Mastering the Performance and Attribution Puzzle in Private Credit Funds

Private credit continues to shine. Investors desire for portfolio diversification, fund managers seeking increased returns, and borrowing firms’ attraction to certainty and speed of execution are some of the key drivers leading to private credit’s ascending popularity. Private credit emerged as a substitute source for procuring corporate loans after the 2008 global financial crisis and has been growing exponentially since. Global private credit assets currently stand at almost $1.7 trillion and are estimated to reach $2.3 trillion by 2027 according to Preqin.¹

Lower loss rates, and lesser correlation with public markets, are fueling a spree in direct lending. The largest asset class category within private credit at $800 billion, or about one half the total,² direct lending offers the appeal of higher returns and lower volatility compared to leveraged loans and high-yield bonds. The market was shocked by the Covid-19 pandemic, leading to tighter regulations and bank deleveraging to halt the syndicated loan market. Then at the start of 2022, conventional lending also came to a standstill mainly due to uncertainty in the global markets caused by sharp interest rate hikes, regulatory constraints, and geopolitical unrest. Middle-market firms in need of liquidity turned to alternative means of funding when public debt became increasingly inaccessible.

Enter private credit – whose 5% annualized outperformance over broadly syndicated loans during the last 10 years³ helped establish its position as a primary private markets strategy for investors seeking alternatives to fixed income for longer-term holds, attractive risk-adjusted returns, and reduced risk. Adding to its mass appeal, private credit is less risky than private equity based on the capital structure workout.

Since private credit now stands clearly as an asset class of its own, this leads to the question, how do fund managers and investors measure private credit performance and analyze attribution?

The attribution puzzle

In an attempt to augment alpha, private markets investors continue to explore and allocate more to private credit strategies for higher-yield opportunities and diversification appeal. By the same token, they are tackling the tribulations in accurately assessing the performance of their multi-strategy private credit portfolios. Insight into the drivers behind investment performance and attribution can be puzzling due to the variety of credit strategies firms employ, the risk/return spectrum, and the varying performance measures and metrics.

In contrast to public markets, there is no like-for-like asset class or index to accurately benchmark against. A limited number of debt indices, data providers, and sources do exist. However, optimizing this data in a manner that integrates with your ecosystem of investment data, and that enables you to frequently measure fund performance, synthesize attribution, and conduct meaningful analyses, is challenging. Metrics are often stale given they are based on delayed, quarterly published data, and credit assets are held for longer, with infrequently occurring issuance and payment. The different tools, data sources, and calculations required to attain accurate portfolio performance can create complications for private credit fund managers, particularly for those employing numerous investment strategies across multiple funds.

Allocation and attribution

Private credit funds bear intricacies of performance attribution, such as allocation to different instruments, sectors and geographies. The challenge is further convoluted by allocation across the range of credit strategies employed, from direct lending, real assets credit, and infrastructure debt to specialty financing credit such as investment fund financing, or asset-backed lending. The terms ‘private credit’ and ‘direct lending’ are often used interchangeably, however, private credit comprises several sub strategies that include direct lending, as well as mezzanine, distressed, and venture debt. These different types of debt instruments bring various data points to track and measure performance and attribution.

Performance attribution in private credit funds often involves a combination of quantitative analysis, including return attribution models, and qualitative assessments based on expertise, judgment and market knowledge. Methodologies vary across fund managers and will consider the fund’s objectives and investment strategies when interpreting attribution.

Attribution analyses with data

Private credit funds generate returns primarily through interest income and fees. Performance attribution can include evaluating the contribution of yield generation strategies, such as the fund’s ability to identify attractive lending opportunities or negotiate favorable terms.

Returns attribution analysis may require comparing actual yields against benchmark or target yields. Private credit funds lack equivalent reliable benchmarks and market value information. These illiquid assets have a longer time horizon (5-10 years), lower regulatory constraints, and disclosure requirements that pose challenges for fund managers and investors in seeking, obtaining, and analyzing data to help draw comparisons.

In addition, private credit assets require more due diligence efforts from the investor. A lack of transparency and availability of private credit data and returns present a barrier to performance analyses, since quarterly NAVs are unrepresentative of true market values, and data availability often lags, limiting estimations.

Performance pieces

Metrics such as internal rate of return (IRR), net asset value (NAV), total return, and cash flow analyses are commonly used to calculate private credit fund performance. Some of these measures include:

• IRR – typically the most used measure of returns across private markets investments to calculate annualized returns based on cash flow timing.
• MOIC (multiple on invested capital) – measures the total amount of cash returned to investors relative to the amount initially invested, are frequently used, and include metrics such as DPI and TVPI.
• DPI (distributed to paid-in capital) – is a measure of the ratio of capital returned to that invested. DPI has overtaken IRR, as a critical metric, says Bloomberg.⁴ Investors are now focusing on cash returns over expected returns, particularly in today’s tumultuous environment, as well as where distributions have recently plunged at major private equity firms.
• TVPI (total value of paid-in capital) – represents the total gross value of an investment.
• PME (public market equivalent) – like the multiples, measures the ratio of cash flow in, to cash flow out.

Whilst PME enables comparison to public benchmarks, reliant on the selection of an equivalent public benchmark, it doesn’t factor time horizons or amounts of investments.  Despite a trending preference towards multiples as credit performance measures, IRR will continue to be an important metric across private markets investments and will play a significant role in investors’ evaluation of whether to back an investment.

Evaluating performance

Performance evaluation is important for private credit managers to determine whether their fund is on par or below benchmark, as well as assessing the investment’s yields, by calculating excess or active returns. Attribution analysis at individual investment level can unveil performance drivers by delving more granularly into a specific asset.

Investors in private credit funds require transparency into the historical performance of potential fund managers to invest with and on due diligence factors such as revenue growth and financing that impact their returns on private credit deals.

Performance reporting

Over time, private credit and other strategies amongst the alternatives landscape will attract higher regulatory attention, reporting demands, increased visibility, and more scrutiny from investors. These demands solidify the need for fund managers to strongly consider their readiness to meet expectations effectively.

Fund managers will need to optimize their reporting and analytical capabilities to manage all the data pertinent to their private credit investments and portfolios. This necessitates capabilities to automate accurate calculations across credit transactions and economics, and link separate investment-related data. The ability to dive into underlying data to observe and analyze derived metrics is integral to perform attribution analyses, manage risk, and to generate periodic performance reports for investors and regulators.

Solving the puzzle

Manager selection is an important criterion for investors in private credit funds. Investors want managers who are agile experts in the different private credit categories and well-versed in investing across capital structures. Comprehensive performance data and reporting enables fund managers to demonstrate their expertise in the private credit space to attract and build trust with investors.

Gathering the pieces

With the proliferation of illiquid assets in varying fund structures and investment vehicles comes the challenges of portfolio management and more specifically, analyzing performance and attribution. Private credit transactions are directly originated, negotiated, and structured, with multiple pieces of data including borrower covenants, credit agreements, and memorandums. This is followed by execution and asset servicing data such as the trade, transactions, and subsequent cash flows and payments in kind (PIK). When it comes to attribution, illiquidity premiums, manager selection, market timing, and idiosyncratic risk are key contributing factors in assessing overall risk-return advantages and performance attribution of private credit portfolios.

Establishing representative benchmarks for a fund’s universe that reflect the desired risk and return characteristics, and calculating portfolio returns from interest income, unrealized and realized gains, and valuation changes, are key elements for appropriately measuring performance attribution. Determining credit selection returns relative to the benchmark, and further decomposing selection to analyze credit rating contribution, are also contributing factors in private credit fund performance.

Capabilities to dynamically calculate and access the various performance metrics benefit private credit fund managers in assessing their investments performance as needed with streamlined, timely, and intuitive access to the relevant underlying and related data. On-demand access lets them track performance of specific assets, analyze underlying drivers, compare against their other investments as well as peers, understand performance attribution, and produce detailed reporting for investors and regulators.

Putting the pieces together

Modern technologies offer a comprehensive view across performance track record, metrics, and benchmarks for private credit investments, with functionality to drill into the underlying components of calculations enabling a more holistic performance analysis and attribution of private credit funds. When data from multiple disconnected sources is easily accessible at any time, fund managers can gain more visibility into and impart meaningful insights from portfolio, fund and investment level data. Fund-level and role-based information access, and full audit trail history on changes made to data, allow firms to confidently enable data access to investment and operations personnel.

The advancement of financial data systems has empowered private credit funds to evaluate, underwrite, and value complex products more dynamically. Contemporary cloud-based technologies help private funds by integrating and aggregating the disparate data across alternative investments, and by harmonizing the data to deliver a broad performance view across their multi-strategy private credit portfolios. Out-of-the-box functionality to model private credit assets being issued, associated transactions, and tracking of PIK inflows, optimizes data management. Features that maintain comprehensive audits on who has transformed or enriched data and when, ensures data governance and compliance and can also help reduce key-person dependencies.

ESG piece

Environmental, social, and governance (ESG) factors continue to be a highly debated topic across the investment industry, though currently with lesser investor and regulatory impact on private markets funds than public ones. Ultimately however, future investments whether public or private are likely to be incrementally scrutinized for their inference on one or all the E, S, and G components. A flexible and scalable data management technology enables a seamless and steady transition into this foray through ESG data ingestion. Customized reporting of private credit performance integrated with ESG data will support fund managers in meeting investors transparency on ESG-related investment impact, and satisfying related regulatory reporting requirements.

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Future-proofing performance and attribution

A sophisticated solution offers data analytics engineered to deliver more granular insights into allocation, performance, attribution, as well as allocation effects based on investment assets, sectors, and geographies. Disparate data aggregation and management tools built with underlying private investment domain-aware models and expertise enables streamlined access to unified information from disparate sources, providers, and files. This transformation process can deliver clean, validated, and accurate data across different funds, portfolios, and investments in a single, unified platform with an intuitive self-service interface to perform the analyses and derive insights that drive future investment decisions.

Suffice it to say, looking beyond today’s requirements can help private credit fund managers select the technology solution that best serves their current needs. A nimble and scalable system to meet evolving future requirements enable firms to explore new investment strategies in private credit and across the broader private markets.