Research Results from Occidental College Update Knowledge of Risk Management (A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series): Insurance – Risk Management

2022 MAR 15 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Research findings on risk management are discussed in a new report. According to news reporting from Occidental College by NewsRx journalists, research stated, “Stock trading has tremendous importance not just as a profession but also as an income source for individuals.”

The news editors obtained a quote from the research from Occidental College: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: A Novel Implementation of Siamese Type Neural Networks in Predicting Rare Fluctuations in Financial Time Series. Risks, 2022,10(39):39. (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks10020039.

Our news journalists report that additional information may be obtained by contacting Treena Basu, Department of Mathematics, Occidental College, Los Angeles, CA 90041, USA. Additional authors for this research include Olaf Menzer, Joshua Ward, Indranil SenGupta.

(Our reports deliver fact-based news of research and discoveries from around the world.)