scholarly journals The Optimal Portfolio Selection Model underg-Expectation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Li Li
Keyword(s):  
Prospect Theory ◽  
Decision Rule ◽  
Portfolio Selection ◽  
Continuous Time ◽  
Optimal Solution ◽  
Selection Model ◽  
Optimal Portfolio ◽  
Optimal Portfolio Selection ◽  
Choquet Expectation ◽  
Portfolio Selection Model

This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by theg-expectation introduced by Peng. This model was established in the general continuous time setting and firstly adopted theg-expectation to replace Choquet expectation adopted in the work of Jin and Zhou, 2008. Using different S-shaped utility functions andg-functions to represent the investors' different uncertainty attitudes towards losses and gains makes the model not only more realistic but also more difficult to deal with. Although the models are mathematically complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.

scholarly journals An Efficient Portfolio with Several Objectives and Varying Parameters

2018 ◽  
Vol 3 (4) ◽  
pp. 335-350 ◽  
Author(s):  
Mrinal Jana ◽  
Geetanjali Panda ◽  
Neelesh Agrawal
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Selection Model ◽  
Optimal Portfolio ◽  
Objective Functions ◽  
Market Data ◽  
Varying Parameters ◽  
Portfolio Selection Model ◽  
Portfolio Return

We investigate a portfolio selection model with several objective functions, whose coefficients are uncertain and vary between some bounds. A preferable efficient portfolio of the model is obtained, which provides the range within which the portfolio return and the moments would vary. An optimal portfolio for the forecasted returns of stocks is found with actual market data from the Bombay Stock Exchange, India.

scholarly journals Hierarchical Clustering as a Dimension Reduction Technique for Markowitz Portfolio Optimization

2020 ◽  
Vol 27 (1) ◽  
pp. 62-71
Author(s):  
Anatoliy Y. Poletaev ◽  
Elena M. Spiridonova
Keyword(s):  
Dimension Reduction ◽  
Portfolio Selection ◽  
Hierarchical Clustering ◽  
Optimization Problem ◽  
Pair Correlation ◽  
Optimal Solution ◽  
Optimal Portfolio ◽  
Practical Applications ◽  
Optimal Portfolio Selection

Optimal portfolio selection is a common and important application of an optimization problem. Practical applications of an existing optimal portfolio selection methods is often difficult due to high data dimensionality (as a consequence of the large number of securities available for investment). In this paper, a method of dimension reduction based on hierarchical clustering is proposed. Clustering is widely used in computer science, a lot of algorithms and computational methods have been developed for it. As a measure of securities proximity for hierarchical clustering Pearson pair correlation coefficient is used. Further, the proposed method’s influence on the quality of the optimal solution is investigated on several examples of optimal portfolio selection according to the Markowitz Model. The influence of hierarchical clustering parameters (intercluster distance metrics and clustering threshold) on the quality of the obtained optimal solution is also investigated. The dependence between the target return of the portfolio and the possibility of reducing the dimension using the proposed method is investigated too. For each considered example in the paper graphs and tables with the main results of the proposed method - application which are the decrease of the dimension and the drop of the yield (the decrease of the quality of the optimal solution) - for a portfolio constructed using the proposed method compared to a portfolio constructed without the proposed method are given. For the experiments the Python programming language and its libraries: scipy for clustering and cvxpy for solving the optimization problem (building an optimal portfolio) are used.

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