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        <title>Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting</title>
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    <abstract>Applying machine learning to financial time series has been an active area of industrial research enabling innovation in market insights, risk management, strategic decision-making, and policy formation. This paper explores the novel use of Large Language Models (LLMs) for explainable financial time series forecasting, addressing challenges in cross-sequence reasoning, multi-modal data integration, and result interpretation that are inherent in traditional approaches. Focusing on NASDAQ-100 stocks, we utilize public historical stock data, company metadata, and economic/financial news. Our experiments employ GPT-4 for zero-shot/few-shot inference and Open LLaMA for instruction-based fine-tuning. The study demonstrates LLMs’ ability to generate well-reasoned decisions by leveraging cross-sequence information and extracting insights from text and price time series. We show that our LLM-based approach outperforms classic ARMA-GARCH and gradient-boosting tree models. Furthermore, fine-tuned public LLMs, such as Open-LLaMA, can generate reasonable and explainable forecasts, although they underperform compared to GPT-4.</abstract>
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%0 Conference Proceedings
%T Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting
%A Yu, Xinli
%A Chen, Zheng
%A Lu, Yanbin
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yu-etal-2023-harnessing
%X Applying machine learning to financial time series has been an active area of industrial research enabling innovation in market insights, risk management, strategic decision-making, and policy formation. This paper explores the novel use of Large Language Models (LLMs) for explainable financial time series forecasting, addressing challenges in cross-sequence reasoning, multi-modal data integration, and result interpretation that are inherent in traditional approaches. Focusing on NASDAQ-100 stocks, we utilize public historical stock data, company metadata, and economic/financial news. Our experiments employ GPT-4 for zero-shot/few-shot inference and Open LLaMA for instruction-based fine-tuning. The study demonstrates LLMs’ ability to generate well-reasoned decisions by leveraging cross-sequence information and extracting insights from text and price time series. We show that our LLM-based approach outperforms classic ARMA-GARCH and gradient-boosting tree models. Furthermore, fine-tuned public LLMs, such as Open-LLaMA, can generate reasonable and explainable forecasts, although they underperform compared to GPT-4.
%R 10.18653/v1/2023.emnlp-industry.69
%U https://aclanthology.org/2023.emnlp-industry.69
%U https://doi.org/10.18653/v1/2023.emnlp-industry.69
%P 739-753

Markdown (Informal)

[Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting](https://aclanthology.org/2023.emnlp-industry.69) (Yu et al., EMNLP 2023)

• Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting (Yu et al., EMNLP 2023)

ACL

• Xinli Yu, Zheng Chen, and Yanbin Lu. 2023. Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 739–753, Singapore. Association for Computational Linguistics.