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A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series

Author

Listed:
  • Treena Basu

    (Department of Mathematics, Occidental College, Los Angeles, CA 90041, USA
    These authors contributed equally to this work.)

  • Olaf Menzer

    (Department of Geography, University of California, Santa Barbara, CA 93117, USA
    These authors contributed equally to this work.)

  • Joshua Ward

    (Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA
    These authors contributed equally to this work.)

  • Indranil SenGupta

    (Department of Mathematics, North Dakota State University, Fargo, ND 58108, USA
    These authors contributed equally to this work.)

Abstract

Stock trading has tremendous importance not just as a profession but also as an income source for individuals. Many investment account holders use the appreciation of their portfolio (as a combination of stocks or indexes) as income for their retirement years, mostly betting on stocks or indexes with low risk/low volatility. However, every stock-based investment portfolio has an inherent risk to lose money through negative progression and crash. This study presents a novel technique to predict such rare negative events in financial time series (e.g., a drop in the S&P 500 by a certain percent in a designated period of time). We use a time series of approximately seven years (2517 values) of the S&P 500 index stocks with publicly available features: the high, low and close price (HLC). We utilize a Siamese type neural network for pattern recognition in images followed by a bootstrapped image similarity distribution to predict rare events as they pertain to financial market analysis. Extending on literature about rare event classification and stochastic modeling in financial analytics, the proposed method uses a sliding window to store the input features as tabular data (HLC price), creates an image of the time series window, and then uses the feature vector of a pre-trained convolutional neural network (CNN) to leverage pre-event images and predict rare events. This research does not just indicate that our proposed method is capable of distinguishing event images from non-event images, but more importantly, the method is effective even when only limited and strongly imbalanced data is available.

Suggested Citation

  • Treena Basu & Olaf Menzer & Joshua Ward & Indranil SenGupta, 2022. "A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series," Risks, MDPI, vol. 10(2), pages 1-16, February.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:2:p:39-:d:747578
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    References listed on IDEAS

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    4. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    5. Semere Habtemicael & Indranil SenGupta, 2016. "Pricing variance and volatility swaps for Barndorff-Nielsen and Shephard process driven financial markets," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 1-35, December.
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    8. Shubham Ekapure & Nuruddin Jiruwala & Sohan Patnaik & Indranil SenGupta, 2021. "A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks," Papers 2107.14695, arXiv.org.
    9. Jinyan Li & Lian-sheng Liu & Simon Fong & Raymond K Wong & Sabah Mohammed & Jinan Fiaidhi & Yunsick Sung & Kelvin K L Wong, 2017. "Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-25, July.
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    Cited by:

    1. Yang Dexiang & Mu Shengdong & Yunjie Liu & Gu Jijian & Lien Chaolung, 2023. "An Improved Deep-Learning-Based Financial Market Forecasting Model in the Digital Economy," Mathematics, MDPI, vol. 11(6), pages 1-18, March.

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