mlsanityData to 7 Oct 2026

TI-Capsule: Capsule Network for Stock Exchange Prediction

arXiv 2102.07718 ↗First posted 15 Feb 2021cs.LG cs.AIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 65 references that are in the map. First posted on arXiv 15 Feb 2021.

Builds on

9 of 65 references.

References · 9

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  4. 2017Dynamic Routing Between Capsulescited by 689
  5. 2019Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysiscited by 39
  6. 2018A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunitiescited by 28
  7. 2016Sentiment Analysis of Twitter Data: A Survey of Techniquescited by 16
  8. 2017Deep Learning applied to NLPcited by 11
  9. 2019Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysiscited by 7

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2021Stock price prediction using BERT and GANcited by 11
  2. 2023TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market datacited by 1
  3. 2025Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)cited by 1
  4. 2024CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twittercited by 0