In Context: Stanford's 2018 Search Fund Primer

The search fund geek that I am, I was giddy with excitement when Stanford’s 2018 Search Fund Primer was released. They do a good job of summarizing the features, history, and trends of the search fund model, and I was eager to see updates, primarily to ensure that what I’m telling searchers is factual. Plus, I was just curious.

I was pleased to see that they’ve expanded the primer to include a look at geography and searcher compensation. To my knowledge, these elements had been difficult to quantify previously.

In this post I’ll provide some reactions to the report, highlighting several sections that caught my eye.

Women

According to the study, only eight women have raised search funds. This astounds me. The number is higher if you include self-funded searchers, but it’s still low.

I can guess as to the reasons women don’t raise search funds, but I would love to hear directly from any women currently considering a search. Then if nobody responds, I’ll post my guesses later.

I will say that, of the woman searchers I’ve spoken with, they’ve decided that the life of an entrepreneur is actually more flexible than that of a corporate employee. You will work long hours, but you have more power to decide when those hours are. You won’t have a boss checking your daily productivity, and you’ll be a role model for other women at your company.

However, travel is a real part of searching. Once you buy, you likely won’t be traveling as much, but during the search, travel may get in the way of life’s plans.

Regardless, I’d love to find the answer to this problem, so we can fix it.

Return Trends

The executive summary notes a decline in IRR, from 36.7% in 2015 to 33.7% in 2017, and in ROI, from 8.4x to 6.9x. These are outstanding returns by any measure, but the fact they’re declining is disheartening at first.

Until you look at breakdown (Figures G & H)! These charts show that when you exclude the outliers, most notably three top performers in the earlier history of the search fund model, the returns are actually growing over time, from 20.2% IRR in the 2009 study to 29.4% in 2018 study, and from 2.4x to 3.3x.

The report points this out, but I thought this data deserves to be highlighted.

Number of Search Funds

The search fund ecosystem is buzzing with the question, “Is the space too crowded?” Outside North America, the answer is “definitely not.” And even within North America, only a few hundred search funds have been raised in the last 30+ years! Given the number of SME transactions happening annually in the US, the search fund model is still tiny.

Figure D | Acquisition Funnel: 2008-2017

The data here is very surprising. Given my anecdotal observations, the most common sourcing methods among search fund entrepreneurs today are high-volume and email-based. Searchers are prospecting thousands of business owners, sometimes tens of thousands in North America.

By contrast, the report seems to say that in 2016-2017, the average number of companies identified was 386 and the average number approached was 26. Both figures are far from what I’ve seen.

Perhaps I need clarification on the definitions of these funnel stages.

Partnerships

The data in this year’s study makes a fairly strong case for partnerships, at least from the investor’s perspective. The average difference in IRR between solo searches and partnerships is greater than 10%, regardless of whether you remove outliers. The study is right to note that the correlation between partnership status and returns does not imply causation, but the correlation certainly has me reevaluating my opinions of partnerships.

I would like to see further analysis on outcomes for the searchers. Because solo searchers generally get 25% carry and partnerships 30%, a searcher gives up 40% of his or her upside by taking on a partner. Is this sacrifice outweighed by greater overall returns? Maybe not.

CEO Compensation

CEOs are paid fairly well in the search fund model. Conclusion: buy a healthy business, and you’ll be fine!

Acquisition Characteristics

I’m eager to see what further analysis Stanford does on the correlation between company characteristics and IRR in the 2020 study. But at a first glance, they seem to have concluded that the company’s “operating leverage, recurring revenue, industry growth rate, and complexity at the time of acquisition” had “low correlation to outcomes”.

Wow! If this turns out to be true (big if), then we really don’t know what produces successful outcomes, despite the fact that search fund investors have been telling searchers for years to seek companies with, for example, recurring revenues in a growing industry.

My guess is that, upon further investigation, we’ll have additional evidence to suggest that these characteristics do matter, but that there are some confounding factors that make drawing conclusions difficult, especially when the companies searchers buy tend to have many similar traits.

Exhibit 1 | Characteristics of First-Time Search Fund Principals

What struck me here was that about 20% of search fund entrepreneurs over the last three years did not have MBAs. Given the model’s reputation as being exclusive to to the top-tier MBA community, this figure is very interesting.

I would be very interested to see analysis on search fund performance by searcher profile. But as with anything in the search fund world, the data set is small, and we have to be careful with drawing conclusions from that small set

Exhibit 5 | Median Statistics for Search Fund Acquisitions

We always aim to buy at 3-5x EBITDA, but it seems we just can’t help ourselves. According to this study, the median purchase price has climbed to 6.3x EBITDA in 2016-17 and 6.0x across all search fund acquisitions.

Again, I’d love to see further analysis here. I’ve seen plenty of “dull” businesses acquired by searchers for 3-5x EBITDA and do well. Which acquisitions are driving up the median multiple? How are they performing in the long run?

Action Items

Read the study! Develop your own analysis and opinions, discuss with your peers, debate with me, and take everything with a grain of salt. This is not a science, but we can still use data to guide our actions.