Private benchmarking

Imagine yourself as a detective in the world of finance, tasked with unraveling the mysteries of successful private equity investments. How can you distinguish the winners from the losers? It all starts with the crucial step of defining and utilizing an appropriate benchmark for comparison. Think of a benchmark as a reference point — a measuring stick that allows you to evaluate the performance of a particular investment. It’s like having a yardstick to determine whether an investment has truly excelled or fallen short of expectations.

In the context of private capital, we can compare fund investments not only to public market alternatives but also to private ones. Such a comprehensive approach allows for a more nuanced assessment of investment performance. Public market benchmarking holds particular relevance for asset allocators who have funded a specific PE program. Their interest lies in monitoring whether their capital is being deployed effectively.

On the other hand, private market benchmarking assumes greater importance for investment managers responsible for selecting the underlying fund investments. It enables them to reevaluate the (relative) performance of the fund managers they have chosen. Private benchmarking offers a more detailed scrutiny, focusing on the individual funds within the investment portfolio. This level of analysis aims to improve the future fund selection process by identifying the strengths and weaknesses of specific investments.

Ultimately, the dearth of theoretical literature on private benchmarking for PE funds has led us to devote an entire Journal of Alternative Investments article exclusively to this aspect, disregarding the public side. Our dedicated focus on private benchmarking in the new paper aims to contribute to the existing body of knowledge and enhance the understanding of this critical aspect of PE investment analysis.

Private Benchmarking

Within the realm of private market benchmarking for private equity funds, the widely adopted approach known as “quartile ranking” stands as the preferred methodology among practitioners. This technique involves a seemingly straightforward two-step process. First, a peer group of PE funds is identified based on their closely aligned characteristics, serving as a reference group for comparison. Second, the performance metrics of all funds within the peer group are analyzed to determine the number of funds that outperform or underperform, ultimately leading to the calculation of “the quartile of the fund.”

Unfortunately, the reliability and usefulness of such peer groups as viable private benchmarks often come into question. They are frequently perceived as subjective, lacking objectivity and standardization. Additionally, these peer groups may be too small to yield meaningful benchmarking results. Furthermore, transparency and disclosure issues related to the construction and selection of peer groups are often neglected, adding further complexities to the benchmarking process.

To shed light on the inherent ambiguities of quartile ranking outcomes, Harris et al. (2012) conducted a study that demonstrated the susceptibility of private benchmarking results to even minor variations in methodologies. Their findings revealed that slight methodological differences could lead to a situation where as many as half of all funds could claim “top quartile” performance.

This highlights the inherent challenges and limitations associated with quartile ranking as a private market benchmarking methodology for private equity funds. The need for greater objectivity, transparency, and standardized approaches becomes evident in order to provide more meaningful and reliable benchmarks for assessing the performance of private equity investments.

Thus, our study follows up on the questions, “Is quartile benchmarking a dead end? Or alternatively, can it be unleashed by more quantitative and data-science-driven methods?”

Key findings

• The availability of data for private benchmarking poses a significant challenge, particularly when considering individual funds or specific investment deals.
• While it is advisable to exercise caution in utilizing excessive data enhancement techniques, it becomes necessary when benchmarking funds from older vintages.
• When evaluating fund-level performance in environments with limited data, parametric models offer a viable approach to derive meaningful statistical insights.
• When examining portfolios at a broader level, simulation-based approaches encounter difficulties in cases of large portfolios due to variance and bias issues.
• Maximum diversified approaches present a natural and comparatively simpler alternative to simulation-based benchmarking when assessing portfolios at a broader level. These approaches offer enhanced accuracy and ease of implementation.

Conclusion

To address the inherent ambiguity in peer group benchmarking results, it is advisable to employ a dual approach that encompasses both simpler and more advanced methods. This entails conducting analyses using both the raw data and an enhanced peer group dataset. At the fund level, a combination of empirical distribution and various parametric models can be utilized for percentile/quartile ranking.

On the portfolio level, the calculation of a maximum diversified benchmark can be accompanied by historical simulation. Subsequently, it is crucial to scrutinize the reasons behind any similarities or discrepancies in benchmarking outcomes between the simple and sophisticated methods. It is important to recognize that the selection of the most suitable method is inherently subjective in any given scenario.

Consequently, the objectivity and validity of private benchmarking results should not be overstated, but rather approached with caution.