{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:05:56Z","timestamp":1753355156833},"reference-count":21,"publisher":"Fuji Technology Press Ltd.","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Adv. Comput. Intell. Intell. Inform.","JACIII"],"published-print":{"date-parts":[[2016,5,19]]},"abstract":"<jats:p>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 \u03b2, 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.<\/jats:p>","DOI":"10.20965\/jaciii.2016.p0484","type":"journal-article","created":{"date-parts":[[2016,5,18]],"date-time":"2016-05-18T21:15:58Z","timestamp":1463606158000},"page":"484-491","source":"Crossref","is-referenced-by-count":2,"title":["Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta"],"prefix":"10.20965","volume":"20","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[]},{"name":"School of Statistics and Management, Shanghai University of Finance and Economics","sequence":"first","affiliation":[]},{"given":"Zhihui","family":"Shi","sequence":"additional","affiliation":[]}],"member":"8550","published-online":{"date-parts":[[2016,5,19]]},"reference":[{"key":"key-10.20965\/jaciii.2016.p0484-1","doi-asserted-by":"crossref","unstructured":"D. Enke and S. Thawornwong, \u201cThe use of data mining and neural networks for forecasting stock market returns,\u201d Expert Systems with Applications, Vol.29, No.4, pp. 927-940, 2005.","DOI":"10.1016\/j.eswa.2005.06.024"},{"key":"key-10.20965\/jaciii.2016.p0484-2","doi-asserted-by":"crossref","unstructured":"A. Fernandez and S. Gomez, \u201cPortfolio selection using neural networks,\u201d Computers & Operations Research, Vol.34, No.4, pp. 1177-1191, 2007.","DOI":"10.1016\/j.cor.2005.06.017"},{"key":"key-10.20965\/jaciii.2016.p0484-3","doi-asserted-by":"crossref","unstructured":"C. C Lin and Y. T. Liu, \u201cGenetic algorithms for portfolio selection problems with minimum transaction lots,\u201d European J. of Operational Research, Vol.185, No.1, pp. 393-404, 2008.","DOI":"10.1016\/j.ejor.2006.12.024"},{"key":"key-10.20965\/jaciii.2016.p0484-4","doi-asserted-by":"crossref","unstructured":"N. Harnpornchai, K. Autchariyapanitkul, J. Sirisrisakulchai, and S. Sriboonchitta, \u201cOptimal Outpatient Appointment System with Uncertain Parameters Using Adaptive-Penalty Genetic Algorithm,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.5, pp. 585-592, 2015.","DOI":"10.20965\/jaciii.2015.p0585"},{"key":"key-10.20965\/jaciii.2016.p0484-5","doi-asserted-by":"crossref","unstructured":"W. Z. Dai and K. Xia, \u201cApproach to Hybrid Flow-Shop Scheduling Problem Based on Self-Guided Genetic Algorithm,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.3, pp. 365-371, 2015.","DOI":"10.20965\/jaciii.2015.p0365"},{"key":"key-10.20965\/jaciii.2016.p0484-6","doi-asserted-by":"crossref","unstructured":"J. Dan, W. Guo, W. R. Shi, B. Fang, and T. P. Zhang, \u201cPSO Based Deterministic ESN Models for Stock Price Forecasting,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.2, pp. 312-318, 2014.","DOI":"10.20965\/jaciii.2015.p0312"},{"key":"key-10.20965\/jaciii.2016.p0484-7","doi-asserted-by":"crossref","unstructured":"S. D. F. Hilado, L. A. G. Lim, R. N. G. Naguib, E. P. Dadios, and J. M. C. Avila, \u201cImplementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.5, pp. 792-797, 2014.","DOI":"10.20965\/jaciii.2014.p0792"},{"key":"key-10.20965\/jaciii.2016.p0484-8","doi-asserted-by":"crossref","unstructured":"K. G. Abistado, C. N. Arellano, and E. A. Maravillas, \u201cWeather Forecasting Using Artificial Neural Network and Bayesian Network,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.5, pp. 812-817, 2014.","DOI":"10.20965\/jaciii.2014.p0812"},{"key":"key-10.20965\/jaciii.2016.p0484-9","doi-asserted-by":"crossref","unstructured":"S. Yokoyama, H. Iizuka, and M. Yamamoto, \u201cPriority Rule-Based Construction Procedure Combined with Genetic Algorithm for Flexible Job-Shop Scheduling Problem,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.6, pp. 892-899, 2015.","DOI":"10.20965\/jaciii.2015.p0892"},{"key":"key-10.20965\/jaciii.2016.p0484-10","doi-asserted-by":"crossref","unstructured":"T. Watanabe, T. Kamai, and T. Ishimaru, \u201cRobust Estimation of Camera Homography by Fuzzy RANSAC Algorithm with Reinforcement Learning,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.6, pp. 833-842, 2015.","DOI":"10.20965\/jaciii.2015.p0833"},{"key":"key-10.20965\/jaciii.2016.p0484-11","doi-asserted-by":"crossref","unstructured":"S. Mabu, K. Hirasawa, and J. Hu, \u201cA graph-based evolutionary algorithm: Genetic network programming and its extension using reinforcement learning,\u201d Evolutionary Computation, MIT Press, Vol.15, No.3, pp. 369-398, 2007.","DOI":"10.1162\/evco.2007.15.3.369"},{"key":"key-10.20965\/jaciii.2016.p0484-12","doi-asserted-by":"crossref","unstructured":"K. Hirasawa, T. Eguchi, J. Zhou, L. Yu, J. Hu, and S. Markon, \u201cA Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming,\u201d IEEE Trans. on Systems, Man and Cybernetics, Part C, Vol.38, No.4, pp. 535-550, 2008.","DOI":"10.1109\/TSMCC.2007.913904"},{"key":"key-10.20965\/jaciii.2016.p0484-13","doi-asserted-by":"crossref","unstructured":"Y. Chen, S. Mabu, K. Shimada, and K. Hirasawa, \u201cTrading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning,\u201d J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.12, No.4, pp. 383-392, 2008.","DOI":"10.20965\/jaciii.2008.p0383"},{"key":"key-10.20965\/jaciii.2016.p0484-14","doi-asserted-by":"crossref","unstructured":"Y. Chen and X. C. Wang, \u201cA Hybrid Stock Trading System Using Genetic Network Programming and Mean Conditional Value-at-Risk,\u201d European J. of Operational Research, Vol.240, pp. 861-871, 2014.","DOI":"10.1016\/j.ejor.2014.07.034"},{"key":"key-10.20965\/jaciii.2016.p0484-15","doi-asserted-by":"crossref","unstructured":"E. Gonzales, K. Taboada, K. Shimada, S. Mabu, and K. Hirasawa, \u201cEvaluating Class Association Rules using Genetic Relation Programming,\u201d Proc. of the IEEE Congress on Evolutionary Computation 2008, pp. 731-736, 2008.","DOI":"10.1109\/CEC.2008.4630877"},{"key":"key-10.20965\/jaciii.2016.p0484-16","doi-asserted-by":"crossref","unstructured":"Y. Chen and K. Hirasawa, \u201cA Portfolio Selection Model using Genetic Relation Algorithm and Genetic Network Programming,\u201d IEEJ Trans. on Electrical and Electronic Engineering, Vol.6, No.5, pp. 403-413, 2011.","DOI":"10.1002\/tee.20676"},{"key":"key-10.20965\/jaciii.2016.p0484-17","doi-asserted-by":"crossref","unstructured":"K. Hirasawa, X. Wang, J. Murata, J. Hu, and C. Z. Jin, \u201cUniversal Learning Network and Its Application to Chaos Control,\u201d Neural Networks, Vol.13, No.2, pp. 239-253, 2000.","DOI":"10.1016\/S0893-6080(99)00100-8"},{"key":"key-10.20965\/jaciii.2016.p0484-18","doi-asserted-by":"crossref","unstructured":"K. Hirasawa, J. Murata, J. Hu, and C. Z. Jin, \u201cUniversal Learning Network and Its Application to Robust Control,\u201d IEEE Trans. on Systems Man and Cybernetics, Part B, Vol.30, No.3, pp. 419-430, 2000.","DOI":"10.1109\/3477.846231"},{"key":"key-10.20965\/jaciii.2016.p0484-19","unstructured":"J. R. Koza, Genetic Programming, on the programming of computers by means of natural selection, Cambridge, Mass.: MIT Press, 1992."},{"key":"key-10.20965\/jaciii.2016.p0484-20","unstructured":"Y. Chen, C. Yue, S. Mabu, and K. Hirasawa, \u201cA Genetic Relation Algorithm with Guided Mutation for the Large-Scale Portfolio Optimization,\u201d Proc. of the ICROS-SICE Int. Joint Conf. 2009, pp. 2579-2584, 2009."},{"key":"key-10.20965\/jaciii.2016.p0484-21","unstructured":"R. S. Sutton, A. G. Barto, Reinforcement Learning-An Introduction, Cambridge: Massachusetts, London, England, MIT Press, 1998."}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/phyosetsu.php?ppno=JACII002000030013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,7]],"date-time":"2019-09-07T16:54:48Z","timestamp":1567875288000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.fujipress.jp\/jaciii\/jc\/jacii002000030484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5,19]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,5,19]]},"published-print":{"date-parts":[[2016,5,19]]}},"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.20965\/jaciii.2016.p0484","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"type":"electronic","value":"1883-8014"},{"type":"print","value":"1343-0130"}],"subject":[],"published":{"date-parts":[[2016,5,19]]}}}