Our research philosophy is grounded in three principles: systematic process, data-driven inference, and reproducible results. Every conclusion must be supported by statistical evidence and survive rigorous out-of-sample validation.
Quantitative research at Zylo is engineering-driven. We treat financial data as an empirical domain requiring the same rigor applied to any scientific discipline: formal hypotheses, controlled experiments, and transparent methodology.
We prioritize reproducibility over novelty. A research finding that cannot be independently replicated under clearly stated assumptions has no place in our framework. All methodologies are documented with sufficient detail to permit third-party verification.
Our approach is inherently skeptical. We actively seek to disconfirm our own results through stress testing, sensitivity analysis, and adversarial validation before incorporating any finding into production systems.
Core research domains spanning quantitative finance, statistical inference, and systematic methodology.
Development and validation of statistical models for financial data analysis. Our work spans regression frameworks, distributional modeling, and multi-factor decomposition methods with emphasis on out-of-sample robustness and reproducibility.
Research into temporal dependencies, regime detection, and forecasting methodologies applied to financial time series. Includes autoregressive models, spectral analysis, and non-stationary process identification.
Quantitative study of market mechanics, order flow dynamics, and liquidity measurement. Research focuses on understanding execution costs, price formation processes, and structural characteristics of electronic markets.
Design and evaluation of systematic, rules-based research frameworks. Includes backtesting methodology, walk-forward validation protocols, and the statistical assessment of strategy robustness across varying market conditions.
Research articles and methodology papers will be published here as they complete peer review and internal validation. Topics will span our core research areas with full methodological disclosure, data specifications, and reproducibility documentation.
All published research represents historical analysis and methodological documentation. It does not constitute advisory content of any kind.