Quantitative portfolio optimization increases risk-adjusted returns by 8% for pension fund
A prominent pension fund managing over $11 billion in assets faced challenges in achieving consistent risk-adjusted returns while managing portfolio volatility. With increasing market uncertainty and pressure to deliver stable returns for beneficiaries, the fund needed a data-driven approach to optimize asset allocation, reduce risk, and improve overall portfolio efficiency.
Fuld & Company partnered with the pension fund to implement a comprehensive quantitative portfolio optimization strategy. Using advanced financial models, scenario analysis, and risk management techniques, we transformed their portfolio management approach to deliver the following measurable results.
- 8% Increase in Risk-Adjusted Returns: Enhanced returns while maintaining an optimal risk profile
- 12% Reduction in Portfolio Volatility: Reduced exposure to market fluctuations, ensuring greater stability
- 18% Improvement in Asset Allocation Efficiency: Streamlined allocation strategies to maximize returns and minimize risk
- Enhanced Risk-Return Balance: Achieved a superior risk-return profile, enabling better investment decisions and long-term financial security for beneficiaries
How We Did It
Fuld & Company employed a comprehensive set of quantitative techniques and financial models to enhance portfolio optimization and risk management:
- Quantitative Asset Allocation Models:
- Leveraged historical market data (e.g., asset performance, correlations) and economic trends to identify the optimal asset mix
- Used optimization algorithms (built with Python) to maximize returns while minimizing risk, ensuring a balanced portfolio
- Scenario Analysis for Risk Assessment:
- Simulated various economic conditions (e.g., recession, inflation, growth) using Bloomberg data and economic indicators to forecast portfolio performance under adverse scenarios
- Ensured that the fund was prepared for potential risks and had optimized exposure to different market environments
- Capital Asset Pricing Model (CAPM):
- Applied CAPM to estimate the expected return of each asset based on market risk and asset beta, using data from Bloomberg Terminal and historical performance metrics
- Prioritized high-performing assets that maximized returns relative to systematic risk
- Value at Risk (VaR) for Risk Management:
- Implemented VaR analysis using Python-based models to estimate the maximum potential loss over a specified period, aligned with the fund’s risk tolerance
- Adjusted asset allocation to ensure alignment with the fund’s risk profile and long-term objectives
- Optimization of Asset Allocation Efficiency:
- Applied Modern Portfolio Theory (MPT) and optimization algorithms (developed in Python) to fine-tune asset allocation
- Achieved the highest return for a given level of risk, improving diversification and capital utilization