Open to opportunities

Carl Hopman

@carlhopman

Portfolio Manager and Quantitative Researcher, I build ML-driven systematic trading strategies and optimized portfolios.

United States

Message

What I'm looking for

I’m looking to lead and build systematic, ML-driven strategies with real portfolio ownership—combining rigorous quantitative research, robust risk/optimization, and production-grade C++/Python execution that resists overfitting.

I’m a Portfolio Manager and Quantitative Researcher with an MIT Sloan Ph.D., focused on building and running profitable systematic trading strategies end-to-end—from research to implementation. I’ve had direct responsibility for a portfolio of $200MM+ in capital, with repeated success across equity market-neutral approaches.

I’ve single-handedly constructed an equity market-neutral strategy using novel machine learning and custom portfolio optimization, and I built proprietary ensemble learning designed to resist overfitting. I architected a portfolio optimization method that combines return forecasts across different time horizons and a training approach that distinguishes “across stocks” versus “across time,” prioritizing time-based accuracy.

At Perot Investments, I led Head, Quantitative Research, overseeing external and internal quantitative strategies for a family office. I enhanced the QSS strategy by adding 300+ features, improved covariance estimation with shrinkage and exposure-penalization, and reduced volatility through more robust risk modeling—while sourcing and evaluating external quantitative hedge funds for new investments.

I also prioritize execution realism and scalable engineering: I built a C++ portfolio optimization engine using MOSEK conic programming with explicit slippage, commission, and borrowing-fee modeling, and led a rewrite from Stata to Python with cloud migration and parallelization. Earlier roles included running an equity market-neutral portfolio across 2K+ US stocks, co-founding a hedge fund with an overfitting-resistant ML ensemble (28% annualized return, 11% volatility), and directing $200MM+ in capital across macro, CTA trend, and equity volatility-neutral strategies.

Experience

Work history, roles, and key accomplishments

Head, Quantitative Research

Perot Investments

Jan 2014 - Jan 2025 (11 years)

Oversaw Perot family office investments across external and internal quantitative strategies, enhancing the proprietary equity market-neutral QSS approach. Improved alpha by adding 300+ features, reduced portfolio volatility via refined covariance estimation, cut slippage from 26bp to 5bp, and built a C++/MOSEK portfolio optimization engine modeling slippage, commissions, and borrowing costs.

Quantitative Stock Selection Machine Learning Feature Engineering Portfolio Optimization C++MOSEK Conic Programming

Portfolio Manager

Rotella Capital Management

Jan 2012 - Jan 2014 (2 years)

Researched and operated an equity market-neutral portfolio across 2K+ US stocks and updated data workflows by switching from Compustat to Capital IQ. Refined strategy construction using full-correlation portfolio optimization with liquidity-varying trading intensity, achieving 11% annualized return with 10% volatility.

Equity Market Neutral Portfolios Portfolio Optimization Correlation Matrices Stock Borrowing Fees Mean Variance Optimization C++

Co-Founding Partner

Fort Sheridan Advisors

Jan 2009 - Jan 2012 (3 years)

Co-founded a hedge fund focused on tail-risk and Black Swan protection, building ML-driven models for non-linear patterns while resisting overfitting. Processed 400+ fundamental, technical, and macro features to forecast next-month returns and deployed a market-neutral strategy achieving 28% annualized return with 11% volatility across 2,500+ liquid US stocks.

Machine Learning Feature Engineering Market Neutral Strategies Overfitting Resistance C++

Director, Quantitative Research

Parkcentral Capital

Jan 2006 - Jan 2009 (3 years)

Managed $200MM+ in capital across macro, CTA trend, and equity market-neutral quantitative strategies. Developed a volatility-neutral equity volatility swap that delivered 43% annualized return, $35MM total profit, and a 1.3 Sharpe ratio, and engineered a robust forecast system with long-term memory, outlier resistance, and optimal lag weighting.

Market Neutral Strategies Volatility Swaps Outlier Resistant Modeling Long Term Memory Forecasting

Strategist

Oak Hill Platinum Partners

Jan 2003 - Jan 2006 (3 years)

Executed and improved US fixed income strategies with a focus on mortgage and swap-based trading and forecasting. Developed a mortgage basis forecasting tool (15% annualized on multi-billion notional), built/operated a CMM book (30% annualized), and designed curve butterfly optimization and swap-spread signals delivering additional annualized returns up to 40%.

Mortgage Basis Forecasting CMM Strategies Curve Optimization Statistical Signal Research Risk Management Yield Curve Modeling