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Deep Learning and Wavelets for High-Frequency Price Forecasting

  • Andrés Arévalo
  • Jaime Nino
  • Diego León
  • German Hernandez
  • Javier Sandoval
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)

Abstract

This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 one-minute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN’s Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.

Keywords

Short-term forecasting High-frequency forecasting Computational finance Deep Neural Networks Discrete Wavelet Transform 

References

  1. 1.
    Arévalo, A., Niño, J., Hernández, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS (LNAI), vol. 9773, pp. 424–436. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42297-8_40CrossRefGoogle Scholar
  2. 2.
    Arévalo Murillo, A.R.: Short-term forecasting of financial time series with deep neural networks. bdigital.unal.edu.co (2016). http://www.bdigital.unal.edu.co/54538/
  3. 3.
    Arnold, L., Rebecchi, S., Chevallier, S., Paugam-Moisy, H.: An introduction to deep learning. In: ESANN (2011). https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2011-4.pdf
  4. 4.
    Chao, J., Shen, F., Zhao, J.: Forecasting exchange rate with deep belief networks. In: 2011 International Joint Conference on Neural Networks, pp. 1259–1266. IEEE, July 2011.  https://doi.org/10.1109/IJCNN.2011.6033368
  5. 5.
    Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734, June 2014. http://arxiv.org/abs/1406.1078
  6. 6.
    De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006).  https://doi.org/10.1016/j.ijforecast.2006.01.001CrossRefGoogle Scholar
  7. 7.
    Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (ICJAI) (2015). http://ijcai.org/papers15/Papers/IJCAI15-329.pdf
  8. 8.
    Gallo, C., Letizia, C., Stasio, G.: Artificial neural networks in financial modelling (2006). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.2740&rep=rep1&type=pdf
  9. 9.
    Gençay, R., Selçuk, F., Whitcher, B.: An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, Cambridge (2002)MATHGoogle Scholar
  10. 10.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016). http://www.deeplearningbook.orgMATHGoogle Scholar
  11. 11.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, Upper Saddle River (2009)Google Scholar
  12. 12.
    He, T.X., Nguyen, T.: Wavelet analysis and applications in economics and finance. Res. Rev. J. Stat. Math. Sci. 1(1), 22–37 (2015)Google Scholar
  13. 13.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997).  https://doi.org/10.1162/neco.1997.9.8.1735CrossRefGoogle Scholar
  14. 14.
    Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(November), 679–688 (2005). http://www.sciencedirect.com/science/article/pii/S0169207006000239%5Cncore.ac.uk/download/pdf/6340761.pdfGoogle Scholar
  15. 15.
    Kamijo, K., Tanigawa, T.: Stock price pattern recognition-a recurrent neural network approach. In: International Joint Conference on Neural Networks, pp. 215–221 (1990). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5726532
  16. 16.
    Krollner, B., Vanstone, B., Finnie, G.: Financial time series forecasting with machine learning techniques: a survey (2010). http://works.bepress.com/bruce_vanstone/17/
  17. 17.
    Li, X., Huang, X., Deng, X., Zhu, S.: Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information. Neurocomputing 142, 228–238 (2014).  https://doi.org/10.1016/j.neucom.2014.04.043CrossRefGoogle Scholar
  18. 18.
    Marszałek, A., Burczyński, T.: Modeling and forecasting financial time series with ordered fuzzy candlesticks. Inf. Sci. 273, 144–155 (2014).  https://doi.org/10.1016/j.ins.2014.03.026MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Medsker, L., Jain, L.C.: Recurrent Neural Networks: Design and Applications. International Series on Computational Intelligence. CRC Press, Boca Raton (1999)CrossRefGoogle Scholar
  20. 20.
    Mills, T.C., Markellos, R.N.: The Econometric Modelling of Financial Time Series. Cambridge University Press, Cambridge (2008).  https://doi.org/10.1017/CBO9780511817380CrossRefMATHGoogle Scholar
  21. 21.
    Preethi, G., Santhi, B.: Stock market forecasting techniques: a survey. J. Theor. Appl. Inf. Technol. 46(1), 24–30 (2012)Google Scholar
  22. 22.
    Schnader, M.H., Stekler, H.O.: Evaluating predictions of change. J. Bus. 63(1), 99–107 (1990)CrossRefGoogle Scholar
  23. 23.
    Sureshkumar, K., Elango, N.: Performance analysis of stock price prediction using artificial neural network. Global journal of computer science and Technology 12, 19–26 (2012). http://computerresearch.org/index.php/computer/article/view/426Google Scholar
  24. 24.
    Takeuchi, L., Lee, Y.: Applying deep learning to enhance momentum trading strategies in stocks (2013)Google Scholar
  25. 25.
    Tsay, R.S.: Analysis of Financial Time Series, vol. 543. Wiley, Hoboken (2005)CrossRefGoogle Scholar
  26. 26.
    Walnut, D.F.: An Introduction to Wavelet Analysis. Birkhäuser, Boston (2002)MATHGoogle Scholar
  27. 27.
    Wilder, J.W.: New Concepts in Technical Trading Systems. Trend Research (1978)Google Scholar
  28. 28.
    Yeh, S., Wang, C., Tsai, M.: Corporate Default Prediction via Deep Learning (2014). http://teacher.utaipei.edu.tw/~cjwang/slides/ISF2014.pdf

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Andrés Arévalo
    • 1
  • Jaime Nino
    • 1
  • Diego León
    • 2
  • German Hernandez
    • 1
  • Javier Sandoval
    • 1
  1. 1.Universidad Nacional de ColombiaBogotáColombia
  2. 2.Universidad Externado de ColombiaBogotáColombia

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