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Advanced Neural Networks

In: Machine Learning in Finance

Author

Listed:
  • Matthew F. Dixon

    (Illinois Institute of Technology, Department of Applied Mathematics)

  • Igor Halperin

    (New York University, Tandon School of Engineering)

  • Paul Bilokon

    (Imperial College London, Department of Mathematics)

Abstract

This chapter presents various neural network models for financial time series analysis, providing examples of how they relate to well-known techniques in financial econometrics. Recurrent neural networks (RNNs) are presented as non-linear time series models and generalize classical linear time series models such as AR(p). They provide a powerful approach for prediction in financial time series and generalize to non-stationary data. This chapter also presents convolution neural networks for filtering time series data and exploiting different scales in the data. Finally, this chapter demonstrates how autoencoders are used to compress information and generalize principal component analysis.

Suggested Citation

  • Matthew F. Dixon & Igor Halperin & Paul Bilokon, 2020. "Advanced Neural Networks," Springer Books, in: Machine Learning in Finance, chapter 0, pages 239-276, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41068-1_8
    DOI: 10.1007/978-3-030-41068-1_8
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