Investor's Last Exam (ILE) is a new equity‑research benchmark created by experienced buy‑side investors and fund managers. It comprises a versioned financial database of 3.43 million document versions, a task taxonomy aligned with professional analyst deliverables, and an expert‑led framework that links scoring criteria to sources, calculations, and assumptions via financial dependency graphs. The benchmark evaluates 86 assignments, jointly measuring research quality, forecast accuracy, and simulated investment performance, and finds that even top models like GPT‑6 Astra and Claude Opus 5.5 struggle to translate research capability into predictive performance.
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
FinSkillBench is an evaluation suite that tests whether language model agents can use financial domain skills to solve investment management tasks across portfolio construction, risk management, and fundamental analysis. The benchmark contains 12 subtasks with 2,603 episodes, each providing point‑in‑time inputs, hidden ground truth, and a verifier. Experiments show that curated skill packages improve performance significantly, while self‑generated skills offer little benefit, indicating that reliable procedural skills are crucial for effective AI agents in this domain.
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand.
Hedge-Bench is a benchmark comprising 92 real-world environments derived from hedge fund analysts' reasoning traces, designed to test AI agents’ ability to forecast future events from incomplete information. Agents are evaluated on joint success—correct forecasts with over 60% research coverage—across 11 frontier models, achieving rates between 2.54% and 31.52%. Training Muse‑Glimmer with GRPO on 165 environments improves coverage on held‑out tasks and demonstrates generalization to out‑of‑distribution evaluations.
FinAutoRubric is a system that lets experts define reusable evaluation guidance for financial research agents, which the agents then use to automatically generate, review, and validate task‑specific rubrics. The approach replaces costly fixed rubrics with query‑specific ones that incorporate expert standards and can be updated as new information becomes available. On three expert‑authored finance benchmarks, the automatically generated rubrics closely match human scoring and are preferred by in‑house analysts in blind reviews.
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
AgenticFinBench is a new benchmark comprising 230 real‑world financial tasks across eight categories, two languages, and four complexity levels, designed to capture the breadth and open‑ended nature of financial AI work. It introduces a reference‑free evaluation framework that uses question‑specific rubrics and a two‑stage claim audit to detect hallucinations, aligning assessment more closely with real‑world financial practice. Experiments on 18 agent configurations show that ignoring hallucinations inflates performance, that harness differences drive evidence grounding more than model differences, and that task complexity reduces quality scores, highlighting challenges in explaining financial impacts and comparing data consistently.
arXiv:2607. 27853v2 Announce Type: replace-cross Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products.
arXiv:2608. 16386v1 Announce Type: cross Abstract: Financial agents must do more than recall domain knowledge: they must be both reliable, executing precise operations over grounded evidence, and executive, sustaining long-horizon research whose conclusions remain auditable.
Artificial Intelligence now underpins investment workflows from data and prediction to execution and tool use, yet its technical prowess does not automatically translate into profitability. A comprehensive review of public research up to 31 August 2026 across equities, ETFs, crypto spot, perpetual futures, and on‑chain markets shows real progress in prediction, text processing, portfolio design, and workflow integration, but evidence for durable net performance remains thin. The study highlights that factors such as temporal contamination, survivorship bias, weak benchmarks, implementation costs, and venue mechanics can erode alpha, and no single AI architecture has proven to deliver persistent, cross‑regime, capacity‑aware net alpha. "whyItMatters":"The findings underscore that while AI advances are evident, investors must rigorously test and govern AI systems to avoid overestimating their profitability potential."
arXiv:2605. 22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions.
FinForesight is a diagnostic backtesting benchmark designed to evaluate deep research systems for financial forecasting. It tests four forecasting tasks—valuation, return ranking, event direction, and future financial fundamentals—across Chinese, U.S., and Hong Kong markets, while also including two diagnostic tasks for counterfactual reasoning and financial calculation. The framework limits evidence to what was available at the forecast date and compares pretraining-era, post-cutoff, and live evaluations to assess how well controlled backtests predict future performance.