A Practitioner's Guide to AI+ML in Portfolio Investing
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914064874078208 |
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| author | Fan, Mehmet Caner Qingliang |
| author_facet | Fan, Mehmet Caner Qingliang |
| contents | In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions. In other words, there should be joint analysis of Machine Learning (ML) technique with the possible portfolio choice-objective functions in terms of test period Sharpe Ratio or returns. The ML method with the best objective function should provide the weight for portfolio formation. Empirically we analyze five time periods of interest, that are out-sample and show performance of some ML-Artificial Intelligence (AI) methods. We see that nodewise regression with Global Minimum Variance portfolio based weights deliver very good Sharpe Ratio and returns across five time periods in this century we analyze. We cover three downturns, and 2 long term investment spans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Practitioner's Guide to AI+ML in Portfolio Investing Fan, Mehmet Caner Qingliang Portfolio Management In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions. In other words, there should be joint analysis of Machine Learning (ML) technique with the possible portfolio choice-objective functions in terms of test period Sharpe Ratio or returns. The ML method with the best objective function should provide the weight for portfolio formation. Empirically we analyze five time periods of interest, that are out-sample and show performance of some ML-Artificial Intelligence (AI) methods. We see that nodewise regression with Global Minimum Variance portfolio based weights deliver very good Sharpe Ratio and returns across five time periods in this century we analyze. We cover three downturns, and 2 long term investment spans. |
| title | A Practitioner's Guide to AI+ML in Portfolio Investing |
| topic | Portfolio Management |
| url | https://arxiv.org/abs/2509.25456 |