This project is the repository of datasets used in "Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction" (CIKM 2025).

How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data. In this paper, we propose ReVol (Return-Volatility Normalization for Mitigating Distribution Shift in Stock Price Data), a robust method for stock price prediction that explicitly addresses the distribution shift problem. ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove samplespecific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market anomalies, and (3) reintegrating the sample characteristics into the predictive process, restoring the traits lost during normalization. Additionally, ReVol combines geometric Brownian motion for longterm trend modeling with neural networks for short-term pattern recognition, unifying their complementary strengths. Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0.03 in IC and over 0.7 in SR across various settings.

• Input format: .csv file, shape (T, 4)
• Each row contains 4 features: Opening, Highest, Lowest, and Closing prices
• Multivariate time series of stock prices

Please cite the following paper if you use our data:

@inproceedings{Lee2025ReVol,
  author    = {Hyunwoo Lee and
               Jihyeong Jeon and
               Jaemin Hong and
               U Kang},
  title     = {Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction},
  booktitle = {{CIKM} '25: The {ACM} International Conference on Information and Knowledge Management, Seoul, Republic of Korea, November 10--14, 2025},
  publisher = {{ACM}},
  year      = {2025},
  url       = {https://doi.org/10.1145/3746252.3761067},
  doi       = {10.1145/3746252.3761067}
}