{"type":"ScholarlyArticle","title":"RM-CVaR: Regularized Multiple β-CVaR Portfolio","authors":[{"name":"Kei Nakagawa","slug":"kei-nakagawa","given":"Kei","family":"Nakagawa","affiliation":"Innovation Lab , Nomura Asset Management Co Ltd , Japan"},{"name":"Shuhei Noma","slug":"shuhei-noma","given":"Shuhei","family":"Noma","affiliation":"Innovation Lab , Nomura Asset Management Co Ltd , Japan"},{"name":"Masaya Abe","slug":"masaya-abe","given":"Masaya","family":"Abe","affiliation":"Innovation Lab , Nomura Asset Management Co Ltd , Japan"}],"abstract":"The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance).\r\nAlthough the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. \r\nConditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity.\r\nCVaR is defined as the expected value of the loss that occurs beyond a certain probability level (β).\r\nHowever, portfolio optimization problems that use CVaR as a risk measure are formulated with a single β and may output significantly different portfolios depending on how the β is selected.\r\nWe confirm even small changes in β can result in huge changes in the whole portfolio structure.\r\nIn order to improve this problem, we propose RM-CVaR: Regularized Multiple β-CVaR Portfolio.\r\nWe perform experiments on well-known benchmarks to evaluate the proposed portfolio.\r\nCompared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.","doi":"10.24963/ijcai.2020/629","url":"https://ijcai.org/proceedings/2020/629","pdf":"https://ijcai.org/proceedings/2020/0629.pdf","markdown":"https://ijcai.org/proceedings/2020/629.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/629","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":629,"pages":{"first":4562,"last":4568},"keywords":["Other areas: Financial decision-support system","AI for trading: General","AI for wealth: AI for roboadvising","AI for risk and security: AI for financial risk analytics","AI for trading: AI for portfolio analytics","AI for risk and security: AI for institutional risk modeling"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24290/file_24290.m3u8","length_sec":278,"provider":"hls"}],"references":[{"position":1,"raw":"[Ardia et al., 2017] David Ardia, Guido Bolliger, Kris Boudt, and Jean-Philippe Gagnon-Fleury. 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