Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction
<|reference_start|>Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction: Understanding the duration of news events' impact on the stock market is crucial for effective time-series forecasting, yet this facet is largely overlooked in current research. This paper addresses this research gap by introducing a novel dataset, the Impact Duration Estimation Dataset (IDED), specifically designed to estimate impact duration based on investor opinions. Our research establishes that pre-finetuning language models with IDED can enhance performance in text-based stock movement predictions. In addition, we juxtapose our proposed pre-finetuning task with sentiment analysis pre-finetuning, further affirming the significance of learning impact duration. Our findings highlight the promise of this novel research direction in stock movement prediction, offering a new avenue for financial forecasting. We also provide the IDED and pre-finetuned language models under the CC BY-NC-SA 4.0 license for academic use, fostering further exploration in this field.<|reference_end|>
@article{chiu2024pre-finetuning,
    title={Pre-Finetuning with Impact Duration Awareness for Stock Movement
  Prediction},
    author={Chr-Jr Chiu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen},
    journal={arXiv preprint arXiv:2409.17419},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17419},
    primaryClass={cs.CL}
}