Generating Market Comments from Stock Price Sequences: A Comparative Study of Prompting Strategies

Core Concepts

Effective prompting strategies can significantly improve the performance of large language models in generating market commentary from time-series stock price data.

Abstract

This study explores various input representations and prompting strategies for the task of generating market comments from numerical stock price data. The key findings are:

Prompts that resemble programming language expressions, such as Python dictionaries and nested lists, perform better than prompts that are closer to natural language or use longer formats like HTML and LaTeX tables.

The authors hypothesize that prompts with characteristics similar to the text used for pretraining the language models yield better outcomes, as the models can better capture the correspondence between timestamps and prices.

While an existing fine-tuned encoder-decoder model achieves high scores in automatic evaluation metrics, a human evaluation reveals that the prompt-based methods generate comments that are more consistent with the reference.

The study provides insights into effective prompt design for tasks that involve generating text from numerical sequences, which can be extended to other data-to-text generation settings beyond market commentary.

Stats

The Nikkei 225 begins to rebound, opening at 17,193 yen, up 263 yen.
The Nikkei 225 rebounded, closing of the morning session at 17,243 yen, an increase of 312 yen.
Tokyo Stock Exchange, 2:00 p.m., up more than 500 yen, buying continues in a wide range of stocks.
The Nikkei 225 rebounded for the first time in 3 days, closing at 17,388 yen, up 457 yen.

Quotes

"Contrary to our expectations, the results show that prompts resembling programming languages yield better outcomes, whereas those similar to natural languages and longer formats, such as HTML and LaTeX, are less effective."
"These findings suggest that converting numerical data into prompts that closely resemble the format used during pretraining can lead to improved performance."

Key Insights Distilled From

Prompting for Numerical Sequences

by Masayuki Kaw... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02466.pdf

Deeper Inquiries

How can the insights from this study be applied to generate market commentary in other languages beyond Japanese?

The insights from this study can be applied to generate market commentary in other languages by adapting the prompt-based approach to the specific linguistic characteristics of the target language. For languages other than Japanese, the templates and prompts used for generating market comments can be translated and adjusted to suit the syntax, grammar, and style of the new language. Additionally, the programming language-like prompts that showed better performance in the study can be tailored to incorporate language-specific elements while maintaining the structure that aligns with the pretraining data of the language model. By customizing the prompts for different languages, the model can effectively generate market commentary in a variety of linguistic contexts.

What are the potential limitations of the prompt-based approach, and how can they be addressed to further improve the performance?

One potential limitation of the prompt-based approach is the reliance on a fixed set of prompts, which may not cover the full range of variations in market commentary. To address this limitation and improve performance, the prompt library can be expanded to include a more diverse set of templates that capture different styles, tones, and nuances of market commentary. Additionally, incorporating dynamic prompts that adapt to the specific characteristics of the input numerical sequences can enhance the model's ability to generate more accurate and contextually relevant comments. Furthermore, fine-tuning the prompts based on feedback from human evaluations and continuous monitoring of model outputs can help refine the prompts for better performance.

How can the understanding of numerical trends and text style be combined to generate market comments that are both accurate and natural-sounding?

To generate market comments that are both accurate in reflecting numerical trends and natural-sounding in terms of text style, a hybrid approach can be adopted. This approach involves designing prompts that not only convey the numerical information effectively but also incorporate language patterns, expressions, and stylistic elements that mimic human-generated market commentary. By combining the understanding of numerical trends with the ability to generate text in a coherent and engaging manner, the model can produce market comments that are both informative and engaging to readers. Additionally, incorporating sentiment analysis and tone detection in the prompt design can help ensure that the generated comments align with the intended tone and style of market commentary. Regular feedback loops and iterative improvements based on human evaluations can further refine the model's ability to strike a balance between accuracy and naturalness in generating market comments.