انتخاب پرتفوی سهام با استفاده از شواهد نظریه دمپستر-شفر
محورهای موضوعی : دانش سرمایهگذاریشعبان محمدی 1 , نادر نقش بندی 2 , هادی سعیدی 3
1 - کارشناسی ارشد حسابداری، موسسه آموزش عالی حکیم نظامی قوچان، قوچان، ایران. (نویسنده مسئول)
2 - استادیار گروه حسابداری،موسسه آموزش عالی حکیم نظامی قوچان، قوچان، ایران
3 - استادیار گروه حسابداری، واحد شیروان، دانشگاه آزاد اسلامی، شیروان، ایران
کلید واژه: پرتفوی سهام, دمپستر-شفر, بهینه سازی, روش فازی,
چکیده مقاله :
درمدل ریسک و بازده برای انتخاب پرتفوی سهام از داده های تاریخی دارایی استفاده می گردد. عوامل بحرانی نیز وجود دارد که به طور مستقیم یا غیر مستقیم بر بازار سهام تاثیر می گذارد.در پژوهش حاضر از روش دلفی فازی برای شناسایی عوامل بحرانی و از فاکتورهای دارای ضریب همبستگی پایین استفاده شد.از عوامل بحرانی و داده های تاریخی برای تطبیق تئوری شواهد دمپستر-شفر برای رتبه بندی سهام استفاده شد. نمونه گیری با استفاده از سهام موجود در بورس اوراق بهادار تهران و شبیه سازی توسط بهینه سازی کولونی مورچه و همچنین از نرم افزار متلب برای پیاده سازی استفاده گردید. عملکرد نتایج در مقایسه با عملکرد اخیر دارایی ها رضایت بخش است.
Markovitz's risk-taking model is to select stocks based on historical asset data. In addition to the impact of historical returns, there are many other critical factors that directly or indirectly affect the stock market. The present study first uses the Fuzzy Delphi method to identify critical factors and ultimately considers factors with low correlation coefficients. Critical factors and historical data were used to adapt Dempestor-Schafer evidence theory for stock rankings. Then, in the sampling model, stocks with a higher rank are proposed. Sampling was carried out using stock held on Tehran Stock Exchange and simulated by optimization of colonization of ant. The performance of the results is satisfactory in comparison with the recent performance of assets.
* H. Markowitz, Portfolio selection, Journal of Finance 7 (1952) 7791.
* S. J. Grossman, J. E. Stiglitz, On the impossibility of informationally efficient markets, The American economic review 70 (3) (1980) 393–408.
* M. G. Yunusoglu, H. Selim, A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to istanbul stock exchange, Expert Systems with Applications 40 (3) (2013) 908–920.
* P. Xidonas, E. Ergazakis, K. Ergazakis, K. Metaxiotis, D. Askounis, G. Mavrotas, J. Psarras.(2009).On the selection of equity securities: An expert systems methodology and an application on the athens stock exchange, Expert Systems with Applications 36, 11966 11980.
* P. Xidonas, G. Mavrotas, C. Zopounidis, J. Psarras, Ipssis(2011).: An integrated multicriteria decision support system for equity portfolio construction and selection, European Journal of Operational Research 210 (2) 398–409.
* R. D. Edwards, J. Magee, W. Bassetti,(2007).Technical analysis of stock trends, CRC Press.
* F. Abdollahzadeh, Investment management and tehran stock exchange (2002).
* Y. Siskos, A. Spyridakos, D. Yannacopoulos, Minora: A multicriteria decisionaiding system for discrete alternatives, Jounal of Information Science and Technology 2 (2) (1993) 136–149.
* A. Adebiyi, C. Ayo, M. O. Adebiyi, S. Otokiti, Stock price prediction using neural network with hybridized market indicators, Journal of Emerging Trends in Computing and Information Sciences 3 (1) (2012) 1–9.
* A. Fern´andez, S. G´omez, Portfolio selection using neural networks, Computers & Operations Research 34 (4) (2007) 1177–1191.
* P. C. Ko, P. C. Lin, Resource allocation neural network in portfolio selection, Expert Systems with Applications 35 (1) (2008) 330–337.
* S. O. Olatunji, M. S. Al-Ahmadi, M. Elshafei, Y. A. Fallatah, Saudi Arabia stock prices forecasting using artificial neural networks, in: Applications of Digital Information and Web Technologies (ICADIWT), 2011 Fourth International Conference on the, IEEE, 2011, pp. 81–86.
* J.-S. Chen, Y.-T. Lin, A partitioned portfolio insurance strategy by a relational genetic algorithm, Expert Systems with Applications 36 (2) (2009) 2727–2734.
* J.-S. Chen, J.-L. Hou, S.-M. Wu, Y.-W. Chang-Chien, Constructing investment strategy portfolios by combination genetic algorithms, Expert Systems with Applications 36 (2) (2009) 3824–3828.
* J. R. Jiao, Y. Zhang, Y. Wang, A heuristic genetic algorithm for productportfolio planning, Computers & Operations Research 34 (6) (2007) 1777–1799.
* Y. Chen, S. Mabu, K. Hirasawa, A model of portfolio optimization using time adapting genetic network programming, Computers & operations research 37 (10) (2010) 1697–1707.
* J. D. Bermudez, J. V. Segura, E. Vercher, A fuzzy ranking strategy for portfolio selection applied to the spanish stock market, in: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007., 2007, pp. 1–4.
* A. Bilbao-Terol, B. M. P´er-Gladish, Arenas-Parra, M. V. Rodr´ıguez-Uria, Fuzzy compromise programming for portfolio selection, Applied Mathematics and Computation 173 (2006) 251264.
* M. Fasanghari, G. A. Montazer, Design and implementation of fuzzy expert system for tehran stock exchange portfolio recommendation, Expert Systems with Applications 37 (9) (2010) 6138–6147.
* F. Tiryaki, M. Ahlatcioglu, Fuzzy stock selection using a new fuzzy ranking and weighting algorithm, Applied Mathematics and Computation 170 (2005) 144–157.
* X. Huang, Risk curve and fuzzy portfolio selection, Computers & Mathematicswith Applications 55 (6) (2008) 1102–1112.
* R. Bhattacharyya, S. Kar, D. D. Majumder, Fuzzy mean–variance– skewness portfolio selection models by interval analysis, Computers &Mathematics with Applications 61 (1) (2011) 126–137.
* R. Bhattacharyya, S. A. Hossain, S. Kar, Fuzzy cross-entropy, mean, variance, skewness models for portfolio selection, Journal of King Saud University-Computer and Information Sciences 26 (1) (2014) 79–87.
* R. Bhattacharyya, M. B. Kar, S. Kar, D. D. Majumder, Mean-entropyskewness fuzzy portfolio selection by credibility theory approach, in: Pattern Recognition and Machine Intelligence, Springer, 2009, pp. 603–608.
* R. Bhattacharyya, S. Kar, Multi-objective fuzzy optimization for portfolio selection: an embedding theorem approach, Turkish Journal of Fuzzy Systems 2 (1) (2011) 14–35.
* T. H. Hsu, T. H. Yang, Application of fuzzy analytic hierarchy process in the selection of advertising media, Journal of Management and Systems 7 (1) (2000) 19–39.
* T. J. Murry, L. L. Pipino, J. P. Gigch, A pilot study of fuzzy set modification of delphi, Human Systems Management 5 (1) (1985) 76–80.
* A. Ishikawa, M. Amagasa, T. Shiga, G. Tomizawa, R. Tatsuta, H. Mieno, The maxmin delphi method and fuzzy delphi method via fuzzy integration, Fuzzy Sets and Systems 55 (3) (1993) 241–253.
* Y. Kuo, P. Chen, Constructing performance appraisal indicators for mobility of the service industries using fuzzy delphi method, Expert Systems with Applications 35 (2008) 1930–1939.
* N. Falsafi, R. Y. Zenouz, M. M. Mozaffari, Employees performance appraisal with topsis under fuzzy environment, International Journal of Society Systems Science 3 (3) (2011) 272–290.
* K. Subramanyam, M. Venkatachalam, The role of book value in equity valuation: does the stock variable merely proxy for relevant past flows?, Available at SSRN 113388.
* W. C. Barbee Jr, S. Mukherji, G. A. Raines, Do sales-price and debt-equityexplain stock returns better than book-market and firm size?, Financial Analysts Journal 52 (2) (1996) 56–60.
* C. C. Ying, Stock market prices and volumes of sales, Econometrica: Journal of the Econometric Society (1966) 676–685.
* R. G. Bowman, The importance of a market-value measurement of debt in assessing leverage, Journal of Accounting Research (1980) 242–254.
* A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, The annals of mathematical statistics (1967) 325–339.
* G. Shafer, et al., A mathematical theory of evidence, Vol. 1, Princeton university press Princeton, 1976.
* L. Hong-dong, Z. Jing, X. Lin, L. Hai-ping, F. Yi, Application of ds evidence theory in combined price forecasting, in: Electric Utility Deregulationand Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on, IEEE, 2008, pp. 1025–1029.
* A. Maseleno, M. M. Hasan, Skin diseases expert system using dempstershafer theory, International Journal of Intelligent Systems and Applications 4 (5) (2012) 38.
* C. Zhang, W. Zhu, S. Yang, Banking operational risk management on ds evidence theory, in: Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference on, IEEE, 2007, pp. 4640–4644.
* R. Bhattacharyya, Possibilistic sharpe ratio based novice portfolio selection models, in: National Conference on Advancement of Computing in Engineering Research (ACER 13) Krishnagar, West Bengal, India, 2013, pp. 33–45.
* M. Dorigo, M. Birattari, T. St¨utzle, Ant colony optimization, Computational Intelligence Magazine, IEEE 1 (4) (2006) 28–39.
* J.-L. Deneubourg, S. Aron, S. Goss, J. M. Pasteels, The self-organizing exploratory pattern of the argentine ant, Journal of insect behavior 3 (2) (1990) 159–168.
* C.-F. Huang, A hybrid stock selection model using genetic algorithms and support vector regression, Applied Soft Computing 12 (2) (2012) 807–818.
* T.-D. Nguyen, A. W. Lo, Robust ranking and portfolio optimization, European Journal of Operational Research 221 (2) (2012) 407–416.
_||_
