| Challenge: | Recent studies have defined forecasting financial volatility from annual reports as text regression problem. |
| Approach: | They propose to replace word features with word embedding vectors to remove lexicon dependency. |
| Outcome: | The proposed model provides more accurate volatility predictions than lexicon based models. |
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VolTAGE: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls (2020.emnlp-main)
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| Challenge: | Existing approaches to stock volatility forecasting ignore correlations between stocks. |
| Approach: | They propose to combine vocal cues with verbal and financial cue data to create a multimodal stock volatility prediction model that accounts for stock interdependence via graph convolutions. |
| Outcome: | The proposed model outperforms existing methods showing that it can predict volatility using multimodal learning. |
KeFVP: Knowledge-enhanced Financial Volatility Prediction (2023.findings-emnlp)
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| Challenge: | Current studies ignore the role of financial metrics knowledge in earnings calls and little consideration is given to integrating text and price information. |
| Approach: | They propose to integrate financial metrics knowledge into text comprehension by knowledge-enhanced adaptive pre-training and effectively incorporating text and price information by introducing a conditional time series prediction module. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three real-world datasets and is effective and reliable. |
Measuring Consistency in Text-based Financial Forecasting Models (2023.acl-long)
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| Challenge: | Recent advances in natural language processing (NLP) have allowed financial forecasting to gain significant accuracy and reliability. |
| Approach: | They propose a tool that assesses logical consistency in financial text and compares it with other models to assess their performance. |
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DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents (2022.findings-emnlp)
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Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha
| Challenge: | Existing research focuses on textual and audio modalities of financial disclosures but ignores the rich tabular data available in financial reports. |
| Approach: | They propose to combine tabular financial data with text transcripts and audio recordings to improve stock volatility and price movement prediction by 5-12% and reduce gender bias by over 30%. |
| Outcome: | The combined data improves stock volatility and price movement prediction by 5-12% and reduces gender bias caused due to audio-based neural networks by over 30%. |
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues (P19-1)
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| Challenge: | Existing studies have shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. |
| Approach: | They propose to model CEO’s verbal (from text) and vocal (from audio) information in a conference call. |
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Encoding Sentiment Information into Word Vectors for Sentiment Analysis (C18-1)
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| Challenge: | Existing methods for embedding sentiment knowledge into word vectors are generally trained independently of the downstream task. |
| Approach: | They propose to encode sentiment knowledge into pre-trained word vectors to improve sentiment analysis. |
| Outcome: | The proposed method improves sentiment analysis on four popular sentiment datasets compared to benchmark methods. |
Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization (2020.acl-main)
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| Challenge: | Recent studies have shown that news articles can be leveraged to improve price prediction. |
| Approach: | They propose a method to encode the influence of news articles through a vector representation of stocks . they use a deep learning framework to acquire the vector representation using news articles and price history . |
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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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Financial Forecasting from Textual and Tabular Time Series (2024.findings-emnlp)
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| Challenge: | Existing models that combine multiple data sources and combine them to form accurate financial predictions are challenging to model without inductive biases. |
| Approach: | They propose to use numerical financial results, macroeconomic states, and long financial documents to model company earnings relative to analyst expectations. |
| Outcome: | The proposed model outperforms existing models in a simulated trading environment and demonstrates that each modality contains unique information. |
Learning to Compare Financial Reports for Financial Forecasting (2024.findings-eacl)
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| Challenge: | Public companies in the US are required to publish annual reports that contain over 25,000 words across all sections and a high percentage of boilerplate content that does not change much year-to-year. |
| Approach: | They propose to model complex, cross-document relationships between financial reports using paired financial reports. |
| Outcome: | The proposed model can predict company risk and correlation from financial reports . the proposed model is able to recognize complex, nuanced relationships with complex signals . |