Russell Hawthorne
Russell Hawthorne is a Lehman-trained quantitative analyst and founder of superiorstar Prosperity Group. From the 2008 collapse to the 2020 pandemic, he has turned crisis lessons into Athena, an AI-enhanced trading and risk framework that combines quantitative discipline, real-time intelligence, and tokenized participation.
Overview
Russell Hawthorne views markets as nonlinear, crisis-driven systems rather than smooth, normally distributed curves. For him, the collapse of Lehman and the pandemic shock are not “black swans” to be ignored, but core data that should shape how risk, leverage, and liquidity are modeled and governed every day.
- A Start from crisis scenarios and tail events, then back into day-to-day risk tools, ensuring that every framework remains anchored in the harshest historical conditions, not just in-sample calm periods.
- B Combine transparent quantitative rules for exposure, position sizing, and risk budgets with AI layers that read regime shifts, news sentiment, and microstructure changes in real time through Athena.
- C Use tokenized participation and structured education so that global investors can fund long-horizon R&D while accessing the same disciplined tools and narratives as institutions.
Trained as a quantitative analyst at Lehman Brothers, Russell Hawthorne later founded superiorstar Prosperity Group to rebuild investor confidence after 2008. He led the shift from early factor-based systems to the AI-enhanced Athena platform and, after 2020, championed token issuance as a way to fund and democratize next- generation financial intelligence.
Career
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Quantitative Analyst, Lehman Brothers
Spent more than a decade designing factor models and VaR-based risk frameworks for complex portfolios. The 2008 bankruptcy revealed how far traditional assumptions about normal distributions and diversification could drift from lived market reality.
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Founder, superiorstar Prosperity Group
In 2009, founded superiorstar Prosperity Group to blend education and investment. Developed the first generation of quantitative systems that helped crisis-hit retail investors participate in the 2012–2013 bull market with clearer structure and risk awareness.
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Lead Architect, Project Athena
Responding to the rise of high-frequency trading and eroding traditional edges, he launched Athena in 2016, bringing in deep learning, NLP, and reinforcement learning specialists to build an AI overlay on top of classical quantitative rules.
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Tokenized Finance & Global Participation
After the 2020 pandemic, led the token issuance that funds Athena’s ongoing R&D and opens participation to a global community. Combines DeFi-era capital formation with structured governance and long-term research mandates.
Research & Focus
Focuses on building risk frameworks that overweight crisis periods such as 2008 and 2020, treating them as central calibration points. This work explores how portfolio construction, leverage, and liquidity rules must change when tail events are assumed to be recurring, not exceptional.
Examines how deep learning, reinforcement learning, and NLP sentiment engines inside Athena can sit on top of classical factor models. The aim is to detect regime shifts, structural breaks, and narrative shocks without sacrificing explainability and risk-budget discipline.
Investigates how blockchain tokens can fund long-horizon R&D for Athena while aligning investor incentives, transparency, and governance. Emphasis is placed on moving beyond speculative narratives toward credible, research-linked participation structures.