Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction

• URL: http://arxiv.org/abs/2409.17419v1
• Date: Wed, 25 Sep 2024 23:06:55 GMT
• Title: Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction
• Authors: Chr-Jr Chiu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen,
• Abstract summary: This paper introduces 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.
• Score: 25.67779910446609
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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