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Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting

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arxiv 2306.11025 v1 pith:T43CKFCY submitted 2023-06-19 cs.LG cs.AIcs.CLq-fin.ST

Xinli Yu , Zheng Chen , Yuan Ling , Shujing Dong , Zongyi Liu , Yanbin Lu This is my paper · ORCID
classification cs.LGcs.AIcs.CLq-fin.ST
keywords financialmodelseriestimeexplainablehistoricalknowledgellms
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This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence reasoning and inference, the hurdle of incorporating multi-modal signals from historical news, financial knowledge graphs, etc., and the issue of interpreting and explaining the model results. In this paper, we focus on NASDAQ-100 stocks, making use of publicly accessible historical stock price data, company metadata, and historical economic/financial news. We conduct experiments to illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. Our experiments include trying zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model Open LLaMA. We demonstrate our approach outperforms a few baselines, including the widely applied classic ARMA-GARCH model and a gradient-boosting tree model. Through the performance comparison results and a few examples, we find LLMs can make a well-thought decision by reasoning over information from both textual news and price time series and extracting insights, leveraging cross-sequence information, and utilizing the inherent knowledge embedded within the LLM. Additionally, we show that a publicly available LLM such as Open-LLaMA, after fine-tuning, can comprehend the instruction to generate explainable forecasts and achieve reasonable performance, albeit relatively inferior in comparison to GPT-4.

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Forward citations

Cited by 22 Pith papers

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  21. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

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  22. Forecast-Then-Optimize Deep Learning Methods

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