Mean-Entropy Model of Uncertain Portfolio Selection Problem

2018 ◽  
pp. 25-54 ◽  
Author(s):  
Saibal Majumder ◽  
Samarjit Kar ◽  
Tandra Pal
Keyword(s):  
Portfolio Selection ◽  
Selection Problem ◽  
Entropy Model ◽  
Portfolio Selection Problem

scholarly journals Uncertain portfolio optimization problem with liquidity and diversification

2020 ◽  
Author(s):  
Ranran Zhang ◽  
Bo Li
Keyword(s):  
Portfolio Selection ◽  
Selection Problem ◽  
Investment Risk ◽  
Deterministic Models ◽  
Entropy Model ◽  
Portfolio Selection Problem ◽  
Uncertain Variables ◽  
Proposed Model ◽  
Portfolio Optimization Problem ◽  
Mean Variance

This paper deals with a portfolio selection problem with uncertain returns. Here, the returns of the assets are regarded as uncertain variables which are estimated by experienced experts. First, an uncertain mean-variance-entropy model for portfolio selection problem is presented by taking into account four criteria viz., return, risk, liquidity and diversification degree of portfolio. In the proposed model, the investment return is quantified by uncertain expected value, the investment risk is characterized by uncertain variance and entropy is used to measure the diversification degree of portfolio. Moreover, different from the previous bi-objective optimization model, our model achieves both the maximum return and the minimum risk in a single objective form by introducing a risk aversion factor and the dimensional influence caused by different units is eliminated by normalization. Then, two auxiliary portfolio selection models are transformed into different equivalent deterministic models. Finally, a numerical simulation is given to verify the practicability of our model.

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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