scholarly journals Investment Portfolio Optimization on Russian Stock Market in Context of behavioral theory

2019 ◽  
Vol 23 (4) ◽  
pp. 99-116
Author(s):  
N. M. Red’kin
Keyword(s):  
Stock Market ◽  
Portfolio Optimization ◽  
Prospect Theory ◽  
Research Result ◽  
Return Level ◽  
Objective Probability ◽  
Investment Portfolio ◽  
Profitability Analysis ◽  
Russian Stock Market ◽  
Investment Portfolio Optimization

The paper investigates possible investment portfolio optimization considering behavioral errors. The research rationale is due to the adaption of the investment recommendations for unqualified investors on the Russian stock market. In economic literature, the consequences of behavioral effects are not detailed enough when making a portfolio of Russian securities. The aim of the article is to make the most optimal portfolio based on the risk/reward ratio. The author made a hypothesis on applying various periods of profitability analysis to improve profitability indicators and increase the subjective probability of its achievement. To build a portfolio model, the behavioral portfolio theory and its optimization through linear programming were used. The study was based on modeling the investment portfolio of the most liquid stocks on the Russian stock market. Modified elements of the cumulative prospect theory with behavioral coefficients were used as indicators of profitability and probability. Based on the analysis results, the model of semi-annual portfolio analysis was proposed as a tool for portfolio optimization. The investor review of the portfolio semi-annual rate of profitability led to its best final index of effectiveness. In the medium-term assessment of portfolio profitability, the influence of behavioral factors decreases while maximizing returns with medium high risk. The research result is consistent with the basics of behavioral economics as the prospect theory regarding risk and loss aversion. Moreover, the factor of frequency of access to information and the degree of naive portfolio diversification with high profitability are promising areas for the development of research in behavioral finance. However, determining by the investor the objective probability to achieve the expected return level by using specific benchmarks is controversial.

scholarly journals A Personalized Mean-CVaR Portfolio Optimization Model for Individual Investment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chunxia Yu ◽  
Yuru Liu
Keyword(s):  
Portfolio Optimization ◽  
Optimization Model ◽  
Evaluation Method ◽  
Garch Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Risk Tolerance ◽  
Financial Assets ◽  
Investment Portfolio ◽  
Portfolio Optimization Model ◽  
Investment Portfolio Optimization

Investment as an important issue in daily life is accompanied by the occurrence of various financial assets, such as stocks, bonds, and mutual funds. However, risk tolerances vary across individuals. Individual investors have to select corresponding personalized investment portfolios to satisfy their own needs. Moreover, it is difficult for ordinary people to select a personalized investment portfolio by themselves, and it is too expensive and inefficient to look for professional consultation. Therefore, the objective of this research is to propose a personalized portfolio recommendation model, which can build the personalized portfolio based on investors’ risk tolerances. In this research, investors’ risk tolerance is determined by the fuzzy comprehensive evaluation method based on investors’ demographic characteristics. The CVaR is used as the risk measurement of financial assets. The dynamics of the distribution of returns are described in the combined Copula-GARCH model, and the future scenarios of returns are generated by the Monte Carlo simulation based on the combined Copula-GARCH model to estimate CVaR. The mean-CVaR portfolio optimization model is used to find out the best personalized portfolio. Finally, experiments are conducted to validate the applicability and feasibility of the personalized investment portfolio optimization model. Results show that the proposed investment portfolio optimization model can recommend personalized investment portfolio according to investor’s risk tolerance.

scholarly journals INVESTMENT PORTFOLIO OPTIMIZATION BY APPLYING A GENETIC ALGORITHM-BASED APPROACH

Ekonomika ◽  
2017 ◽  
Vol 96 (2) ◽  
pp. 66-78 ◽  
Author(s):  
Petras Dubinskas ◽  
Laimutė Urbšienė
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Optimization Approach ◽  
Investment Portfolio ◽  
Market Index ◽  
Key Issues ◽  
Genetic Algorithm Approach ◽  
One Year ◽  
Investment Portfolio Optimization

The investment portfolio optimization issues have been widely discussed by scholars for more than 60 years. One of the key issues that emerge for researchers is to clarify which optimization approach helps to build the most efficient portfolio (in this case, the efficiency refers to the minimization of the investment risk and the maximization of the return). The objective of the study is to assess the fitness of a genetic algorithm approach in optimizing the investment portfolio. The paper analyzes the theoretical aspects of applying a genetic algorithm-based approach, then it adapts them to practical research. To build an investment portfolio, four Lithuanian enterprises listed on the OMX Baltics Stock Exchange Official List were selected in accordance with the chosen criteria. Then, by applying a genetic algorithm-based approach and using MatLab software, the optimum investment portfolio was constructed from the selected enterprises. The research results showed that the genetic algorithm-based portfolio in 2013 reached a better risk-return ratio than the portfolio optimized by the deterministic and stochastic programing methods. Also, better outcomes were achieved in comparison with the OMX Baltic Market Index. As a result, the hypothesis of the superiority of a portfolio, optimized on the basis of a genetic algorithm, is not rejected. However, it should be noted that in seeking for more reliable conclusions, further research should include more trial periods as the current study examined a period of one year. In this context, the operation of the approach in the context of a market downturn could be of particular interest.

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