# TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

arXiv: https://arxiv.org/abs/2304.02094 · HTML: https://mlsanity.com/paper/2304.02094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 75 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN](https://mlsanity.com/paper/1803.04831/) (2018)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)
- [Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction](https://mlsanity.com/paper/1603.07893/) (2016)

## Built on by (most cited first, 1 of 1)

- [CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter](https://mlsanity.com/paper/2403.15391/) (2024)

## Abstract (arXiv)

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.