Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation

Financial large language models (LLMs) powering robo-advisors and trading agents may harbor inherent biases toward specific assets, a critical concern largely unaddressed in current research. A study published on arXiv explores whether these advanced AI systems systematically prefer certain financial instruments, specifically examining Bitcoin representations and their influence on portfolio allocation¹. The investigation also seeks to identify internal representations within the LLM that causally drive these preferences and determine how such biases ultimately affect downstream financial decisions. This research highlights the potential for embedded predispositions within AI models to guide investment strategies, raising questions about the fairness and impartiality of automated financial advice. Understanding how LLMs develop and act on these asset-specific leanings is crucial, as unacknowledged biases could lead to distorted market behavior and unintended financial consequences for users. The implications extend to the security and integrity of AI in finance, demanding rigorous auditing of these emerging capabilities.