Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios

Deng, Geng; Dulaney, Tim; McCann, Craig; Wang, Olivia

doi:10.1057/jam.2013.21

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…Another study [16] utilized semi-variance to assess portfolio risk, while still another study [17] utilized the downside risk. Some studies [18]- [23] used Value at Risk (VaR) to adjust the risk in the Sharpe ratio, which assesses risk via the probabilities of losing money. In order to improve VaR, some studies [24]- [26] employed the Conditional Value-at-Risk (CVaR) model to assess risk.…”

Section: Related Studiesmentioning

confidence: 99%

“…GNQTS uses the quantum not gate to inverse the Q-matrix probability, which can help GNQTS to jump out of the local optima. When the global-best solution has incompatible preference with the probability of the Q-matrix, then the quantum not-gate is applied to inverse the probability, as shown in Equation (18). Equation (19) shows the quantum not-gate applied to inverse the probability of the Q-matrix; for example, when the Q-matrix has higher probability tend to select 1, the global-best solution is 0, and the quantum notgate applies the process, as shown in Figure 9.…”

Section: ) Updatementioning

confidence: 99%

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Using Trend Ratio and GNQTS to Assess Portfolio Performance in the U.S. Stock Market

et al. 2021

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Stock selection is an important issue in the stock market, and when assessing portfolio performance, return and risk are important conditions. The Sharpe ratio is a well-known assessment strategy that simultaneously considers portfolio return and risk. However, as the Sharpe ratio uses the average line to assess portfolio risk, it can easily assess a portfolio with stable uptrend as high risk, thus, this paper uses the trend ratio to address this problem. The trend ratio can assess a stable uptrend portfolio with low risk and identify a portfolio that has a higher daily expected return per unit daily risk. As the solution space in stock solution is huge, it is hard to use brute-force method to exhaust selections within a limited time. Thus, this paper uses the Global-best guided Quantum-inspired Tabu Search algorithm with Not-gate (GNQTS) to effectively optimize a portfolio given limited time. In addition, this paper uses 13 different kinds of sliding windows to train and test data, and identify a suitable period with a high trend ratio. The experimental results show that the proposed method can identify a portfolio with stable uptrend in the U.S. stock market. This paper does not exclude any stocks in the solution space; therefore, our method can comprehensively consider all the possible combinations of a portfolio. Moreover, we find some interesting phenomenon: the best single stock may not be contained in a portfolio and the stock with negative return cannot be excluded in the solution space. In addition, this study compares our portfolio performance with the portfolio guided by the Sharpe ratio; according to the experimental results, a portfolio guided by the trend ratio has more stable uptrend and lower risk than the Sharpe ratio. Thus, our method can outperform other assessment strategies.INDEX TERMS Stock selection, portfolio optimization, metaheuristic algorithm, trend ratio, quantuminspired tabu Search algorithm (QTS), the U.S. Stock Market.

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Section: Related Studiesmentioning

confidence: 99%

Section: ) Updatementioning

confidence: 99%

Using Trend Ratio and GNQTS to Assess Portfolio Performance in the U.S. Stock Market

et al. 2021

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“…Bailey and de Prado (2012) consider the probabilistic Sharpe ratio measuring the probability that the ratio of excess return to standard deviation exceeds a certain value. Deng et al (2013) propose the VaR-adjusted Sharpe ratio measure to construct a portfolio with the largest worstcase scenario Sharpe ratio within a given confidence interval. They show that the original Sharpe ratio and the probabilistic Sharpe ratio are both special cases of the VaR-adjusted Sharpe ratio.…”

Section: Risk-adjusted Return Performance Measuresmentioning

confidence: 99%

Data-Driven Optimization of Ambiguous Reward-Risk Ratio Measures

Lejeune

2015

SSRN Journal

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“…We should also mention Sharpe ratio model which is applied by construction of only one evaluation function that models the whole problem formulation. This model uses information from mean and variance of the assets when evaluating investments [16].…”

Section: Portfolio Optimization Problem Definitionmentioning

confidence: 99%

Upgraded Firefly Algorithm for Portfolio Optimization Problem

Tuba

Bačanin

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

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Portfolio selection is a well-known intractable research problem in the area of economics and finance. There are many definitions of the problem that by introduction of additional constraints try to make it closer to the realword conditions. Firefly algorithm is one of the latest swarm intelligence metaheuristics that was very successfully applied to both, unconstrained and constrained hard optimization problems. In this paper we adjusted firefly algorithm to the portfolio optimization problem and since the results were not completely satisfactory, we modified it so that better exploitation/exploration balance was achieved. We tested our improved algorithm on unconstrained portfolio problem, as well as on the problem formulation with cardinality and bounding constraints. We used official benchmark data sets from the OR-Library, and included data from Hang Seng in Hong Kong, DAX 100 in Germany and FTSE 100 in UK with 31, 85 and 89 assets respectively. Our upgraded algorithm proved to be uniformly better than the original one. Additionally, we compared it on the same data set to five other optimization metaheuristics from the literature and our upgraded firefly algorithm was better in most cases measured by all performance indicators.

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Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios

Cited by 29 publications

References 13 publications

Using Trend Ratio and GNQTS to Assess Portfolio Performance in the U.S. Stock Market

Using Trend Ratio and GNQTS to Assess Portfolio Performance in the U.S. Stock Market

Data-Driven Optimization of Ambiguous Reward-Risk Ratio Measures

Upgraded Firefly Algorithm for Portfolio Optimization Problem

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