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This project presents the application of a MS-QRNN model designed to estimate Value at Risk accurately by integrating both numerical financial time-series data
This project presents the application of a MS-QRNN model designed to estimate Value at Risk accurately by integrating both numerical financial time-series data
Source: GitHub API · observed Oct 11, 2026. Stars and forks are recorded counts. Repository code licenses may differ from dataset or model-weight licenses.
References explain how this resource entered the collection. They do not represent an endorsement.
Agent Trading Arena probes numerical understanding of LLM-based trading agents in a zero-sum market simulation
InvestorBench evaluates LLM-based agents on financial decision-making tasks across multiple asset classes and markets
FinMTEB is a finance-specific massive text embedding benchmark with 64 datasets across seven financial NLP task types
