Author: Hongwei ZhangPublisher:
ISBN: 9789056686697
Category :
Languages : en
Pages : 0
Book Description
Empirical Asset Pricing and Ensemble Machine Learning
Author: Hongwei ZhangPublisher:
ISBN: 9789056686697
Category :
Languages : en
Pages : 0
Book Description
Machine Learning in Asset Pricing
Author: Stefan NagelPublisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156
Book Description
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.
Machine Learning in Empirical Asset Pricing
Author: Colm KellyPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
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Essays in Empirical Asset Pricing with Machine Learning
Author: Matthias BüchnerPublisher:
ISBN:
Category : Capital assets pricing model
Languages : en
Pages : 0
Book Description
Essays on Empirical Asset Pricing Via Machine Learning
Author: Gerrit LiedtkeEssays in Empirical Asset Pricing with Machine Learning
Author: Matthias BûchnerEssays in Empirical Asset Pricing with Machine Learning
Author: Felix KempfEmpirical Asset Pricing Via Machine Learning
Author: Shihao GuPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. Our implementation establishes a new standard for accuracy in measuring risk premia summarized by an unprecedented out-of-sample return prediction R2. We identify the best performing methods (trees and neural nets) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premia measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.
Essays on the Application of Machine Learning Techniques in the Empirical Asset Pricing Research
Author: Tizian OttoEmpirical Asset Pricing
Author: Wayne FersonPublisher: MIT Press
ISBN: 0262039370
Category : Business & Economics
Languages : en
Pages : 497
Book Description
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.