# Stock Market Prediction using Natural Language Processing -- A Survey

arXiv: https://arxiv.org/abs/2208.13564 · HTML: https://mlsanity.com/paper/2208.13564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 26 Aug 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02745/) (2016)
- [Improving Stock Market Prediction via Heterogeneous Information Fusion](https://mlsanity.com/paper/1801.00588/) (2018)
- [Price graphs: Utilizing the structural information of financial time series for stock prediction](https://mlsanity.com/paper/2106.02522/) (2021)
- [Modeling Financial Time Series using LSTM with Trainable Initial Hidden States](https://mlsanity.com/paper/2007.06848/) (2020)

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

- [Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark](https://mlsanity.com/paper/2610.10727/) (2026)

## Abstract (arXiv)

The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage profit opportunities. This attractiveness of finding a solution has prompted researchers to find a way past problems like volatility, seasonality, and dependence on time. This paper surveys recent literature in the domain of natural language processing and machine learning techniques used to predict stock market movements. The main contributions of this paper include the sophisticated categorizations of many recent articles and the illustration of the recent trends of research in stock market prediction and its related areas.