Harnessing Alternative Data for Smarter Financial Decision-Making
Despite its multi-billion-dollar valuation and strong public perception, FTX’s underlying fundamentals showed weaknesses that were overlooked by mainstream analysts. While investor confidence remained high, our team sought to uncover discrepancies between FTX’s perceived market position and its actual user engagement data.
bridge_ci conducted a data-driven risk assessment of FTX using alternative data sources, including web traffic patterns, search interest, and competitive benchmarks. By analyzing digital engagement metrics alongside traditional financial indicators, we identified early warning signs of instability before the exchange’s collapse.
FTX lagged behind competitors in key engagement metrics, including web traffic and organic search interest.
Declining user activity signaled potential liquidity issues, contradicting public narratives of strong market positioning.
Clear misalignment between valuation and real market traction, exposing a high-risk financial structure.
Our findings provided an early warning signal that could have helped investors and institutions mitigate billions in losses. This case underscores the power of alternative data analysis in risk detection, demonstrating how financial decision-makers can identify vulnerabilities before they escalate into crises.
Policy advocacy is a high-stakes game where influence is often hidden beneath the surface. Organizations struggle to identify key stakeholders, understand their relationships, and predict their impact on policy outcomes.
The analysis used topological data analysis to examine the geometric structure of store performance data, revealing distinct patterns and clusters that weren't visible through traditional statistical approaches. Store locations with similar characteristics were connected in a network visualization, with different metrics represented by color coding.
Harnessing Alternative Data for Smarter Financial Decision-Making
