Behavioral Finance Analytics with Large Language Models: Exploring the Relationship Between Investor Self-Perception, Decision Biases, and Market Outcomes

Abstract

The integration of large language models into behavioral finance opens a novel frontier for studying how investors’ self-perception interacts with cognitive biases to shape aggregate market outcomes. Traditional behavioral research relies on surveys and experimental proxies that lack ecological richness and scalability. This paper presents a system-level examination of behavioral finance analytics powered by large language models, focusing on the extraction of self-perception narratives, bias signatures, and their empirical linkages to trading volumes, volatility, and bubble formation. We articulate a multi-layered architecture that ingests heterogeneous textual streams—social media discussions, earnings call transcripts, and financial news—and deploys fine-tuned transformer models to detect overconfidence, loss aversion, herding, and confirmation biases embedded in first-person investor discourse. The analysis highlights structural trade-offs among model capacity, inference cost, and analytical fidelity, as well as the deployment challenges of streaming pipelines and model drift. Governance dimensions receive sustained attention, including fairness disparities arising from demographic imbalances in training corpora, robustness against adversarial narrative manipulation, and interpretability requirements mandated by evolving AI regulatory frameworks. We further discuss sustainability considerations such as energy footprints of large foundation models versus distilled variants. The paper concludes with policy implications for market surveillance, systemic risk detection, and the design of transparent AI-assisted behavioral analytics. By linking fine-grained linguistic self-representation to macro-financial dynamics, the proposed analytics framework opens pathways toward more resilient, fair, and context-aware financial decision-support systems.