Advanced Neural Networks

Matthew F. Dixon, Igor Halperin and Paul Bilokon
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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

Chapter Chapter 8 in Machine Learning in Finance, 2020, pp 239-276 from Springer

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41068-1_8

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DOI: 10.1007/978-3-030-41068-1_8

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