# Stock trend prediction using news sentiment analysis

arXiv: https://arxiv.org/abs/1607.01958 · HTML: https://mlsanity.com/paper/1607.01958/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2016.

## Built on by (most cited first, 9 of 9)

- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis](https://mlsanity.com/paper/2407.16150/) (2024)
- [NewsPanda: Media Monitoring for Timely Conservation Action](https://mlsanity.com/paper/2305.01503/) (2023)
- [LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma](https://mlsanity.com/paper/2403.00418/) (2024)
- [Learning the Market: Sentiment-Based Ensemble Trading Agents](https://mlsanity.com/paper/2402.01441/) (2024)
- [Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data](https://mlsanity.com/paper/2412.10540/) (2024)
- [Towards Financial Sentiment Analysis in a South African Landscape](https://mlsanity.com/paper/2106.10004/) (2021)
- [A Comprehensive Review on Summarizing Financial News Using Deep Learning](https://mlsanity.com/paper/2109.10118/) (2021)
- [Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark](https://mlsanity.com/paper/2201.12283/) (2022)

## Abstract (arXiv)

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is an attempt to study relationship between news and stock trend. To show this, we created three different classification models which depict polarity of news articles being positive or negative. Observations show that RF and SVM perform well in all types of testing. Na"ive Bayes gives good result but not compared to the other two. Experiments are conducted to evaluate various aspects of the proposed model and encouraging results are obtained in all of the experiments. The accuracy of the prediction model is more than 80% and in comparison with news random labeling with 50% of accuracy; the model has increased the accuracy by 30%.