MEME: Modeling the evolutionary modes of financial markets

Large Language Models (LLMs) have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human-like analytical workflows. However, current LLM-based methods primarily follow either an Asset-Centric paradigm focused on individual stock prediction or a Market-Centric approach for portfolio allocation, often remaining agnostic to the underlying reasoning that drives market movements. In this paper, we propose a Logic-Oriented perspective, modeling the financial market as a dynamic, evolutionary ecosystem of competing investment narratives, termed Modes of Thought. To operationalize this view, we introduce MEME (Modeling the Evolutionary Modes of Financial Markets), a framework designed to reconstruct market dynamics through the lens of evolving logics. MEME employs a multi-agent extraction module to transform noisy data into high-fidelity Investment Arguments and utilizes Gaussian Mixture Modeling (GMM) to uncover latent consensus within a semantic space. To mitigate “logical noise” and semantic drift, we also implement a temporal evaluation and alignment mechanism to track the lifecycle and historical profitability of these modes. By prioritizing enduring market wisdom over transient anomalies, MEME ensures that portfolio construction is guided by robust, verified reasoning. Extensive experiments on three heterogeneous Chinese stock pools (SSE 50, CSI 300, and CSI 500) from 2023 to 2025 demonstrate that MEME consistently outperforms eight state-of-the-art baselines. Further ablation studies, sensitivity analyses, and lifecycle visualizations validate MEME's capacity to identify and adapt to the evolving consensus of financial markets. Our implementation can be found at https://anonymous.4open.science/r/MEME-FA3C.