Financial Machine Learning
Seiten
2023
now publishers Inc (Verlag)
978-1-63828-290-7 (ISBN)
now publishers Inc (Verlag)
978-1-63828-290-7 (ISBN)
Surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. Designed for financial economists, statisticians and machine learners.
Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.
Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.
1. Introduction: The Case for Financial Machine Learning
2. The Virtues of Complex Models
3. Return Prediction
4. Risk-Return Tradeoffs
5. Optimal Portfolios
6. Conclusions
Acknowledgements
References
Erscheinungsdatum | 14.11.2023 |
---|---|
Reihe/Serie | Foundations and Trends® in Finance |
Verlagsort | Hanover |
Sprache | englisch |
Maße | 156 x 234 mm |
Gewicht | 251 g |
Themenwelt | Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung |
ISBN-10 | 1-63828-290-0 / 1638282900 |
ISBN-13 | 978-1-63828-290-7 / 9781638282907 |
Zustand | Neuware |
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