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Adaptive Machine Learning-Based Stock Prediction Using Financial Time Series Technical Indicators

  • Ahmed K. TahaEmail author
  • Mohamed H. Kholief
  • Walid AbdelMoez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

Stock market prediction is a hard task even with the help of advanced machine learning algorithms and computational power. Although much research has been conducted in the field, the results often are not reproducible. That is the reason why the proposed workflow is publicly available on GitHub [1] as a continuous effort to help improve the research in the field. This study explores in detail the importance of financial time series technical indicators. Exploring new approaches and technical indicators, targets, feature selection techniques, and machine learning algorithms. Using data from multiple assets and periods, the proposed model adapts to market patterns to predict the future and using multiple supervised learning algorithms to ensure the adoption of different markets. The lack of research focusing on feature importance and the premise that technical indicators can improve prediction accuracy directed this research. The proposed approach highest accuracy reaches 75% with an area under the curve (AUC) of 0.82, using historical data up to 2019 to ensure the applicability for today’s market, with more than a hundred experiments on a diverse set of assets publicly available.

Keywords

Stock price prediction Technical indicators feature importance Adaptive stock prediction Machine learning Feature selection 

References

  1. 1.
    Taha, A.: feature importance for ml stock prediction. https://github.com/ahmedengu/feature_importance
  2. 2.
    Fama, E.F.: Random walks in stock market prices. Financ. Anal. J. 51, 75–80 (1995).  https://doi.org/10.2469/faj.v51.n1.1861CrossRefGoogle Scholar
  3. 3.
    Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finance. 25, 383 (1970).  https://doi.org/10.2307/2325486CrossRefGoogle Scholar
  4. 4.
    López de Prado, M.M.: Advances in financial machine learning (2018)Google Scholar
  5. 5.
    Murphy, J.J., Murphy, J.J.: Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. Institute of Finance, New York (1999)Google Scholar
  6. 6.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep Learning for Event-Driven Stock Prediction. aaai.org
  7. 7.
    Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based Twitter sentiment for stock prediction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2 Short Pap.), pp. 24–29 (2013)Google Scholar
  8. 8.
    Jung, H.J., Aggarwal, J.K.: A binary stock event model for stock trends forecasting: Forecasting stock trends via a simple and accurate approach with machine learning. In: International Conference on Intelligent Systems Design and Applications, ISDA, pp. 714–719. IEEE (2011).  https://doi.org/10.1109/ISDA.2011.6121740
  9. 9.
    Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E.W.T., Liu, M.: A causal feature selection algorithm for stock prediction modeling. Neurocomputing 142, 48–59 (2014).  https://doi.org/10.1016/J.NEUCOM.2014.01.057CrossRefGoogle Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002).  https://doi.org/10.1007/b98835CrossRefzbMATHGoogle Scholar
  11. 11.
    Quinlan, J.R.: Learning efficient classification procedures and their application to chess end games. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, pp. 463–482. Springer, Heidelberg (1983).  https://doi.org/10.1007/978-3-662-12405-5_15CrossRefGoogle Scholar
  12. 12.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B. 58, 267–288 (1996).  https://doi.org/10.1111/j.2517-6161.1996.tb02080.xMathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    TA-Lib : Technical Analysis Library. http://ta-lib.org/
  14. 14.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015).  https://doi.org/10.1038/nature14539CrossRefGoogle Scholar
  15. 15.
    Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2016, pp. 785–794. ACM Press, New York (2016).  https://doi.org/10.1145/2939672.2939785
  16. 16.
    McCue, T., Carruthers, E., Dawe, J., Liu, S.: Evaluation of generalized linear model assumptions using randomization (2008). http://www.mun.ca/biology/dschneider/b7932/B7932Final10Dec2008.pdf

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ahmed K. Taha
    • 1
    Email author
  • Mohamed H. Kholief
    • 1
  • Walid AbdelMoez
    • 1
  1. 1.College of Computing and Information TechnologyArab Academy for Science, Technology and Maritime TransportAlexandriaEgypt

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