Fuzzy Portfolio Allocation Models Through a New Risk Measure and Fuzzy Sharpe Ratio

Nguyen, Thanh; Gordon-Brown, Lee; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

doi:10.1109/tfuzz.2014.2321614

Cited by 50 publications

(11 citation statements)

References 48 publications

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“…To measure the efficiency of the proposed model, the Sharpe ratio was computed for performance evaluation of the optimal portfolio. Based on [27], Nguyen et al [28] modeled stock returns by the LR triangular fuzzy random variables and introduced a portfolio performance ratio named "reward-to-uncertainty" ratio to measure a portfolio performance.…”

Section: Introductionmentioning

confidence: 99%

A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

Liu¹,

Li²

2017

Mathematical Problems in Engineering

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When facing to make a portfolio decision, investors may care more about every portfolio's performance on a return and risk tradeoff. In this paper, a new low partial moment measurement that only punishes the loss risk is defined for selection variables based on L-S integral. Furthermore, a new performance measure for portfolio evaluation is proposed to generalize the Sharpe ratio in the fuzzy context. With the optimal performance criterion, a new parametric Sharpe ratio portfolio optimization model is developed wherein uncertain returns are presented as parametric interval-valued fuzzy variables. To make the proposed model easy to solve, we transform the fractional programming into an equivalent form and solve it with domain decomposition method (DDM). Finally, we apply the proposed performance measure into a portfolio selection problem, compare the computational results in different cases, and analyze the influence of different parameters on the optimal portfolio.

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Section: Introductionmentioning

confidence: 99%

A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

Liu¹,

Li²

2017

Mathematical Problems in Engineering

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“…A metaheuristic solution to complicated futures portfolio optimization problem is presented in [22]. Fuzzy theory based models are given in [23][24][25] to portfolio selection problem. A optimization method based on particle swarm optimization is given in [26], has applied to portfolio selection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

Kumar

Doja

2018

Computers

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This paper proposes a novel framework for solving the portfolio selection problem. This framework is excogitated using two newly parameters obtained from an existing basic mean variance model. The scheme can prove entirely advantageous for decision-making while using computed values of these significant parameters. The framework combines effectiveness of the mean-variance model and another significant parameter called Conditional-Value-at-Risk (CVaR). It focuses on extracting two newly parameters viz. αnew and βnew, which are demarcated from results obtained from mean-variance model and the value of CVaR. The method intends to minimize the overall cost, which is computed in the framework using quadratic equations involving these newly parameters. The new structure of ANFIS is designed by changing existing structure of ANFIS and this new structure contains six layers instead of existing five-layered structure. Fuzzy sets are harnessed for the design of the second layer of this new ANFIS structure. The output parameter acquired from the sixth layer of the new ANFIS structure serves as an important index for an investor in the decision-making. The numerical results acquired from the framework and the new six-layered structure is presented and these results are assimilated and compared with the results of the existing ANFIS structure.

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“…Likewise, Zhou, Li, and Pedrycz [22] introduce a concept of fuzzy semientropy to quantify the down side uncertainty, which formulates two mean-semi-entropy portfolio selection models with simulation-based genetic algorithm. Moreover, Nguyen, Gordon-Brown, Khosravi, Creighton, and Nahavandi [19] introduce a new portfolio risk measure and fuzzy Sharpe ratio. Correspondingly, two portfolio optimization problems are formulated and solved by fuzzy approach or genetic algorithm, where an experimental result shows their approach is more effective than the existing one.…”

Section: Extension Of Markowitz Model With Fuzzy Logicmentioning

confidence: 99%

“…For example, although a mean-variance (MV) portfolio [3] has been one of the most famous strategies, there is a well-known serious problem that the direct MV optimization amplifies the effects of estimation errors (e.g., [4]). Consequently, many researchers introduce the fuzziness in portfolio optimization problems from various perspectives (e.g., [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]).…”

Section: Introductionmentioning

confidence: 99%

Robust Technical Trading with Fuzzy Knowledge-Based Systems

Nakano

Takahashi

2017

SSRN Journal

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This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e., (i) a module for preparing candidate investment proposals and (ii) a module for their evaluation to construct a well-performed portfolio. Moreover, our framework effectively utilizes fuzzy KBSs for representation of human expert knowledge: Precisely, in the 1st module, three sets of fuzzy IF-THEN rules implement linguistic technical trading rules, which are designed specifically for getting well performance in different market phases. On the other hand, the 2nd module exploits fuzzy logic to evaluate the prepared investment candidates in terms of multilateral performance measures frequently used in practice. In an out-of-sample numerical experiment, our framework successfully generates a series of portfolios, which show long-term satisfactory records in the prolonged slumping Japanese stock market.

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Fuzzy Portfolio Allocation Models Through a New Risk Measure and Fuzzy Sharpe Ratio

Cited by 50 publications

References 48 publications

A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

A Novel Framework for Portfolio Selection Model Using Modified ANFIS and Fuzzy Sets

Robust Technical Trading with Fuzzy Knowledge-Based Systems

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