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.
Highlights
Rare event prediction has recently been a field of substantial quantitative model development (Ali and Ariturk 2014; Cheon et al 2009; Janjuaa et al 2019; Li et al 2017; Lin and SenGupta 2001; Rao et al 2020; Weiss and Hirsh 2000)
This study aims to extend the literature of rare event prediction in the field of financial analytics by proposing an implementation of image pattern recognition
We are interested in testing various class imbalance levels, defining the binary outcome variable we are predicting as whether an intra-day negative percent change in close price was exceeded in X% of the dataset where X is 1, 5, and 10
Summary
Rare event prediction has recently been a field of substantial quantitative model development (Ali and Ariturk 2014; Cheon et al 2009; Janjuaa et al 2019; Li et al 2017; Lin and SenGupta 2001; Rao et al 2020; Weiss and Hirsh 2000). This study aims to extend the literature of rare event prediction in the field of financial analytics by proposing an implementation of image pattern recognition. While fundamental analytics utilizes a company’s financial information to make purchase decisions by comparing a stock’s intrinsic value to the prevailing market price, technical analytics utilizes technical charts and other instruments and variables such as key performance indicators and trend analytics to identify patterns that might be able to advocate stock movements without measuring a stock’s intrinsic value (Drakopoulou 2016). While several papers identify fundamental analytics as promising to identify potential stock value appreciation (Cheng and Chen 2007), others show the importance of technical analytics (Chavarnakul and Enke 2008; Hu et al 2021; Sezer and Ozbayoglu 2018; Vijh et al 2020) or the combination of both (Bettman et al 2009; Drakopoulou 2016)
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