Results of the summarizing process for the arXiv paper: 2306.11025v1

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 study, the authors focus specifically on NASDAQ-100 stocks and aim to address these challenges by leveraging LLMs. They make use of publicly accessible historical stock price data, company metadata, and historical economic/financial news related to AAPL (Apple Inc.) as an example. The experiments conducted in this paper illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. The authors explore zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model called Open LLaMA. Through performance comparison results and examples, they demonstrate that LLMs can make well-thought decisions by reasoning over information from both textual news and price time series data. They show that LLMs are able to extract insights, leverage cross-sequence information, utilize inherent knowledge embedded within them, generate explainable forecasts and achieve reasonable performance. Moreover, it is noted that GPT-4 outperforms Open LLaMA in terms of performance. The paper also provides additional context regarding the prompt structure used for generating summaries and keywords. The authors empirically tweaked the instructions to better guide the model's task of predicting next week's summary and keywords based on noisy but relevant news data extracted from web page HTML. They experimented with dividing the instruction into two parts positioned at the beginning and end of the prompt as well as incorporating a "step-by-step thinking" approach which notably improved the performance of the model. Overall, this paper contributes to the field of financial time series forecasting by showcasing the potential of LLMs in addressing challenges related to cross sequence reasoning multi modal signal incorporation and interpretability. The experiments conducted highlight their effectiveness in making informed decisions based on information from textual news and price time series data.

• Large Language Models (LLMs) can be harnessed for explainable financial time series forecasting
• Challenges in applying machine learning models to financial time series include cross-sequence reasoning, incorporating multi-modal signals, and interpreting results
• This study focuses on NASDAQ-100 stocks and addresses these challenges using LLMs
• Historical stock price data, company metadata, and economic/financial news related to AAPL are used as examples
• Experiments demonstrate the potential of LLMs in offering a unified solution to the challenges
• Zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with Open LLaMA are explored
• LLMs can make well-thought decisions by reasoning over textual news and price time series data
• LLMs extract insights, leverage cross-sequence information, utilize inherent knowledge, generate explainable forecasts, and achieve reasonable performance
• GPT-4 outperforms Open LLaMA in terms of performance
• The paper provides additional context on the prompt structure used for generating summaries and keywords
• Tweaking instructions improved the model's performance in predicting next week's summary and keywords based on relevant news data extracted from web page HTML
• Overall, this paper contributes to financial time series forecasting by showcasing the potential of LLMs in addressing challenges related to cross-sequence reasoning, multi-modal signal incorporation, and interpretability

Harnessing Large Language Models for Explainable Financial Time Series Forecasting

The application of machine learning models to financial time series forecasting comes with several challenges, such as difficulty in cross-sequence reasoning and inference, incorporating multi-modal signals from historical news, financial knowledge graphs etc., and interpreting and explaining the model results. This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities to address these challenges. The authors focus specifically on NASDAQ-100 stocks and use publicly accessible historical stock price data, company metadata, and historical economic/financial news related to AAPL (Apple Inc.) as an example.

Experimental Setup

The experiments conducted in this paper illustrate the potential of LLMs in offering a unified solution to the aforementioned challenges. The authors explore zero-shot/few-shot inference with GPT-4 and instruction-based fine-tuning with a public LLM model called Open LLaMA. Through performance comparison results and examples, they demonstrate that LLMs can make well thought decisions by reasoning over information from both textual news and price time series data. They show that LLMs are able to extract insights, leverage cross sequence information, utilize inherent knowledge embedded within them, generate explainable forecasts and achieve reasonable performance. Moreover it is noted that GPT-4 outperforms Open LLaMA in terms of performance.

Prompt Structure Used For Generating Summaries & Keywords

The paper also provides additional context regarding the prompt structure used for generating summaries & keywords. The authors empirically tweaked the instructions to better guide the model's task of predicting next week's summary & keywords based on noisy but relevant news data extracted from web page HTML. They experimented with dividing the instruction into two parts positioned at the beginning & end of the prompt as well as incorporating a "step by step thinking" approach which notably improved the performance of the model.

Conclusion

Overall this paper contributes to field of financial time series forecasting by showcasing potential of LLMs in addressing challenges related to cross sequence reasoning multi modal signal incorporation & interpretability . Experiments conducted highlight their effectiveness in making informed decisions based on information from textual news & price time series data