An analysis of machine learning risk factors and risk parity portfolio optimization.
Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio's risk into risk factor comm...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272521&type=printable |
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| author | Liyun Wu Muneeb Ahmad Salman Ali Qureshi Kashif Raza Yousaf Ali Khan |
| author_facet | Liyun Wu Muneeb Ahmad Salman Ali Qureshi Kashif Raza Yousaf Ali Khan |
| author_sort | Liyun Wu |
| collection | DOAJ |
| description | Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio's risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R. |
| format | Article |
| id | doaj-art-0b040f2da65c4c6c8fb3ecbc7bac5e0b |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0b040f2da65c4c6c8fb3ecbc7bac5e0b2025-08-20T02:57:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01179e027252110.1371/journal.pone.0272521An analysis of machine learning risk factors and risk parity portfolio optimization.Liyun WuMuneeb AhmadSalman Ali QureshiKashif RazaYousaf Ali KhanMany academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio's risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272521&type=printable |
| spellingShingle | Liyun Wu Muneeb Ahmad Salman Ali Qureshi Kashif Raza Yousaf Ali Khan An analysis of machine learning risk factors and risk parity portfolio optimization. PLoS ONE |
| title | An analysis of machine learning risk factors and risk parity portfolio optimization. |
| title_full | An analysis of machine learning risk factors and risk parity portfolio optimization. |
| title_fullStr | An analysis of machine learning risk factors and risk parity portfolio optimization. |
| title_full_unstemmed | An analysis of machine learning risk factors and risk parity portfolio optimization. |
| title_short | An analysis of machine learning risk factors and risk parity portfolio optimization. |
| title_sort | analysis of machine learning risk factors and risk parity portfolio optimization |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272521&type=printable |
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