◆ MBA Search Fund Alliance

Protecting searchers
and students from
learning the hard way.

We publish investor rankings, original reporting, and governance research to protect student and searcher safety — and to counter the corporate-governance playbook that has quietly become the industry's default.
CoverageSearch Funds · ETA · SMB M&A
Rankings40 investors · 233 firsthand responses
MethodSearcher-reported, quality-reviewed
PositionIndependent · Founder-aligned
Only ~50% of searchers acquire◆ ~33% of operator-CEOs are removed or forced out◆ Stanford Primer reports ~35% IRR · 4.5x MOIC◆ Pitchbook & Yale suggest ~2.5x MOIC · 1.6x median◆ Preferred equity stacks & board control defaults◆ We publish what the Primer leaves out◆ Only ~50% of searchers acquire◆ ~33% of operator-CEOs are removed or forced out◆ Stanford Primer reports ~35% IRR · 4.5x MOIC◆ Pitchbook & Yale suggest ~2.5x MOIC · 1.6x median◆ Preferred equity stacks & board control defaults◆ We publish what the Primer leaves out◆
§ 01 / The Problem

The traditional search model
is quietly broken.

Power sits with capital. Searchers carry the operating risk and the reputational risk. What's framed as "poor performance" is usually the downstream symptom of governance built to favor investors over operators.

20mo
According to the Stanford Primer, it takes 20 months to acquire a business.
~33%
Of operator-CEOs later removed, forced out, or quietly replaced post-close.
30%
Recent data shows that as low as just 30% of searchers are acquiring businesses.
~2.5x
Estimated weighted MOIC when you triangulate against Pitchbook data and the Yale study, opposed to the 4.5x claimed by the Stanford Reports.
§ 02 / Manifesto

We exist to publish what the rest of the industry treats as off-the-record — the numbers, the post-close removals — so the next cohort of searchers signs with their eyes open.

◆ MBA Search Fund Alliance · Partner@mbasearchfundalliance.org
§ 03 / Rankings

2025 Search Investor
Rankings.

First-hand, searcher-reported evaluations of the forty most active traditional search fund investors — scored across leadership, honesty, insight, friendliness, engagement, and likelihood to recommend.

◆ Leadership — Top 10

01Peterson3.59
02TTCER3.54
03Pacific Lake3.53
04SF Partners3.51
05Relay3.46
06M203.43
07Cambria3.41
08Red Forest3.38
09Futalafeu3.34
10TD Investments3.33

◆ Criterion Defined

Ability to lead in a deal context, influence other investors toward closing, and move a transaction forward when it matters.

◆ Field Note

Leadership scores cluster tightly at the top. The gap between the #1 and #10 investor here is smaller than in any other category.

◆ Honesty — Top 10

01TD Investments3.83
02Cambria3.81
03TTCER3.75
04Red Forest3.71
05SF Partners3.67
06ETA3.64
07Nashton3.57
08Housatonic3.50
09Liberty3.50
10Futalafeu3.47

◆ Criterion Defined

Candor, integrity, and willingness to share bad news directly — including when it's inconvenient to the investor's own position.

◆ Field Note

The highest-rated category across the dataset. Honesty is also the most bimodal — investors are either clearly trusted or clearly not.

◆ Quality of Insight — Top 10

01Cambria3.66
02Red Forest3.46
03TTCER3.45
04SF Partners3.40
05Pacific Lake3.40
06Futalafeu3.30
07Peterson3.29
08Milk Street3.27
09M203.26
10Housatonic3.25

◆ Criterion Defined

Practical, relevant guidance that actually moved the deal forward or changed the operator's thinking in a measurable way.

◆ Field Note

Cambria's lead here is the widest margin in any single category — roughly 0.2 above the nearest competitor.

◆ Searcher-Friendly — Top 10

01ETA3.79
02SF Partners3.73
03Red Forest3.71
04TTCER3.50
05TD Investments3.50
06Peterson3.47
07Futalafeu3.46
08Liberty3.41
09Cambria3.39
10AIJ3.36

◆ Criterion Defined

Genuine support and advocacy for the operator — particularly post-close, when power dynamics shift fastest.

◆ Field Note

Scores here diverge most sharply from Leadership scores — several "strong leaders" rank poorly on operator advocacy.

◆ Helpfulness — Top 10

01SF Partners3.54
02Cambria3.47
03Red Forest3.46
04ETA3.43
05M203.29
06Pacific Lake3.28
07Relay3.24
08Liberty3.23
09Endurance3.23
10Peterson3.21

◆ Criterion Defined

Measurable impact through timely, hands-on involvement. Responsiveness, operating help, introductions that actually close.

◆ Field Note

Helpfulness is the single strongest predictor of Likelihood-to-Recommend in the dataset.

◆ Recommend — Top 10

01SF Partners3.61
02Red Forest3.60
03TD Investments3.58
04Cambria3.53
05Peterson3.50
06TTCER3.49
07ETA3.43
08Greyheart3.36
09Milk Street3.35
10Housatonic3.33

◆ Criterion Defined

Whether searchers would encourage others — candidly, without caveats — to work with this investor again.

◆ Field Note

This is the category that most closely approximates an NPS-style reputation score. The top three are tight. The bottom quartile is not.

◆ Methodology

How investors were evaluated.

Firsthand feedback from searchers, prospective searchers, and operating CEOs — collected through direct outreach, industry platforms, and self-reported polls. Submissions were quality-reviewed for internal consistency and alignment with stated firsthand experience. Respondents were instructed to skip investors they lacked sufficient experience with, so the data reflects informed, experience-based evaluations.

Leadership
Ability to lead, influence other investors, and help close deals.
Honesty & Transparency
Candor, integrity, and willingness to share bad news.
Quality of Insight
Practical, relevant guidance that added real value.
Searcher Friendliness
Genuine support and advocacy for the operator.
Helpfulness & Engagement
Measurable impact through timely, hands-on involvement.
Likelihood to Recommend
Whether searchers would encourage others to work with them.
233
Participants
40
Investors ranked
6
Criteria
2025
Cycle
Animal Search Farm book cover by Kevin Hong
◆ December 2026

Animal Search Fund Farm.

A new book by Kevin Hong — hundreds of candid interviews, one unvarnished look at traditional search.

The tougher, often unspoken questions surrounding the traditional search fund model — the governance, the economics, the removals, the "mafia," and what the Primer conveniently leaves out. Told through interviews with the searchers, operators, and investors who lived it.

◆ Inside the book
  • How has search actually evolved over the past decade?
  • How does each business school rank each search fund investor?
  • Why are fewer than 50% of searchers closing deals?
  • Why are up to one-third of searchers fired or forced out?
  • Is the rate of CEO termination influenced by race or gender?
  • What is the "search fund mafia" — and why does it matter?
  • What hidden terms during deal closing should searchers watch?
  • What can search fund investors do to actually improve the community?
  • What does the future of search look like?
Get notified at launch →
§ 04 / Media

News, media &
field notes.

Reporting, essays, and commentary from our contributors. Some of it is uncomfortable. That's usually the point.

◆ Feature · Investigation

This is why most search fund case studies are marketing — not data.

Triangulating the Stanford Primer's widely-cited 4.5x MOIC against Pitchbook and the Yale study, and what the gap implies for aspiring searchers and LPs making allocation decisions on circulated returns.

Kevin Hong · LinkedInRead the piece →
 ◆ Essay · Governance

Be careful who you choose as your investors.

Why the cap-table and the board-resolution language matter more than the check size — and the patterns that separate aligned capital from the other kind.

LinkedInRead →
 ◆ Commentary

If I were an MBA student today.

What an MBA would actually be weighting — and avoiding — before signing a traditional search term sheet.

LinkedInRead →
 ◆ First-person

One year after the decision to speak up.

A retrospective on going public — what it cost, what it changed, and why silence was never a safer option.

LinkedInRead →
 ◆ Op-ed

"I'm not a DEI guy" — & why that's not the frame.

Why the real question about CEO termination patterns isn't ideological. It's actuarial.

LinkedInRead →

Upcoming events.

◆ Roundtables · Speaking · Panels
May · 21 · 2026
What Gets Funded: The 2026 SBA ETA Playbook — with Kevin Hong, Lisa Forrest & Sarah Andrews (Northwest Bank)
Virtual · 12 PM PT · 50 seats
Register ↗
Dec · 01 · 2026
Animal Search Farm book launch
Book Launch
Details ↗
§ 05 / Join Us

Student. Searcher.
Operator. Academia.

If you're inside the ETA / search fund ecosystem — and you want cleaner governance, better term sheets, and a place to actually speak candidly — we want to hear from you. Members get access to the full unredacted rankings, quarterly roundtables, and early access to original research.

Sign up to join the WhatsApp group — open exclusively to MBA students, recent MBA graduates, and active searchers.

Join the Alliance →
Investors, faculty & ecosystem partners — reach out for the institutional track.