Opportunities vs. Risks: Exploring Automatic Annotation of Financial Polarity Biases via Large Language Models - Romero, Viviane; Assis, Gabriel; Carvalho, Jonnathan; Mann, Paulo; Paes, Aline (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247742, 2025)
The financial market encompasses investors with distinct risk profiles - conservative, moderate and aggressive — each reflecting different attitudes toward gains and losses. These profiles influence the interpretation of financial news, particularly regarding the comparison between the labels "positive"/"negative" and the categories "opportunity"/"risk". Although these pairs of terms may initially appear equivalent, their practical application reveals notable inconsistencies. This paper employs large language models (LLMs) to annotate financial news, investigating whether such models capture the biases associated with each investor profile. We analyze the correlation between "opportunity" and "positive" and between "risk" and "negative" in the labels generated by the models. Furthermore, we examine whether, in the absence of explicit instructions regarding risk preference, the LLMs implicitly adopt a default bias when performing sentiment analysis on financial texts. Our findings provide insights into how risk profiles influence model behavior and suggest directions for improving both the personalization and accuracy of polarity detection in financial news analysis.