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

Treena Basu, Olaf Menzer, Joshua Ward and Indranil SenGupta
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Treena Basu: Department of Mathematics, Occidental College, Los Angeles, CA 90041, USA
Olaf Menzer: Department of Geography, University of California, Santa Barbara, CA 93117, USA
Joshua Ward: Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA
Indranil SenGupta: Department of Mathematics, North Dakota State University, Fargo, ND 58108, USA

Risks, 2022, vol. 10, issue 2, 1-16

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.

Keywords: Convolutional Neural Network (CNN); image processing; rare event prediction; siamese neural networks; time series (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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