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On Feature Reduction using Deep Learning for Trend Prediction in Finance

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  • Luigi Troiano
  • Elena Mejuto
  • Pravesh Kriplani

Abstract

One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning tools is effective in price trend prediction. The focus has been put mainly on Restricted Boltzmann Machines (RBM) and on output obtained by them. Few attention has been payed to Auto-Encoders (AE) as an alternative means to perform a feature reduction. In this paper we investigate the application of both RBM and AE in more general terms, attempting to outline how architectural and input space characteristics can affect the quality of prediction.

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  • Luigi Troiano & Elena Mejuto & Pravesh Kriplani, 2017. "On Feature Reduction using Deep Learning for Trend Prediction in Finance," Papers 1704.03205, arXiv.org.
    • Handle: RePEc:arx:papers:1704.03205
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    Cited by:

    1. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    2. Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.

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