Case Study
March 18, 2026
How a research team in India built, tested, and adopted a new risk metric for alternative assets
Lead Analyst Ananya Rao
Building, Testing, and Adopting a Custom Metric
You hand us an outcome you want to measure. We challenge it, test its boundaries, and co-design a metric you can explain to your stakeholders. In this case study, our team worked with a mid-sized Indian research group seeking to capture liquidity shifts in a non-traditional asset class. We started by mapping the operational environment, then worked through cycles of hypothesis and rejection. Collaboration with both internal and external reviewers pushed us to clarify every variable. The final metric emerged not from theory, but from lived data and feedback loops. Here’s how the process unfolded.
Identifying Hidden Biases
Together, we reviewed the research group’s data environment and existing measurement tools. The team’s first metric draft was based on a widely used volatility proxy, but we identified a blind spot: it missed temporal clustering effects. To address this, we integrated a time-weighted component, validating it through historical back-testing and stress scenarios.
Iterative Peer Review
Each proposed tweak was subjected to peer challenge sessions. Team members from diverse backgrounds questioned every formula’s fairness, interpretability, and reproducibility. This led to a scoring system that balanced sensitivity with clarity. Results were presented in open review, where even negative findings were documented and discussed.
Long-Term Adoption and Feedback
Once the metric was final, our client integrated it into their workflow for six months. They shared logs, performance notes, and discrepancies. We used these to refine reporting protocols, ensuring the metric remained robust when facing unexpected data variations. The group now reports higher confidence in their risk discussions, citing improved alignment with real-world scenarios.