FinForesight benchmark evaluates deep research systems in financial forecasting
Original title: FinForesight: A Diagnostic Backtesting Benchmark for Deep Research Systems in Financial Forecasting
FinForesight is a backtesting benchmark for financial forecasting that avoids post-cutoff information.
FinForesight provides a diagnostic backtesting benchmark for financial deep research systems across Chinese, U.S., and Hong Kong markets. It evaluates valuation, return ranking, event direction, and future fundamentals. Pretraining-era scores exceed post-cutoff scores, and stronger retrieval controls narrow the gap to live results. The code and benchmark data are publicly available.
- Four forecasting tasks: valuation, return ranking, event direction, future financial fundamentals
- Two capability diagnostic tasks: counterfactual reasoning and financial calculation
- Harness restricts retrieved evidence to forecast-date information
- Code and benchmark data publicly available
From the original article
1 min read at huggingface.co
Anticipating financial outcomes requires synthesizing evolving evidence and reasoning under uncertainty, making deep research systems promising tools for forecasting. Yet evaluating forecasting ability is challenging because historical backtests may reward memorized outcomes or information retrieved after the forecast date.
By Fengyuan Liu, Mukai Li, Jieshun You · huggingface.co
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