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Developing a model based on fuzzy logic for identifying reversal points in capital markets derived from technical analysis | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 05 خرداد 1405 اصل مقاله (3.14 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.35389.5267 | ||
| نویسندگان | ||
| Valiollah Mehri1؛ Mehrdad Ghanbari* 2؛ Babak Jamshidinavid2؛ Alireza Moradi3 | ||
| 1Department of Financial Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| 2Department of Accounting, Faculty of Humanities, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| 3Department of Economics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran | ||
| تاریخ دریافت: 30 مرداد 1403، تاریخ بازنگری: 30 شهریور 1403، تاریخ پذیرش: 15 مهر 1403 | ||
| چکیده | ||
| This research aims to present a model for identifying reversal points in capital markets using technical analysis based on fuzzy logic. From the perspective of its objectives, this is an applied research, meaning it seeks to acquire the necessary knowledge to develop a tool that addresses key needs of shareholders, such as identifying reversal points, which are crucial in decision-making for buying and selling. Additionally, since the study aims to find relationships between variables to identify reversal points and assess the impact of their changes on the overall outcome, it is classified as causal or experimental research in terms of its methodology. From the perspective of data type, this research is based on quantitative data. In terms of timing, this study employs a cross-sectional design followed by a prospective approach. In this research, fuzzy logic and genetic algorithms were used to provide a method for identifying reversal points in financial markets. For this purpose, a Mamdani fuzzy system was employed. After implementing the proposed structure, the optimized membership functions were evaluated to ensure their alignment with the research objective (identifying reversal points). The proposed method, due to its desirable accuracy in identifying reversal points, has increased the returns from trading. If users enter or exit trades based on alerts with a probability higher than 85%, according to the type of reversal point, they will enter or exit trades at the right time in about 94% of cases, which will significantly contribute to improving the profitability of their trades. | ||
| کلیدواژهها | ||
| Return points؛ capital market؛ technical analysis؛ fuzzy logic | ||
| مراجع | ||
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[1] N. Adams, D. Jacobs, S. Kenny, R. Serena, and M. Sutton, China's evolving financial system and its global importance, RBA Bulletin, 2021.
[2] E. Afshari Rad, S.E. Alavi, and H.A. Sinaii, An intelligent model for predicting stock trends using technical analysis methods, Financ. Res. 20 (2017), no. 2, 249-264.
[3] K.P. Anagnostopoulos and G. Mamani, The mean-variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms, Expert Syst. Appl. 38 (2011), no. 11, 14208-14217.
[4] M. Asghar Tabar and A. Jafari Samimi, Optimizing the moving average of stock prices in Tehran Stock Exchange: The meta-heuristic approach of adaptive genetic algorithm, Invest. Knowledge 7 (2017), no. 25, 127-148.
[5] Y. Bilan, B. Gavurova, G. Stanislaw, and A. Tkacova, The composite coincident indicator (CCI) for business cycles, Acta Polytechnica Hungarica, 14 (2017), no. 7, 71-90.
[6] E.F. Brigham and J.F. Houston, Fundamentals of financial management: Concise, Cengage Learning, 2021.
[7] E. Feyen, J. Frost, L. Gambacorta, H. Natarajan, and M. Saal, Fintech and the digital transformation of financial services: Implications for market structure and public policy, BIS papers, (2021), no. 117.
[8] J. Hair Jr, M. Page, and N. Brunsveld, Essentials of Business Research Methods, Routledge, 2019.
[9] A. Hayes, Financial Markets: Role in the Economy, Importance, Types and Examples, Investopedia, 2022.
[10] M. Hervani and M. Khalili Iraqi, Designing an algorithmic trading strategy with the introduction of the adjustable moving average (AMA) indicator to predict future stock price movements in the Iranian capital market, First Int. Conf. Challeng. New Solut. Ind. Engin. Manag. Account., Sari, 2019.
[11] E.C. Hui and K.K.K. Chan, Can we still beat "buy-and-hold" for individual stocks?, Phys. A: Statist. Mech. Appl. 410 (2014), 513-534.
[12] A.D. Ijegwa, V.O. Rebecca, F. Olusegun, and O.O. Isaac, A predictive stock market technical analysis using fuzzy logic, Comput. Info. Sci. 7 (2014), no. 3, 1.
[13] S. Jadhav, B. Dange, and S. Shikalgar, Prediction of stock market indices by artificial neural networks using forecasting algorithms, In: S. Dash, S. Das and B. Panigrahi, (eds), Int. Conf. Intell. Comput. Appl. Adv. Intell. Syst. Comput., Springer, Singapore, 632 (2018), 455-464.
[14] A.A. Jan, F.-W. Lai, and M. Tahir, Developing an Islamic corporate governance framework to examine sustainability performance in Islamic banks and financial institutions, J. Cleaner Prod. 315 (2021), 128099.
[15] K. Kaczmarczyk and M. Hernes, Financial decisions support using the supervised learning method based on random forests, Proc. Comput. Sci. 176 (2020), 2802-2811.
[16] H. Khanjarpanah, D. Doroush, S. Shawalpour, and A. Jabbarzadeh, Application of technical method for stock price forecasting: The approach of nonlinear probability models and artificial neural networks, Financ. Manag. Strat. 6 (2017), no. 3, 59-79.
[17] J. Lee, R. Kim, Y. Koh, and J. Kang, Global stock market prediction based on stock chart images using deep Q-network, IEEE Access 7 (2019), 167260-167277.
[18] Q. Lin, Technical analysis and stock return predictability: An aligned approach, J. Financ. Markets 38 (2018), 103-123.
[19] S.F. Memarzadeh, H. Khosravi Faresani, and T. Javedani Gandomani, Presenting a method based on deep learning to predict stock prices, Int. Web Res. Conf., 2021.
[20] J. Raisi and Z. Beheshti, Prediction of Tehran stock market trend based on technical analysis and multi-layer perceptron neural network optimization based on evolutionary difference algorithm, 5th Nat. Conf. Elect. Engin. Intell. Syst. Iran, Najafabad, 2017.
[21] A. Sadeghi, M. Madanchi Zaj, and A. Daneshvar, Presenting a meta-heuristic hybrid model in the forex market to optimize investment strategies based on forecasting the market trend, Invest. Knowledge 12 (2023), no. 47, 113-134.
[22] U. Sahin and A. Murat Ozbayoglu, TN-RSI: Trend-normalized RSI indicator for stock trading systems with evolutionary computation, Proc. Comput. Sci. 36 (2014), 240-245.
[23] X. Yu, W. Wu, X. Liao, and Y. Han, Dynamic stock-decision ensemble strategy based on deep reinforcement learning, Appl. Intell. 53 (2023), no. 2, 2452-2470. | ||
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آمار تعداد مشاهده مقاله: 2 تعداد دریافت فایل اصل مقاله: 1 |
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