BSL prepayments: How quantitative models could enhance performance

The large, established and growing broadly syndicated loan market presents both opportunities and challenges for underlying investors and their respective portfolio managers.

The US Broadly Syndicated Loan market (“BSL”) is a proven asset class with a track record of over 30 years (from the earliest leverage loan ‘prime rate’ mutual funds). CLOs are significant investors in this $1,5tn+ market. Other investors include regulated mutual funds and ETFs, and private account investors.

While BSLs offer high yields and could be attractive under an environment of increasing interest rates, they also come with several risk factors that can affect their pricing and complicate the management of a portfolio of BSLs.

Credit risk, interest rate risk, market risk, and prepayment risk are several key factors that BSL managers consider. In this article, we’ll explore the drivers of leveraged corporate loan prepayments and how BSL portfolio managers (whether it is a CLO, ETF, open end mutual fund, or private account) can potentially improve their performance and better manage investor expectations by using more sophisticated quantitative models.

Specifically, we’ll examine some of the key factors that would drive critical quantitative models to estimate prepayment risk and how these models can help BSL managers make more informed decisions to improve investment performance. 

Why prepayments matter for investors

The benefits of accurately anticipated prepayments in a BSL portfolio could be significant for investors in terms of better performance, lowers costs and more efficient portfolio management. A few examples of how a market participant could benefit from a model driven approach to better anticipate and manage prepayment risk include:

  • Managing prepayment cashflows can allow BSL managers to better prepare for their next investment decisions (e.g., complying with reinvestment criteria, avoiding incurring transactions costs to meet a purchase commitment) or to better manage investor redemptions – as in the case of open-end mutual funds.
  • Any time a prepayment occurs, the loan’s correspond rating bucket will make up a lower portion of the total portfolio (this is especially relevant for CLOs where ratings criteria are critical drivers for ensuring compliance with trading rules). This balance shift can have downstream consequences on whether the portfolio manager can meet the ratings portfolio criteria (i.e., WARF, Caa/CCC limit). Being able to anticipate the distribution of prepayments by rating buckets can allow a manager to better plan for these risks and enhance their ability to preserve the credit risk profile of a fund/portfolio.
  • Prepayment modeling can complement the underlying investor’s expectations of returns and cash flows. For example, in static private accounts or for vehicles that are no longer in their reinvestment period, the portfolio prepayments will have a significant impact on the underlying cash flows distributed to investors.
  • Proper management and projections of prepayments will assist managers to more efficiently redeploy those funds to minimize negative carry. Negative carry could have a significant impact on a BSL portfolio’s returns.
  • For funds that are part of a larger fund (some CLOs and SPVs for example are consolidated into a larger fund) can incorporate prepayment management into the asset-liability management of a parent fund.
  • As BSL investors increasingly demand sophisticated and quantitative approaches before investing in a particular fund, a manager that utilizes cutting-edge methods increases their opportunity of raising funds and meeting investors’ expectations – above and beyond direct links to better performance, such as market best practices, surveillance, forecasting, etc.

Factors that can influence loan prepayments

BSL prepayments are driven by a host of factors. Our analysis shows that the most pertinent fall into three categories – (i) Age of Loan, (ii) Loan Spreads and Prices, and (iii) Loan’s Recent Prepayment Activity.

Age of Loan

The first significant driver is the number of months-on-book. Recently originated loans are highly unlikely to prepay. However, as prepayments continue to increase linearly with time, there are some nuances to consider.

The linear trajectory of prepayments may have a different slope depending on the loan’s term structure, with shorter-term loans having steeper slopes. Additionally, towards the end of a loan’s term structure, there is a substantial increase in prepayment as loans get closer to their final repayment.

Loan Spreads and Prices

Loan Spreads – at the loan level

The loan’s spread is another factor worth discussing. The loan’s spread can influence prepayments in either direction. A higher spread can indicate the credit worthiness of a borrower who must allocate more cash to consider a prepayment, making doing so more challenging. However, higher spreads can also increase the borrower’s incentive to prepay, as the potential to reduce interest payments is higher.

This can especially be the case where a borrower’s credit profile has improved. Our findings suggest that higher spreads have a positive relationship with prepayment, indicating that the benefits of prepayment outweigh the costs of higher rates.

Market Loan Spread

However, loan spreads don’t affect the loan in isolation. The average spread of other loans on the market is important. The lower the average market spread, the higher the current loan’s propensity to prepay holding all else equal. This relationship suggests that when spreads are lower elsewhere, borrowers attempt to prepay either to refinance or partially take advantage of lower spreads elsewhere.

We found that when the broader market’s spread is included in a model, the current loan’s spread exhibits a stronger effect. This change suggests that the difference between the two spreads plays a key role in driving prepayments. However, this relationship becomes complicated once the next factor – Price – is introduced into the model.

Loan Price – Current

A loan’s current price is a strong predictor of prepayment probability. More specifically, a loan has a marked increase in prepayment rates if its price is between 99 and 100.5, with the peak occurring at 100. A very large proportion of all prepayments occur when the price of the loan is within that range.

Furthermore, certain factors including the loan’s spread and the market spread become irrelevant once price is controlled for – this is not surprising since the specific loan facility spread and market spreads are generally considered in the price. Thus, even though spreads likely play an important causal role in driving prepayments, the market is accounting for loan spreads and market spread when pricing loans.

Thus, when trying to predict prepayments, the loan’s price carries a good degree of information available in other loan characteristics and will generally dominate many other, though not all, explanatory variables.

Loan Price History

Another factor that matters for a loan’s prepayment activity is the loan’s price history. If a loan is priced at 100 because it was recently issued, one wouldn’t expect that loan to prepay. However, once the loan has been on the books for some time and experienced price fluctuations, then its higher price becomes more meaningful.

Recent Loan Repayment Activity

A loan’s recent behavior can affect the loan’s propensity to prepay in one of two ways. A borrower could have just made a substantial prepayment and consequently have little capacity to prepay for some time. Or they could stagger their prepayments across multiple periods. We found the latter to happen more often. Thus a loan’s most recent period’s prepayment indicates a higher likelihood to prepay again.

A few words about private debt prepayments 

In this paper we provide some insight into prepayment modeling for BSLs, and we find that a very powerful indicator of prepayments in the price of the BSL. For pure private lending, however, market prices are not readily available. In such cases more modeling around fundamental prepayment drivers – such as economic variables, broad market variable or even underlying company financial statements – would need to be explored and analyzed.

Alter Domus has performed extensive fundamental prepayment analysis for private loan portfolios. Our Alter Domus Risk Modeler provides the platform to perform and deliver such analytics. Risk Modeler is also applicable to the broader BSL marketplace for advanced analytics, including prepayment.


The large, established and growing BSL market presents both opportunities and challenges for underlying investors and their respective portfolio managers. Prepayment risk is a critical factor to consider, as it can impact a BSL’s portfolio performance. By employing a model-driven approach to managing prepayment risk, BSL managers can better plan their investment decisions, maintain the risk profile of a fund, manage investor cash flows (including redemptions) and better cater to investor expectations.

Key factors that influence prepayment activity for BSLs include months-on-book, loan and market spreads, current and historical loan prices, and recent prepayment behavior. By understanding these factors and their relationship with prepayment probabilities, BSL managers can make more informed decisions to optimize their portfolios. For private loan investors, modeling and managing prepayment risk may be a little more challenging given the lack of observable market prices. However, more fundamental techniques around prepayments is a field where Alter Domus has significant expertise and capabilities through our alter Domus Risk Modeler platform.

Embracing advanced quantitative models and keeping abreast of factors affecting loan pricing and prepayment risk will ensure that BSL managers can successfully navigate the complexities of the market, ultimately benefiting both the portfolios they manage and their investors. While the insights above based on aggregated market data can provide value to Alter Domus’ clients, a deep dive into an individual portfolio can significantly improve a manager’s ability to better manage prepayment risk. 

Key contacts

Steve Kernytsky

United States

Manager, Quantitative Analytics


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