From Time Series to Images: Revolutionizing Stock Market Predictions with Convolutional Deep Neural Networks

Abstract

Predicting the trend of stock prices is a hard task due to numerous factors and prerequisites that can affect price movement in a specific direction. Various strategies have been proposed to extract relevant features of stock data, which is crucial for this domain. Due to its powerful data processing capabilities, deep learning has demonstrated remarkable results in the financial field among modern tools. This research suggests a convolutional deep neural network model that utilizes a 2D-CNN to process and classify images. The process for creating images involves transforming the top technical indicators from a financial time series, each calculated for 21 different day periods, to create images of specific sizes. The images are labeled Sell, Hold, or Buy based on the original trading data. Compared to the Long Short Time Memory Model and to the one-dimensional Convolutional Neural Network and the model exhibits the best performance.