Papers with risk management
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. |
Semantic search with domain-specific word-embedding and production monitoring in Fintech (2020.coling-demos)
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| Challenge: | a novel system with domain-specific custom language models for accurate search terms expansion addresses several challenges faced in an industry-setting . many industry-grade search engines exist, but their accuracy can be sub-optimal due to domain specificity and terms provided by the users. |
| Approach: | They propose an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion. |
| Outcome: | The proposed system is used in risk management and has wide applicability to other domains. |
Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting (2023.emnlp-industry)
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| Challenge: | Recent advances in machine learning and artificial intelligence have opened up numerous opportunities and challenges in financial time series forecasting. |
| Approach: | They propose to use Large Language Models for explainable financial time series forecasting to leverage cross-sequence information and extract insights from text and price time series. |
| Outcome: | The proposed model outperforms ARMA-GARCH and gradient-boosting tree models while underperforming on other models. |
DialogueGAT: A Graph Attention Network for Financial Risk Prediction by Modeling the Dialogues in Earnings Conference Calls (2022.findings-emnlp)
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| Challenge: | Existing models focus on extracting useful semantic information from conference call transcripts but ignore subtle yet important information of dialogue structures. |
| Approach: | They propose a graph attention network called DialogueGAT for financial risk prediction by simultaneously modeling the speakers and their utterances in conference calls. |
| Outcome: | The proposed model outperforms baseline models on a dataset of S&P1500 companies. |
ForestCast: Open-Ended Event Forecasting with Semantic News Forest (2025.findings-emnlp)
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| Challenge: | Existing approaches and datasets overlook the complex relationships among events . current research lacks comprehensive evaluation methods to evaluate OEEF . |
| Approach: | They propose a prediction pipeline that extracts forecast-relevant events from news data . forestcast organizes news events into a story tree and predicts subsequent events along each path . |
| Outcome: | The proposed pipeline extracts forecast-relevant events from news data and predicts subsequent events along each path. |
Scattered Hypothesis Generation for Open-Ended Event Forecasting (2026.findings-acl)
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| Challenge: | Existing methods for event forecasting focus on the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. |
| Approach: | They propose a reinforcement learning framework that optimizes inclusiveness and diversity of the hypothesis by integrating validity-gated score into the overall objective. |
| Outcome: | The proposed framework outperforms baselines on two real-world benchmark datasets. |