{"title":"受限最大缩减:快速稳健的投资组合优化方法","authors":"Albert Dorador","doi":"arxiv-2401.02601","DOIUrl":null,"url":null,"abstract":"We propose an alternative linearization to the classical Markowitz quadratic\nportfolio optimization model, based on maximum drawdown. This model, which\nminimizes maximum portfolio drawdown, is particularly appealing during times of\nfinancial distress, like during the COVID-19 pandemic. In addition, we will\npresent a Mixed-Integer Linear Programming variation of our new model that,\nbased on our out-of-sample results and sensitivity analysis, delivers a more\nprofitable and robust solution with a 200 times faster solving time compared to\nthe standard Markowitz quadratic formulation.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Constrained Max Drawdown: a Fast and Robust Portfolio Optimization Approach\",\"authors\":\"Albert Dorador\",\"doi\":\"arxiv-2401.02601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an alternative linearization to the classical Markowitz quadratic\\nportfolio optimization model, based on maximum drawdown. This model, which\\nminimizes maximum portfolio drawdown, is particularly appealing during times of\\nfinancial distress, like during the COVID-19 pandemic. In addition, we will\\npresent a Mixed-Integer Linear Programming variation of our new model that,\\nbased on our out-of-sample results and sensitivity analysis, delivers a more\\nprofitable and robust solution with a 200 times faster solving time compared to\\nthe standard Markowitz quadratic formulation.\",\"PeriodicalId\":501045,\"journal\":{\"name\":\"arXiv - QuantFin - Portfolio Management\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Portfolio Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2401.02601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Portfolio Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.02601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Constrained Max Drawdown: a Fast and Robust Portfolio Optimization Approach
We propose an alternative linearization to the classical Markowitz quadratic
portfolio optimization model, based on maximum drawdown. This model, which
minimizes maximum portfolio drawdown, is particularly appealing during times of
financial distress, like during the COVID-19 pandemic. In addition, we will
present a Mixed-Integer Linear Programming variation of our new model that,
based on our out-of-sample results and sensitivity analysis, delivers a more
profitable and robust solution with a 200 times faster solving time compared to
the standard Markowitz quadratic formulation.