Machine Learning in Asset Pricing (eBook)

Stefan Nagel (Autor)

eBook Download: PDF

2021
160 Seiten
Princeton University Press (Verlag)
978-0-691-21871-7 (ISBN)

Infos

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors 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.Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Produkt-Details

• Erscheint lt. Verlag: 11.5.2021
• Reihe/Serie: Princeton Lectures in Finance
• Reihe/Serie: Princeton Lectures in Finance
• Zusatzinfo: 17 b/w illus. 4 tables.
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Recht / Steuern ► Wirtschaftsrecht
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Schlagworte: Advances in Financial Learning • Arbitrage • Artificial Neural Network • Asset Allocation • asset prices • autoencoder • Bayesian • Bayesian estimation • Bayesian inference • Bayesian linear regression • Bayesian regression • Bayesian Statistics • Behavioral Economics • Benchmarking • Bias of an estimator • Bias–variance tradeoff • bootstrap aggregating • Bootstrapping (statistics) • Calculation • Capital Asset Pricing Model • Cash Flow • cash flow forecasting • Center for Research in Security Prices • coefficient • computational complexity theory • concept drift • Cost curve • Covariance matrix • Covariate • credit risk • Cross-Sectional Data • Cross-sectional regression • Cross-section of stock returns • Cross-validation (statistics) • data-driven methods of tuning • Data Science • data set • decision tree learning • Demand curve • Dimension • Econometrics • elastic-net estimator • ensemble learning • Errors and residuals • estimation • Estimation theory • Estimator • Factor Analysis • factor models • Financial Economics • firm fundamentals • Forecast Error • Forecasting • greedy algorithm • high-dimensional prediction • Hyperparameter • Hyperparameter Optimization • Igor Halperin • Interaction (statistics) • investment advice • Investment Management • Investment Strategy • Kalman Filter • kernel regression • linear regression • Loss Function • machine learning • Machine learning in finance • Marcos Lopez de Prado • market capitalization • Market Clearing • Market Data • market efficiency • market liquidity • Market Participant • market portfolio • Market Price • Martingale (probability theory) • Mathematical Optimization • Matthew Dixon • mean-variance optimization framework • Moment (mathematics) • Neural networks • optimization problem • out-of-sample performance • Parameter (computer programming) • Paul Bilokon • Point Estimation • Portfolio Weight • Predictability • Prediction • predictive power • Preference (economics) • price change • Price elasticity of demand • Pricing • Principal Component Analysis • Prior probability • Profit (economics) • Proportionality (mathematics) • Quasi-Newton method • Rational Expectations • Regularization • Regularization (mathematics) • Return Predictability • Ridge regression • Risk Aversion • Risk Management • risk premia estimation • Risk Premium • Sharpe Ratio • Shrinkage Estimator • Sparse matrix • stochastic discount factor • Stochastic Gradient descent • supervised learning • Supply (economics) • test statistic • Tikhonov regularization • Trading Strategy • trees and random forests • Valuation (finance) • Variable (mathematics) • Weighted arithmetic mean
• ISBN-10: 0-691-21871-4 / 0691218714
• ISBN-13: 978-0-691-21871-7 / 9780691218717