Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
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| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
| Outcome: | The proposed method achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. |
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Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading (2021.findings-acl)
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| Challenge: | Existing models that use textual features and sentiments to make stock predictions are poor explainability and low signal-to-noise ratio. |
| Approach: | They propose a bi-level event detection model that detects corporate events from news articles and an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations (2020.emnlp-main)
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| Challenge: | Existing models that predict stock movements are based on time series and technical analysis, but price signals alone fail to capture market surprises and impacts of sudden unexpected events. |
| Approach: | They propose a model that integrates chaotic temporal signals from financial data and social media to create hierarchical temporal networks. |
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Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
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| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data (P18-4)
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| Challenge: | Existing methods to extract events from documents are limited due to the high cost of labeling . Experimental results demonstrate the effectiveness of a document-level Chinese financial event extraction system. |
| Approach: | They propose a document-level Chinese financial event extraction framework which detects event mentions and extracts events from financial news. |
| Outcome: | The proposed system detects event mentions and extracts events from financial news . it can generate large scale labeled data and extract events from entire document . |
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction (C18-1)
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| Challenge: | Recent work considers learning dense representations for news titles and abstracts . text representations can address the sparsity of discrete indicators in statistical models . |
| Approach: | They propose to use news abstracts to combine the most informative sentences in news content to learn dense representations for text elements. |
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Forecasting Firm Material Events from 8-K Reports (D19-51)
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| Challenge: | In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents of the company’s 8-K Current Reports. |
| Approach: | They exploit state-of-the-art neural architectures, including sequence-to-sequence architecture and attention mechanisms, to build a deep learning model that can forecast firm material event sequences based on company 8-K Current Reports. |
| Outcome: | The proposed model can forecast firm material event sequences based on the contents of the firm's 8-K Current Reports. |
Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
| Outcome: | The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis . |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |