Applying Convolutional Neural Networks for Stock Market Trends Identification

TL;DR

This paper explores using convolutional neural networks to identify long-term stock market trends based on expert-labeled image data, addressing challenges like data imbalance and label contradictions.

Contribution

It introduces a CNN-based framework with three submodels for trend detection, emphasizing expert-labeled image data and addressing practical issues like dataset imbalance.

Findings

CNN effectively identifies trend change points.

Expert-labeled images provide valuable training data.

Framework addresses dataset imbalance and label contradictions.

Abstract

In this paper we apply a specific type ANNs - convolutional neural networks (CNNs) - to the problem of finding start and endpoints of trends, which are the optimal points for entering and leaving the market. We aim to explore long-term trends, which last several months, not days. The key distinction of our model is that its labels are fully based on expert opinion data. Despite the various models based solely on stock price data, some market experts still argue that traders are able to see hidden opportunities. The labelling was done via the GUI interface, which means that the experts worked directly with images, not numerical data. This fact makes CNN the natural choice of algorithm. The proposed framework requires the sequential interaction of three CNN submodels, which identify the presence of a changepoint in a window, locate it and finally recognize the type of new tendency - upward, downward or flat. These submodels have certain pitfalls, therefore the calibration of their hyperparameters is the main direction of further research. The research addresses such issues as imbalanced datasets and contradicting labels, as well as the need for specific quality metrics to keep up with practical applicability. This paper is the full text of the research, presented at the 20th International Conference on Artificial Intelligence and Soft Computing Web System (ICAISC 2021)