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RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data (2404.07452v1)

Published 11 Apr 2024 in q-fin.RM, cs.AI, cs.CE, cs.LG, and q-fin.PM

Abstract: The integration of AI techniques, particularly LLMs, in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering (Q$&$A), and stock movement prediction (binary classification), with a notable gap in the application of LLMs for financial risk prediction. Addressing this gap, in this paper, we introduce \textbf{RiskLabs}, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely combines different types of financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data surrounding ECC release dates. Our approach involves a multi-stage process: initially extracting and analyzing ECC data using LLMs, followed by gathering and processing time-series data before the ECC dates to model and understand risk over different timeframes. Using multimodal fusion techniques, RiskLabs amalgamates these varied data features for comprehensive multi-task financial risk prediction. Empirical experiment results demonstrate RiskLab's effectiveness in forecasting both volatility and variance in financial markets. Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context. Our findings not only contribute to the AI in finance application but also open new avenues for applying LLMs in financial risk assessment.

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References (51)
  1. An exploration of automatic text summarization of financial reports. In Proceedings of the Third Workshop on Financial Technology and Natural Language Processing, pp.  1–7.
  2. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  3. Identifying corporate credit risk sentiments from financial news. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pp.  362–370.
  4. The distribution of realized stock return volatility. Journal of financial economics 61(1), 43–76.
  5. Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change 30(3), 211–222.
  6. Predicting companies’ esg ratings from news articles using multivariate timeseries analysis. arXiv preprint arXiv:2212.11765.
  7. wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems 33, 12449–12460.
  8. Becker, P. (2023). Sustainability science: Managing risk and resilience for sustainable development. Elsevier.
  9. Artificial intelligence, machine learning and big data in finance opportunities, challenges, and implications for policy makers.
  10. Breiman, L. (2001). Random forests. Machine learning 45, 5–32.
  11. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
  12. Volatility distribution in the s&p500 stock index. Physica A: Statistical Mechanics and its Applications 245(3-4), 441–445.
  13. Support-vector networks. Machine learning 20, 273–297.
  14. Forecasting stock market volatility using (non-linear) garch models. Journal of forecasting 15(3), 229–235.
  15. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821.
  16. Learning to forget: Continual prediction with lstm. Neural computation 12(10), 2451–2471.
  17. Empirical asset pricing via machine learning. The Review of Financial Studies 33(5), 2223–2273.
  18. Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003.
  19. Kim, H. Y. and C. H. Won (2018). Forecasting the volatility of stock price index: A hybrid model integrating lstm with multiple garch-type models. Expert Systems with Applications 103, 25–37.
  20. Predicting risk from financial reports with regression. In Proceedings of human language technologies: the 2009 annual conference of the North American Chapter of the Association for Computational Linguistics, pp.  272–280.
  21. Kruschke, J. (2014). Doing bayesian data analysis: A tutorial with r, jags, and stan.
  22. Lakhani, A. (2023). Enhancing customer service with chatgpt transforming the way businesses interact with customers.
  23. Lee, Y.-C. (2007). Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications 33(1), 67–74.
  24. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33, 9459–9474.
  25. Large language models in finance: A survey. In Proceedings of the Fourth ACM International Conference on AI in Finance, pp.  374–382.
  26. Train big, then compress: Rethinking model size for efficient training and inference of transformers. In International Conference on machine learning, pp.  5958–5968. PMLR.
  27. Foundations and trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430.
  28. Investigating deep stock market forecasting with sentiment analysis. Entropy 25(2), 219.
  29. Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114.
  30. Mitchell, A. (2013). Risk and resilience: From good idea to good practice.
  31. Stock price prediction using news sentiment analysis. In 2019 IEEE fifth international conference on big data computing service and applications (BigDataService), pp.  205–208. IEEE.
  32. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.  390–401.
  33. A survey of hallucination in large foundation models. arXiv preprint arXiv:2309.05922.
  34. Multimodal multi-task financial risk forecasting. In Proceedings of the 28th ACM international conference on multimedia, pp.  456–465.
  35. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567.
  36. The performance of lstm and bilstm in forecasting time series. In 2019 IEEE International conference on big data (Big Data), pp.  3285–3292. IEEE.
  37. A review of supervised machine learning algorithms. In 2016 3rd international conference on computing for sustainable global development (INDIACom), pp.  1310–1315. Ieee.
  38. Soni, V. (2023). Large language models for enhancing customer lifecycle management. Journal of Empirical Social Science Studies 7(1), 67–89.
  39. Enhanced news sentiment analysis using deep learning methods. Journal of Computational Social Science 2(1), 33–46.
  40. Improving customer service quality in msmes through the use of chatgpt. Jurnal Minfo Polgan 12(1), 380–386.
  41. Managing risk and resilience.
  42. Rank-normalization, folding, and localization: An improved r for assessing convergence of mcmc (with discussion). Bayesian analysis 16(2), 667–718.
  43. A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating. Research in International Business and Finance 64, 101869.
  44. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems 35, 24824–24837.
  45. An effective application of decision tree to stock trading. Expert Systems with applications 31(2), 270–274.
  46. Fingpt: Open-source financial large language models. arXiv preprint arXiv:2306.06031.
  47. Html: Hierarchical transformer-based multi-task learning for volatility prediction. In Proceedings of The Web Conference 2020, pp.  441–451.
  48. Finmem: A performance-enhanced llm trading agent with layered memory and character design. arXiv preprint arXiv:2311.13743.
  49. Enhancing financial sentiment analysis via retrieval augmented large language models. In Proceedings of the Fourth ACM International Conference on AI in Finance, pp.  349–356.
  50. Benchmarking large language models for news summarization. Transactions of the Association for Computational Linguistics 12, 39–57.
  51. Joint abstractive and extractive method for long financial document summarization. In Proceedings of the 3rd Financial Narrative Processing Workshop, pp.  99–105.
Citations (10)
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Summary

  • The paper introduces a novel LLM-driven framework that integrates multi-modal financial data to enhance risk prediction accuracy.
  • It employs specialized encoders for processing earnings calls, time series, and market news, extracting key signals for risk assessment.
  • Experimental results demonstrate lower MSE and improved VaR estimates compared to conventional models, underscoring its practical impact.

Introducing RiskLabs: Enhancing Financial Risk Prediction with LLMs

Overview of RiskLabs Framework

RiskLabs presents a novel approach to financial risk prediction by exploiting the capabilities of LLMs to analyze and integrate diverse types of financial data. The framework stands out for its ability to combine textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data, through a series of sophisticated encoders and analytic modules. This multi-source, multi-modal fusion technique facilitates a comprehensive financial risk assessment, marking a significant advancement in the application of AI within the domain of finance.

Methodological Contributions

RiskLabs introduces several key methodological innovations:

  • Multi-Source Data Integration: By leveraging different types of financial data, RiskLabs achieves a holistic view of the financial landscape, enhancing the accuracy of risk prediction.
  • Earnings Conference Call Encoder: Utilizes LLMs to process both audio and textual data from ECCs, extracting salient features that are critical for understanding company performance and market expectations.
  • Time-Series Encoder: Adopts a dual approach, incorporating time-series embeddings and a Bayesian-based method to model the relationships among multiple financial metrics, this enhances the prediction of volatility and VaR (Value at Risk) metrics.
  • News-Market Reactions Encoder: Employs an enriched news pipeline to assess the impact of financial news on market behaviors, aiding in the prediction of stock movements and market dynamics.

Experimental Evaluation and Findings

RiskLabs was evaluated against several baselines, including classical financial models, LSTM-based models, and other AI-driven approaches. The framework demonstrated superior predictive performance, particularly in volatility forecasting and VaR estimation, as reflected in its lower mean squared error (MSE) across different forecasting intervals. Additionally, an ablation paper revealed the incremental value added by each component of RiskLabs, confirming the efficacy of its integrated approach.

Practical Implications and Theoretical Contributions

The success of RiskLabs underscores the potential of LLMs in financial risk assessment, offering a viable alternative to traditional and existing AI-based models. From a practical standpoint, RiskLabs provides investors and financial analysts with more accurate and comprehensive risk predictions, facilitating better-informed decision-making processes. Theoretically, this work contributes to the growing body of literature on the applicability of LLMs in finance, particularly in addressing the challenges of financial risk prediction.

Future Directions

Building on the current capabilities of RiskLabs, ongoing enhancements focus on refining the News-Market Reactions Encoder and expanding the dataset for a broader validation of the model. The introduction of "Dynamic Moving Time Window" training and "Time Decay Hyper-parameter" settings is anticipated to further improve the timeliness and accuracy of risk predictions. These developments reflect a continuous commitment to advancing the frontiers of using LLMs for financial analysis.

Conclusion

RiskLabs represents a breakthrough in financial risk prediction by effectively harnessing the power of LLMs to process and analyze multimodal financial data. Its comprehensive approach not only advances the state of AI applications in finance but also opens up new avenues for research and innovation in this critical and complex domain.

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