# The Effects of Twitter Sentiment on Stock Price Returns

arXiv: https://arxiv.org/abs/1506.02431 · HTML: https://mlsanity.com/paper/1506.02431/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2015.

## Built on by (most cited first, 6 of 6)

- [AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment](https://mlsanity.com/paper/2206.11072/) (2022)
- [In the Red(dit): Social Media and Stock Prices](https://mlsanity.com/paper/2311.09252/) (2023)
- [Twitter Permeability to financial events: an experiment towards a model for sensing irregularities](https://mlsanity.com/paper/2312.11530/) (2023)
- [The irruption of cryptocurrencies into Twitter cashtags: a classifying solution](https://mlsanity.com/paper/2312.11531/) (2023)
- [Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading](https://mlsanity.com/paper/2508.07408/) (2025)
- [Human Agreement and Return Association Are Not Interchangeable Criteria](https://mlsanity.com/paper/2609.11144/) (2026)

## Abstract (arXiv)

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.