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Predicting Stock Market Trends for Japanese Candlestick Using Cloud Model

  • Magda M. Madbouly
  • Mohamed Elkholy
  • Yasser M. GharibEmail author
  • Saad M. Darwish
Conference paper
  • 172 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

Cloud model covers the randomness gap in fuzzy logic model and represents the uncertainty transformation between two different concepts. First concept is the linguistic term that represent the qualitative mean. While the second is the crisp term which represent the quantitative mean. The proposed work presents promising model which combines cloud model, fuzzy time series, and Heikin-Ashi candlestick to predict and confirm accurate stock trend. The model solves several challenging such as: nonlinearity, uncertainty and noises in stock market trend. Heikin-Ashi Candlesticks are an extended branch of Japanese candlesticks, such candlestick filters out stock noise and effort to highlight the trend. Heikin-Ashi Candlestick is constructed by calculating averages of the previous and current period prices. Cloud model handle the ambiguous and uncertainty in the Japanese candlestick definitions (qualitative information) and actual stock prices (quantitative data). It is applied to build membership functions by handling the uncertainty and vagueness of the stock historical data. Then the suggested model constructs dynamic weighted fuzzy logical relationships based on the membership functions to predict the next open and close prices of the stock as well as the high and low values. Finally it constructs the next Heikin-Ashi Japanese candlestick pattern that clarify the trend direction based on the patterns sequence. The imperial evaluation proves that the proposed model has high forecasting accuracy and is feasible to be implemented.

Keywords

Cloud model Fuzzy time series Stock trend Heikin-Ashi candlestick Japanese candlestick 

References

  1. 1.
    Gunduz, H., Yaslan, Y., Cataltepe, Z.: Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. Knowl.-Based Syst. 137, 138–148 (2017).  https://doi.org/10.1016/j.knosys.2017.09.023CrossRefGoogle Scholar
  2. 2.
    Abbad, J., Fardousi, B., Abbad, M.: Advantages of using technical analysis to predict future prices on the amman stock exchange. Int. J. Bus. Manag. 9(2), 1–16 (2014).  https://doi.org/10.5539/ijbm.v9n2p1CrossRefGoogle Scholar
  3. 3.
    Goswami, M.M., Bhensdadia, C.K., Ganatra, A.P.: Candlestick analysis based short term prediction of stock price fluctuation using SOM-CBR. In: 2009 IEEE International Advance Computing Conference, Patiala, India, pp. 1448–1452. IEEE (2009).  https://doi.org/10.1109/IADCC.2009.4809230
  4. 4.
    Lee, C.-H.L., Chen, W.S., Liu, A.: Pattern discovery of fuzzy time series for financial prediction. IEEE Trans. Knowl. Data Eng. 18(5), 613–625 (2006)CrossRefGoogle Scholar
  5. 5.
    Nison, S.: Japanese Candlestick Charting Techniques. New York Institute of Finance, USA (1991). ISBN 0-13-931650-7. http://pdfs.semanticscholar.org/e1b0/56855d8725748a2a250048418f7846b2f9c3.pdf. Accessed 15 Nov 2019
  6. 6.
    Kamo, T.: Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting (2011). Doctoral dissertations (1908). http://scholarsmine.mst.edu/doctoral_dissertations/1908. Accessed 15 Nov 2019
  7. 7.
    Chandrinos, S.K., Lagaros, N.D.: Construction of currency portfolios by means of an optimized investment strategy. Oper. Res. Perspect. 5, 32–44 (2018).  https://doi.org/10.1016/j.orp.2018.01.001CrossRefGoogle Scholar
  8. 8.
    Di Lorenzo, R.: Basic technical analysis of financial markets. Perspect. Bus. Cult. 38, 86–101 (2013).  https://doi.org/10.1007/978-88-470-5421-9CrossRefGoogle Scholar
  9. 9.
    Valcu, D.: Using the Heikin-Ashi technique. Tech. Anal. Stocks Commodities Mag. 22(2), 16–29 (2004). http://iticsoftware.com/media/upload/forex-e-books/Using_The_Heikin_Ashi_Technique_D_Valcu.pdfGoogle Scholar
  10. 10.
    Di Lorenzo, R.: Heikin Ashi. In: How to Make Money by Fast Trading. Perspectives in Business Culture, pp. 165–169. Springer, Milano (2012).  https://doi.org/10.1007/978-88-470-2534-9_34
  11. 11.
    Wang, S., Li, D., Shi, W., Li, D., Wang, X.: Cloud model-based spatial data mining. Ann. GIS 9(1–2), 60–70 (2003).  https://doi.org/10.1080/10824000309480589CrossRefGoogle Scholar
  12. 12.
    Bao, Y.P., Li, X., Wang, M.: A novel method for endpoint temperature prediction in RH. Ironmaking Steelmaking 46, 1–4 (2017).  https://doi.org/10.1080/03019233.2017.1392104CrossRefGoogle Scholar
  13. 13.
    Yan, G., Jia, S., Ding, J., Xu, X., Pang, Y.: A time series forecasting based on cloud model similarity measurement. Soft. Comput. 23, 6443–6454 (2018).  https://doi.org/10.1007/s00500-018-3190-1CrossRefGoogle Scholar
  14. 14.
    Wang, P., Xu, X., Cai, C., Huang, S.: A linguistic large group decision making method based on the cloud model. IEEE Trans. Fuzzy Syst. 26(6), 3314–3326 (2018).  https://doi.org/10.1109/tfuzz.2018.2822242CrossRefGoogle Scholar
  15. 15.
    Yang, X., Xu, Z., He, R., Xue, F.: Credibility assessment of complex simulation models using cloud models to represent and aggregate diverse evaluation results. In: Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science, vol. 11645, pp. 306–317. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-26766-7_28
  16. 16.
    Xu, X., Huang, Q., Ren, Y., Sun, H.-B.: Condition assessment of suspension bridges using local variable weight and normal cloud model. KSCE J. Civil Eng. 22(10), 4064–4072 (2018).  https://doi.org/10.1007/s12205-018-1819-3CrossRefGoogle Scholar
  17. 17.
    Li, T., Yang, X.: Risk assessment model for water and mud inrush in deep and long tunnels based on normal grey cloud clustering method. KSCE J. Civil Eng. 22(5), 1991–2001 (2017).  https://doi.org/10.1007/s12205-017-0553-6CrossRefGoogle Scholar
  18. 18.
    Wu, X., Duan, J., Zhang, L., AbouRizk, S.M.: A hybrid information fusion approach to safety risk perception using sensor data under uncertainty. Stoch. Env. Res. Risk Assess. 32(1), 105–122 (2017).  https://doi.org/10.1007/s00477-017-1389-9CrossRefGoogle Scholar
  19. 19.
    Li, W., Li, F., Du, J.: A level set image segmentation method based on a cloud model as the priori contour. SIViP 13(1), 103–110 (2018).  https://doi.org/10.1007/s11760-018-1334-5MathSciNetCrossRefGoogle Scholar
  20. 20.
    Li, W.S., Du, J., Zhao, Z., Long, J.: Fusion of medical sensors using adaptive cloud model in local laplacian pyramid domain. IEEE Trans. Biomed. Eng. 66(4), 1172–1183 (2018).  https://doi.org/10.1109/tbme.2018.2869432CrossRefGoogle Scholar
  21. 21.
    Ji, H., Han, Q., Li, X., You, H., Ye, Z.: Air combat situation assessment based on improved cloud model theory. In: 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, pp. 754–758. IEEE (2019).  https://doi.org/10.1109/itaic.2019.8785869
  22. 22.
    Lü, X., Chen, C., Wang, P., Meng, L.: Status evaluation of mobile welding robot driven by fuel cell hybrid power system based on cloud model. Energy Convers. Manag. 198(111904), 1–18 (2019).  https://doi.org/10.1016/j.enconman.2019.111904CrossRefGoogle Scholar
  23. 23.
    Zhang, T., Yan, L., Yang, Y.: Trust evaluation method for clustered wireless sensor networks based on cloud model. Wireless Netw. 24(3), 777–797 (2016).  https://doi.org/10.1007/s11276-016-1368-yCrossRefGoogle Scholar
  24. 24.
    Wang, T., Shang, L., Ma, X.: Application of cloud model and matter element theory in transformer fault diagnosis. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, pp. 2089–2092. IEEE (2018)  https://doi.org/10.1109/iaeac.2018.8577253
  25. 25.
    Lv, C., Tian, L., Wang, Z.: A fuzzy comprehensive evaluation model of power quality based on normal cloud model. In: EEET ‘18 Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology, Tianjin, China, pp. 34–38. ACM (2018).  https://doi.org/10.1145/3277453.3277459
  26. 26.
    Li, J., Fang, H., Song, W.: Sustainable supplier selection based on SSCM practices: a rough cloud TOPSIS approach. J. Clean. Prod. 222, 606–621 (2019).  https://doi.org/10.1016/j.jclepro.2019.03.070CrossRefGoogle Scholar
  27. 27.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets Syst. 54(1), 1–9 (1993).  https://doi.org/10.1016/0165-0114(93)90355-lCrossRefGoogle Scholar
  28. 28.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series - part II. Fuzzy Sets Syst. 62(1), 1–8 (1994).  https://doi.org/10.1016/0165-0114(94)90067-1CrossRefGoogle Scholar
  29. 29.
    Bose, M., Mali, K.: Designing fuzzy time series forecasting models: a survey. Int. J. Approx. Reason. 111, 78–99 (2019).  https://doi.org/10.1016/j.ijar.2019.05.002MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Li, D.Y.: Knowledge representation in KDD based on linguistic atoms. J. Comput. Sci. Technol. 12(6), 481–496 (1997).  https://doi.org/10.1007/BF02947201CrossRefGoogle Scholar
  31. 31.
    Chen, S.-M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996).  https://doi.org/10.1016/0165-0114(95)00220-0MathSciNetCrossRefGoogle Scholar
  32. 32.
    Hwang, J.R., Chen, S.-M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100(2), 217–228 (1998).  https://doi.org/10.1016/s0165-0114(97)00121-8CrossRefGoogle Scholar
  33. 33.
    Yu, H.-K.: Weighted fuzzy time series models for TAIEX forecasting. Physica A: Stat. Mech. Appl. 349(3:4), 609–624 (2005).  https://doi.org/10.1016/j.physa.2004.11CrossRefGoogle Scholar
  34. 34.
    Vovan, T.: An improved fuzzy time series forecasting model using variations of data. Fuzzy Optim. Decis. Making 18(2), 151–173 (2018).  https://doi.org/10.1007/s10700-018-9290-7MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Fiess, N.M., MacDonald, R.: Towards the fundamentals of technical analysis: analysing the information content of high, low and close prices. Econ. Model. 19(3), 353–374 (2002).  https://doi.org/10.1016/S0264-9993(01)00067-0CrossRefGoogle Scholar
  36. 36.
    Lee, K.H., Jo, G.S.: Expert system for predicting stock market timing using a candlestick chart. Expert Syst. Appl. 16(4), 357–364 (1999).  https://doi.org/10.1016/s0957-4174(99)00011-1CrossRefGoogle Scholar
  37. 37.
    Leon, C.-H., WenSung, L., Liu, C.A.: Candlestick tutor: an intelligent tool for investment knowledge learning and sharing. In: Fifth IEEE International Conference on Advanced Learning Technologies (ICALT 2005), Kaohsiung, Taiwan, pp. 238–240. IEEE (2005).  https://doi.org/10.1109/icalt.2005.82
  38. 38.
    Lee, C.L.: Modeling personalized fuzzy candlestick patterns for investment decision making. In: 2009 Asia-Pacific Conference on Information Processing, Shenzhen, China, pp. 286–289. IEEE (2009)  https://doi.org/10.1109/apcip.2009.207
  39. 39.
    Naranjo, R., Arroyo, J., Santos, M.: Fuzzy modeling of stock trading with fuzzy candlesticks. Expert Syst. Appl. 93, 15–27 (2018).  https://doi.org/10.1016/j.eswa.2017.10.002CrossRefGoogle Scholar
  40. 40.
    Hu, W., Si, Y.W., Fong, S., Lau, R.Y.K.: A formal approach to candlestick pattern classification in financial time series. Appl. Soft Comput. 84, 1–28 (2019).  https://doi.org/10.1016/j.asoc.2019.105700CrossRefGoogle Scholar
  41. 41.
    Li, D., Di, K., Li, D.: Knowledge representation and uncertainty reasoning in GIS based on cloud model. In: 2000 Proceedings 9th International Symposium on Spatial Data Handing, Beijing, pp. 3a.3—14. http://www.researchgate.net/publication/237515742_Knowledge_representation_and_uncertainty_reasoning_in_GIS_based_on_cloud_models. Accessed 15 Nov 2019
  42. 42.
    Thi, N.: Forecasting and Trading Stock Using Technical Analysis and Neural Fuzzy Network, Master of Science in Information Technology Thesis, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand (2006). http://www.scribd.com/document/233931961/4870180041. Accessed 15 May 2015

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Magda M. Madbouly
    • 1
  • Mohamed Elkholy
    • 2
  • Yasser M. Gharib
    • 1
    Email author
  • Saad M. Darwish
    • 1
  1. 1.Institute of Graduate Studies and ResearchesAlexandria UniversityAlexandriaEgypt
  2. 2.Faculty of EngineeringPharos University in AlexandriaAlexandriaEgypt

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