Robust CAPM Estimation through Cross Validation
| Autor: | Kekoura Sakouvogui, William E. Nganje |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Estimation
Information Systems and Management Mean squared error Strategy and Management Cross-validation Absolute deviation Computational Theory and Mathematics Artificial Intelligence Financial crisis Econometrics Business Management and Accounting (miscellaneous) Capital asset pricing model Portfolio Business and International Management Finance Information Systems Mathematics |
| Zdroj: | The Journal of Financial Data Science. 1:153-167 |
| ISSN: | 2640-3943 |
| DOI: | 10.3905/jfds.2019.1.2.153 |
| Popis: | Limitations of the capital asset pricing model (CAPM) continue to present inconsistent empirical results despite CAPM’s firm mathematical foundations provided in numerous studies. In this article, the authors examine how estimation errors of the CAPM can be minimized using the cross-validation technique, a concept that is widely applied in machine learning (ML-CAPM). They apply their approach to test the assumption that the CAPM is a well-diversified portfolio model by using data from the S&P 500 and the Dow Jones Industrial Average. The results from the ML-CAPM validate both market indexes as well diversified, with statistically insignificant variation in unsystematic risks during and after the 2007 financial crisis. Furthermore, the ML-CAPM provides smaller root mean square error and mean absolute deviations compared to the traditional CAPM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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