We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns.
Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts.
The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.
Inhaltsverzeichnis (Table of Contents)
- INTRODUCTION
- WHAT IS MACHINE LEARNING?
- WHAT MACHINE LEARNING CAN(NOT) DO
- LITERATURE
- DATA AND METHODOLOGY
- DATA
- METHODOLOGY
- MODELS
- BENCHMARK
- PENALIZED LINEAR
- TREE-BASED MODELS
- NEURAL NETWORKS
- RESULTS
- PREDICTIVE SLOPE
- PORTFOLIOS
- ROBUSTNESS CHECKS
- CONCLUSION
- GLOSSARY
- REFERENCES
- APPENDIX
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis investigates the potential of advanced modeling approaches to synthesize and dissect a high-dimensional set of factors into a return prediction model that yields more precise predictions than traditional methods. The study examines whether out-of-sample predictions can be improved by considering potential nonlinearities in the relationship between expected returns and characteristics, as well as nonlinear interactions among factors.
- Effectiveness of machine learning techniques in predicting stock returns.
- Comparison of machine learning techniques to traditional forecasting methods.
- Exploration of nonlinear relationships and interactions between expected returns and stock characteristics.
- Evaluation of the economic value of cross-sectional return forecasts.
- Identifying the strongest performing machine learning methods for stock return prediction.
Zusammenfassung der Kapitel (Chapter Summaries)
- Introduction: This chapter introduces the concept of machine learning in quantitative finance and explores its potential to predict stock returns with greater precision. It highlights the challenges faced by traditional factor models, particularly in the wake of the 2008 financial crisis, and suggests that machine learning techniques, with their ability to handle nonlinearities and interactions, can offer a more robust solution.
- Literature: This chapter provides a comprehensive review of existing literature on the use of machine learning in finance, focusing on its application in stock selection and return prediction. It examines previous studies that have explored the effectiveness of machine learning techniques in this domain, highlighting their advantages and limitations.
- Data and Methodology: This chapter outlines the data sources and methodology employed in the study. It describes the data set used, including the specific stock and firm characteristics, and details the machine learning techniques applied to develop and evaluate the predictive models. This includes a discussion of the benchmark model used for comparison.
- Models: This chapter provides a detailed description of the machine learning models used in the study. It covers various techniques, including penalized linear models, tree-based models, and neural networks, highlighting their strengths and weaknesses in the context of stock return prediction.
- Results: This chapter presents the findings of the study, focusing on the predictive performance of the different machine learning models. It analyzes the results in terms of their ability to accurately predict stock returns out-of-sample and compares their performance to the benchmark model. The chapter also examines the impact of different factors on the predictive ability of the models.
- Robustness Checks: This chapter explores the robustness of the findings by conducting sensitivity analyses and testing the models under different conditions. It examines the stability of the results when using alternative data sets or different model specifications. This helps to assess the reliability and generalizability of the study's conclusions.
Schlüsselwörter (Keywords)
This study focuses on the use of machine learning techniques to predict the cross-section of stock returns. Key themes and concepts include machine learning, deep learning, big data, cross-sectional expected stock returns, characteristic-based anomalies, penalized linear models, (group) lasso, elastic-net, random forest, gradient boosting, and neural networks.
- Quote paper
- David Dümig (Author), 2019, Dissecting Characteristics via Machine Learning for Stock Selection, Munich, GRIN Verlag, https://www.grin.com/document/502999
