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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.

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Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Coins, banknotes, medals, seals (numismatics) --- Index parameter --- estimation --- wrapped stable --- Hill estimator --- characteristic function-based estimator --- asymptotic --- efficiency --- GARCH model --- HARCH model --- PHARCH model --- Griddy-Gibs --- Euro-Dollar --- safe-haven assets --- gold price --- Swiss Franc exchange rate --- oil price --- generalized Birnbaum–Saunders distributions --- ACD models --- Box-Cox transformation --- high-frequency financial data --- goodness-of-fit --- banking competition --- credit risk --- NPLs --- Theil index --- convergence analysis --- interest rates --- yeld curve --- no-arbitrage --- bonds --- B-splines --- time series --- multifractal processes --- fractal scaling --- heavy tails --- long range dependence --- financial models --- Bitcoin --- capital asset pricing model --- estimation of systematic risk --- tests of mean-variance efficiency --- t-distribution --- generalized method of moments --- multifactor asset pricing model --- Lerner index --- stochastic frontiers --- shrinkage estimator --- seemingly unrelated regression model --- multicollinearity --- ridge regression --- financial incentives --- public service motivation --- job performance --- job satisfaction --- intention to leave