Financial language agents are commonly evaluated through static prediction tasks or isolated historical backtests, where an agent’s decisions cannot affect subsequent prices or the behavior of competing strategies. This overlooks a defining property of real markets: interpretations of financial news influence order flow, price formation, and the competitive environment faced by other participants. We introduce FINEVO, a closed-loop evaluation framework that integrates time-stamped financial news, heterogeneous trading agents, a continuous double-auction market, and population-level capital reallocation. FINEVO evaluates language-based agents alongside technical, forecasting, reinforcement-learning, and informed strategies, reporting not only returns but also market concentration, strategic reorganization, regime-dependent standing, and recovery from shocks. Across 128-run suites of controlled market shocks and historical-news-conditioned episodes, we find that language-agent performance is strongly contingent on the surrounding market regime and agent population. Endogenous price formation also causes identical information schedules to produce different outcomes under different competitive compositions. Mechanism ablations further show that innovation is critical for maintaining contestability: removing it increases mean HHI from 0.189 to 0.477 and mean Top-1 capital share from 0.344 to 0.531. These results suggest that financial language agents should be evaluated not only by how accurately they interpret information, but also by how their decisions reshape the markets in which they operate.

Financial management is high-stakes, where small errors can propagate into reporting deviations and costly downstream decisions, yet real-world workflows remain labor-intensive and fragmented, and existing automation supports only isolated steps rather than complete workflows. Large language models (LLMs) show promise in automating financial workflows, but current benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow-level evaluation. To address these gaps, we present FinMaster, a benchmark for evaluating large language models on full financial management workflows spanning financial literacy, accounting, auditing, and consulting. FinMaster comprises three modules: FinSim generates synthetic datasets compliant with real-world accounting standards for diverse company types, enabling realistic evaluation without relying on proprietary financial records. FinSuite offers 183 tasks across core financial domains. FinEval provides a unified evaluation framework. Extensive experiments on state-of-the-art models including GPT-4o-mini, Claude-3.7-Sonnet, and DeepSeek-V3 reveal critical capability gaps in financial reasoning, with accuracy dropping from over 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning. This degradation reflects error propagation, where accuracy reaches 58% for single-metric calculations but decreases to 37% in multi-metric settings. FinMaster provides scalable and reproducible benchmarking for realistic end-to-end financial workflows, helping advance reliable deployment of LLMs in financial practice.

Financial decision-making presents unique challenges for language models, requiring them to handle temporally evolving, risk-sensitive, and event-driven contexts. While large language models (LLMs) demonstrate strong general reasoning abilities, they often overlook key behavioral patterns underlying human financial behavior—such as expert reliance under information asymmetry, loss-averse risk adjustment, and temporal adaptation. We propose FinHEAR, a multi-agent framework for Human Expertise and Adaptive Risk-aware reasoning. FinHEAR coordinates multiple LLM-based agents to capture historical trends, interpret current events, and incorporate expert knowledge within a unified, event-aware pipeline. Grounded in behavioral economics, FinHEAR features mechanisms for expert-guided retrieval to reduce information asymmetry, dynamic position sizing to reflect loss aversion, and feedback-driven refinement to enhance temporal consistency. Experiments on a curated real-world financial dataset show that FinHEAR consistently outperforms strong baselines in both trend forecasting and decision-making.