1دانشجوی دکتری، مدیریت صنعتی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
2استاد، مدیریت صنعتی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
3استاد، مهندسی صنایع، دانشکدۀ مهندسی صنایع، دانشگاه صنعتی شریف، تهران، ایران
4استاد، مدیریت مالی، دانشکدۀ مدیریت، دانشگاه تهران، تهران، ایران
چکیده
در رویکردهای سنتی مقادیر مرتبط با اهداف یک مدل تصمیمگیری اغلب معین و قطعی فرض می‌شود، درحالی‌که در دنیای واقعی این مقادیر احتمالی است و تصمیمگیرنده نمیتواند آنها را بهطور قطعی تعیین کند. بهینهسازی مالی یکی از حوزههای جذاب در تصمیمگیری در شرایط عدم اطمینان است. در مسئلۀ انتخاب سبد سرمایهگذاری، تصمیمگیرنده همزمان با اهداف مختلف و گاه متعارض مانند نرخ بازده، نقدینگی، سود تقسیمی و ریسک مواجه است. تاکنون روش‌های مختلف برنامهریزی چندهدفه از جمله برنامهریزی آرمانی و برنامهریزی توافقی که بیشترین میزان ترجیحات و آرمان‌های تصمیمگیرنده را ارضا کند و روش‌های مختلف برنامهریزی چندمعیاره برای مواجهه با مسئلۀ انتخاب سبد سهام استفاده شده‌اند. در این پژوهش با تشکیل برنامهریزی تصادفی چندهدفه که شاخص‌های مرتبط با اهداف، تصادفی و مبتنی بر توزیع نرمال هستند، از مدل برنامهریزی توافقی با محدودیت تصادفی به‌منظور انتخاب سبد استفاده شد. پس از توسعه و حل مدل، در نهایت نتایج مدل برنامهریزی برای انتخاب سبد سهام در بازار بورس تهران ارائه شده است.
Designing a Multi-objective Stochastic programming model for portfolio selection
نویسندگان [English]
Alireza Sharifisalim1؛ Mansour Momeni2؛ Mohammad Modarres Yazdi3؛ Reza Rayi4
1Ph.D. Student in Operational Research Management, Faculty of Management, Tehran University, , Tehran, Iran.
2Prof., Industrial Management, Faculty of Management, Tehran University, Tehran, Iran.
3Prof., Industrial Engineering, Sharif University of Technology, Tehran, Iran.
4Prof., Financial Management, Faculty of Management, Tehran University, Tehran, Iran.
چکیده [English]
In traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. So decision maker cannot estimate them exactly. Financial optimization is one of the most attractive areas in decision under uncertainty. In the portfolio selection problem the Decision Maker considers simultaneously conflicting objectives such as rate of return, liquidity, Dividend and risk. Multi-objective programming techniques such as goal programming and compromise programming are used to choose the portfolio best satisfying the Decision Maker’s aspirations and preferences; additionally Multi Criteria Decision Making (MCDM)Techniques for dealing with portfolio selection have been used. In this article, we assume that the parameters associated with the objectives are random and normally distributed. We propose a chance constrained compromise programming model is based on compromise programming and chance constrained programming models as a deterministic transformation to multi-objective stochastic programming portfolio model. To determine the share of industry investment planning MCDM were used. The result of the planning model for portfolio selection in Tehran Stock Exchange is shown.
Akbarpour Shirazi, M. Ahmadpor, A. (2010). The Use of Multiple Attribute Decision making in selecting stock. Quarterly Journal of Securities Exchange, 5, 5–38 (in persian).
Anvary Rostamy, A. Hoseinian, S. Rezaei Asl, M. (2013). Financial Ranking of Firms Listed in Tehran Stock Exchange Corporations Using MADM and Mixed Methods. Financial Research,14: 31–54 (in persian).
Aouni, B., Ben Abdelaziz, F., & Martel, J. M. (2005). Decision-maker's preferences modeling in the stochastic goal programming. European Journal of Operational Research, 162(3): 610-618.
Arditti, F. D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1): 19-36.
Baker, H. K., & Haslem, J. A. (1974). The impact of investor socioeconomic characteristics on risk and return preferences. Journal of Business Research, 2(4): 469-476.
Ballestero, E. (2001). Stochastic goal programming: a mean–variance approach. European Journal of Operational Research, 131(3): 476-481.
Ballestero, E., & Romero, C. (1996). Portfolio selection: A compromise programming solution. Journal of the Operational Research Society,47: 1377-1386.
Bell, D. E., Raiffa, H., & Tversky, A. (Eds.). (1988). Decision making: Descriptive, normative, and prescriptive interactions. Cambridge University Press, 99–112.
Ben Abdelaziz, F. B., Lang, P., & Nadeau, R. (1995). Distributional efficiency in multiobjective stochastic linear programming. European Journal of Operational Research, 85(2): 399-415.
Ben Abdelaziz, F., Lang, P., Nadeau, R. (1999). Efficiency in multiple criteria under uncertainty. Theory and Decision, 47: 191–211.
Ben Abdelaziz, F., Mejri, S. (2001). Application of goal programming in a multi-objective reservoir operation model in Tunisia, European Journal of Operational Research, 133(2): 352-361.
Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science, 6(1): 73-79.
Charnes, A., Cooper, W.W. (1963). Deterministic equivalents for optimizing and satisfying under chance constraints. Operations Research, 11: 18–39.
Chen; long , (2007). what drives stock price movement?, the Eli Broad College of business , Michigan state university.
Delbari, M. (2001). Evaluation criteria for effective stock selection in Tehran Stock Exchange, based on the analytic hierarchy process. M.A. Theses, Isfahan University (in persian).
Elton, E.J., Gruber, M.J., Padberg, M. (1976). Simple rules for optimal portfolio selection. Journal of Finance, 31: 1341–1357.
Hadavinejad, M. (2009). Factors affecting stock selection in Tehran Stock Exchange by Using of MADM. M.A. Theses, Emam Sadegh University (in persian).
Hamediyan, M. (2000). Factors affecting share prices and investors' decisions in Tehran Stock Exchange. M.A. Theses, Shahid Beheshti University (in persian).
Heybati, F. (1999). Evaluation of maternal investment firms based on AHP. Financial Research, 13, 32–49 (in persian).
Hosainzadeh, M., Menhaj, M., Kazemi, A. (2014). A method for solving possibilistic multi-objective linear programming problems with fuzzy decision variables. Jornal of Industrial Management, 6(4): 709-724. (in persian)
Jerry; W.R, (2011). Combined DEMATEL technique with a novel MCDM model for exploring portfolio selection based on CAPM, Expert Systems with Applications, 38:16-25.
Kumar, P.C., Philippatos, G.C., Ezzell, J.R. (1978). Goal programming and the selection of portfolios by dual-purpose funds. The Journal of Finance, 33:303–310.
Lai, T.Y. (1991). Portfolio selection with skewness: A multiple-objective approach. Review of Quantitative Finance and Accounting, 293–305.
Lee, S.M., Chesser, D.L. (1980). Goal Programming for Portfolio Selection, the Journal of Portfolio Management (Spring), 22–26.
Levary, R.R., Avery, M.L. (1984). On practical application of weighting equities in a portfolio via goal programming. Operation Research, 21: 246–261.
Levy, H. (1992). Stochastic dominance and expected utility: Survey and analysis. Management Science, 38: 555–593.
Liu, L. (1999). Approximate portfolio analysis. European Journal of Operational Research, 119: 35–49.
Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7: 77-91.
Markowitz, H. M. (1959). Portfolio selection: efficient diversification of investments. New York, Wiley.
Moutameni, A. Sharifi Salim, A. (2013). Propounding a Model for Portfolio Selection in Stock Exchange by Using of MCDM (Case Study: 50 Better Companies). Journal of Industrial Management Perspective, 5, 73-89 (in persian).
Muhlemann, A.P., Lockett, A.G., Gear, A.E. (1978). Portfolio modeling in multiple-criteria situations under uncertainty. Decision Sciences, 9: 612–626.
Nawrocki, D.N., Carter, W.L. (1998). Earnings announcements and portfolio selection. Do they add value? International Review of Financial Analysis, 7: 37–50.
Ogryczak, W., (2000). Multiple criteria linear programming model for portfolio selection. Annals of Operations Research, 97: 143–162.
Onil; W. J, (1991). How to make money in stocks, New York: McGrow-Hill.
Potter; R. E, (1971). An empirical study of motivations of common stock investors, Southern Journal of Business, 6: 41–48.
Pourzand, F. Zibaei, M. (2012). Application of Stochastic Goal Programming in Water Resources Use Management: A Case Study of Firozabad Plain. Agricultural Economics & Development, 25: 420–427 (in persian).
Prakash, A. J., Chang, C. H., & Pactwa, T. E. (2003). Selecting a portfolio with skewness: Recent evidence from US, European, and Latin American equity markets. Journal of Banking & Finance, 27(7): 1375-1390.
Samuelson, P. (1970). The fundamental approximation of theorem of portfolio analysis in terms of means, variances and higher moments. Review of Economic Studies, 37: 537–542.
Sharpe, W.F. (1967). A linear programming algorithm for mutual fund portfolio selection. Management Science, 13: 499–510.
Shing, C., & Nagasawa, H. (1999). Interactive decision system in stochastic multiobjective portfolio selection. International Journal of Production Economics, 60: 187-193.
Steuer, R.E., Na, P. (2003). Multiple criteria decision making combined with finance: A categorized bibliographic study. European Journal of Operational Research, 150: 496–515.
Tamiz, M., Hasham, R., Jones, D.F., Hesni, B., Fargher, E.K. (1996). A two staged goal programming model for portfolio selection. In:Tamiz, M., (Ed.), Lecture Notes in Economics and Mathematical Systems, 432: 286–299.
Lee, w.sh., Tzeng, G.H.,Guan,J.L., Chien,K.T., Huang, J.M.. (2009). Combined MCDM techniques for exploring stock selection based on Gordon model,Expert Systems with Applications.
Zeleny, M. (1982). Multiple Criteria Decision Making. McGraw-Hill, New York.
Zopounidis, C., & Doumpos, M. (2002). Multi‐criteria decision aid in financial decision making: methodologies and literature review. Journal of Multi‐Criteria Decision Analysis, 11(4‐5): 167-186.
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