# Support for Stock Trend Prediction Using Transformers and Sentiment Analysis

arXiv: https://arxiv.org/abs/2305.14368 · HTML: https://mlsanity.com/paper/2305.14368/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 18 May 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition](https://mlsanity.com/paper/2208.00883/) (2022)

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

- [MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs](https://mlsanity.com/paper/2408.15034/) (2024)
- [COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inference](https://mlsanity.com/paper/2504.16269/) (2025)
- [Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism](https://mlsanity.com/paper/2406.06594/) (2024)
- [TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search](https://mlsanity.com/paper/2404.00271/) (2024)

## Abstract (arXiv)

Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years. Stock prediction based solely on technical data can suffer from lag caused by the inability of stock indicators to effectively factor in breaking market news. The use of sentiment analysis on top headlines can help account for unforeseen shifts in market conditions caused by news coverage. We measure the performance of our model against RNNs over sequence lengths spanning 5 business days to 30 business days to mimic different length trading strategies. This reveals an improvement in directional accuracy over RNNs as sequence length is increased, with the largest improvement being close to 18.63% at 30 business days.