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The paper introduces MEME, a novel framework that models financial markets as an evolving ecosystem of investment narratives ("Modes of Thought") to improve portfolio construction. MEME uses a multi-agent extraction module to convert noisy data into Investment Arguments, then employs Gaussian Mixture Modeling to identify consensus within a semantic space and a temporal evaluation mechanism to track the lifecycle of these modes. Experiments on Chinese stock pools from 2023-2025 show MEME outperforms seven state-of-the-art baselines, demonstrating its ability to adapt to evolving market consensus.
LLMs can beat SOTA quantitative finance baselines by modeling financial markets as a dynamic competition between "Modes of Thought" and prioritizing enduring market wisdom over transient anomalies.
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), 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 to uncover latent consensus within a semantic space. To model semantic drift among different market conditions, 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 reasoning. Extensive experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines. Further ablation studies, sensitivity analysis, lifecycle case study and cost analysis validate MEME's capacity to identify and adapt to the evolving consensus of financial markets. Our implementation can be found at https://github.com/gta0804/MEME.
