Risk management strategies for finding universal portfolios

Mohr, Esther ; Dochow, Robert

DOI: https://doi.org/10.1007/s10479-016-2176-6
URL: https://www.researchgate.net/publication/305309691...
Additional URL: https://link.springer.com/article/10.1007%2Fs10479...
Document Type: Article
Year of publication: 2017
The title of a journal, publication series: Annals of Operations Research
Volume: 256
Issue number: 1
Page range: 129-147
Place of publication: New York, NY
Publishing house: Springer Science + Business Media
ISSN: 0254-5330 , 1572-9338
Publication language: English
Institution: Business School > Service Operations Management (Schön 2014-)
Subject: 330 Economics
Keywords (English): Universal portfolio , Distribution-free investing , Worst-case analysis , Minimax regret , Competitive analysis
Abstract: We consider Cover’s universal portfolio and the problem of risk management in a distribution-free setting when learning from experts. We aim to find optimal portfolios without modelling the financial market at the outset. Although it exists, the price distribution of the constituent assets is neither known nor given as part of the input. We consider the portfolio selection problem from the perspective of online algorithms that process input piece-by-piece in a serial fashion. Under the minimax regret criterion, we propose two risk-adjusted algorithms that track the expert with the lowest maximum drawdown. We obtain upper bounds on the worst-case performance of our algorithms that equal the bounds obtained by Cover (Math Finance 1(1):1–29, 1991). We also present computational evidence using NYSE data over a 22-year period, which shows superior performance of investment strategies that take risk management into account.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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