Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers (2025)

Abstract: Financial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity of high-quality datasets. To address these issues, we leverage recent advancements in large language models (LLMs) and propose to adapt proxy tuning for financial sentiment analysis. Proxy tuning efficiently transfers knowledge from a pre-trained expert model to a controllable base model by incorporating logit differences, steering the base model toward the desired sentiment representation. Our method offers significant advantages: (1) it is training-free, reducing computational demands and data dependency; (2) it achieves promising performance, with a 36.67% improvement over the base model and over 90% of the tuned model’s performance; and (3) it is highly adaptable, functioning in a plug-and-play manner without requiring access to model architectures or weights. These results demonstrate the potential of proxy tuning as an efficient and practical solution for financial sentiment analysis in data-scarce scenarios.

## introduction financial sentiment analysis @xcite @xcite is a critical task in the financial domain with significant practical applications. for investors, it serves as a barometer of market trends, aiding in predicting fluctuations, formulating strategies, and assessing risks. for financial institutions, it provides valuable signals for algorithmic trading and quantitative investment, enabling strategy innovation and improved asset pricing. for regulators, it helps identify risks such as fraud, market manipulation, and systemic instability by reflecting market participants' decision-making tendencies. ## conclusion in this paper, we propose a framework leveraging large language models and proxy-tuning to address financial sentiment analysis, overcoming common challenges such as limited data and high computational costs. our method achieves performance comparable to directly tuned models while being resource-efficient. we hope this work provides insights into efficiently transferring knowledge from expert black-box models to controllable ones and inspires broader applications in the financial domain.