انتخاب پرتفوی بهینه با استفاده از سیستم خبره در محیط فازی ممدانی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد مدیریت بازرگانی مالی، دانشکده مدیریت،گروه مدیریت و حسابداری، دانشگاه یزد

2 دانشیار، دانشکده مدیریت،گروه مدیریت و حسابداری، دانشگاه یزد، یزد

3 استادیار، دانشکده مدیریت، گروه مدیریت و حسابداری، دانشگاه یزد

چکیده

اصلاحات جزء جدایی ناپذیر تحولات اقتصادی در بخش مالی می باشد که شامل شکل دهی دوباره ساختار بازار
سرمایه، نوآوری در ابزارها و محیطی با نظم جامع تر است. دو مکتب فکری غالب بر ادبیات بازار سهام، تجزیه
و تحلیل های بنیادی و تکنیکی می باشند. مسئله انتخاب پرتفوی بسیار مهم می باشد. به همین علت، این پژوهش
ارزیابی سهام، شرکت ها با استفاده از هر دو روش تجزیه و تحلیل بنیادی و تکنیکی را برگزیده و سپس به منظور
تشکیل پرتفویی که حالات مختلف ریسک و ترجیحات سرمایه گذارن را لحاظ کند، از مدل فازی ممدانی و
مدل برنامه ریزی خطی عدد صحیح ترکیبی استفاده نموده است. دلیل استفاده از سیستم فازی ممدانی، کارا
بودن آن در محیط های مبهم و استفاده از دانش انسانی و مدل برنامه ریزی خطی عدد صحیح ترکیبی، قابلیت
یافتن جواب بهینه مسئله از میان تعداد زیاد جواب موجود می باشد. نتایج ارزیابی عملکرد پرتفوی های تشکیل
شده برای سه حالت سرمایه گذار ریسک گریز، ریسک خنثی و ریسک پذیر، نشان می دهد که عملکرد پرتفوی
پیشنهادی مثبت بوده و عملکرد مناسبی را نشان می دههد، اما در مقیاسی  دقیق تر  پرتفوی تشکیل شده برای
سرمایه گذار ریسک گریز در وضعیت مطلوب تری قرار دارد

کلیدواژه‌ها


عنوان مقاله [English]

Selection of Optimal Portfolio Using Expert System in Mamdani Fuzzy Environment

نویسندگان [English]

  • Seyedeh Farnaz Kouhbanani Nejad 1
  • Darush farid 2
  • Hojatallah sadeghi 3
چکیده [English]

Modifications are an integrated part of economic evolution in financial section which include reforming the capital market structure, innovating in tools and an environment with more comprehensive discipline. Two dominant schools of thought in the literature on stock markets are fundamental and technical analysis. Selection the portfolio would be so important. So we use both fundamental and technical analysis to evaluate companies' stock and then in order to form a portfolio which consider different risk states and investor’s preferences utilize Mamdani Fuzzy and mixed integer linear programming model. The reasons for the use Mamdani Fuzzy system are its capability of working in vague environment and using human knowledge and for mixed integer linear programming model is its capability in finding the optimum solution among the several available ones. The results of evaluating the performance of formed portfolio for three cases of Risk averse, Risk neutral and Risk prone investor show that the performance of proposed portfolio is positive and proper, but in a more accurate scale the formed portfolio has a more proper condition for the risk averse investor.

کلیدواژه‌ها [English]

  • Stock Evaluation
  • Optimal Portfolio
  • Expert System
  • Portfolio Management
  • Fuzzy Logic
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Maknickiene, N. Selection of orthogonal investment portfolio using Evolino RNN trading model. Social and Behavioral Sciences. (110) . 1158 – 1165. 2014.

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Fernández, A. & Gómez, S. Portfolio selection using neural networks. Computers & Operations Research 34 ,1177–1191. 2007

Gupta, P.; Inuiguchi, M. & Mehlawat, M. A hybrid approach for constructing suitable and potimal portfolios. Expert Systems with Applications, 38 (5): 5620., 908–920. 2013.

Karsak, E. Fuzzy multiple objective programming framework to prioritize decision requirements in prioritize function deployment. Computers quality function deployment, computers & industrial engineering (47), 149-163. 2004.

Levy,H; Levy, M. The benefits of differential variance- based constraints in portfolio optimization. European Journal of Operational Research, Volume 234, Issue 2, PP. 372-381. 2014

Rada, R. Expert systems and evolutionary computing for financial investing: A review. Expert Systems with Applications, 34(4), 2232–2240. 2008.

Reilly, F. & Brown, C Investment Analysis and Portfolio Management. Publisher: Cengage Learning; 10 edition. P. 840. 2011.

Xidonas, P. & Psarras, J. Equity portfolio management within the MCDM frame: A literature review. International Journal of Banking, Accounting and Finance, 1(3), 285–309. 2009.

Yunusoglu, G. & Selim, H. A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange. Expert Systems with Applications. (40). 908-920. 2013.

Zhou, R., Yang, Z., Yu, M., Ralescu, D. A.,. A portfoliooptimization model based on informationentropy and fuzzy time series. Fuzzy Optimization and Decision Making, 14(4), 381-397. 2015

Zhang, WG. & Nic, ZK. On admissible efficient portfolio selection problem. Applied Mathematic and Computations. 159 (2): 357-371. 2004.

Mamdani, E.H. & Assilian, S. (1975). An Experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1-13. 1975.

Ross, T.J. Fuzzy logic with engineering application john wiley & sons. 2005

 

 

 

 

 

 

 

Fama, E. & Ferench, K. The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives 18. P:20. 2003

Maknickiene, N. Selection of orthogonal investment portfolio using Evolino RNN trading model. Social and Behavioral Sciences. (110) . 1158 – 1165. 2014.

Markowitz H. Portfolio selection; Journal of Finance: 77-91. 1952.

Strong، Robert A. Portfolio Construction, Management & Protection, 2d Edition، South-Western College، P .431. 2000.

Yunusoglu, G. & Selim, H. A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange. Expert Systems with Applications. (40). 908-920. 2013.

Alvarz Grima, M.; Bruines, PA.; Verhoef, p. Modeling tunnel machine performance by neure-fuzzy methods. Tunnelling and underground space technology. Pp. 259-269. 2000.

Asmuni, H.. Fuzzy methodologies for automated university. Timetabling Solution Construction and Evaluation. Ph.D Thesis. University of Nottingham, UK. P. 71. 2009.

Baralis, E.; cagliero, L.; Garza, P. Planning stock portfolios by means of weighted frequent itemsets. Expert Systems with Applications Volume 86, 15 November, Pages 1-17. 2017

Chong, H.Y.; Yap, H.; Loong, Y. Fuzzy-based risk prioritization for a hydrogen refueling facility in Malaysia. Applied Physics & Engineering. (24) Pp. 565-573. 2013.

Fernández, A. & Gómez, S. Portfolio selection using neural networks. Computers & Operations Research 34 ,1177–1191. 2007

Gupta, P.; Inuiguchi, M. & Mehlawat, M. A hybrid approach for constructing suitable and potimal portfolios. Expert Systems with Applications, 38 (5): 5620., 908–920. 2013.

Karsak, E. Fuzzy multiple objective programming framework to prioritize decision requirements in prioritize function deployment. Computers quality function deployment, computers & industrial engineering (47), 149-163. 2004.

Levy,H; Levy, M. The benefits of differential variance- based constraints in portfolio optimization. European Journal of Operational Research, Volume 234, Issue 2, PP. 372-381. 2014

Rada, R. Expert systems and evolutionary computing for financial investing: A review. Expert Systems with Applications, 34(4), 2232–2240. 2008.

Reilly, F. & Brown, C Investment Analysis and Portfolio Management. Publisher: Cengage Learning; 10 edition. P. 840. 2011.

Xidonas, P. & Psarras, J. Equity portfolio management within the MCDM frame: A literature review. International Journal of Banking, Accounting and Finance, 1(3), 285–309. 2009.

Yunusoglu, G. & Selim, H. A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange. Expert Systems with Applications. (40). 908-920. 2013.

Zhou, R., Yang, Z., Yu, M., Ralescu, D. A.,. A portfoliooptimization model based on informationentropy and fuzzy time series. Fuzzy Optimization and Decision Making, 14(4), 381-397. 2015

Zhang, WG. & Nic, ZK. On admissible efficient portfolio selection problem. Applied Mathematic and Computations. 159 (2): 357-371. 2004.

Mamdani, E.H. & Assilian, S. (1975). An Experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1-13. 1975.

Ross, T.J. Fuzzy logic with engineering application john wiley & sons. 2005