How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting

Abstract

Stock market forecasting is a time series problem that aims to predict possible future prices or directions of an index/stock. The stock data contains high uncertainty and is influenced by too many factors; hence it isn't easy to achieve the goal by traditional time series methods. In literature, the convolutional neural networks (CNN) models were used for stock market forecasting and gave successful results. But, data imbalance due to labeling and feature selection problems were seen when considering these models. Hence, this study proposed a new rule-based labeling algorithm and a new feature selection approach to solve the issues. In addition, a CNN-based model, which was presented to predict the next day's trade action of stocks in the Dow30 index, was constructed to check the effectiveness of the data labeling and the feature selection approach. Different image-based input variable sets were created using technical indicators, gold, and oil price data to feed the CNN model. The prediction performance of CNN models was compared with other studies in the literature. The experimental results showed that the CNN prediction model, which uses the proposed feature selection and labeling approaches in this study, performs 3-22% higher accuracy than the CNN-based models taking part in other studies. Also, the labeling approach proposed is more successful than Chen and Huang's data weighting approach to solve the stock data imbalance problem. This algorithm reduced the ratio between labeled data from 15 times to 1.8 times.