Artificial Intelligence for Modeling Investor Behavior and Market Sentiment Dynamics
DOI:
https://doi.org/10.66280/ijair.v1i1.83Abstract
The traditional paradigms of financial market analysis have historically relied upon the Efficient Market Hypothesis and the assumption of the rational agent. However, the emergence of high-frequency social data and complex global interdependencies has exposed the limitations of these classical frameworks in capturing the reflexive and often irrational nature of market participants. This research paper investigates the systematic integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in modeling investor behavior and the shifting dynamics of market sentiment. We conduct an interdisciplinary system-level analysis that situates AI not merely as a predictive tool, but as a critical socio-technical infrastructure capable of decoding the latent emotional and cognitive biases that drive capital flows. The discussion encompasses the architectural trade-offs involved in multi-modal sentiment analysis, the challenges of deploying real-time behavioral models within legacy financial systems, and the systemic risks associated with algorithmic herding. Furthermore, we address the ethical imperatives of fairness and data privacy, arguing that the governance of behavioral AI must account for the potential manipulation of retail sentiment by institutional actors. By examining the infrastructure of "opinion mining" and its environmental costs, this paper proposes a robust framework for behavioral modeling that prioritizes transparency, sustainability, and market integrity. This investigation concludes with forward-looking perspectives on the role of autonomous behavioral agents in stabilizing future financial ecosystems against collective panic and informational cascades.
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