The Effectiveness of the Automatic System of Fuzzy Logic-Based Technical Patterns Recognition: Evidence from Tehran Stock Exchange

الموضوعات :

Abdolmajid Abdolbaghi Ataabadi ¹ , Sayyed Mohammad Reza Davoodi ² , Mohammad Salimi Bani ³

1 - Department of Management, Industrial Engineering, Amp and Management Sciences, Shahrood University of Technology
2 - Department of Management ,Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.
3 - Department of Financial Engineering, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

تاريخ الإرسال : 08 الأربعاء , جمادى الثانية, 1440 تاريخ التأكيد : 17 السبت , ذو القعدة, 1440 تاريخ الإصدار : 28 الإثنين , شوال, 1440

الکلمات المفتاحية: Technical patterns, moving average, pattern recognition, fuzzy logic,

ملخص المقالة :

The present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was used on 38 indicators of Tehran Stock Exchange within the period 2014-2017 in order to evaluate the effectiveness of technical patterns. Having compared the conditional distribution of daily returns under the condition of the discovered patterns and the unconditional distribution of returns at various levels of confidence driven from fuzzy logic with the mean returns of all normalized market indicators, we observed that in the desired period, after recognizing the pattern, all patterns investigated at the confidence level 0.95 with a fuzzy point 0.5 contained useful information, practically leading to abnormal returns.

المصادر:

[1] Alejandro Escobar, A.,  Moreno Cadavid, J.,  Múnera, S., A Technical Analysis Indicator Based On Fuzzy Logic, Electronic Notes in Theoretical Computer Science, 2013, 292(27), P.1571-0661, Doi:10.1016/j.entcs.20 13.02.003.

[2] Anand, S., Chin, W., Siau-Cheng, K., Charting patterns on price history. ACM Sigplan Notices, 2001, 36(10), P.134-145.‏ Doi:10.1145/507635.507653

[3] Ehteshami, S., Hamidian, M., Hajiha, Z., Shokrollahi, S.,  Forecasting Stock Trend by Data Mining Algorithm, Advances in mathematical finance & applications, 2018, 3(1), P.97-105. Doi:10.22034/amfa.539138.

[4] ‏Escobar, A., Moreno, J., Múnera ,S., A technical analysis indicator based on fuzzy logic, Electronic Notes in Theoretical Computer Science,2013, 5(292),P.27-37. Doi:10.1016/j.entcs.2013.02.003.

[5] García, F.,  Guijarro, F., Oliver, J., Tamošiūnienė, R., Hybrid Fuzzy Neural Network to Predict Price Direction in the German DAX-30 Index,  Technological and Economic Development of Economy, 2018, 24(6), P.2161–2178. Doi:10.3846/tede.6394.

[6] Gonzalez, A,A., Análisis bursátil con fines speculative: un enfoque técnico modern. Ed. Limusa, México,2007, ISBN 9789681865146.

[7] Gradojevic, N., Gençay, R., Fuzzy logic, trading uncertainty, and technical trading. Journal of Banking & Finance,2013, 37(2). P.578-586.‏ Doi:10.1016/j.jbankfin.2012.09.012

[8] Guo, X., Automatically Recognizing stock patterns using RPCL Neural Networks. In: International Conference on Intelligent Systems and Knowledge Engineering. Atlantis Press, 2007. Doi:10.2991/iske.2007.28

[9] Ijegwa, Acheme David., et al. A predictive stock market technical analysis using fuzzy logic. Computer and information science, 2014, 7(3),P.1.‏ Doi:10.5539/cis.v7n3p1

[10] Jegadeesh, Narasimhan., Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation: Discussion. The Journal of Finance, 2000, 55(4), P.1765-1770.

[11] Johnson, NF., Jefferies, P., Hui, PM., Financial market complexity. OUP Catalogue, 2003,  Doi:10.1002/ij fe.278.

[12] Kamijo, K., Tanigawa, T., Stock price pattern recognition-a recurrent neural network approach. In: 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990, P.215-221.‏ Doi:10.1109/IJCNN.19 90.137572

[13] Kenny, A., Advanced Technical Analysis, Publisher Abbas Kenny,2005.

[14] Leigh,W., Modani, N., Hightower, R., A computational implementation of stock charting: abrupt volume increase as a signal for movement in New York stock exchange composite index. Decision Support Systems,2004, 37(4) P.515-530. Doi:10.1016/S0167-9236(03)00084-8

[15]Liacay, Pintat B., Peffer, G., Simulación realista de los mercados financieros con sistemas multi-agentes,2008.

[16] Lo, A., Mamaysky, W ., Wang, H., Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. The journal of finance, 2000, 55(4), P.1705-1765.‏  Doi:10.1111/0022-1082.00265

 [17] Nakano, M., Takahashi, A., Takahashi, S., Robust technical trading with fuzzy knowledge-based systems (No. CIRJE-F-1053). CIRJE, Faculty of Economics, University of Tokyo,2017.

[18] Naranjo, R., Arroyo, J., Santos, M., Fuzzy modeling of stock trading with fuzzy candlesticks. Expert Systems with Applications, 2018, 93, P.15-27.‏ Doi:10.1016/j.eswa.2017.10.002

 [19] Peachavanish, R., Dual Time Frame Relative Strength Stock Selection Using Fuzzy Logic, International MultiConference of Engineers and Computer Scientists, 2018, 2 IMECS, P. 14-16,

 [20] Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M. S., Rostamy-Malkhalifeh, M. , Fuzzy Data Envelopment Analysis: An Adjustable Approach, Expert Systems with Applications,2019, Doi:10.1016/j.eswa. 2019.06.039.

 [21] Peykani, P., Mohammadi, E., Rostamy-Malkhalifeh, M., Hosseinzadeh Lotfi, F., Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange, Advances in mathematical finance & applications, 2019,4 (1), P.31-43, Doi:10.22034/amfa.2019.581412.1155.

[22]Peykani, P., Mohammadi, E., Pishvaee, M.S., Rostamy-Malkhalifeh, M. and Jabbarzadeh, A., A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches, RAIRO-Operations Research,2018, 52(4), P.1445-1463. Doi:10.1051/ro/2018019.

[23] Radfar, M.,  Zomorodian, G., Aligholi, M.,  Minouei, M., Hanifi ,F., Designing Native Decision-Making Model for Selecting Venture Capital Investment in Emerging Companies, Advances in mathematical finance & applications, 2019, 4 (2), 75-88, Doi:10.22034/amfa.2019.584735.1178.

 [24] Taherinia, M., Rashidi Baghi, M., Prediction the Return Fluctuations with Artificial Neural Networks’ Approach, Advances in mathematical finance & applications, 2019, 4(2), P.103-114, Doi:10.22034/amfa.2019 .580643.1149.

 [25] Tealab, A., Hefny, H., Badr, A., Short-Term Stock Market Fuzzy Trading System with Fuzzy Capital Management, International Journal of Intelligent Engineering and Systems, 2018, 11(3),  Doi:10.22266/ijies2018 .0630.06.

[26] Volna, E., Kotyrba, M., Jarusek, Rt., Multi-classifier based on Elliott wave’s recognition. Computers & Mathematics with Applications,2012, 66(2), P.213-225.‏Doi:10.1016/j.camwa.2013.01.012.

[27] Volna, E., Kotyrba, M., Jarušek, R., Prediction by means of Elliott waves recognition. In: Nostradamus: Modern Methods of Prediction, Modeling, and Analysis of Nonlinear Systems. Springer, Berlin, Heidelberg,2013, P.241-250. Doi:10.1007/978-3-642-33227-2_25‏ .

[28] Zapranis, A., Tsinaslanidis, P E., A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the US stock market. Expert Systems with Applications,2012, 39(7), P.6301-6308. Doi:10.1016/j.eswa.2011.11.079‏

[29] Zhou, X., Dong, g., Can fuzzy logic make technical analysis 20/20?. Financial Analysts Journal, 2004, 60(4), P. 54-75.‏ Doi:10.2469/faj.v60.n4.2637.