# Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading arXiv: https://arxiv.org/abs/2105.12825 ยท HTML: https://mlsanity.com/paper/2105.12825/ ## Key facts - Cited by 39 papers in the map (data through 14 Oct 2026). - Builds on 6 of its 24 references that are in the map. - First posted on arXiv 26 May 2021. ## Builds on (most cited first, 6 of 6) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Financial Event Extraction Using Wikipedia-Based Weak Supervision](https://mlsanity.com/paper/1911.10783/) (2019) - [Predicting the Effects of News Sentiments on the Stock Market](https://mlsanity.com/paper/1812.04199/) (2018) - [Stock trend prediction using news sentiment analysis](https://mlsanity.com/paper/1607.01958/) (2016) - [Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network](https://mlsanity.com/paper/1811.06173/) (2018) - [Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method](https://mlsanity.com/paper/1909.12789/) (2019) ## Built on by (most cited first, 30 of 39) - [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023) - [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024) - [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023) - [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024) - [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023) - [INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent](https://mlsanity.com/paper/2412.18174/) (2024) - [Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications](https://mlsanity.com/paper/2408.11878/) (2024) - [FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models](https://mlsanity.com/paper/2402.10986/) (2024) - [Demystifying Domain-adaptive Post-training for Financial LLMs](https://mlsanity.com/paper/2501.04961/) (2025) - [FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents](https://mlsanity.com/paper/2411.05764/) (2024) - [FinMTEB: Finance Massive Text Embedding Benchmark](https://mlsanity.com/paper/2502.10990/) (2025) - [Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model](https://mlsanity.com/paper/2206.06606/) (2022) - [Do We Need Domain-Specific Embedding Models? An Empirical Investigation](https://mlsanity.com/paper/2409.18511/) (2024) - [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report](https://mlsanity.com/paper/2108.07865/) (2021) - [Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models](https://mlsanity.com/paper/2407.03689/) (2024) - [Feature Programming for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.06252/) (2023) - [No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks](https://mlsanity.com/paper/2403.06249/) (2024) - [SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation](https://mlsanity.com/paper/2412.10906/) (2024) - [STEER-ME: Assessing the Microeconomic Reasoning of Large Language Models](https://mlsanity.com/paper/2502.13119/) (2025) - [Self-Specialization: Uncovering Latent Expertise within Large Language Models](https://mlsanity.com/paper/2310.00160/) (2023) - [D'olares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English](https://mlsanity.com/paper/2402.07405/) (2024) - [CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial Forecasting by Integrating Time Series Patterns and Salient Macroeconomic Announcements](https://mlsanity.com/paper/2502.04592/) (2025) - [L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization](https://mlsanity.com/paper/2408.03033/) (2024) - [Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models](https://mlsanity.com/paper/2411.06272/) (2024) - [FANAL -- Financial Activity News Alerting Language Modeling Framework](https://mlsanity.com/paper/2412.03527/) (2024) - [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025) - [Finance Language Model Evaluation (FLaME)](https://mlsanity.com/paper/2506.15846/) (2025) - [1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector](https://mlsanity.com/paper/2210.17157/) (2022) - [Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis](https://mlsanity.com/paper/2503.16575/) (2025) - [Generative AI Enhanced Financial Risk Management Information Retrieval](https://mlsanity.com/paper/2504.06293/) (2025) ## 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.