scholarly journals A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ying Liu ◽  
Ya-Nan Li
Keyword(s):  
Portfolio Selection ◽  
Domain Decomposition Method ◽  
Equivalent Form ◽  
Performance Criterion ◽  
Performance Measure ◽  
Sharpe Ratio ◽  
Selection Problem ◽  
Optimization Approach ◽  
Fuzzy Variables ◽  
Portfolio Selection Problem

When facing to make a portfolio decision, investors may care more about every portfolio’s performance on a return and risk trade-off. 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.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

2010 ◽  
Author(s):  
Gour Sundar Mitra Thakur ◽  
Rupak Bhattacharyya ◽  
Swapan Kumar Mitra ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Portfolio Selection ◽  
Ant Colony ◽  
Selection Problem ◽  
Optimization Approach ◽  
Portfolio Selection Problem ◽  
Fuzzy Random

A DOWNSIDE RISK APPROACH FOR THE PORTFOLIO SELECTION PROBLEM WITH FUZZY RETURNS

2004 ◽  
Vol 09 (01) ◽  
Author(s):  
Teresa León ◽  
Vicente Liern ◽  
Paulina Marco ◽  
Enriqueta Vercher ◽  
José Vicente Segura
Keyword(s):  
Portfolio Selection ◽  
Downside Risk ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Risk Approach

scholarly journals A Two-level Reinforcement Learning Algorithm for Ambiguous Mean-variance Portfolio Selection Problem

2020 ◽  
Author(s):  
Xin Huang ◽  
Duan Li
Keyword(s):  
Reinforcement Learning ◽  
Portfolio Selection ◽  
Current Knowledge ◽  
Lower Layer ◽  
Experimental Studies ◽  
Selection Problem ◽  
Multiple Time ◽  
Portfolio Selection Problem ◽  
Mean Variance Portfolio ◽  
Mean Variance

Traditional modeling on the mean-variance portfolio selection often assumes a full knowledge on statistics of assets' returns. It is, however, not always the case in real financial markets. This paper deals with an ambiguous mean-variance portfolio selection problem with a mixture model on the returns of risky assets, where the proportions of different component distributions are assumed to be unknown to the investor, but being constants (in any time instant). Taking into consideration the updates of proportions from future observations is essential to find an optimal policy with active learning feature, but makes the problem intractable when we adopt the classical methods. Using reinforcement learning, we derive an investment policy with a learning feature in a two-level framework. In the lower level, the time-decomposed approach (dynamic programming) is adopted to solve a family of scenario subcases where in each case the series of component distributions along multiple time periods is specified. At the upper level, a scenario-decomposed approach (progressive hedging algorithm) is applied in order to iteratively aggregate the scenario solutions from the lower layer based on the current knowledge on proportions, and this two-level solution framework is repeated in a manner of rolling horizon. We carry out experimental studies to illustrate the execution of our policy scheme.

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