Research Reports
Original research on quantitative methodologies, risk premia strategies, and portfolio construction techniques.
Advancing Quantitative Research
Our research team produces original work that bridges academic theory and practical implementation. We investigate new approaches to alpha generation, risk management, and portfolio construction, always with an eye toward real-world applicability.
Our research process emphasizes reproducibility, robustness, and out-of-sample validation. We're skeptical of data mining and careful about multiple testing issues. When we publish findings, they meet standards we would apply to our own investment decisions.
- Factor construction and enhancement methodologies
- Machine learning applications in finance
- Alternative data integration and signal extraction
- Portfolio optimization under realistic constraints
- Transaction cost modeling and execution optimization
Recent Publications
Machine Learning in Factor Timing
Evaluating the efficacy of ML models for dynamic factor allocation, with emphasis on avoiding overfitting and ensuring robustness.
Tail Risk Hedging Strategies
Comparative analysis of tail risk hedging approaches, including options-based strategies and trend-following alternatives.
Transaction Cost Optimization
Framework for incorporating realistic transaction costs into portfolio optimization, with applications to factor strategies.
Access Our Research
Contact us to learn more about our research publications and collaboration opportunities.