Leseprobe
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Machine Learning in Asset Pricing
Stefan Nagel
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Sozialwissenschaften, Recht, Wirtschaft / Wirtschaft
Beschreibung
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing
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.
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.
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Quasi-Newton method, Risk premium, Demand curve, Interaction (statistics), Cash flow forecasting, Forecast error, Predictability, Bayesian inference, Kernel regression, Bayesian, Moment (mathematics), Pricing, Bayesian statistics, Credit risk, Data science, Covariate, Cross-validation (statistics), Supply (economics), Linear regression, Capital asset pricing model, Sharpe ratio, Financial economics, Investment Advice, Decision tree learning, Mathematical optimization, Weighted arithmetic mean, Bootstrapping (statistics), Estimation, Trading strategy, Machine learning, Supervised learning, Tikhonov regularization, Arbitrage, Hyperparameter optimization, Prior probability, Estimation theory, Cross-sectional data, Market liquidity, Estimator, Profit (economics), Behavioral economics, Covariance matrix, Optimization problem, Bias–variance tradeoff, Investment strategy, Calculation, Regularization (mathematics), Ensemble learning, Parameter (computer programming), Computational complexity theory, Principal component analysis, Econometrics, Sparse matrix, Greedy algorithm, Price elasticity of demand, Variable (mathematics), Prediction, Forecasting, Factor analysis, Bayesian linear regression, Test statistic, Hyperparameter, Market clearing, Stochastic discount factor, Cross-sectional regression, Rational expectations, Cost curve, Cash flow, Errors and residuals, Coefficient