Forecasting financial time series trends by pattern recognition

Document Type : Research Paper

Authors

Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

Stock and price index prediction are among the main challenges for market players, traders, and economic analysts. Pattern recognition is one of the most common methods for analyzing complex data such as financial data. Elliot waves are used as one of the most robust models for predicting many markets, and it works based on a hypothesis that argued that upward and downward market price action always showed up in the same repetitive patterns. The need for expert knowledge and skills to detect these waves makes using it difficult for many traders. So far, little research has been done on the automatic identification of these waves. In this paper, we have attempted to recognize these patterns automatically and use them in predicting future upward/downward trends in prices. For this purpose, twelve patterns have been selected as representing Elliot waves. These patterns are stored in a self-organized map neural network and the network is used to identify the waves in the target stock. The proposed algorithm has been tested with several stocks from the Forex financial market. The results have an average accuracy of 93.94 percent in predicting stock trends and it indicates an improvement in prediction accuracy compared to other works.

Keywords

[1] B. Akdemir and L. Yu, Elliot waves predicting for stock marketing using euclidean based normalization method merged with artificial neural network, Fourth Int. Conf. Comput. Sci. Convergence Inf. Technol., 2009.
[2] D. Anveshini, V. Revathi, A. Eswari, P. Mounika and K. Meghana, Pattern recognition based fingerprint authentication for ATM system, Int. Conf. Electron. Renew. Syst. (ICEARS), 2022.
[3] G. Atsalakis and E. Dimitrakakis, Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction:The WASP system, Expert Syst. Appl. 38 (2011), 9196–9206.
[4] G. Atsalakis and K. A. Valavanis, Forecasting stock market short-term trends using a neuro-fuzzy based methodology, J. Expert Syst. Appl. 36 (2009), 10696–10707.
[5] S. Chen, S. Bao and Y. Zhou, The predictive power of Japanese candlestick charting in Chinese stock market, Phys. A: Statist. Mech. Appl. 457 (2016), 148–165.
[6] R. Elliott, The Wave Principle, Lulu Press, 1938.
[7] Forex, 2021, Available: http://wikipedia.org.
[8] A. Ganti, Foreign Exchange Market, 2021, Available: https://www.investopedia.com.
[9] Forex historical data, 2020, Available: http://www.fxhistoricaldata.com/.
[10] T.M. Ghazal, Convolutional neural network based intelligent handwritten document recognition, Comput. Materials Contin. 70 (2022), no. 3, 4563–4581.
[11] R. Gonzalez and M. Thomas, Syntatic Pattern Recognition:an Introduction, MA: Addison Wesley, Reading, 1978.
[12] N. Gorelik, J. Chong and D. Lin, Pattern recognition in musculoskeletal imaging using artificial intelligence, Seminars in musculoskeletal radiology. 24(1) (2020) 38–49.
[13] M.J. Horton, Stars, crows, and doji: the use of candlesticks in stock selection, Quart. Rev. Econ. Finance 49 (2009), no. 2, 283–294.
[14] M. Kotyrba, E. Volna, M. Janosek, H. Habiballa and D. Braz, Methodology for Elliott waves pattern recognition, Ratio 34 (2013), no. 55, 0–618.
[15] T. Lu and Y. Shiu, Tests for two-day candlestick patterns in the emerging equity market of Taiwan, Emerg. Markets Finance Trade 48 (2012), no. , 41–57.
[16] B.R. Marshall, M.R. Young and L.C. Rose, Candlestick technical trading strategies: Can they create value for investors?, J. Bank. Finance 30 (2006), no. 8, 2303–2323.
[17] S.T. Mndawe, B.S. Paul and W. Doorsamy, Development of a stock price prediction framework for intelligent media and technical analysis, Appl. Sci. 12 (2022), no. 2.
[18] R. Naranjo and M. Santos, A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition, Expert Syst. Appl. 133 (2019), 34–48.
[19] K. Ostaszewski, P. Heinisch, I. Richter, H. Kroll, W. Balke, D. Fraga and K. Glassmeier, Pattern recognition in time series for space missions: A rosetta magnetic field case study, Acta Astron. 168 (2020), 123–129.
[20] M. Paluch and L. Jackowska-Strumi l lo, Decision System For Stock Data Forecasting Based on Hopfield Artificial neural network, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska., 2016. ´
[21] S. Rath, P. Samal and J. Behera, Fundamental and technical analysis in future trading, Biotica Res. Toda. 2 (202), no. 4, 60–63.
[22] T.J. Strader, J.J. Rozycki, T.H. Root and Y.H.J. Huang, Machine learning stock market prediction studies: Review and research directions, J. Int. Technol. Inf. Manag. 28 (2020), no. 4, 63–83.
[23] I. Swietlicka, W. Kuniszyk-J´o´zkowiak and M. Swietlicki, A ´ rtificial neural networks combined with the principal component analysis for non-fluent speech recognition, Sensors 22 (2022), no. 1.
[24] E. Volna, M. Kotyrba and R. Jarusek, Multi-classifier based on Elliott wave’s recognition, Comput. Math. Appl. 66 (2013), 213–225.
[25] E. Voln´a, M. Kotyrba, Z. Oplatkov´a and R. Senkerik, Elliott waves classification by means of neural and pseudo neural networks, Soft Comput. 22 (2018), no. 6, 1803–1813.
[26] J.L. Wu, L.C. Yu and P.C. Chang, An intelligent stock trading system using comprehensive features, Appl. Soft Comput. 23 (2014), 39–50.
[27] M. Zhu, S. Atri and E. Yegen, Are candlestick trading strategies effective in certain stocks with distinct features?, Pacific-Basin Finance J. 37 (2016), 116–127.
[28] F. Zulkernine, M.M. Kumbure, C. Lohrmann, P. Luukka and J. Porras, Machine learning techniques and data for stock market forecasting: a literature review, Expert Syst. Appl. 197 (2022), no. 1, 116659.
[29] A. Frost and R. Prechter, Elliott Wave Principle: Key to Market Behavior, John Wiley & Sons, 2001.
[30] P. Dost´al and Z. Sojka, Elliottovy vlny, Tribuns.r.o, 2008.
[31] T. Kohonen, Self-Organizing Maps, Berlin, Germany, Springer Series in Information Sciences, 2001.
[32] L. Yang, S. Liu, S. Tsoka and L.G. Papageorgiou, Mathematical programming for piecewise linear regression analysis, Expert Syst. Appl. 44 (2016), 156–167.
Volume 14, Issue 1
January 2023
Pages 2587-2600
  • Receive Date: 24 February 2022
  • Revise Date: 09 April 2022
  • Accept Date: 30 June 2022