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Trend Prediction in Finance Based on Deep Learning Feature Reduction

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Key Digital Trends in Artificial Intelligence and Robotics (ICDLAIR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 670))

Abstract

One of the main features of Deep Learning is to encode the information content of a complex phenomenon in a latent representation space. This represents an element of definite interest in Finance, as it allows time series data to be compressed into a smaller feature space. Among the different models that are used to accomplish this task are Restricted Boltzmann Machines (RBM) and Auto-Encoders (AE). In this paper we present a preliminary comparative study in the use of these techniques in predicting the trend of time series finance. We attempt to outline the impact of architectural and input space characteristics have on the quality of prediction.

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Author information

Authors and Affiliations

  1. Dept of Engineering, University of Sannio, Viale Traiano 1, 82100, Benevento, Italy

    Vincenzo Benedetto, Francesco Gissi & Elena Mejuto Villa

  2. Kebula srl, Via della Biblioteca 2, 84084, Fisciano, Italy

    Vincenzo Benedetto, Francesco Gissi & Elena Mejuto Villa

  3. DISA-MIS, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Italy

    Elena Mejuto Villa & Luigi Troiano

Authors
  1. Vincenzo Benedetto
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  2. Francesco Gissi
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  3. Elena Mejuto Villa
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  4. Luigi Troiano
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Corresponding author

Correspondence to Vincenzo Benedetto .

Editor information

Editors and Affiliations

  1. Department of Management and Innovation Systems, University of Salerno, Salerno, Italy

    Luigi Troiano

  2. Department of Engineering, University of Sannio, Benevento, Italy

    Alfredo Vaccaro

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Department of Computer Science, University of Oviedo, Gijón, Spain

    Irene Díaz Rodriguez

  5. EMLYON Business School, Écully, France

    Imene Brigui

  6. University College London, London, UK

    David Pastor-Escuredo

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Benedetto, V., Gissi, F., Villa, E.M., Troiano, L. (2023). Trend Prediction in Finance Based on Deep Learning Feature Reduction. In: Troiano, L., Vaccaro, A., Kesswani, N., Díaz Rodriguez, I., Brigui, I., Pastor-Escuredo, D. (eds) Key Digital Trends in Artificial Intelligence and Robotics. ICDLAIR 2022. Lecture Notes in Networks and Systems, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-30396-8_11

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