From Bias to Behavior: Learning Bull-Bear Market Dynamics with Contrastive Modeling

2025-07-12

AI Summary

This paper addresses the challenge of modeling bull-bear market dynamics driven by investor cognitive biases. We propose B4, a novel framework that explicitly represents biases as learnable latent embeddings for the first time. B4 introduces inertia pairing and dual-force competition mechanisms to jointly characterize long–short force antagonism, sentiment divergence, and behavioral adaptation. By integrating contrastive learning, temporal embedding, and latent-space alignment, it fuses heterogeneous multi-source data within a shared latent space to capture asymmetry and heterogeneity in market trend evolution. Evaluated on real-world financial datasets, B4 significantly improves trend prediction accuracy. Moreover, it enables interpretable analysis of bias propagation pathways and investor behavioral interactions. The framework establishes a new paradigm for behavioral finance modeling and intelligent investment research.

Technology Category

• Machine Learning: Bayesian Learning
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Financial markets exhibit highly dynamic and complex behaviors shaped by both historical price trajectories and exogenous narratives, such as news, policy interpretations, and social media sentiment. The heterogeneity in these data and the diverse insight of investors introduce biases that complicate the modeling of market dynamics. Unlike prior work, this paper explores the potential of bull and bear regimes in investor-driven market dynamics. Through empirical analysis on real-world financial datasets, we uncover a dynamic relationship between bias variation and behavioral adaptation, which enhances trend prediction under evolving market conditions. To model this mechanism, we propose the Bias to Behavior from Bull-Bear Dynamics model (B4), a unified framework that jointly embeds temporal price sequences and external contextual signals into a shared latent space where opposing bull and bear forces naturally emerge, forming the foundation for bias representation. Within this space, an inertial pairing module pairs temporally adjacent samples to preserve momentum, while the dual competition mechanism contrasts bullish and bearish embeddings to capture behavioral divergence. Together, these components allow B4 to model bias-driven asymmetry, behavioral inertia, and market heterogeneity. Experimental results on real-world financial datasets demonstrate that our model not only achieves superior performance in predicting market trends but also provides interpretable insights into the interplay of biases, investor behaviors, and market dynamics.

Problem

• Modeling bull-bear market dynamics with bias-behavior relationships
• Integrating price sequences and external signals for trend prediction
• Capturing investor bias asymmetry and market heterogeneity

Innovation

• Joint embedding of price sequences and contextual signals
• Inertial pairing module preserves momentum
• Dual competition mechanism captures behavioral divergence