# Incorporating Fine-grained Events in Stock Movement Prediction

arXiv: https://arxiv.org/abs/1910.05078 · HTML: https://mlsanity.com/paper/1910.05078/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation](https://mlsanity.com/paper/2402.06656/) (2024)
- [Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling](https://mlsanity.com/paper/2108.11318/) (2021)
- [Enhancing Financial Market Predictions: Causality-Driven Feature Selection](https://mlsanity.com/paper/2408.01005/) (2024)
- [Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction](https://mlsanity.com/paper/2309.05608/) (2023)
- [Hierarchical Organization Simulacra in the Investment Sector](https://mlsanity.com/paper/2410.00354/) (2024)

## Abstract (arXiv)

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.