Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
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Abstract (arXiv)
In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behind stock movements and aim to profit from the temporary stock mispricing that may occur when corporate events take place. The core of the proposed strategy is a bi-level event detection model. The low-level event detector identifies events' existences from each token, while the high-level event detector incorporates the entire article's representation and the low-level detected results to discover events at the article-level. We also develop an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. EDT includes 9721 news articles with token-level event labels as well as 303893 news articles with minute-level timestamps and comprehensive stock price labels. Experiments on EDT indicate that the proposed strategy outperforms all the baselines in winning rate, excess returns over the market, and the average return on each transaction.
Key facts. Cited by 39 papers in the map (data through 13 Oct 2026). Builds on 6 of its 24 references that are in the map. First posted on arXiv 26 May 2021.
Builds on
6 of 24 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019Financial Event Extraction Using Wikipedia-Based Weak Supervisioncited by 11
- 2018Predicting the Effects of News Sentiments on the Stock Marketcited by 10
- 2016Stock trend prediction using news sentiment analysiscited by 9
- 2018Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Networkcited by 6
- 2019Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Methodcited by 1
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Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 16; 2025: 10; 2026: 4.
39 papers.
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12 most cited of 33 All 33 (JSON)
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- 2025A symbolic Perl algorithm for the unification of Nahuatl word spellings24 Nov 2025
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12 newest of 33 All 33 (JSON)