Uncovering Representation Bias for Investment Decisions in Open-Source Large Language Models
This addresses potential biases in financial LLM applications for investors and regulators, though it is incremental as it focuses on specific models and metrics.
This paper investigates representation bias in open-source Qwen large language models when used for investment decisions, finding that firm size and valuation consistently increase model confidence while risk factors decrease it, with confidence varying significantly across sectors and best aligning with fundamental data.
Large Language Models are increasingly adopted in financial applications to support investment workflows. However, prior studies have seldom examined how these models reflect biases related to firm size, sector, or financial characteristics, which can significantly impact decision-making. This paper addresses this gap by focusing on representation bias in open-source Qwen models. We propose a balanced round-robin prompting method over approximately 150 U.S. equities, applying constrained decoding and token-logit aggregation to derive firm-level confidence scores across financial contexts. Using statistical tests and variance analysis, we find that firm size and valuation consistently increase model confidence, while risk factors tend to decrease it. Confidence varies significantly across sectors, with the Technology sector showing the greatest variability. When models are prompted for specific financial categories, their confidence rankings best align with fundamental data, moderately with technical signals, and least with growth indicators. These results highlight representation bias in Qwen models and motivate sector-aware calibration and category-conditioned evaluation protocols for safe and fair financial LLM deployment.