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.

Portfolio Optimization with Risk Decomposition

2017 ◽  
Vol 2017 (5) ◽  
pp. 61-85
Author(s):  
Konstantin Asaturov
Keyword(s):  
Portfolio Optimization ◽  
Performance Indicators ◽  
Equity Market ◽  
Optimization Approach ◽  
Risk Decomposition ◽  
Market Index ◽  
Specific Risk ◽  
Capm Model

The paper offers the modification of traditional portfolio optimization approach to construct the portfolio with possibility to control both systematic and specific risk (portfolio with risk decomposition). Built on modern econometric tools, the author estimates and forecasts the dynamics of alphas and betas of stocks in the frame of CAPM model, which are further applied for portfolio optimization. The closing weekly prices of 10 Australian stocks and ASX Index as the market index during the period from July 2000 to July 2016 were used. Within the sample there is no evidence of arbitrage on the Australian equity market employing neutral beta portfolio. The study confirms that portfolios with risk decomposition outperform Markowitz’s one according to various performance indicators.

scholarly journals Machine Reliability Optimization by Genetic Algorithm Approach

2020 ◽  
pp. 35-40
Author(s):  
Ngnassi Djami Aslain Brisco ◽  
Nzie Wolfgang ◽  
Doka Yamigno Serge
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Method ◽  
Reliability Assessment ◽  
Optimal Solution ◽  
Assessment Method ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Machine Reliability ◽  
Long Time ◽  
Genetic Algorithm Approach

To define the reliability network of a system (machine), we start with a set of components arranged in an appropriate topology (series, parallel, or parallel-series), choose the best terms of the ratio performance / cost, and gather by links with the aim to combine them. This process requires a long time and effort, given the very large number of possible combinations, which becomes tedious for the analyst. For this reason, it is essential to use an appropriate optimization approach when designing any product. However, before trying to optimize, it is necessary to have a reliability assessment method. The objective of this paper is to display a meta-heuristic method, which is sustained on the genetic algorithm (GA) to improve the machines reliability. To achieve this objective, a methodology that consists of presenting the functionalities of genetic algorithms is developed. The result achieved is the proposal of a reliability network for the optimal solution.

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.

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