A Hybrid DEA Based CHAID and Imperialist Competitive Algorithm for Stock Selection
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
1 - Department of Industrial Management, Semnan Branch, Islamic Azad University, Semnan,Iran.
کلید واژه: DEA Based CHAID, Stock Selection, data mining, Imperialist Competitive Algorithm, Classification,
چکیده مقاله :
In this paper, the investment portfolio is formed based on the data mining algorithm of CHAID on the basis of the risk status criteria. In the next step, the second investment portfolio is created based on the decision rules extracted by the DEA-BCC model. The final portfolio is created through a two-objective mathematical programming model based on the Imperialist Competitive algorithm.
مقاله حاضرچهارچوب جدیدی برای ایجاد یک پرتفوی جدید از سهام ارایه می دهد . در این مقاله بحث می شود که چگونه یک پرتفوی بهینه ی سهام از طریق رویکرد حاضر در مقایسه با روش های قبلی طراحی می شود.در این مقاله ، پرتفوی سرمایه گذاری بر اساس الگوریتم داده کاوی چید بر مبنای معیار ریسک تشکیل می شود.در مرحله ی بعد ، دومین پرتفوی سرمایه گذاری بر مبنای قواعد تصمیم استخراج شده بر مبنای مدل DEA-BCC ایجاد می شود.پرتفوی نهایی از طریق یک مدل برنامه ریزی دو هدفه مبتنی بر الگوریتم رقابت استعماری ایجاد می شود.متدولوژی ارایه شده از طریق یک مطالعه موردی در بورس اوراق بهادار تهران بکار گرفته شده است.نتایج الگوریتم چید بر مبنای فیلد خروجی ریسک نشان می دهد که تمام سهام های کاندیدا در یک کلاس قرار نمی گیرد و ضروری است که هر کلاس از سهام های کاندیدا در مقایسه با سایر کلاس های سهام به صورت مستقل مورد ارزیابی قرار بگیرد.نتایج بکارگیری الگوریتم رقابت استعماری در مقیاس های کوچک و متوسط بر مبنای روش تاگوچی نشان می دهد که سهام های مورد مطالعه در روش بکار گرفته شده کالیبره می باشد.بر خلاف سایر روش های انتخاب پرتفوی سهام ،این مقاله ابتدا سهام ها را از طریق الگوریتم چید طبقه بندی می کند . سهام های طبقه بندی شده در هر کلاس به طور مستقل از طریق مدل DEA-BCC موردارزیابی قرار می گیرد.در نهایت پرتفوی بهینه از طریق الگوریتم رقابت استعماری انتخاب می شود.
[1] E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, In 2007 IEEE congress on evolutionary computation 4 (2007) 4661-4667.
[2] T. Back, Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford university press, 1996.
[3] R. Castellano, R. Cerqueti, Mean Variance portfolio selection in presence of infrequently traded stocks, European Journal of Operational Research 234 (2014) 442-449.
[4] C. C¸ iflikli, E. Kahya-Ozyirmidokuz, Implementing a data mining solution for enhancing carpet manufacturing productivity, Knowledge-Based Systems 23 (2010) 783-788.
[5] W. W. Cooper, Data envelopment analysis, Encyclopedia of Operations Research and Management Science (2001) 183-191.
[6] K. Deb, Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, 2001.
[7] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, InMHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science 6 (1995) 39-43.
[8] A. T. Eshlaghy, F. F. Razi, A hybrid greybased KOHONEN and genetic algorithm to integrated technology selection, International Journal of Industrial and Systems Engineering 20 (2015) 323-342.
[9] F. Faezy Razi, A. T. Eshlaghy, J. Nazemi, M. Alborzi, A. Pourebrahimi, A hybrid grey based KOHONEN model and biogeographybased optimization for project portfolio selection, Journal of Applied Mathematics (2014).
[10] K. Gnanendran, J. K. Ho, R. P. Sundarraj, Stock selection heuristics for interdependent items, European Journal of Operational Research 145 (2003) 585-605.
[11] F. Gorunescu, Data Mining: Concepts, models and techniques, Springer Science & Business Media, 2011.
[12] J. Han, J. Pei, M. Kamber, Data mining: concepts and techniques, Elsevier, 2011.
[13] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, 1992.
[14] H. C. Huang, T. K. Lin, P. W. Ngui, Analysing a mental health survey by chisquared automatic interaction detection, Annals of The Academy of Medicine, Singapore 22 (1993) 332-337.
[15] K. Y. Huang, C.-J. Jane, A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories, Expert systems with applications 36 (2009) 5387-5392.
[16] S. Hwang, J. Park, Performance Evaluation with Information on Portfolio Compositions, AsiaPacific Journal of Financial Studies 40 (2011) 710-730.
[17] A. Ishizaka, P. Nemery, Assigning machines to incomparable maintenance strategies with ELECTRE-SORT, Omega 47 (2014) 45-59.
[18] M. Kantardzic, Data mining: concepts, models, methods, and algorithms, John Wiley & Sons, 2011.
[19] A. Kaveh, Advances in metaheuristic algorithms for optimal design of structures, Springer, 2014.
[20] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, Optimization by simulated annealing, science 220 (1983) 671-680.
[21] R. K. Lai, C.-Y. Fan, W.-H. Huang,P.-C. Chang, Evolving and clustering fuzzy decision tree for financial time series data forecasting, Expert Systems with Applications 36 (2009) 3761-3773.
[22] D. T. Larose, C. D. Larose, Discovering knowledge in data: an introduction to data mining, John Wiley & Sons, 2014.
[23] C. A. K. Lovell, J. T. Pastor, Units invariant and translation invariant DEA models, Operations research letters 18 (1995) 147-151.
[24] D. Maringer, Portfolio management with heuristic optimization, Springer, 2005.
[25] J. A. McCarty, M. Hastak, Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression, Journal of business research 60 (2007) 656-662.
[26] D. L. Olson, D. Delen, Advanced data mining techniques, Springer Science, Business Media, 2008.
[27] J.-L. Prigent, Portfolio optimization and performance analysis, CRC Press, 2007.
[28] R. V. Rao, Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods, Springer Science & Business Media, 2007.
[29] S. C. Ray, Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research, Cambridge university press, 2004.
[30] F. F. Razi, A. T. Eshlaghy, J. Nazemi, M. Alborzi, A. Poorebrahimi, A hybrid greybased fuzzy C-means and multiple objective genetic algorithms for project portfolio selection, International Journal of Industrial and Systems Engineering 21 (2015) 154-179.
[31] L. M. Seiford, J. Zhu, Modeling undesirable factors in efficiency evaluation, European Journal of Operational Research 142 (2002) 16-20.
[32] J. Shadbolt, J. G. Taylor, Neural Networks and the Financial Markets: Bpredicting, Combining, and Portfolio Optimisation, Springer, 2002.
[33] K.-Y. Shen, M.-R. Yan, G.-H. Tzeng, Combining VIKOR-DANP model for glamor stock selection and stock performance improvement, Knowledge-Based Systems 58 (2014) 86-97.
[34] F. Tiryaki, B. Ahlatcioglu, Fuzzy portfolio selection using fuzzy analytic hierarchy process, Information Sciences 179 (2009) 53-69.
[35] F. Tiryaki, M. Ahlatcioglu, Fuzzy stock selection using a new fuzzy ranking and weighting algorithm, Applied Mathematics and Computation 170 (2005) 144-157.
[36] M. van Diepen, P. H. Franses, Evaluating chi-squared automatic interaction detection, Information Systems 31 (2006) 814-831.
[37] M. C. Wong, Y. L. Cheung, The practice of investment management in Hong Kong: market forecasting and stock selection, Omega 27 (1999) 451-465.
[38] P. Xidonas, G. Mavrotas, T. Krintas, J. Psarras, C. Zopounidis, Multicriteria portfolio management, Springer, 2012.
[39] B. Xing, W.-J. Gao, Introduction to Computational Intelligence, In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms 5 (2014) 3-17. Springer, Cham.
[40] H. Yu, R. Chen, G. Zhang, A SVM stock selection model within PCA, Procedia computer science 31 (2014) 406-412.
[41] X. Zhang, Y. Hu, K. Xie, S. Wang, E. W. T. Ngai, M. Liu, A causal feature selection algorithm for stock prediction modeling, Neurocomputing 142 (2014) 48-59.
