In the ever-evolving landscape of investment management, constructing portfolios that not only meet client objectives but also intelligently manage risk is paramount. For decades, Tracking Error (TE) has been a cornerstone metric for benchmark-aware portfolio construction. However, as investor needs become more sophisticated and the demand for hyper-personalization grows, the limitations of TE are becoming increasingly apparent. InvestSuite, a global B2B embedded wealth and InvestTech provider, hosted a webinar detailing a novel approach that moves "Beyond Tracking Error," leveraging their proprietary iVaR (InvestSuite Value at Risk) metric and powerful Portfolio Optimizer tool.
This article delves into the insights shared, exploring the challenges of traditional methods and showcasing how InvestSuite's innovative solutions are shaping the future of quantitative portfolio construction.
The Traditional Pillar: Understanding Tracking Error
Benchmark-aware portfolio construction is a strategy where portfolios are built with a specific market benchmark (like the S&P 500 or STOXX 600) as a reference. The goal is typically to either closely replicate the benchmark's performance (as in direct indexing or ETF replication) or to modestly outperform it while keeping deviations within acceptable limits (enhanced indexing or factor-based investing).
Tracking Error is a key financial metric that quantifies how closely a portfolio's returns follow those of its benchmark. A lower tracking error indicates a more consistent alignment with the benchmark's performance.
The concept of using standard deviation of returns as a risk measure has a long history, with roots in Irving Fisher's work in 1906 and significantly popularized by Harry Markowitz's Nobel Prize-winning Portfolio Selection thesis in 1952.
Advantages of Tracking Error:
Closed-Form Equations: For simpler problems, TE allows for neat, analytical solutions.
Established Technology: It can be solved with computational techniques available since the 1950s, even for more complex scenarios.
The Cracks in the Pillar: Limitations of Tracking Error
Despite its widespread use, TE has significant drawbacks, particularly when aligning with actual investor perceptions of risk:
Doesn't Match Real Investor Risk Perception: Investors are typically more concerned about drawdowns (the peak-to-trough decline during a specific period) and the time to recovery from such losses. TE, being a symmetrical measure of volatility, doesn't directly capture these downside risk aspects that truly impact investor sentiment and financial well-being. A portfolio could have a low TE but still experience significant, albeit infrequent, drawdowns.
Information Loss: TE condenses the entire complex interaction and co-dependence between a portfolio and its benchmark into a single number. This oversimplification throws away a vast amount of valuable information about the nature, frequency, and magnitude of deviations.
Overfitting and Unintuitive Solutions: Optimizing solely on TE can sometimes lead to unintuitive portfolio compositions or overfitting to historical data, which may not hold in future market conditions. For instance, a portfolio constantly underperforming by a fixed amount would have a TE of 0%, which is clearly undesirable. Conversely, a portfolio that sometimes underperforms but often outperforms might have a higher TE, yet be preferred by investors.
Introducing iVaR: InvestSuite's More Intuitive Risk Measure
To address these shortcomings, InvestSuite developed iVaR (InvestSuite Value at Risk), a drawdown-based risk measure designed to be more aligned with how investors genuinely perceive relative risk.
"iVaR is a coherent risk measure that combines three crucial drawdown properties," as was explained in the webinar. . These are:
Frequency: How often does the portfolio underperform the benchmark? (i.e., how many drawdown periods are there?)
Depth: What is the maximum underperformance experienced during these periods? (i.e., the height of the drawdown area).
Width: How long does it take to recover from these underperformance periods and get back to outperforming the benchmark? (i.e., the duration of the drawdown area).
By focusing on these three dimensions, iVaR captures the "pain points" of relative underperformance more effectively than TE. The sum of these "pink areas" (visualizing underperformance relative to a running maximum outperformance) on a graph gives a single iVaR number, providing a more holistic and investor-centric view of relative risk.
Driving Innovation: InvestSuite Portfolio Optimizer
The iVaR metric is a key component of InvestSuite's broader Portfolio Optimizer, a sophisticated quantitative portfolio construction framework. It was at the very start of the company, and a core part of fulfilling the company's mission of helping financial institutions realize their digital wealth transformation journey, democratizing wealth management, and creating the most delightful investing experiences for their customers. The Portfolio Optimizer is a testament to this, offering highly scalable, AI-supported, and cloud-native solutions.
This framework is incredibly flexible, allowing for:
Diverse Objectives: Optimizing for expected return, managing transaction costs, implementing factor tilts, incorporating ESG preferences, and managing tax liabilities.
Comprehensive Risk Measures: Utilizing traditional measures like volatility and TE, but also advanced metrics like Value at Risk (VaR), expected (relative) shortfall, equal risk contribution, maximum diversified portfolio, and, crucially, the proprietary (Relative) iVaR.
Granular Exposure Constraints: Setting limits on cash levels, industry/sector allocations, country/region exposures, factor tilts, ESG scores, and even custom data types.
Practical Technical Constraints: Implementing exclusion screening, limiting the number of instruments, setting min/max trade and position sizes, incorporating tax-loss harvesting, and managing liquidity.
Broad Instrument Support: Handling stocks, ETFs, mutual funds, and even crypto assets.
The Portfolio Optimizer can be accessed in three ways:
Robo Advisor: A retail investor front-end.
Optimizer API: For direct system-to-system integration.
Model Builder: A light-weight portfolio manager or researcher front-end, which was showcased in the live demo.
Relative iVaR in Action: Backtesting and Live Demo Insights
The webinar goes into what Optimizer can really do by providing compelling evidence of iVaR's superiority through backtests and a live demo.
Backtest Results (STOXX 600 & S&P 500):
Two primary backtests were conducted on both the STOXX 600 and S&P 500 indices, each limiting portfolios to a maximum of 100 instruments:
Minimize Tracking Error.
Minimize Relative iVaR.
The results were striking. For the STOXX 600:
Ex-post Tracking Error: Minimizing TE resulted in a TE of 1.58%, while minimizing Relative iVaR yielded a slightly higher TE of 1.6%.
Maximum Relative Drawdown: Crucially, the TE-minimized portfolio experienced a maximum relative drawdown of 7.03%. In contrast, the Relative iVaR-minimized portfolio saw a significantly lower maximum relative drawdown of only 2.77%.
Outperformance: Over the long term (past 12 years), the portfolio optimized using Relative iVaR consistently outperformed the one optimized using TE, often by a substantial margin. This is because Relative iVaR inherently penalizes negative deviations more, allowing for positive deviations to contribute to outperformance.
Similar superior results for Relative iVaR were observed for the S&P 500 backtest, with a maximum relative drawdown of 3.37% for the iVaR strategy versus 4.58% for the TE strategy.
Live Demo with Model Portfolio Builder:
In the showcased demo, the following characteristics contribute to the flexibility of the Portfolio Optimizer using the Model Builder interface, focusing on the STOXX 600:
Baseline: A portfolio was constructed to track the STOXX 600, minimizing Relative iVaR with a maximum of 100 instruments. This already showed good tracking with a tendency to outperform slightly over time due to the nature of iVaR.
ESG Tilt: An ESG preference was added to boost the portfolio's overall ESG Combined Score. The optimizer successfully increased the ESG score from around 68 to 73, while still maintaining close tracking of the STOXX 600 and keeping within the 100-instrument limit.
Specific Social Factor Tilt (Women% Employees): Further demonstrating hyper-personalization, a tilt was added to increase the percentage of women employees in the portfolio companies. The optimizer managed to increase this metric from approximately 40% (benchmark level) to over 51%, again, without significantly compromising tracking or other constraints.
Fundamental Factor Tilt (Value): Finally, a tilt towards "Value" stocks (using InvestSuite's proprietary X-Ray Factor Scores) was introduced. The portfolio successfully reflected this value tilt compared to the benchmark.
These demonstrations powerfully illustrated the Portfolio Optimizer's ability to create highly customized, benchmark-aware portfolios that cater to specific investor preferences (ESG, social impact, factor exposure) while intelligently managing risk through the iVaR framework.
The Future is Personalized and Purposeful
Our approach points perfectly at a vision of the future of investing that is digital, AI-supported, hyper-personal, and purposeful. By moving beyond the one-size-fits-all nature of Tracking Error and embracing more nuanced, investor-centric risk measures like iVaR, combined with a flexible optimization engine, financial institutions can:
Offer truly differentiated investment solutions.
Cater to the growing demand for ESG and impact investing.
Provide more intuitive risk management that resonates with end-clients.
Achieve cost-efficient and scalable portfolio construction.
Conclusion: Embracing a Smarter Way to Manage Portfolios
Our "Beyond Tracking Error" webinar argues for a paradigm shift in benchmark-aware portfolio construction. While Tracking Error has served its purpose, its limitations in capturing true investor risk perception and its inability to facilitate nuanced personalization are significant.
