Temporal Data Meets LLM – Explainable Financial Time Series Forecasting

by   Xinli Yu, et al.

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.


page 4

page 6

page 7


Leveraging Vision-Language Models for Granular Market Change Prediction

Predicting future direction of stock markets using the historical data h...

LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs

In this work, we leverage pre-trained Large Language Models (LLMs) to en...

Beyond Classification: Financial Reasoning in State-of-the-Art Language Models

Large Language Models (LLMs), consisting of 100 billion or more paramete...

The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges

Recently, large language models (LLMs) like ChatGPT have demonstrated re...

Industry Classification Using a Novel Financial Time-Series Case Representation

The financial domain has proven to be a fertile source of challenging ma...

GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models

Annual Reports of publicly listed companies contain vital information ab...

Financial Time Series Representation Learning

This paper addresses the difficulty of forecasting multiple financial ti...

Please sign up or login with your details

Forgot password? Click here to reset