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

• URL: http://arxiv.org/abs/2507.14182v1
• Date: Sat, 12 Jul 2025 11:36:26 GMT
• Title: From Bias to Behavior: Learning Bull-Bear Market Dynamics with Contrastive Modeling
• Authors: Xiaotong Luo, Shengda Zhuo, Min Chen, Lichun Li, Ruizhao Lu, Wenqi Fan, Shuqiang Huang, Yin Tang,
• Abstract summary: This paper explores the potential of bull and bear regimes in investor-driven market dynamics.<n>We propose the Bias to Behavior from Bull-Bear Dynamics model (B4), a unified framework that embeds temporal price sequences and external contextual signals into a shared latent space.<n>Our model achieves superior performance in predicting market trends and provides interpretable insights into the interplay of biases, investor behaviors, and market dynamics.
• Score: 13.039189005779534
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index [0.0]
  This paper investigates the structural dynamics of stock market volatility through the Financial Chaos Index.<n>We identify three distinct market regimes, low-chaos, intermediate-chaos, and high-chaos, each characterized by differing levels of systemic stress.<n>We find that shifts in macroeconomic, financial, policy, and geopolitical uncertainty exhibit strong predictive power for volatility dynamics across regimes.
  arXiv  Detail & Related papers  (2025-04-26T15:48:11Z)
• CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization [14.241290261347281]
  We introduce the framework of Cross-market Synergy with Pseudo-volatility Optimization (CSPO)<n>CSPO implements an effective deep neural architecture to leverage external futures knowledge.<n>CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence.
  arXiv  Detail & Related papers  (2025-03-26T18:58:15Z)
• BiasConnect: Investigating Bias Interactions in Text-to-Image Models [73.76853483463836]
  We introduce BiasConnect, a novel tool designed to analyze and quantify bias interactions in Text-to-Image models.<n>Our method provides empirical estimates that indicate how other bias dimensions shift toward or away from an ideal distribution when a given bias is modified.<n>We demonstrate the utility of BiasConnect for selecting optimal bias mitigation axes, comparing different TTI models on the dependencies they learn, and understanding the amplification of intersectional societal biases in TTI models.
  arXiv  Detail & Related papers  (2025-03-12T19:01:41Z)
• Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets [0.0]
  We develop a form of virtual traders that use deep generative models to make buy/sell decisions. Under our framework, neuro-symbolic traders are agents that use vision-language models to discover a model of the fundamental value of an asset. We find that this leads to price suppression compared to the historical data, highlighting a future risk to market stability.
  arXiv  Detail & Related papers  (2024-10-18T16:37:52Z)
• Nested replicator dynamics, nested logit choice, and similarity-based learning [56.98352103321524]
  We consider a model of learning and evolution in games with action sets endowed with a partition-based similarity structure. In this model, revising agents have a higher probability of comparing their current strategy with other strategies that they deem similar. Because of this implicit bias toward similar strategies, the resulting dynamics do not satisfy any of the standard monotonicity rationalitys for imitative game dynamics.
  arXiv  Detail & Related papers  (2024-07-25T07:09:53Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Multi-Asset Spot and Option Market Simulation [52.77024349608834]
  We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators.
  arXiv  Detail & Related papers  (2021-12-13T17:34:28Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets [12.020797636494267]
  We propose CLVSA, a hybrid model that captures variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence-to-sequence model with attention. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to prevent itself from overfitting traps.
  arXiv  Detail & Related papers  (2021-04-08T20:31:04Z)
• Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning [2.741266294612776]
  We apply deep learning to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile markets. Our findings show that accuracies, while remaining significant, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers.
  arXiv  Detail & Related papers  (2020-02-24T17:19:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.