Expertise
01
Review & Analysis
QP conducts independent, rigorous reviews of existing quantitative structures, models, and workflows, identifying conceptual weaknesses and quantitative blind spots that are often missed in standard consulting engagements.
Our assessments are grounded in academic standards of model validation, statistical inference, and economic coherence, and are designed to determine whether a framework is internally consistent, methodologically sound, and fit for its intended decision-making purpose. Typical applications include NAV and valuation frameworks (DCF calibration, terminal value methodologies, WACC estimation, peer-group definition, and multiple selection/refinement), as well as risk management models relying on VaR, CVaR/expected shortfall, Monte Carlo simulation, and scenario analysis. We also evaluate operational improvement potential across data-provider choice and integration, as well as coding landscapes and implementation practices.
02
Model Design & Implementation
QP designs and implements advanced quantitative models tailored to clients’ decision-making environments, translating economic insight and empirical evidence into robust, production-ready frameworks. Grounded in rigorous econometrics and modern machine learning, we emphasise robustness, interpretability, and long-term maintainability across portfolio construction, asset allocation, risk, valuation, and forecasting in public and private markets.
Implementation spans the full pipeline, from data sourcing and optimisation to backtesting and integration, ensuring models are transparent, well-documented, and embedded in workflows that support monitoring and informed oversight. We modernise Excel-heavy processes and work across Python, MATLAB, R, Stata, C++ and Docker, while optimising data-provider stacks (FactSet, Bloomberg, Preqin, Infront). We strengthen risk and valuation with Bayesian methods and advanced scenario simulation.
03
Research & Development
QP acts as an external quantitative R&D sparring partner, complementing internal research teams with exploratory projects that build durable intellectual capital. We develop new analytical perspectives, proprietary datasets, and experimental methodologies beyond standard practice to test hypotheses, assess structural risks, and identify new return drivers, with emphasis on experimental design, statistical validity, and economic interpretation.
Research support suits clients launching or refining internal initiatives through independent, academically grounded pre-submission reviews of papers, technical reports, and methodological documentation, plus third-party verification to mitigate conflicts of interest and model risk. We also strengthen internal knowledge bases via structured literature reviews, concise research digests, and tailored workshops for research, risk, and investment teams.