TI-Capsule: Capsule Network for Stock Exchange Prediction

TL;DR

This paper introduces TI-Capsule, a novel capsule network model that combines social media text and candlestick images to predict EUR/USD stock behavior with high accuracy.

Contribution

The study presents a new multi-modal capsule network model that integrates text and image data for stock prediction, demonstrating improved performance over existing methods.

Findings

Achieved 91% prediction accuracy on the dataset.

Effectively combines social media sentiment and financial images.

Capsule network maintains feature relationships for better prediction.

Abstract

Today, the use of social networking data has attracted a lot of academic and commercial attention in predicting the stock market. In most studies in this area, the sentiment analysis of the content of user posts on social networks is used to predict market fluctuations. Predicting stock marketing is challenging because of the variables involved. In the short run, the market behaves like a voting machine, but in the long run, it acts like a weighing machine. The purpose of this study is to predict EUR/USD stock behavior using Capsule Network on finance texts and Candlestick images. One of the most important features of Capsule Network is the maintenance of features in a vector, which also takes into account the space between features. The proposed model, TI-Capsule (Text and Image information based Capsule Neural Network), is trained with both the text and image information simultaneously. Extensive experiments carried on the collected dataset have demonstrated the effectiveness of TI-Capsule in solving the stock exchange prediction problem with 91% accuracy.