عوامل موثر بر پیش‌بینی بازدۀ سهام؛ استفاده از تحلیل حوزۀ دانش و تکنیک دلفی-فازی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

2 استاد، گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

3 استادیار، گروه مدیریت صنعتی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

10.48301/kssa.2022.327544.1982

چکیده

روند حرکت و تغییرات بازده بازار سهام دریچه‌ای برای رفتار اقتصادی آینده است، زیرا انتظارات سرمایه‌گذاران از شرایط اقتصادی آینده را نشان می‌دهد. هدف اصلی این پژوهش، شناسایی و رتبه بندی عوامل موثر بر پیش‌بینی بازده سهام، در شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران می‌باشد. در این پژوهش دو سؤال کلی مطرح شده است؛ اول این‌که چه عواملی بر پیش‌بینی بازدۀ سهام شرکت‌ها تاثیرگذار هستند؟ و دوم این‌که اولویت‌بندی عوامل موثر چگونه بوده و هر عامل از چه درجه اهمیتی برخوردار است؟ بر پایۀ تحلیل حوزۀ دانش اقدام به شناسایی عوامل خرد اقتصادی گردیده و بر پایۀ نظر سنجی از خبرگان و تکنیک دلفی-فازی به اهمیت سنجی و پالایش عوامل شناسایی شده اقدام گردید. طبق نتایج به دست آمده، از میان عوامل اصلی مؤثر بر پیش‌بینی بازدۀ سهام شرکت‌ها به ترتیب اولویت عوامل سود تقسیمی به قیمت، نسبت قیمت به سود، نسبت تعدیلی قیمت به سود، نسبت رشد سود، بازده بدون ریسک، پراکندگی بازده، دامنۀ نوسان بازده، ضریب چولگی پیرسون، ضریب چولگی استاندارد، ضریب کشیدگی، نسبت سود تقسیمی و انحراف متوسط بازده، در پیش‌بینی بازدۀ سهام شرکت‌ها نقش دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Factors Affecting the Forecast of Stock Returns using Delphi-Fuzzy Knowledge Analysis and Technique

نویسندگان [English]

  • Maryam Bahmani 1
  • Mohammad Ebrahim Pourzarandi 2
  • Mehrzad Minoei 3
1 PhD Student, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3 Assistant Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

The trend of stock market movements and changes is a window for future economic behavior because it reflects investors' expectations of future economic conditions. The main purpose of this study was to identify and rank the factors affecting the forecast of stock returns in companies listed on the Tehran Stock Exchange. The present research raises two general questions. The first question is related to the factors affecting the forecast of companies' stock returns. The second question deals with the method of prioritizing effective factors and the importance assigned to each factor? Based on the analysis of the field of knowledge, microeconomic factors were identified and based on a survey of experts and Delphi-fuzzy technique, the importance of the identified factors was assessed and refined. According to the results, among the main factors affecting the forecast of stock returns of companies, the priority factors of dividend to price, price-to-profit ratio, price-to-profit adjustment ratio, profit growth ratio, risk-free return, return on distribution, fluctuation range yield, Pearson skewness coefficient, standard skewness coefficient, elongation coefficient, dividend ratio and average return deviation play an important role in the order mentioned.

کلیدواژه‌ها [English]

  • Identification of effective factors Stock return forecast Stock return Knowledge field Delphi
  • fuzzy

مراجع

Ahmadkhani, M., Abdul Rahimian, M. H., & Mirjafari Ardakani, S. A. (2017). Investigating the relationship between investment factors and the performance and stock returns of companies listed on the Tehran Stock Exchange. Applied studies in management and development sciences, 5(5), 1-8. https://civilica.com/doc/994990/
Ananthi, M., & Vijayakumar, K. (2021). RETRACTED ARTICLE: Stock market analysis using candlestick regression and market trend prediction (CKRM). Journal of Ambient Intelligence and Humanized Computing, 12(5), 4819-4826. https://doi.org/10.1007/s12 652-020-01892-5
Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March 26-28). Stock Price Prediction Using the ARIMA Model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, UK. https://doi.org/10.1109/UKSim.2014.67
Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056. https://doi.org/10.1016/j.eswa.2015.05.013
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), 1-24. https://doi.org /10.1371/journal.pone.0180944
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control (5 ed.). John Wiley & Sons. https://www.wiley.com/en-au/Time+Series+Analysis:+Forecasting+and+Control,+5th+Edition-p-978111867 5021
Cakra, Y. E., & Trisedya, B. D. (2015, October 10-11). Stock price prediction using linear regression based on sentiment analysis. 2015 International Conference on Advanced Computer Science and Information Systems, Depok, Indonesia. https://doi.org/10.1109/ ICACSIS.2015.7415179
Chen, T-L., & Chen, F-Y. (2016). An intelligent pattern recognition model for supporting investment decisions in stock market. Information Sciences, 346-347, 261-274. http s://doi.org/10.1016/j.ins.2016.01.079
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83(4), 187-205. https://doi.org/10.1016/j.eswa.2017.04.030
Creighton, J., & Zulkernine, F. H. (2017, December 11-14). Towards building a hybrid model for predicting stock indexes. 2017 IEEE International Conference on Big Data, Boston, MA, USA.  https://doi.org/10.1109/BigData.2017.8258433
Dey, S., Kumar, Y., Saha, S., & Basak, S. (2016). Forecasting to Classification: Predicting the direction of stock market price using Xtreme Gradient Boosting. PESIT South Campus, 1-10. https://doi.org/10.13140/RG.2.2.15294.48968
Di Persio, L., & Honchar, O. (2017). Recurrent neural networks approach to the financial forecast of Google assets. International journal of Mathematics and Computers in simulation, 11, 7-13. https://iris.univr.it/retrieve/handle/11562/959057/66085/Recu rrent
Ghosh, I., & Datta Chaudhuri, T. (2021). FEB-Stacking and FEB-DNN Models for Stock Trend Prediction: A Performance Analysis for Pre and Post Covid-19 Periods. Decision Making: Applications in Management and Engineering, 4(1), 51-84. https://doi.org/ 10.31181/dmame2104051g
Hossain, M. A., Karim, R., Thulasiram, R., Bruce, N. D. B., & Wang, Y. (2018, November 18-21). Hybrid Deep Learning Model for Stock Price Prediction. 2018 IEEE Symposium Series on Computational Intelligence Bangalore, India . https://doi.org/ 10.1109/SSCI.2018.8628641
Kaviani, M., Fakhrehosseini, S. F., & Dastyar, F. (2020). An Overview of the Importance and Why the Stock Return Prediction, with Emphasis on Macroeconomic Variables. Journal of Accounting and Social Interests, 10(2), 113-131. https://doi.org/10.2205 1/ijar.2020.26185.1505
Kim, S. H., Lee, H. S., Ko, H. J., Jeong, S. H., Byun, H. W., & Oh, K. J. (2018). Pattern matching trading system based on the dynamic time warping algorithm. Sustainability, 10(12), 1-18. https://doi.org/10.3390/su10124641
Kohansal Kafshgari, M., Zarei, A., & Behmanesh, R. (2021). Presentation of intelligent Meta-heuristic Hybrid models (ANFIS -MGGP ) to predict stock returns with more accuracy and speed than other Meta-heuristic methods. Financial Engineering and Portfolio Management, 12(47), 390-413. https://fej.ctb.iau.ir/article_681209.html?l ang=en
Lv, D., Yuan, S., Li, M., & Xiang, Y. (2019). An empirical study of machine learning algorithms for stock daily trading strategy. Mathematical Problems in Engineering, 2019, 1-31. https://doi.org/10.1155/2019/7816154
Maghsoud, H., Vakilifard, H., & Torabi, T. (2020). Factor Variability Test in Stock Return Forecasting Using Dynamic Model Averaging (DMA). Financial Engineering and Portfolio Management, 11(45), 639-660. https://fej.ctb.iau.ir/article_671420.html
Mohamadi, M., Hemmati, H., & Sharhani, E. (2021). Investigating the relationship between risk of stock price falls, financial turmoil and stock returns on the Tehran Stock Exchange. Journal of Accounting and Management Vision, 4(47), 122-138. http:// www.jamv.ir/article_138596.html?lang=en
Mousavi, S. N., Momenimofrad, M., & Mehrabi, M. (2019). Identify and prioritize the Factors affecting Organizational envy using Delphi Fuzzi Approach. Public Administration Perspaective, 10(1), 95-114. https://doi.org/10.52547/jpap.2019.96 498
Naseer, M., & Bin Tariq, D. (2015). The efficient market hypothesis: A critical review of the literature. The IUP Journal of Financial Risk Management, 12(4), 48-63. https://pap ers.ssrn.com/sol3/papers.cfm?abstract_id=2714844
Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611. https:// doi.org/10.1016/j.eswa.2015.07.052
Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016, October 03-05). Sentiment analysis of Twitter data for predicting stock market movements. 2016 International Conference on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India. https://doi.org/10.1109/SCOPES.2016.7955659
Peachavanish, R. (2016, March 16-18). Stock selection and trading based on cluster analysis of trend and momentum indicators. Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, China. https://www.iaeng.org/publicati on/IMECS2016/IMECS2016_pp317-321.pdf
Roy, R., & Shijin, S. (2018). A six-factor asset pricing model. Borsa Istanbul Review, 18(3), 205-217. https://doi.org/10.1016/j.bir.2018.02.001
Seng, J-L., & Yang, H-F. (2017). The association between stock price volatility and financial news – a sentiment analysis approach. Kybernetes, 46(8), 1341-1365. https://doi.org /10.1108/K-11-2016-0307
Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 1-22. https://doi.org/10.3390/ijfs7020026
Velay, M., & Daniel, F. (2018). Stock chart pattern recognition with deep learning. arXiv, 1-6. https://doi.org/10.48550/arXiv.1808.00418
Xu, Y., & Cohen, S. B. (2018, July 15-20). Stock movement prediction from tweets and historical prices. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia. https://doi.org/10.18653/v1/P18-1183
Yang, B., Gong, Z. J., & Yang, W. (2017, July 26-28). Stock market index prediction using deep neural network ensemble. 2017 36th Chinese Control Conference, Dalian, China . https://doi.org/10.23919/ChiCC.2017.8027964
Zhang, J., Cui, S., Xu, Y., Li, Q., & Li, T. (2018). A novel data-driven stock price trend prediction system. Expert Systems with Applications, 97, 60-69. https://doi.org/10.1 016/j.eswa.2017.12.026
فصلنامه علمی کارافن
دوره 19، شماره 2 - شماره پیاپی 58
علوم انسانی
شهریور 1401
صفحه 431-453

فایل ها

سابقه مقاله

  • تاریخ دریافت: 21 بهمن 1400
  • تاریخ بازنگری: 25 فروردین 1401
  • تاریخ پذیرش: 05 اردیبهشت 1401

هم رسانی

ارجاع به این مقاله

آمار