Deep Learning and Wavelets for High-Frequency Price Forecasting

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


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%.


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


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Andrés Arévalo
    • 1
    Email author
  • 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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