Allocation to alternative lending has experienced a significant increase among institutional investors. This significant growth has been driven in part by the attractive risk/seward characteristics of alternative lending and the stability of the cash flows it generates. In this white paper we aim to systematically evaluate these characteristics and compare them with other, similar assets.
Our first challenge was to select the appropriate investment indexes. At the time of publication, the non-confidential alternative lending index information available in Continental Europe was very limited. We therefore selected the Orchard US Consumer Online Lending Index as a proxy for the asset class. This index measures the performance of consumer loans originated and funded in the United States. It includes more than one million loans with an outstanding balance of over USD 11 billion.
Our analysis covers seven years of monthly return data ranging from January 2011 until September 2017. We used the following ETFs, taking into account dividends and splits, as a proxy for the following indexes:
Although our selection of indexes has limitations, we still believe our analysis sheds light on this new asset class. As further information and more indexes become available, we plan to update our research to deepen our insights.
Alternative lending has produced solid returns at a fraction of the volatility of other asset classes and at a shorter weighted average maturity, as shown in Table 1.
In the following sections, we will explore in more detail the characteristics of this asset class. We will finalise our analysis by evaluating the impact of alternative lending on traditional portfolios.
As shown in Fig.1, alternative lending has produced solid returns since 2011. We believe there are several drivers for this return premium.
First, alternative lending has a particular type of credit risk premium not available with traditional assets. By this we mean access to consumer and SME (small and medium-sized enterprise) credit at a single loan level, with no leverage or tranching, enabling access to a credit risk premium in its purest form.
Second, alternative lending platforms and fintech companies have filled a credit void left by banks and other institutional lenders. Excessive regulation and cost have discouraged most bank lenders from making small loans to consumers and SMEs. Bank lenders’ fixed operating costs per loan stayed almost constant, meaning that small loans started to be unprofitable. As a consequence, banks shifted their focus to large loan transactions. The void left by this was filled by more efficient technology-centric companies capturing what we consider the fintech return premium.
Finally, investors in alternative lending are exposed to a semi-illiquid asset, for which they are compensated in the form of an illiquidity return premium.
We believe that these excess return premiums are significant and have helped drive a higher allocation of institutional investors’ portfolios to alternative lending.
The excess returns of alternative lending are driven by credit, fintech and illiquidity premiums.
A common way to measure the financial risk of an asset is to evaluate the consistency of its returns, also known as its volatility. Volatility is measured as the dispersion of returns from the average return, expressed as the standard deviation of those returns. The higher the volatility, the lower the consistency of returns and thus the higher the risk.
In Table 1 we saw that alternative lending had a volatility of 0.59% per year, the lowest volatility among the assets compared. We might argue that this comparison is not appropriate since we are comparing a semi-illiquid asset with more liquid ones. However, all returns from alternative lending are driven by monthly interest payments on loans, and it is therefore important to highlight the stability and low volatility of those monthly cash flows.
We further explore this in a graphical way in Fig.2. Each point represents the monthly return of the corresponding asset. The dispersion of these returns represents the volatility. The higher the dispersion of the points, the higher the volatility and the higher the risk.
In the case of alternative lending, returns are driven exclusively by monthly interest payments on loans, and we can conclude that these returns are stable and predictable. Additionally, investors care much more about negative volatility (dispersion where returns are negative) than positive volatility (dispersion of positive returns). As we see in Fig.2, in the case of alternative lending the monthly returns are almost always positive.
We believe that low volatility and predictability of cash flows have driven institutional investors to increase their allocation to alternative lending. Specifically, we have seen institutional investors that depend on predictable cash flows to finance their commercial or philanthropic activities allocating a higher portion of their portfolios to alternative lending
A well-diversified portfolio should be constructed with assets whose returns are not related to each other – or, in mathematical terms, assets that have a low correlation to each other. This ensures that the portfolio is truly diversified and generates stable returns in different economic environments.
Adding more assets to a portfolio will not necessary increase diversification. Investors often add assets that are highly correlated, creating bloated portfolios that are not truly diversified.
Understanding the correlation between assets in a portfolio allows us to better understand which ones add true diversification. Correlation measures both the direction and magnitude of returns, and it is usually expressed as a percentage between +100% and -100%, or, written without the percentage sign, between +1 and -1. If the return of two assets behaves exactly the same way, they are said to have a correlation of +1 or 100%. If returns are exactly the opposite, the correlation is -1 or -100%. To obtain true diversification, portfolios should be constructed with assets of low correlation or negative correlation.
Fig.3 shows the correlation matrix and heat map for the assets in our analysis. We can see that most assets do not add consistent diversification. For example, 10 year treasury notes add diversification when compared with US equities (-40% correlation), but only limited diversification when compared with investment grade corporate bonds (48% correlation). However, alternative lending adds persistent diversification to the portfolio irrespective of the asset it’s compared with, with a maximum correlation of just 19%.
We believe institutional investors have realised the true diversifying value of alternative lending, and this has prompted them to allocate a greater share of their investment portfolios to this asset class.
To obtain true diversification, portfolios must be constructed with assets that are minimally correlated to each other.
In recent years the trend has been for institutional investors to allocate more capital to real tangible assets, such as farmland and real estate, that produce stable cash flows. This behaviour is driven in part by a desire to diversify cash flow sources and limit the speculative risks usually seen in the traditional capital markets.
Another reason institutional investors have allocated more to tangible assets is the possibility of accessing the real economy at a very granular level. Institutions have been able to build an alternative lending portfolio targeted at specific geographic areas based on their convictions and investment goals. For example, Swiss and American consumer loans are very likely to behave differently from each other, and each geographic location is likely to be somehow resilient to different types of economic fluctuations. Institutional investors can use this advantage to build custom investments to ensure the stability of their portfolios.
Furthermore, institutional investors can choose to allocate capital in a much more granular way by only investing in loans originated in a specific city or regions within a city. The possibility of creating granular loan portfolios helps them insulate risk and focus on certain opportunities. Additionally, some institutions view this characteristic as a way of helping local communities by providing people living in these geographic areas with access to fair credit.
In the final section of our analysis we consider the impact of adding alternative lending to a simple base portfolio composed of 50% equities and 50% fixed income divided equally between high-yield corporate bonds and investment-grade corporate bonds.
We then allocate 15% of the portfolio to alternative lending, reducing fixed-income exposure while keeping the 50% equity allocation constant. Comparing the investment performance statistics with the initial base portfolio, as shown in Table 2 it’s clear that adding alternative lending not only increases the return, but also reduces the risk (volatility).
The Sharpe ratio measures the risk-adjusted return, in other words the unit of return per unit of risk. Adding 15% alternative lending significantly improves the Sharpe ratio of our hypothetical portfolio by 11% (Fig.4).
Deciding the final allocation of alternative lending in a portfolio depends on several factors, and professional investors have tools to help them make this type of decision. However, our illustrative example demonstrates that allocating a portion to alternative lending has a significant and positive impact for investors. We believe institutional and sophisticated investors have reached similar conclusions, which is why we’re seeing an increased allocation to this asset class.
We have systematically analysed and compared alternative lending with other asset classes. We have found that alternative lending provides solid returns with short weighted average maturity and predictable cash flows. Furthermore, as an asset class, alternative lending adds true diversification to investment portfolios.
We extended our analysis by simulating the impact of alternative lending on a traditional portfolio, and found that replacing a portion of the fixed-income allocation with alternative lending significantly improves the risk-adjusted return, by 11%.
We plan to expand our research to other jurisdictions as more data becomes available and more liquid products are developed to access this exciting asset class.