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genetic relation algorithm with guided mutation for the large-scale portfolio optimization

EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3353-3363页

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Shanghai Univ Finance & Econ Sch Stat & Maragement Shanghai 200433 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (CRA/G) for the portfolio optimization is proposed in this paper. genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with CRA/G. The results show that CRA/G approach is successful in portfolio optimization. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Portfolio optimization genetic relation algorithm Guided mutation genetic network programming

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A Portfolio Selection Model Using genetic relation algorithm and genetic Network Programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2011年第5期6卷 403-413页

作者： Chen, Yan Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： portfolio selection genetic relation algorithm genetic network programming

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Generating Trading Rules for Stock Markets Using Robust genetic Network Programming and Portfolio Beta

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2016年第3期20卷 484-491页

作者： Chen, Yan Shi, Zhihui Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China

In this paper, Robust genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio beta, optimization by the genetic relation algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed.

关键词： portfolio beta genetic relation algorithm robust genetic network programming stock trading

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Guided genetic relation algorithm on the Adaptive Asset Allocation

Guided Genetic Relation Algorithm on the Adaptive Asset Allo...

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SICE Annual Conference

作者： Victor Parque Shingo Mabu Kotaro Hirasawa The Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9784907764388

One important question in investment is how to build adaptive asset allocation strategies, i.e. portfolios which adjust to the changing conditions of the economic environments. This paper proposes an evolutionary approach for the adaptive asset allocation by using Guided genetic relation algorithm(GRA-g), whose main role is to model and evolve the optimal adaptive portfolio structures. Simulations using asset classes in USA show that the proposed scheme offers competitive economic advantages. This paper suggests that the use of evolutionary computing techniques is an excellent tool to aid the asset allocation, whose advantages imply the usefulness to manage the exposure to risk.

关键词： Adaptive asset allocation genetic relation algorithm Evolutionary computing

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A Portfolio Selection Strategy Using genetic relation algorithm

A Portfolio Selection Strategy Using Genetic Relation Algori...

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2010 IEEE World Congress on Computational Intelligence

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China Waseda Univ Grad Sch Informat Product & Syst Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9781424481262

This paper proposes a new strategy beta-GRA for portfolio selection in which the return and risk are considered as measures of strength in genetic relation algorithm (GRA). Since the portfolio beta beta efficiently measures the volatility relative to the benchmark index or the capital market, beta is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which selects stocks based on beta using GRA. GRA is a new evolutionary algorithm designed to solve the optimization problem due to its special structure. We illustrate the proposed strategy by experiments and compare the results with those derived from the traditional models.

关键词： Î²-GRA strategy benchmark index capital market evolutionary algorithm genetic algorithms genetic relation algorithm investment optimization problem portfolio construction process portfolio evaluation portfolio selection strategy

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A genetic relation algorithm for portfolio selection

A genetic relation algorithm for portfolio selection

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3rd International Conference on Management Science and Engineering Management

作者： Chen, Yan Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

ISBN: (纸本)9781846260032

In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem.. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed model considers the correlation coefficient between stocks as strength, which indicates the relationship between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. The efficiency of GRA method is confirmed by the stock trading model of genetic network programming (GNP). We present the experimental results obtained by GRA and compare them with those obtained by traditional method, and it is clarified that the proposed model can obtain much higher profits than the traditional one.

关键词： portfolio selection genetic relation algorithm genetic network programming financial applications

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A Portfolio Selection Model using genetic relation algorithm and genetic Network Programming

A Portfolio Selection Model using Genetic Relation Algorithm...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781424427932

In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed model considers the correlation coefficient between stocks as strength, which indicates the relationship between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. The efficiency of GRA method is confirmed by the stock trading model using genetic network programming (GNP) that has been proposed in the previous study. We present the experimental results obtained by GRA and compare them with those obtained by traditional method, and it is clarified that the proposed model can obtain much higher profits than the traditional one.

关键词： portfolio selection genetic relation algorithm genetic network programming

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