ACL18

Goal

The ACL18 dataset is designed to advance stock movement prediction using social media and historical prices. Its primary goals are:

  • To predict stock price movements from tweets and historical data

  • To benchmark multimodal financial forecasting combining text and time series

  • To evaluate sentiment-driven trading signals

Description

ACL18 is a stock movement prediction dataset containing tweets and historical stock prices. Models must predict whether a stock’s price will rise or fall based on social media sentiment and price trends.

Dataset Composition

  • Source: Twitter posts and historical stock prices

  • Time Period: Historical trading data with corresponding tweets

  • Sample Size: 27,053 instances

  • Data Modalities: Text (tweets) and time series (stock prices)

Example

Stock: Apple Inc. (AAPL)

Historical Prices (5-day window): - Day -5: $120.50 → $119.80 (-0.58%) - Day -4: $119.80 → $121.30 (+1.25%) - Day -3: $121.30 → $120.90 (-0.33%) - Day -2: $120.90 → $122.10 (+0.99%) - Day -1: $122.10 → $121.75 (-0.29%)

Tweets: - “$AAPL new iPhone pre-orders exceed expectations! Strong demand across all models” - “Apple suppliers ramping up production, bullish signal for Q4” - “Analysts raising price targets on $AAPL after product launch”

Prediction Target: Rise or Fall (next day movement > ±0.5%)

Ground Truth: Rise (+1.2% next day)

Task Description

Stock Movement Prediction

  • Input: Historical stock prices (time series) and related tweets (text)

  • Output: Binary classification (Rise or Fall)

  • Challenge: Combining noisy social media signals with price patterns to predict short-term movements

Key Use Cases

  1. Algorithmic Trading: Short-term trading signals from social sentiment

  2. Market Sentiment Analysis: Tracking investor sentiment on social media

  3. Risk Management: Early warning signals from social media discussions

Evaluation Metrics

  1. Accuracy (ACC): Percentage of correct predictions

  2. Matthews Correlation Coefficient (MCC): Balanced measure for binary classification

References

Yumo Xu and Shay B. Cohen. “Stock Movement Prediction from Tweets and Historical Prices.” In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1970–1979, 2018.

For dataset access, visit HuggingFace.