Applied Intelligence

, Volume 49, Issue 3, pp 897–911 | Cite as

A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction

  • Yoojeong Song
  • Jae Won Lee
  • Jongwoo LeeEmail author


From past to present, the prediction of stock price in stock market has been a knotty problem. Many researchers have made various attempts and studies to predict stock prices. The prediction of stock price in stock market has been of concern to researchers in many disciplines, including economics, mathematics, physics, and computer science. This study intends to learn fluctuation of stock prices in stock market by using recently spotlighted techniques of deep learning to predict future stock price. In previous studies, we have used price-based input-features to measure performance changes in deep learning models. Results of this studies have revealed that the performance of stock price models would change according to varied input-features configured based on stock price. Therefore, we have concluded that more novel input-feature in deep learning model is needed to predict patterns of stock price fluctuation more precisely. In this paper, for predicting stock price fluctuation, we design deep learning model using 715 novel input-features configured on the basis of technical analyses. The performance of the prediction model was then compared to another model that employed simple price-based input-features. Also, rather than taking randomly collected set of stocks, stocks of a similar pattern of price fluctuation were filtered to identify the influence of filtering technique on the deep learning model. Finally, we compared and analyzed the performances of several models using different configuration of input-features and target-vectors.


Deep learning Stock prediction Novel input feature Technical analysis Novel filtering technique 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2018R1D1A1B07040312)


  1. 1.
    Lee JW (2013) A stock trading system based on supervised learning of highly volatile stock price patterns. Journal of KIISE : Computing Practices and Letters 19(1):23–29CrossRefGoogle Scholar
  2. 2.
    Kim IM, Park SK (2009) The predictability of korean stock returns and volatility clock samples. The Korean Economic Association 57(3):195–221Google Scholar
  3. 3.
    Kim SD (2012) Data mining tool for stock investors’ decision support. The Journal of the Korea Contents Association 12(2):472–482CrossRefGoogle Scholar
  4. 4.
    Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Kudlur M (2016) Tensorflow: A system for large-scale machine learning. In OSDI 16:265–283Google Scholar
  5. 5.
    Song YJ, Lee JW (2017) A design and implementation of deep learning model for stock prediction using tensorflow. Korea Computer Congress 2017, pp 799–801Google Scholar
  6. 6.
    Song YJ, Lee JW, Lee JW (2017) Performance evaluation of price-based input features in stock price prediction using tensorflow. KIISE Transactions on Computing Practices 23(11):625–631CrossRefGoogle Scholar
  7. 7.
    Lee JW, Kim SY, Kim SD, Lee JW, Chae JS (2003) A two-phase stock trading system based on pattern matching and automatic rule induction. Korea Information Processing Society 10(3):257–264Google Scholar
  8. 8.
    Bollinger J (2001) Bollinger On bollinger bandGoogle Scholar
  9. 9.
    Arnat L (2016) Stock price prediction by deep learningGoogle Scholar
  10. 10.
    Guthrie D, Allison B, Liu W, Guthrie L, Wilks Y (2006) A closer look at skip-gram modelling. In: Proceedings of the 5th international Conference on Language Resources and Evaluation (LREC-2006), pp 1–4Google Scholar
  11. 11.
    Akita R, Yoshihara A, Matsubara T, Uehara K (2016) Deep learning for stock prediction using numerical and textual information. In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 2016. IEEE, pp 1–6Google Scholar
  12. 12.
    Gers F (2001) Long short-term memory in recurrent neural networks Unpublished PhD dissertation. Ecole Polytechnique fédérale de Lausanne, LausanneGoogle Scholar
  13. 13.
    Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp 1188–1196Google Scholar
  14. 14.
    Goldberg Y, Levy O (2014) word2vec explained: Deriving mikolov et al.’s negative-sampling word-embedding method. arXiv:1402.3722
  15. 15.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680Google Scholar
  16. 16.
    Zhou X, Pan Z, Hu G, Tang S, Zhao C (2018) Stock market prediction on high-frequency data using generative adversarial nets. mathematical problems in engineeringGoogle Scholar
  17. 17.
    Abhinandan G, Dev Kumar C, Tanupriya C (2017) Stock prediction using functional link artificial neural network (FLANN). In: 2017 3rd International Conference on Computational Intelligence and Networks (CINE), pp 10–16Google Scholar
  18. 18.
    Ding X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In Ijcai, pp 2327–2333Google Scholar
  19. 19.
    Troiano L, Villa EM, Loia V (2018) Replicating a trading strategy by means of LSTM for financial industry applications. IEEE transactions on industrial informaticsGoogle Scholar
  20. 20.
    Yao Y, Rosasco L, Caponnetto A (2007) On early stopping in gradient descent learning. Constructive Approx 26(2):289– 315MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. The Journal of finance 47(5):1731–1764CrossRefGoogle Scholar
  22. 22.
    Blume L, Easley D, O’hara M (1994) Market statistics and technical analysis: The role of volume. The Journal of Finance 49(1):153–181CrossRefGoogle Scholar
  23. 23.
    Broder AZ, Glassman SC, Manasse MS, Zweig G (1997) Syntactic clustering of the web. Computer Networks and ISDN Systems 29(8-13):1157–1166CrossRefGoogle Scholar
  24. 24.
    Colby RW (2002) The encyclopedia of technical market indicators. Hardcover, 2nd edn. McGraw-Hill Education, pp 832Google Scholar
  25. 25.
  26. 26.
  27. 27.
  28. 28.
    Python Available:
  29. 29.
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958MathSciNetzbMATHGoogle Scholar
  30. 30.
    Lee JW (2007) Integrated multiple simulation for optimizing performance of stock trading systems based on neural networks. The KIPS Transactions: PartB. 14(2):127–134Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication 2018

Authors and Affiliations

  1. 1.Sookmyung Women’s UniversitySeoulKorea
  2. 2.Sungshin Women’s UniversitySeoulKorea

Personalised recommendations