Quantitative Research & Portfolio Management
Pfäffikon, Switzerland | Nov 2012 – Dec 2014
From November 2012 to December 2014, I was responsible for quantitative modeling, Python programming, and implementation of the AHL TargetRisk strategy at Man Investments. This involved systematic approaches to multi-asset investing, focusing on dynamic risk management and volatility targeting.
The AHL TargetRisk is a systematic, dynamic, multi-asset allocation strategy designed to manage risk actively while achieving stable, long-term returns. It dynamically allocates across equities, credit, government bonds, commodities, and inflation-linked instruments, adapting its portfolio based on market conditions, volatility, and correlation signals.
Quantitative Modeling: Developed systematic allocation methods using Python, emphasizing volatility targeting, risk budgeting, and correlation monitoring.
Python Programming:
Created automated rebalancing and execution scripts using Python libraries such as NumPy, Pandas, and scikit-learn.
Built extensive backtesting frameworks in Python for parameter optimization, stress testing, and historical scenario analysis.
Developed Python-based dashboards for real-time risk analytics, including VaR, expected shortfall, and volatility measures.
Risk Management Techniques:
Implemented volatility scaling to systematically control portfolio volatility at the asset-class and portfolio levels.
Integrated momentum overlays to reduce exposures proactively during market downturns.
Developed correlation overlays to manage and mitigate losses during bond-driven market sell-offs, employing advanced volatility models (e.g., the HEAVY model).
Successfully supported the live launch of AHL TargetRisk in December 2014, adhering strictly to the strategy's volatility target (~10%).
Demonstrated robust performance, achieving notable risk-adjusted returns with lower drawdowns compared to traditional benchmarks:
Annualized return: 9.2% (versus 2.9% for the Morningstar Moderate Allocation index).
Annualized volatility: 7.8%.
Sharpe ratio: 1.06, significantly higher than peer benchmarks.
Contributed directly to the strategy's recognition and its continuing use in institutional portfolios, where it manages billions in assets.
Systematic Multi-Asset Allocation: Broad diversification across global markets, dynamically balanced based on quantitative signals.
Enhanced Return Profile: Strategic volatility targeting and momentum overlays resulted in lower drawdowns and smoother returns, validated by quantitative analysis and simulated performance studies.
Active Risk Controls: Advanced models helped the portfolio to proactively adjust risk exposure, significantly reducing volatility spikes during turbulent periods.
The AHL TargetRisk strategy holds a high industry reputation, recognized by a Morningstar "Gold" analyst rating for its sophisticated systematic approach and performance consistency.
The AHL TargetRisk Strategy has demonstrated strong performance compared to a traditional 60/40 benchmark portfolio across key metrics:
Annualized Return:
AHL TargetRisk: 13.0%
60/40 Benchmark: 7.1%
Annualized Volatility:
AHL TargetRisk: 7.8%
60/40 Benchmark: ~10%
Maximum Drawdown:
AHL TargetRisk: -15.9%
60/40 Benchmark: -32.1%
Sharpe Ratio:
AHL TargetRisk: 1.08
60/40 Benchmark: 0.51
Performance metrics are derived from simulated and historical data.
My role involved the complete quantitative and technical development of AHL TargetRisk, translating complex risk management and systematic investing strategies into production-quality code and robust investment solutions. The strategy remains a cornerstone product at Man AHL, recognized for superior risk-adjusted returns and resilience during market stress periods.