# MEME: Modeling the Evolutionary Modes of Financial Markets arXiv: https://arxiv.org/abs/2602.11918 ยท HTML: https://mlsanity.com/paper/2602.11918/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 50 references that are in the map. - First posted on arXiv 12 Feb 2026. ## Builds on (most cited first, 28 of 28) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025) - [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025) - [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025) - [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025) - [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024) - [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024) - [TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets](https://mlsanity.com/paper/2502.01506/) (2025) - [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021) - [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025) - [AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay](https://mlsanity.com/paper/2502.16789/) (2025) - [Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning](https://mlsanity.com/paper/2306.12964/) (2023) - [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://mlsanity.com/paper/2402.03659/) (2024) - [MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model](https://mlsanity.com/paper/2409.07486/) (2024) - [AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors](https://mlsanity.com/paper/2406.18394/) (2024) - [Kronos: A Foundation Model for the Language of Financial Markets](https://mlsanity.com/paper/2508.02739/) (2025) - [MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction](https://mlsanity.com/paper/2402.06633/) (2024) - [Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining](https://mlsanity.com/paper/2505.11122/) (2025) - [Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.11420/) (2025) - [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022) - [Multi-period Learning for Financial Time Series Forecasting](https://mlsanity.com/paper/2511.08622/) (2025) - [Ploutos: Towards interpretable stock movement prediction with financial large language model](https://mlsanity.com/paper/2403.00782/) (2024) - [FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction](https://mlsanity.com/paper/2502.05218/) (2025) - [MASS: Muli-agent simulation scaling for portfolio construction](https://mlsanity.com/paper/2505.10278/) (2025) - [Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading](https://mlsanity.com/paper/2508.06312/) (2025) - [TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis](https://mlsanity.com/paper/2508.17565/) (2025) - [Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools](https://mlsanity.com/paper/2311.10801/) (2023) - [Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation](https://mlsanity.com/paper/2107.05201/) (2021) ## Built on by (most cited first, 2 of 2) - [TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agents](https://mlsanity.com/paper/2603.22567/) (2026) - [RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting](https://mlsanity.com/paper/2609.34004/) (2026) ## Abstract (arXiv) 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.