About
Mission
Evaluating thousands of predictive factors is a complex and challenging task, requiring years of dedicated research, infrastructure investment and a team of experts. This makes using alternative data & exogenous risk factors unaccessible for most asset managers, funds and family offices.
Unravel is an investment research platform designed to address this gap by surfacing the exogenous predictive risk factors that matter most for any asset.
Our public and proprietary research provides unprecedented transparency to make:
- The most volatile and risky assets investable
- Portfolios more resilient by exposing their vulnerability to exogenous risk factors
- Quantitative strategies easier to implement and deploy with model portfolios and expert guidance
Leadership Team
Unravel is built by a team of experienced quantitative analysts, software engineers and data scientists. We're pragmatists, committed to delivering results that are testable, repeatable, and transparent.
Origin Story
In 2020, Mark exited his previous company, and started his family office, where using this technical and statistical expertise, he deployed a wide range of quantitative (cross-sectional, long/short) strategies across all asset classes with a small team of quants.
At the same time, Daniel & Mark started working together building infrastructure and data pipelines that will enable the evaluation of thousands of predictive factors, with the goal to uncover the hidden predictive relationships that drive asset returns.
Unravel is a manifestation of our passion for research and our belief that exogenous predictive factors will become a key ingredient for both discretionary and quantiative investing in the future.
Unravel was launched in 2025.
Timeline
Our Research & Development path so far.
Released Unravel
We publicly launched the research platform uncovering the exogenous predictive factors that matter most for any asset.
API released for partners
Launched a private programmable interface enabling clients to build systematic strategies using our core technology.
Packaged infrastructure for re-usable internal and external workflows
Standardized our technology stack to support both in-house research and partner funds.
Ranking and discovery of linear relationships
Built tools to automatically identify and prioritize significant linear patterns in financial data for more effective analysis.
Interpretability layer with human-in-the-loop step
Created systems that combine algorithmic insights with human expertise, ensuring models remain transparent and understandable.
Initial research into linear & non-linear forecasting methods with exogenous data
Began exploring advanced forecasting techniques that would enable us to surface exogenous predictive factors reliably.