Unlocking Key Drivers of Private Debt Fund Performance

Introduction

Private debt funds present a unique opportunity for investors seeking attractive risk premium, steady cash flow and portfolio diversification, but they often come with layers of complexity. As institutional investors increasingly turn to private debt as an alternative asset class, understanding the underlying drivers of fund performance has never been more critical.

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Beyond headline metrics like Net IRR, investors must dive deeper into the nuances that affect returns, such as capital deployment efficiency, fund size, and sensitivity to macroeconomic factors. Traditional analysis methods often miss these subtle yet powerful forces that shape performance over time. This is where advanced analytical techniques like Principal Component Analysis (PCA) and clustering come into play.

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This study leverages PCA to reduce dimensionality and uncover key components driving fund performance, and then applies clustering to identify distinct fund archetypes based on those components. The result? Actionable insights that enable investors to categorize funds not just by return, but by their underlying risk and economic exposure, thus offering a roadmap for more informed allocation decisions in the private debt market.

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Contextualizing the Importance of this Analysis

In today’s competitive private debt market, traditional metrics like Net IRR only scratch the surface of a fund’s health and future potential. This analysis goes deeper, addressing critical factors such as capital deployment, fund size, and sensitivity to economic shifts. By applying Principal Component Analysis (PCA) and clustering, the study identifies core performance drivers and organizes funds into diverse profiles, offering a multidimensional view of private debt dynamics.

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Key Findings - Implications for Investors

This framework equips investors with actionable insights that support strategic allocation decisions based on fund characteristics, economic exposure, and capital reserves. With the following fund profiles, investors can align their strategies with long-term objectives, tailoring their portfolios to balance risk, growth potential, and economic conditions effectively. Note: Mentioned Figures refer to average values on each of the generated clusters.

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High-Performance Small Caps

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• These funds, while relatively small in size (USD MN 830), demonstrate modest Net IRR (11.1%) and Net Multiple (1.4). They have a high Called Percentage (88.9%), indicating that the majority of capital has already been deployed, which limits the potential for imminent capital calls. As additional capital is called and deployed, the current IRR may stabilize at a lower level, particularly if future investments yield lower returns. This dynamic highlights both the growth potential and risks associated with these funds, including size-related volatility and uncertain performance outcomes as capital is deployed.
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• Investors seeking growth can use these funds as satellite holdings, supplementing a core portfolio of more stable assets. While the high Called Percentage suggests limited room for immediate capital deployment, smaller allocators looking to invest capital more quickly could still find opportunities for future capital calls, especially as existing investments mature and new opportunities arise. However, they should consider balancing the risks associated with smaller fund size and higher volatility.

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Capital-Heavy Giants

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• This group is composed of large, capital-heavy funds with high unrealized value and substantial reserves (Dry Powder). These reserves amount to 3,480.7 USD MN in absolute terms, which represents 37.5% of fund size in relative terms, compared to 21.6% for High-Performance Small Caps. The larger reserves enable sustained strategic investments even during volatile market conditions. These funds enjoy good performance metrics, such as Net IRR of 12.7%, which are closer to industry benchmarks and provide a more conservative profile for investors.
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• These funds may suit larger institutional investors or sizeable pension funds looking to avoid excessive volatility. Their larger dry powder reserves and moderate returns make them reliable options for investors prioritizing consistent, stable growth of invested capital. Investors can integrate these funds as foundational positions, relying on their capital-heavy profile to support strategic asset allocation in volatile markets. The substantial reserves offer liquidity and flexibility, allowing these funds to capitalize on emerging opportunities without immediate capital calls, making them appealing for long-term growth and stability.

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Balanced Rate-Sensitive Performers

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• These funds exhibit moderate performance across key metrics like Net IRR and Net Multiple, but are highly sensitive to interest rate fluctuations, particularly linked to the selected indices.
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• For investors seeking balance, these funds provide steady returns aligned with broader market conditions, making them suitable for those concerned with macroeconomic factors, such as rising interest rates. This cluster offers a middle ground between performance and stability.

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By understanding how each fund profile aligns with market conditions and economic sensitivity, investors can position their portfolios not only for growth but for resilience during economic shifts, particularly in response to interest rate changes and other macroeconomic factors.

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Unveiling the Drivers Through Data Analysis

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Data Collection and Preprocessing

Our analysis is based on a performance dataset of 2,200+ private debt funds with vintages from 1983 to 2024, covering multiple geographies (North America, Europe, Asia)  sourced from Preqin as of October 2024. The dataset includes variables such as:

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1. Net IRR (%): Reflects the fund’s ability to generate returns on invested capital. It provides investors with a clear view of performance after all costs have been considered.

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2. Net Multiple (X): This metric indicates the total value generated by the fund relative to the initial capital invested. It is a crucial measure for understanding the overall return on investment, combining both realized and unrealized gains.

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3. Fund Size (USD MN):  Reflects the total capital committed to the fund. Larger funds often offer more diversification and stability, while smaller funds may provide higher growth potential but with greater risk.

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4. Called (%): The percentage of committed capital that has been called by the fund for investment. This variable is vital for understanding capital deployment efficiency and the growth potential of the fund.

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5. Fund Dry Powder (USD MN): Represents the amount of capital the fund has available for future investments. It is important for assessing a fund’s capacity for additional investments and its ability to respond to future opportunities.

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6. Fund Unrealized Value (USD MN): This refers to the current market value of the fund’s assets that have not yet been realized or sold. High unrealized value can indicate future potential gains but also represents unrealized risk.

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7. Fund Manager Total AUM (USD MN): Larger fund managers often have more experience and resources, which can translate into greater stability and investor confidence.

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8. Average Benchmark Net IRR (%): This is used to gauge a fund’s performance relative to the broader market. Including this helps in assessing how well the fund is performing compared to its peers.

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9. US Generic Government 10 Year Index: This index is widely used to measure interest rate risk. It is included to capture the impact of interest rate fluctuations on fund performance, particularly in debt markets.

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10. Bloomberg U.S Corporate Bond Index: This index captures the performance of higher-quality corporate bonds. This index is included to assess the sensitivity of private debt funds to broader corporate credit market conditions.

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These selection of variables will be used for the multivariate statistical analysis. To ensure each variable contributed equally to the analysis, data was standardized due to the different units and scales involved.

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Principal Component Analysis (PCA)

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Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variance as possible. It transforms the original variables into a new set of uncorrelated variables called ‘principal components’, which are linear combinations of the original variables, effectively addressing issues of collinearity by consolidating redundant information.

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These generated principal components are ranked by the amount of variance they capture, with the first few components retaining most of the information from the original data. By simplifying the complexity of a dataset, PCA helps to identify the underlying structure and key drivers that contribute most to the variation in the data, making it a powerful tool for uncovering patterns and insights in high-dimensional datasets like those in private debt fund performance analysis.

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To distill the most significant factors influencing fund performance, PCA techniques were employed, transforming the mentioned ten performance-related variables into a set of uncorrelated principal components. This approach preserves the majority of the variance while simplifying the complexity of the analysis. Retaining the first seven principal components, as shown by the triangle, is possible to capture approximately 85.7% of the total variance in the data , as it can be evidenced on Plot 1.

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In Principal Component Analysis, a loadings plot depicts how the original variables influence each principal component (PC1, PC2, … PCn). This presents the coefficients, or weights, that connect the original variables to the new, uncorrelated principal components. Variables with high absolute values in the loadings significantly impact a specific principal component. Analysing the loadings on Table 1, helps to identify the factors driving the variation captured by each component, thus revealing insights into the data's underlying structure.

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Plot 1:Cumulative Explained Variance by PCA Components

Interpretation of Key Principal Components

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PC1 - Fund Size and Capital Reserves: Driven primarily by fund size (loading: 0.610), fund dry powder (0.521), and unrealized value (0.516), highlights the significance of these factors in determining a fund's future investment capacity. Larger funds with higher reserves have the flexibility to deploy capital in the future, offering a stable, long-term growth potential. This component is essential for investors focused on the capacity of funds to manage future investments in illiquid markets.

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PC2 - Performance Metrics: Dominated by performance metrics, with Net IRR (loading: 0.649) and Net Multiple (0.586) being the strongest contributors. These metrics reflect a fund's ability to generate returns and its performance relative to the broader market, indicated by Average Benchmark Net IRR (0.443). This component is critical for investors evaluating the long-term success of funds in generating value for investors.

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PC3 - Capital Deployment and Sensitivity to Rates: Emphasizes capital deployment efficiency (Called percentage: 0.679) alongside interest rate sensitivity. This component helps investors understand the interplay between deployment and economic conditions. Funds with higher sensitivity to interest rates may need to carefully manage deployment strategies to optimize returns during rate shifts.

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PC4 - Interest Rate Exposure: This component is heavily influenced by interest rate exposure, with a significant loading on the US Generic Government 10 Year Index  (0.716) and the Bloomberg U.S Corporate Bond Index  (0.398). This component underscores how fluctuations in interest rates can impact private debt funds, making it relevant for investors considering macroeconomic factors. Funds with higher sensitivity to these indices may require strategic adjustments in periods of rate volatility.

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Table 1: PCA Loadings

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The remaining principal components (PC5 to PC7) capture additional aspects such as fund manager size, benchmark alignment, and the influence of fund managers on capital deployment. These components, while less individually impact, provide further context for understanding how large fund managers operate within the broader market and how closely a fund's performance aligns with industry benchmarks.

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While the objective of this study delves into the internal Private Debt fund performance drivers and metrics, it is worth noting that standard practices, such correlating principal components with exogenous variables were considered during the analysis phase, which reaffirmed the statistical robustness and interpretability of the findings.

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By focusing on these key components, the analysis uncovers the most significant drivers of fund performance, giving investors actionable insights into how fund size, performance metrics, capital deployment, and interest rate exposure interact. These insights help investors better understand the underlying dynamics of private debt funds and make more informed decisions based on historical data.

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Cluster Analysis

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To complement the insights drawn from Principal Component Analysis (PCA), clustering techniques were applied to group the private debt funds into unique categories using the generated principal components (PCn). This approach allows for the identification of fund archetypes that share similar characteristics, offering a clearer picture of how funds behave across a range of performance metrics and macroeconomic factors. The clusters provide actionable groupings that help investors evaluate the diversity and structure of private debt funds, simplifying fund selection based on strategic objectives.

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K-means clustering was applied to the selected seven principal components, revealing natural groupings in the data. To determine the optimal number of clusters, Elbow Method, Silhouette Score and Dendrograms were used, which indicated that three clusters offered the best fit to the data.

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After establishing the solution, characteristics of each cluster were examined. The analysis revealed the following attributes summarized on Table 2:

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Table 2: Cluster Overview / Summary

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Each variable in the table helps investors understand how the clusters differ, from performance metrics (Net IRR, Net Multiple) to factors like fund size, capital deployment (Called %), and sensitivity to interest rates. The table serves as a guide for identifying the types of funds that may align with specific investment strategies or risk appetites.

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The clustering analysis reveals different fund characteristics within the private debt market, offering valuable insights for investors seeking to navigate this complex asset class. By categorizing funds based on their performance metrics (Plot 2 & Plot 3), capital deployment efficiency, and sensitivity to macroeconomic factors, the analysis uncovers three clear profiles:

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Plot 2: Net IRR (%) by generated Cluster

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Plot 3: Net Multiple by generated Cluster

High-Performance Small Caps: This Cluster showcases funds with moderate Net IRR (11.1%) and high Net Multiple (1.4), reflecting strong relative performance among smaller funds. With a Called Percentage of 88.9%, most of their capital is already deployed, though their lower reserves suggest limited capacity for further investments. It is important to note that IRRs for this cluster may diminish as more capital is deployed, particularly if future investments yield less favorable returns. These funds remain an attractive option for growth-oriented investors but should be considered within a broader, balanced strategy to mitigate risks associated with their smaller size and deployment dynamics.

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Capital-Heavy Giants: Characterized by large fund sizes and high unrealized value, with significant dry powder reserves still available. Unlike Cluster High-Performance Small Caps , these funds possess significantly larger reserves, averaging 37.5% of fund size, compared to 21.6%. These funds tend to perform closer to benchmarks with a Net IRR of 12.7%, slightly below the benchmark average of 13.1%.

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With an average Fund Size of 9,285.9 USD MN, these funds dominate the private debt market in terms of asset size. The substantial Fund Dry Powder (3,480.7 USD MN) highlights their capacity for future investment, but the lower Called Percentage (59.6%) suggests a more conservative approach to capital deployment. These “Capital-Heavy Giants” offer a strong option for institutional investors seeking large, well-capitalized funds that balance moderate returns with significant potential for future growth.

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Balanced Rate-Sensitive Performers: Shows a balance between fund size and performance, with moderate sensitivity to interest rates and corporate bond indices. These funds provide stable returns while being sensitive to macroeconomic factors like interest rates.

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The last cluster consist of funds with a moderate Fund Size (2,668.9 USD MN) and lower Net IRR of 9.1%. What distinguishes this cluster is the high sensitivity to macroeconomic factors, as reflected in the US Generic Government 10 Year Index (4.1) and Bloomberg U.S Corporate Bond Index (3,210.9), indicating these funds are more affected by fluctuations in interest rates and corporate bond markets. These funds are managed by institutions with substantial Assets Under Management, offering a balance between size and exposure to macroeconomic trends. Investors focused on long-term stability with attention to economic shifts may find these funds to be a better fit for their strategic goals.

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Closing Remarks

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This analysis highlights the value of advanced techniques like Principal Component Analysis (PCA) and clustering in navigating the complexities of private debt funds. By reducing the data to key components, we identified critical drivers of fund performance, such as fund size, capital deployment efficiency, and sensitivity to interest rate changes. The clustering process further enables another categorization of funds into three relevant groups, each with unique performance and risk profiles.

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These clusters provide a framework for investors to refine their portfolio strategies, aligning fund selection with specific risk preferences and long-term objectives. Rather than relying solely on headline metrics like Net IRR, market participants can now evaluate funds based on their capital management, economic exposure, and growth potential. This approach offers a structured pathway for decision-making in the illiquid and complex private debt market, where understanding underlying fund dynamics is crucial for long-term success.

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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, regulatory 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.

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