Author: Gerrit LiedtkePublisher:
ISBN:
Category :
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 BüchnerPublisher:
ISBN:
Category : Capital assets pricing model
Languages : en
Pages : 0
Book Description
Essays in Empirical Asset Pricing with Machine Learning
Author: Matthias BûchnerEssays in Empirical Asset Pricing with Machine Learning
Author: Felix KempfEssays on the Application of Machine Learning Techniques in the Empirical Asset Pricing Research
Author: Tizian OttoEmpirical Asset Pricing Via Machine Learning
Author: Shihao GuPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best performing methods (trees and neural networks) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. All methods agree on the same set of dominant predictive signals which includes variations on momentum, liquidity, and volatility. Improved risk premium measurement through machine learning simplifies the investigation into economic mechanisms of asset pricing and highlights the value of machine learning in financial innovation.
Essays on Empirical Asset Pricing
Author: Steffen WindmüllerPublisher:
ISBN: 9783754155042
Category :
Languages : en
Pages :
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.
Author: Liang Zhang
Author: Junyan Shen