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Design of an Artificial Neural Network battery for an optimal recognition of patterns in financial time series

Author

Listed:
  • Simone Fioribello

    (Chartered Management Engineer, Italy)

  • Pier Giuseppe Giribone

    (#x2020;Department of Economics, University of Genoa, 16126 Genova, Italy†CARIGE Bank, Financial Engineering, 16123 Genova, Italy)

Abstract

The increasingly massive use of advanced Machine Learning methodologies in the financial field sector has led credit institutions to quickly move to new FinTech technologies. This paper deals with how a battery of Artificial Neural Networks (ANN), dedicated to the automatic recognition of financial patterns of potential interest to traders, can be designed and validated. The battery of neural networks that have been designed is composed of a shallow ANN, a deep ANN with ReLu, a deep ANN with Dropout and a convolutional network (ConvNet). Depending on the type of classification problem, the ANN battery dynamically recognizes the best classifier and makes use of it for pattern recognition. The first part of the paper describes how these technologies work, the second one performs a validation of the code and the third one suggests a technical analysis application on financial time series.

Suggested Citation

  • Simone Fioribello & Pier Giuseppe Giribone, 2018. "Design of an Artificial Neural Network battery for an optimal recognition of patterns in financial time series," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-17, December.
    • Handle: RePEc:wsi:ijfexx:v:05:y:2018:i:04:n:s2424786318500317
    DOI: 10.1142/S2424786318500317
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