We survey the nascent literature on machine learning in the study of financial markets. We 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.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Financial Machine Learning for free.
- Title
- Financial Machine Learning
- Author(s)
- Bryan Kelly, Dacheng Xiu
- Published
- 2023-07-13
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 159
- Language
- English
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Introduction: The Case for Financial Machine Learning Prices are Predictions Information Sets are Large Functional Forms are Ambiguous Machine Learning versus Econometrics Challenges of Applying Machine Learning in Finance (and the Benefits of Economic Structure) Economic Content (Two Cultures of Financial Economics) The Virtues of Complex Models Tools For Analyzing Machine Learning Models Bigger Is Often Better The Complexity Wedge Return Prediction Data Experimental Design A Benchmark: Simple Linear Models Penalized Linear Models Dimension Reduction Decision Trees Vanilla Neural Networks Comparative Analyses More Sophisticated Neural Networks Return Prediction Models For ``Alternative'' Data Risk-Return Tradeoffs APT Foundations Unconditional Factor Models Conditional Factor Models Complex Factor Models High-frequency Models Alphas Optimal Portfolios ``Plug-in'' Portfolios Integrated Estimation and Optimization Maximum Sharpe Ratio Regression High Complexity MSRR SDF Estimation and Portfolio Choice Trading Costs and Reinforcement Learning Conclusions References
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