Paper: arXiv 2404.07452

Abstract

The integration of Artificial Intelligence (AI) techniques, particularly large language models (LLMs), in finance has garnered increasing academic attention. Despite progress, existing studies predominantly focus on tasks like financial text summarization, question-answering, and stock movement prediction (binary classification), the application of LLMs to financial risk prediction remains underexplored. Addressing this gap, in this paper, we introduce RiskLabs, a novel framework that leverages LLMs to analyze and predict financial risks. RiskLabs uniquely integrates multimodal financial data, including textual and vocal information from Earnings Conference Calls (ECCs), market-related time series data, and contextual news data to improve financial risk prediction. Empirical results demonstrate RiskLabs’ effectiveness in forecasting both market volatility and variance. Through comparative experiments, we examine the contributions of different data sources to financial risk assessment and highlight the crucial role of LLMs in this process. We also discuss the challenges associated with using LLMs for financial risk prediction and explore the potential of combining them with multimodal data for this purpose.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced techniques like multimodal fusion with LLMs and multi-head self-attention, indicating substantial mathematical complexity. It also presents empirical results with comparative experiments on real financial data (earnings calls, news, time series), demonstrating high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Predict Financial Risk<br>using LLMs on Multimodal Data"] --> B

  subgraph B ["Data Acquisition & Integration"]
      direction LR
      B1["Textual Data<br>ECC Transcripts"]
      B2["Vocal Data<br>ECC Audio Tone"]
      B3["Market Data<br>Time Series"]
      B4["Contextual Data<br>News Sentiment"]
  end

  B --> C["LLM-based Feature Extraction<br>& Multimodal Fusion"]

  C --> D{"Prediction Tasks"}
  D --> E["Market Volatility<br>Forecasting"]
  D --> F["Variance Prediction"]

  E & F --> G["Key Outcomes"]
  
  subgraph G ["Comparative Analysis"]
      G1["Superior Performance<br>vs. Unimodal Models"]
      G2["LLMs are crucial<br>for text processing"]
      G3["Identified optimal<br>data source combinations"]
  end

Cited References (90)

  1. Artificial Intelligence, Machine Learning and Big Data in Finance Opportunities, Challenges, and Implications for Policy Makers (2021)
  2. Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance (2021)
  3. A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating (2023)
  4. A review of supervised machine learning algorithms (2016)
  5. Train big, then compress: Rethinking model size for efficient training and inference of transformers (2020)
  6. Benchmarking large language models for news summarization (2024)
  7. Enhancing financial sentiment analysis via retrieval augmented large language models — → 2310.04027
  8. Chain-of-thought prompting elicits reasoning in large language models (2022)
and 82 more
  1. Fingpt: Open-source financial large language models — → 2306.06031
  2. Sustainability science: Managing risk and resilience for sustainable development (2023)
  3. Risk and resilience: From good idea to good practice (2013)
  4. Managing risk and resilience (2015)
  5. A survey of hallucination in large foundation models (2023) — link
  6. Gpt-4 technical report (2023) — link
  7. Retrieval-augmented generation for knowledge-intensive nlp tasks (2020)
  8. Retrieval augmentation reduces hallucination in conversation (2021) — link
  9. Simcse: Simple contrastive learning of sentence embeddings (2021) — link
  10. The performance of LSTM and BiLSTM in forecasting time series (2019)
  11. Artificial intelligence index report 2023 (2023) — link
  12. Predicting risk from financial reports with regression (2009)
  13. Estimating value at risk of portfolio by conditional copula-GARCH method (2009)
  14. Multimodal multi-task financial risk forecasting (2020)
  15. Markov chain Monte Carlo estimation of regime switching vector autoregressions (1999)
  16. Using conditional copula to estimate value at risk (2006)
  17. Finbert: Financial sentiment analysis with pre-trained language models (2019) — link
  18. Language models are few-shot learners (2020)
  19. Bert: Pre-training of deep bidirectional transformers for language understanding (2018) — link
  20. Attention is all you need (2017)
  21. Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms (2023) — link
  22. Are transformers effective for time series forecasting? (2023)
  23. Temporal Data Meets LLM–Explainable Financial Time Series Forecasting — → 2306.11025
  24. Journey of hallucination-minimized generative ai solutions for financial decision makers (2024)
  25. Scaling laws for neural language models (2020) — link
  26. Llm lies: Hallucinations are not bugs, but features as adversarial examples (2023) — link
  27. A Scoping Review of ChatGPT Research in Accounting and Finance — → 2412.05731
  28. Realized volatility forecasting with neural networks (2020)
  29. Forecasting crude oil market volatility using variable selection and common factor (2023)
  30. Prediction of realized volatility and implied volatility indices using AI and machine learning: A review (2024)
  31. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation (1982)
  32. Generalized autoregressive conditional heteroskedasticity (1986)
  33. Modelling credit risk for SMEs: Evidence from the US market (2007)
  34. The pricing of options and corporate liabilities (1973)
  35. A closed-form solution for options with stochastic volatility with applications to bond and currency options (1993)
  36. Predicting the direction of stock market prices using random forest (2016) — link
  37. Application of support vector machines to corporate credit rating prediction (2007)
  38. An effective application of decision tree to stock trading (2006)
  39. Joint abstractive and extractive method for long financial document summarization (2021)
  40. wav2vec 2.0: A framework for self-supervised learning of speech representations (2020)
  41. An exploration of automatic text summarization of financial reports (2021)
  42. Large language models for enhancing customer lifecycle management (2023)
  43. Improving customer service quality in MSMEs through the use of ChatGPT (2023)
  44. Enhancing Customer Service with ChatGPT Transforming the Way Businesses Interact with Customers (2023)
  45. FinMem: A performance-enhanced LLM trading agent with layered memory and character design — → 2311.13743
  46. Large language models in finance: A survey (2023)
  47. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues (2019)
  48. Predicting Companies’ ESG Ratings from News Articles Using Multivariate Timeseries Analysis (2022) — link
  49. Identifying corporate credit risk sentiments from financial news (2022)
  50. Investigating deep stock market forecasting with sentiment analysis (2023)
  51. Stock price prediction using news sentiment analysis (2019)
  52. Enhanced news sentiment analysis using deep learning methods (2019)
  53. Empirical asset pricing via machine learning (2020)
  54. Support-vector networks (1995)
  55. Random forests (2001)
  56. A transformer-based framework for multivariate time series representation learning (2021)
  57. Financial time series forecasting with deep learning: A systematic literature review: 2005–2019 (2020)
  58. Kernel density estimation with bounded data (2018)
  59. Local likelihood density estimation (1996)
  60. Functional GARCH models: The quasi-likelihood approach and its applications (2019)
  61. Volatility estimation using a rational GARCH model (2018)
  62. Estimation of stochastic volatility models via Monte Carlo maximum likelihood (1998)
  63. Volatility distribution in the S&P500 stock index (1997)
  64. The distribution of realized stock return volatility (2001)
  65. Deep learning for finance: deep portfolios (2017)
  66. PyMC: a modern, and comprehensive probabilistic programming framework in Python (2023)
  67. Bayesian regression and classification (2003)
  68. Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach (2020)
  69. Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions (2022) — link
  70. Forecasting stock market volatility using (non-linear) Garch models (1996)
  71. Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)
  72. Learning to forget: Continual prediction with LSTM (2000)
  73. Multi-task sequence to sequence learning (2015) — link
  74. Empirical evaluation of gated recurrent neural networks on sequence modeling (2014) — link
  75. Html: Hierarchical transformer-based multi-task learning for volatility prediction (2020)
  76. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2014)
  77. Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC (with discussion) (2021)
  78. The finben: An holistic financial benchmark for large language models (2024) — link
  79. FinAgent: A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist — → 2402.18485
  80. When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments — → 2407.18957
  81. Omnipred: Language models as universal regressors (2024) — link
  82. Financial Risk Disclosure Return Premium: A Topic Modeling Approach (2020)

7 of 90 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.