Capital assets --- Capital --- Investments --- Mathematical models --- Advances in Financial Learning. --- Bayesian estimation. --- Bayesian regression. --- Igor Halperin. --- Machine Learning in Finance. --- Marcos Lopez de Prado. --- Matthew Dixon. --- Paul Bilokon. --- Supervised learning. --- asset prices. --- cross-section of stock returns. --- data-driven methods of tuning. --- elastic-net estimator. --- factor models. --- firm fundamentals. --- high-dimensional prediction. --- market efficiency. --- mean-variance optimization framework. --- neural networks. --- out-of-sample performance. --- regularization. --- return predictability. --- ridge regression. --- risk premia estimation. --- trees and random forests. --- Machine learning. --- Financial applications.