Decoding Private Debt Performance: An Analysis Through Transition Matrices

Private Debt has developed into a distinctive asset class within the Alternatives landscape. It has consistently provided attractive yields to investors while maintaining a favourably low correlation with other mainstream asset classes. However, the work required to unlock its subtleties and truly comprehend its performance dynamics may prove elusive for many potential allocators new to the sector. With this in mind, transition matrices may serve as a beacon, lighting the way to a more profitable experience within the Private Debt space.

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Transition Matrices unveil a clear pattern: Private Debt stands out as a performance-resilient asset class. Past performance does predict future return (to some degree!) within the sector – i.e., top and bottom quartile funds display stability across vintages, while the middle tiers present more compelling opportunities for dynamic performance. This crucial insight may assist investors with strategic fund selection by emphasizing the potential for performance upward mobility. Leveraging Klarphos' expertise, investors gain access to a data-driven road-map to navigate this promising yet nuanced investment landscape.

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The Power of Transition Matrices

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Originally developed as a mathematical concept, Markov Transition Matrices have found applications in a myriad of fields,including finance. They offer valuable insights into the performance stability of assets, allowing us to measure the likelihood of transitioning from one performance state to another. Essentially, they provide a probabilistic map of the historical performance of an asset.

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Applying Transition Matrices to Private Debt

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In this study, we employed transition matrices to gain insights into the performance dynamics of Private Debt. We focused principally on direct lending funds since they have grown into the predominant strategy within the Private Debt space, particularly in the aftermath of the 2008 financial crisis. For an in-depth analysis of the shift in the lending landscape and the rising opportunities in Direct Lending, please refer to our article 'The Window of Opportunity to Invest in Direct Lending'.

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Furthermore, to better comprehend the obtained results, it was useful to compare them with those from another asset class within the alternative investments space. Thus, we also calculated the transition matrix for Private Equity - Buyout, which is a typical hallmark for Alternatives investments.

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In order to provide a comprehensive outlook, the data used in our analysis was extracted from Preqin and encompassed the relevant Alternative Fund performances from 1980 to 2023 with a focus on North America and Europe. The dataset consisted of over 3,600 observable funds where ranking by quartile¹⁾ was available. By mapping the quartile ranks of funds across different vintages, we obtained a detailed view of performance transitions within the mentioned asset classes. The periodicity of the transition matrices in our analysis is determined by the annual intervals between the Vintage/Inception Year of each fund. The resulting transition matrix is as follows:

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How to interpret the matrices:

• The row index represents the current quartile.
• The column index represents the subsequent quartile.
• Each cell in the matrix indicates the probability of transitioning from the current quartile to the next quartile.

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Table 1: Private Debt (Direct Lending) & Private Equity (Buyout): Quartile Ranking Transition Matrix

^{Source: Klarphos; Preqin data as of 31 December, 2023}

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The transition matrix, which captures the likelihood of funds migrating across different performance quartiles, provides valuable insights into manager performance persistence of Direct Lending. It highlights:

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• Stability of top and bottom performances: both the highest (1) and lowest (4) quartiles exhibit a tendency for funds to remain within their current quartiles, with a 38% probability for both the bottom and the category. This demonstrates that once funds are either top or bottom performers, they often maintain that status in the subsequent vintage.

• Volatility in the middle: quartile 2 is particularly volatile. Whilst it has a 29% chance of maintaining its position, there is a significant 27% probability that GPs could decline to quartile 3 for the subsequent vintage. Similarly, Quartile 3 demonstrates a marked degree of variability, with equal odds (32%) of moving to quartile 2 or staying put. This underlines the relatively unpredictable nature of mid-tier performances.

• Potential for upward mobility: interestingly, funds most recently in the lowest quartile (4) have a substantial probability of advancing to a better quartile. There's a cumulative 61% chance of these funds transitioning to quartiles 1, 2, or 3 in the next vintage. This data point offers a glimpse of hope, indicating that even lower-performing funds have a marked opportunity to improve their standings.

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These insights should provide investors with a more adequate understanding of Direct Lending funds’ historical performance persistence, which is a crucial factor to understand prior to committing capital to any Alternatives asset class.

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Moreover, the illustrated comparison illuminates subtle nuances between Private Debt and Private Equity. It is evident that while certain points such as stability at extremes, central volatility, and the silver lining of upward mobility resonate across both, the Private Equity - Buyout sector embodies a more fluid performance landscape compared to Direct Lending in Private Debt, which means that performance trajectories have been less rigid for managers in the Private Equity – Buyout space.

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This showcases a notable degree of 'stickiness'²⁾ of the performance behaviour of Private Debt, especially within the Direct Lending sector, which represents the consistent behaviour of funds to remain steadfast in their performance quartiles. Conversely, sectors such as Private Equity - Buyout manifest a more adaptable performance trend, insinuating that historical performance in Private Debt can prove to be a sturdier gauge for future projections.

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Practical Implications for Investors

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With the insights derived from transition matrices, investors are able to complement their existing investment decision-making frameworks. For instance, recognizing the resilience of the top and bottom quartiles might indicate that established funds with a proven track records tend to maintain their positions and therefore should be favored, ceteris paribus, by allocators.

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The following visualization shows 10 Direct Lending fund managers (by number of funds), mapping their average quartile ranks over their respective vintage/inception years. It offers a complementary visual cue for evaluating manager persistence.

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In particular, Chart 1 features the quartile ranks of these leading managers over vintage years. Even a cursory study reveals a pattern which corroborates the transition matrices above:established funds, especially those consistently performing in the top quartiles, seem to maintain their ranks with relative consistency. This showcases the robustness of the asset class

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Chart 1: Average Quartile Rank of Leading Fund Managers in Private Debt (Direct Lending) by Funds Managed

^{Excludes funds in investment phase from the last three years to focus on a relevant sample for assessing performance trends.}

^{Source: Performance data using funds from 2010 to 2020. Klarphos; Preqin data as of 31 December, 2023.}

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How to seek Performance? - Private Debt Performance Quartile Overview

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As our analysis thus far has focused solely on manager performance persistence, a reasonable question is – does this even matter? I.e., how disperse are returns within Private Debt across manager, anyway? Table 2 below provides a quantitative perspective on the returns based on quartile rankings. A stark difference in the Net IRR is revealed.

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The top quartile boasts a whopping 14.2% average Net IRR, almost seven-fold the 2.9% IRR of the bottom quartile. Such significant disparities between quartiles emphasize the need for prudent fund election, as the potential difference in returns is substantial. Additionally, manager use of leverage and subscription lines must also be assessed in detail and taken into account when assessing past and future returns. Read our ‘ROCC & MOCC: Unveiling an Alternative Approach to Private Market Return Analysis’.

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Table 2: Private Debt – Direct Lending: Average Net IRR (%) per Quartile Ranking

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^{Source: Performance data using funds from 1982 to 2023. Klarphos; Preqin data as of 31 December, 2023.}

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Diving deeper into the factors influencing the performance of private debt funds, Chart 2 offers a compelling view of how the size of a fund can have a significant impact on the delivered performance. The chart delineates the inter quartile range of Net IRR (%) segmented by varying fund sizes, measured in USD.

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As we traverse the chart, it becomes evident that there is a nuanced relationship between fund size and the potential for high returns. It underscores the idea that not only the track record of a fund matters but also its scale and structure.

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Investigating the relationship between fund size and return potential, the chart distinctly highlights the $500 million to $1 billion category as a notable performer. This bracket not only showcases a competitive median Net IRR, but also a relatively narrow return range. This pattern of returns indicates a level of stability and consistency that sets it apart from the rest. In contrast to both the smaller funds and those significantly larger,this middle range offers a promising mix of efficiency and return potential. The data suggests that investors looking for optimal returns might find the $500 million to $1 billion fund size bracket to be the 'sweet spot', balancing the benefits of scale with the agility that often comes with more moderately sized funds.

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It's important to note that while fund size is a critical factor, it is not the sole determinant of success. The investment style and timing within the investment cycle are equally pivotal in selecting the right fund. These factors collectively influence a fund’s capacity to deliver top-tier performance and position itself within the upper quartiles. At Klarphos, we leverage this insight and strive to help our clients target these optimal-sized, top quartile investments in an all-weather manner. By implementing sophisticated quantitative techniques, as illustrated above, in conjunction with rigorous qualitative assessments, we navigate our clients towards informed and strategic investment decisions.

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Chart 2: Inter quartile Range of Net IRR (%) by Fund Size (USD)

^{Source: Performance data using funds from 1982 to 2023. Klarphos; Preqin data as of 31 December, 2023.}

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Reasons Behind Transitions

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Before concluding our analysis, it’s worth considering potential reasons for manager performance transitions in the first place. After all, if one cannot understand why performance changes across vintages, how could an allocator expect to predict such a development during due diligence. A few possibilities emerge.

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Firstly, as revealed above, size is an important determinant of performance for funds, and GPs frequently raise smaller or bigger funds depending on performance, opportunity sets, and macro preferences within the LP community. For example,‘performance chasing’ (Arnott, R., Kalesnik, V., & Wu, L. (2017). The Folly of Hiring Winners and Firing Losers. The Journal of Portfolio Management. Volume 45. Number 1. Fall 2018) is an observable phenomenon within most asset classes of finance and Private Debt is no different. As managers perform well, they gain attention among allocators and, barring a few exceptions with exceptional discipline, tend to grow until their performance mean reverts.

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Secondly, strategies vary based on macroeconomic conditions and managers may see performance improve or erode despite no change in execution.

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On the other hand, and as a third point, managers may experience ‘style drift’, or the pursuit of a strategy different than that presented in marketing and due diligence materials and/or distinct from prior fund approaches, for a myriad of reasons including team change, market conditions, perceived investment landscapes, etc. While a myriad of reasons exist to answer the question of ‘why’ manager performance may vary over time,these are what surface most often in our own diligence process at Klarphos.

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What Lies Ahead for Private Debt?

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The consistent performance of top and bottom quartiles suggests a maturing market where established players continue to dominate. However, the fluidity in the middle quartiles indicates that there is still room for disruption. As environmental, social, and governance (ESG) factors gain prominence, funds adhering to these principles might experience performance mobility that could potentially reshape quartile dynamics in the coming years. Further data on this topic will set the story line.

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Endnotes

Using the Chi-Squared Test of Independence, we evaluated the statistical significance of observed transition probabilities.

For Private Debt – Direct Lending: Chi2 Stat was 28.90 with a P Value of 0.000674

For Private Equity – Buyout: Chi2 Stat: 68.95 and P Value: 2.44×10-11

1) Preqin Quartile Rank: this ranking is based on a comparison against a larger sample size of peers that have reported performance within the most up-to-date range (i.e. the last 5 quarters).Performance Quartile: Refers to a statistical term used to divide performance into four equal groups, each representing 25% of the measured values.

2) When we talk about 'sticky' probabilities in this context, we are referring to the likelihood that a fund will remain in the same quartile from one period to the next. A higher value on the diagonal of the matrix indicates a higher 'stickiness', or tendency to stay in the same quartile.

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Important Information

This document is informative purposes only. It does not constitute research, investment advice nor solicitation to invest in any investment product or service that Klarphos offers or may offer in the future in any jurisdiction. The information contained herein is based on projections, estimates and/or other financial data and has been prepared internally by Klarphos. Opinions expressed therein are current opinions as of the date of this document only and are subject to change at any time without notice.

No representations are made as to the accuracy of the observations, assumptions and projections. No subscriptions to any Klarphos products are possible based solely on this document. Any investment decisions should be made in accordance with the legal documentation of a fund such as its offering memorandum.

Klarphos is not entitled to provide any tax or legal advice.

Past performance is not indicative of future returns. There can be no assurance that the strategy objectives will be realized or that the strategy will not experience losses. Target returns are hypothetical and are neither guarantees nor predictions of future performance. There can be no assurance that the target returns will be achieved.

Klarphos is an Asset Manager specialized in customized portfolio solutions and advisory services for institutional clients based in Luxembourg. Klarphos concentrates its asset management on Alternative Investments and also provides advisory services for strategic asset allocation and ALM optimization. The asset manager employs an international team of specialists and is regulated by the Luxembourg financial regulator CSSF as an Alternative Investment Fund Manager (AIFM).

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