Machine Learning in Asset Pricing
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Sozialwissenschaften, Recht, Wirtschaft / Wirtschaft
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.
Autoencoder, Risk aversion, Market data, Bootstrapping (statistics), Factor analysis, Machine learning, Moment (mathematics), Cross-validation (statistics), Capital asset pricing model, Valuation (finance), Principal component analysis, Concept drift, Supply (economics), Bootstrap aggregating, Cash flow forecasting, Calculation, Kernel regression, Price Change, Econometrics, Optimization problem, Covariance matrix, Ensemble learning, Interaction (statistics), Cash flow, Loss function, Shrinkage estimator, Prediction, Sharpe ratio, Weighted arithmetic mean, Proportionality (mathematics), Investment management, Price elasticity of demand, Kalman filter, Preference (economics), Data set, Decision tree learning, Cross-sectional regression, Bias–variance tradeoff, Forecasting, Risk management, Arbitrage, Bias of an estimator, Artificial neural network, Variable (mathematics), Investment strategy, Coefficient, Stochastic discount factor, Trading strategy, Investment Advice, Predictive power, Mathematical optimization, Cost curve, Sparse matrix, Predictability, Asset allocation, Estimation, Test statistic, Point estimation, Bayesian, Forecast error, Covariate, Computational complexity theory, Supervised learning, Profit (economics), Market liquidity, Financial economics, Parameter (computer programming), Stochastic gradient descent, Market participant, Regularization (mathematics), Estimator, Prior probability, Errors and residuals, Quasi-Newton method, Risk premium, Market capitalization, Center for Research in Security Prices, Greedy algorithm, Dimension, Market price, Rational expectations, Bayesian linear regression, Portfolio Weight, Hyperparameter, Martingale (probability theory), Market clearing, Data science, Market portfolio, Pricing, Bayesian statistics, Behavioral economics, Credit risk, Cross-sectional data, Hyperparameter optimization, Linear regression, Tikhonov regularization, Bayesian inference, Demand curve, Benchmarking, Estimation theory