Man AHL
Quantitative Research & Portfolio Management | Pfäffikon, Switzerland | Nov 2012 – Dec 2014 —
Man Investments – AHL Target Risk Strategy
From November 2012 to December 2014, I was responsible for quantitative modeling, Python programming, and implementation of the AHL Target Risk strategy at Man Investments. This involved systematic approaches to multi-asset investing, focusing on dynamic risk management and volatility targeting.
Strategy Overview
The AHL Target Risk 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.
Specific Contributions & Technical Details
Quantitative Modeling: Developed systematic allocation methods using Python, emphasizing volatility targeting, risk budgeting, and correlation monitoring.
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).
Achievements and Impact
Successfully supported the live launch of AHL Target Risk 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 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.
Strategy Performance Highlights
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.
Current Market Recognition
The AHL Target Risk strategy holds a high industry reputation, recognized by a Morningstar “Gold” analyst rating for its sophisticated systematic approach and performance consistency.
Performance Highlights
The AHL Target Risk Strategy has demonstrated strong performance compared to a traditional 60/40 benchmark portfolio:
| Metric | AHL Target Risk | 60/40 Benchmark |
|---|---|---|
| Annualized Return | 13.0% | 7.1% |
| Annualized Volatility | 7.8% | ~10% |
| Maximum Drawdown | -15.9% | -32.1% |
| Sharpe Ratio | 1.08 | 0.51 |
Performance metrics are derived from simulated and historical data.
Conclusion
My role involved the complete quantitative and technical development of AHL Target Risk, 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.