Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy

TL;DR

This paper introduces a novel prompt-based evaluation method for financial sentiment analysis that incorporates detailed instructions to improve LLM performance and stock prediction accuracy, addressing subjectivity issues in existing datasets.

Contribution

The paper presents the Annotators' Instruction Assisted Prompt (AIAP), a new approach that standardizes sentiment interpretation in LLMs by integrating detailed annotator instructions, and demonstrates its effectiveness with a new WSBS dataset.

Findings

AIAP improves LLM performance by up to 9.08 points.

Enhanced sentiment interpretation leads to better stock prediction.

Context-aware prompts yield more consistent and accurate sentiment analysis.

Abstract

Financial sentiment analysis (FSA) presents unique challenges to LLMs that surpass those in typical sentiment analysis due to the nuanced language used in financial contexts. The prowess of these models is often undermined by the inherent subjectivity of sentiment classifications in existing benchmark datasets like Financial Phrasebank. These datasets typically feature undefined sentiment classes that reflect the highly individualized perspectives of annotators, leading to significant variability in annotations. This variability results in an unfair expectation for LLMs during benchmarking, where they are tasked to conjecture the subjective viewpoints of human annotators without sufficient context. In this paper, we introduce the Annotators' Instruction Assisted Prompt, a novel evaluation prompt designed to redefine the task definition of FSA for LLMs. By integrating detailed task instructions originally intended for human annotators into the LLMs' prompt framework, AIAP aims to standardize the understanding of sentiment across both human and machine interpretations, providing a fair and context-rich foundation for sentiment analysis. We utilize a new dataset, WSBS, derived from the WallStreetBets subreddit to demonstrate how AIAP significantly enhances LLM performance by aligning machine operations with the refined task definitions. Experimental results demonstrate that AIAP enhances LLM performance significantly, with improvements up to 9.08. This context-aware approach not only yields incremental gains in performance but also introduces an innovative sentiment-indexing method utilizing model confidence scores. This method enhances stock price prediction models and extracts more value from the financial sentiment analysis, underscoring the significance of WSB as a critical source of financial text. Our research offers insights into both improving FSA through better evaluation methods.