- Author: Stefan Nagel
The book "Machine Learning in Asset Pricing" is written by Stefan Nagel and is part of the Princeton Finance Lecture Series. It authoritatively introduces how machine learning is applied in the field of asset pricing. In financial markets, investors are faced with large amounts of potentially value-relevant information from a variety of sources. In these data-rich, high-dimensional environments, rapidly evolving machine learning (ML) techniques are well suited to solving prediction problems. As a result, machine learning methods are quickly becoming part of the asset pricing research and quantitative investment toolkits.
This book provides an in-depth exploration of the prospects and challenges of machine learning in asset pricing applications. Asset pricing problems are significantly different from the scenarios for which machine learning tools were originally developed, so in order to realize the full potential of machine learning methods, they must be adapted to the specific conditions of the asset pricing application. Economic considerations, such as portfolio optimization, the no-arbitrage principle, and investor learning, can guide the selection and modification of machine learning tools.
The book begins with a brief overview of basic supervised learning methods, then discusses the application of these techniques to empirical research on asset pricing and shows how they hold promise for advancing the theoretical modeling of financial markets. The book covers core issues such as predicting return differences between assets, capturing predictable changes in returns over time, credit risk prediction, and forecasting asset return co-movements. This book offers exciting possibilities for applying cutting-edge methods to the study of financial asset valuation.
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