Finance Agent

This green agent evaluates AI agents on multi-task financial document analysis using 900 real SEC 10-K filings (2015-2020). It assesses three critical capabilities: (1) Risk Factor Classification - identifying and categorizing risks from Section 1A, (2) Business Summary Generation - extracting key business information from Section 1, and (3) Cross-Section Consistency - verifying risk discussions across sections. The benchmark uses Ground Truth caching to ensure reproducible results and provides weighted scoring (40% risk, 30% business, 30% consistency) for comprehensive evaluation of financial AI capabilities.

Our Finance Green Agent evaluates financial research tasks performed by Agentic AI based on real world financial tasks using Edgar SEC search, Google search, and similar tools.

We introduce TS-Bench Agent, a unified benchmarking framework for evaluating the capabilities of agentic systems in solving financial time-series modelling problems. The benchmark assesses whether a time-series agent can autonomously interpret task specifications, retrieve and process data, construct appropriate machine-learning models, and produce valid outputs with the objective of achieving strong performance. TS-Bench Agent focuses on two core classes of tasks: time-series forecasting and time-series generation. Forecasting tasks require agents to predict the future dynamics of financial time series, while generation tasks require agents to synthesize realistic time series that faithfully reproduce the statistical and temporal properties of historical data. To ensure comprehensive coverage, each task class comprises multiple tasks organised into three difficulty levels, ranging from short-horizon stock return and volatility prediction at the easiest level to more complex crypto-market dynamics and regime-switching financial processes at higher difficulty levels. TS-Bench Agent further incorporates a comprehensive and robust evaluation protocol. Forecasting performance is assessed using RMSE, MAE, and MAPE, while generation quality is evaluated using Histogram Loss, Autocorrelation Loss, and Cross-Correlation Loss. Metric values are normalised and aggregated to produce task-level scores, which are then combined across tasks using difficulty-based weighting to yield an overall score for each task class. By considering diverse tasks and difficulty levels, TS-Bench Agent delivers a more robust, comprehensive, and reliable assessment of agent capabilities than evaluations based on individual tasks. Beyond quantitative metrics, TS-Bench Agent provides structured task summaries, including detailed task descriptions, data access links, evaluation code, and explicit output format requirements. This design ensures that agents are evaluated under clearly specified and reproducible conditions. The overarching goal of TS-Bench Agent is to enable fair, transparent, and reproducible ranking of agentic solutions for financial time-series modelling. By standardising task definitions, evaluation metrics, and aggregation rules, TS-Bench Agent offers a consistent and reliable foundation for comparing agent workflow for financial time-series analysis.

Answers fiscal/financial questions from US Treasury bulletin corpus (1939-2025). Supports lookups, percentage changes,table sums, and multi-step reasoning over financial data.

agentic finance qa built by Liang Ding (team name: 1688-alpha)