# TI-Capsule: Capsule Network for Stock Exchange Prediction

arXiv: https://arxiv.org/abs/2102.07718 · HTML: https://mlsanity.com/paper/2102.07718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/1905.05812/) (2019)
- [A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/1812.00315/) (2018)
- [Sentiment Analysis of Twitter Data: A Survey of Techniques](https://mlsanity.com/paper/1601.06971/) (2016)
- [Deep Learning applied to NLP](https://mlsanity.com/paper/1703.03091/) (2017)
- [Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/1907.08696/) (2019)

## Built on by (most cited first, 4 of 4)

- [Stock price prediction using BERT and GAN](https://mlsanity.com/paper/2107.09055/) (2021)
- [TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data](https://mlsanity.com/paper/2304.02094/) (2023)
- [Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)](https://mlsanity.com/paper/2505.08086/) (2025)
- [CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter](https://mlsanity.com/paper/2403.15391/) (2024)

## Abstract (arXiv)

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.