CIKM18

Goal

The CIKM18 dataset is designed to advance stock movement prediction using hybrid deep sequential modeling. Its primary goals are:

  • To predict stock price movements from social text and historical prices

  • To benchmark sequential modeling approaches for financial forecasting

  • To evaluate deep learning methods on financial time series

Description

CIKM18 is a stock movement prediction dataset that combines social text-driven signals with deep sequential modeling of historical prices.

Dataset Composition

  • Source: Social media posts (tweets) and historical stock prices

  • Time Period: Historical trading data with social media activity

  • Sample Size: 4,967 instances

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

Example

Stock: Amazon.com Inc. (AMZN)

Historical Prices (sequential 5-day pattern): - Day -5: Close $3,200 (volume: 4.2M) - Day -4: Close $3,185 (volume: 3.8M, -0.47%) - Day -3: Close $3,210 (volume: 5.1M, +0.79%) - Day -2: Close $3,195 (volume: 4.5M, -0.47%) - Day -1: Close $3,220 (volume: 6.2M, +0.78%)

Social Text: - “Amazon Prime Day sales hit record highs, expect strong Q2 earnings” - “$AMZN cloud services AWS gaining market share from competitors” - “Retail analysts upgrading Amazon on strong e-commerce growth”

Sequential Pattern: Increasing volume with positive price momentum

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

Ground Truth: Rise (+1.5% following continued momentum)

Task Description

Stock Movement Prediction with Sequential Modeling

  • Input: Social media text and sequential historical price data

  • Output: Binary classification (Rise or Fall)

  • Challenge: Capturing sequential dependencies in price movements while integrating social media sentiment signals

Key Use Cases

  1. Quantitative Trading: Sequential pattern recognition for trading strategies

  2. Market Prediction: Combining technical analysis with sentiment analysis

  3. Investment Research: Understanding price momentum influenced by social sentiment

Evaluation Metrics

  1. Accuracy (ACC): Percentage of correct predictions

  2. Matthews Correlation Coefficient (MCC): Correlation between predictions and actual outcomes

References

Huizhe Wu, Wei Zhang, Weiwei Shen, and Jun Wang. “Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction.” In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM), pages 1627–1630, 2018.

For dataset access, visit HuggingFace.