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Robust reward–risk ratio portfolio optimization
Author(s) -
Sehgal Ruchika,
Mehra Aparna
Publication year - 2021
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12652
Subject(s) - sharpe ratio , portfolio , cvar , portfolio optimization , econometrics , robust optimization , expected shortfall , bounded function , mathematical optimization , variance (accounting) , standard deviation , mathematics , computer science , economics , statistics , financial economics , mathematical analysis , accounting
In this paper, we propose robust portfolio optimization models for reward–risk ratios utilizing Omega, semi‐mean absolute deviation ratio, and weighted stable tail adjusted return ratio (STARR). We address the uncertainty in returns on assets by varying them in symmetric bounded intervals. The proposed robust reward–risk ratios preserve linearity in the resulting models, and hence are tractable. However, the robust models involve a sizably voluminous number of constraints, especially when the number of assets and scenarios is large. We employ the cutting plane algorithm to solve the proposed models in a much reduced time efficiently. We evaluate the performance of the robust reward–risk ratio models on the listed stocks of FTSE 100, Nikkei 225, S&P 500, and S&P BSE 500. The robust portfolio optimization models are found to outperform their conventional counterpart models in terms of statistics measured by the standard deviation, value at risk (VaR), conditional value at risk (CVaR), Sharpe ratio, and STARR ratio.