# Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?

arXiv: https://arxiv.org/abs/2505.07078 · HTML: https://mlsanity.com/paper/2505.07078/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 76 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 19 of 19)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024)
- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://mlsanity.com/paper/2402.03659/) (2024)
- [LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction](https://mlsanity.com/paper/2406.10811/) (2024)
- [FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting](https://mlsanity.com/paper/2502.18834/) (2025)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?](https://mlsanity.com/paper/2306.14222/) (2023)
- [TradExpert: Revolutionizing Trading with Mixture of Expert LLMs](https://mlsanity.com/paper/2411.00782/) (2024)
- [Sentiment trading with large language models](https://mlsanity.com/paper/2412.19245/) (2024)

## Built on by (most cited first, 20 of 20)

- [UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios](https://mlsanity.com/paper/2509.21766/) (2025)
- [LiveTradeBench: Seeking Real-World Alpha with Large Language Models](https://mlsanity.com/paper/2511.03628/) (2025)
- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)
- [TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis](https://mlsanity.com/paper/2508.17565/) (2025)
- [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026)
- [Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.23515/) (2025)
- [Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness](https://mlsanity.com/paper/2603.27539/) (2026)
- [Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory](https://mlsanity.com/paper/2604.11544/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models](https://mlsanity.com/paper/2605.24564/) (2026)
- [MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting](https://mlsanity.com/paper/2512.21878/) (2025)
- [Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals](https://mlsanity.com/paper/2607.12233/) (2026)
- [Market-Dependent Communication in Multi-Agent Alpha Generation](https://mlsanity.com/paper/2511.13614/) (2025)
- [MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting](https://mlsanity.com/paper/2603.26797/) (2026)
- [FundaPod: A Multi-Persona Agent Pod Architecture with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research](https://mlsanity.com/paper/2605.27864/) (2026)
- [Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data](https://mlsanity.com/paper/2606.29793/) (2026)
- [Hybrid Analysis for Secure MCP Tool Use in LLM Agents](https://mlsanity.com/paper/2607.25297/) (2026)
- [SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes](https://mlsanity.com/paper/2609.19705/) (2026)
- [Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems](https://mlsanity.com/paper/2609.19789/) (2026)
- [Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents](https://mlsanity.com/paper/2610.04040/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and stock trading applications, enabling AI agents to generate investment decisions from unstructured financial data. However, most evaluations of LLM timing-based investing strategies are conducted on narrow timeframes and limited stock universes, overstating effectiveness due to survivorship and data-snooping biases. We critically assess their generalizability and robustness by proposing FINSABER, a backtesting framework evaluating timing-based strategies across longer periods and a larger universe of symbols. Systematic backtests over two decades and 100+ symbols reveal that previously reported LLM advantages deteriorate significantly under broader cross-section and over a longer-term evaluation. Our market regime analysis further demonstrates that LLM strategies are overly conservative in bull markets, underperforming passive benchmarks, and overly aggressive in bear markets, incurring heavy losses. These findings highlight the need to develop LLM strategies that are able to prioritise trend detection and regime-aware risk controls over mere scaling of framework complexity.