Information Asymmetry

TL;DR - high-quality data can reduce a seller’s advantage in an M&A process.

I hope you like the change of name of the newsletter. Pivoting from using my own name felt overdue. And ‘Asymmetric Advantage’ suggested itself as I wrote this piece. But I reserve the right to change the name again!

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One topic that has recurred in my conversations over the past month is the gathering of data for due diligence during an M&A transaction. So I thought I would take some time to drill into this in a post. So here goes!

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Caveat emptor - buyer beware - is a phrase known to most people. But not many know the full version:

Caveat emptor, quia ignorare non debuit quod jus alienum emit ("Let a purchaser beware, for he ought not to be ignorant of the nature of the property which he is buying from another party.")

When a company or financial sponsor acquires the whole of, or a stake in, another business, their potential ‘ignoran[ce] of the nature of the property” they are buying means that they are at risk of being on the wrong side of information asymmetry - that the seller of the business possesses greater knowledge than them.

Due diligence is the attempt on the buyer’s part to diminish, reduce or reverse this asymmetry. This asymmetry is particularly true when acquiring a private company - where minimal information is publicly available.

According to Harvard Business review, companies spend more than $2 trillion on acquisitions every year, yet the M&A failure rate is between 70% and 90%.

Private Equity firms - professional acquirers of businesses - cannot afford to have failure rates this high. The Private Equity business model relies on proving to their limited partner investors that they should invest in the PE firm’s next fund. And the best way to do that is to produce a positive return on investment from their current fund.

So a Private Equity firm is highly incentivised to reduce, as much as possible, the failure rate of their acquisitions. (Failure, simply put, is the inability to sell the acquired company in a short-to-medium time horizon after acquisition for a larger amount than was initially paid for it.)

This can be done after an acquisition has closed in a number of ways. For example:

• Bringing in a new, more experienced management team - usually with experience of deploying the Private Equity playbook;

• Driving inorganic growth through bolt-on acquisitions - can help to accelerate top-line growth, and mask underperformance of the initially acquired platform;

• Reducing costs to maximise profitability - when the business is sold again the valuation will most likely be a multiple of EBITDA;

• Using financial leverage to return capital to the investors.

But, at the point of acquisition, there is an opportunity to make the best possible decision, and:

• Avoid buying a poor business;

• Pay the right amount for the business;

• Ensure all efforts are made to complete the acquisition if the business is found to be stellar.

Most M&A processes take somewhere between 3-6 months end to end, but there can be an extended courtship for many years in advance of a full-blown process. So a PE investor has ample opportunity to ensure that they are not on the wrong side of information asymmetry.

The majority of PE funds will focus their investment professionals on narrow industry verticals. In the course of their day-to-day work these focused investment professionals meet many CEOs, advisors and consultants in their chosen vertical and become well-versed in its value drivers and dynamics. They will have their junior team perform sector research, and present their theses on why a specific sector is worth investing in to their investment committees. Their origination teams will contact CEOs in that space in the hope that they can arrange off-market transactions.

All of this desktop research is performed using a combination of:

• Company Data & Analytics providers and broad Industry Data & Analytics providers, e.g.

  • Large players

    • CapitalIQ (owned by S&P Global)

    • Orbis (owned by Moody’s)

    • Factset

    • Mergermarket (owned by ION Group)

  • Upstarts

    • Alphasense

    • Pitchbook (owned by Morningstar)

    • Sourcescrub (backed by Francisco Partners)

    • My nascent database tracking the market currently lists another 30+ companies, and I have know that I have barely scratched the surface - there is a particular gold rush going on currently in discovery.

• Specialist industry vertical Data & Analytics providers, e.g.

  • Euromonitor - FMCG

  • 451 Research (owned by S&P Global) - Tech

  • Megabuyte - UK-focused Tech

  • There are too many sector specialists to list out here - thousands in total.

Some of the larger / differentiated VC and PE firms are using AI to scour data to source deals and generate alpha. EQT’s Motherbrain project has got everyone’s attention. (FWIW - I linked out to this article at the end of my post last week. There’s an incentive to read to the end :-))

A well-organised Financial Sponsor will systematise and augment the data they glean from desktop research with the more proprietary information that comes in during their day-to-day work, including:

• Meeting and call notes with CEOs, advisors and consultants - stored in their CRM system;

• Data flowing in from their portfolio companies;

• Teasers, IMs and Due Diligence reports shared by advisors in deal processes;

• Other third-party research.

A well-organised PE firm will consolidate this data in one centralised location with the ability to perform detailed look-ups on each company. How advanced a Financial Sponsor is in doing this varies across the industry.

With all this prior research, informational organisation, and thinking, by the time a PE firm finds themselves participating in an auction process, they should be well versed in the business they are meeting, and in the sector that business operates in.

However, we see that, without exception, PE funds who take the opportunity to bid on an asset, whether in an auction process or in an off-market transaction, will perform additional research at this stage, i.e. they feel more information required in order to make an informed decison.

This is understandable:

• As stated before, there is an understandable concern on the buyer’s part of being on the wrong side of information asymmetry - that is that the seller of the business possesses greater knowledge than the buyer;

• Additional information can assist in arriving at a more accurate valuation;

• No-one wants to be blamed for making a bad investment;

• A fund needs to show its LPs that it has performed due diligence;

• There is no disincentive to do so - costs for additional research can be transferred by the GPs to the LPs;

• Parkinson’s Law - tasks expand to fill the time available.

So there is a strong incentive to, and minimal barriers against, performing the maximum amount of due diligence possible before the deal is signed.

Yes, a Financial Sponsor (or perhaps the vendor and their advisors, if they are keen to educate the market) will commission an expensive report from a brand-name consultant. But often this is done by a Financial Sponsor mainly to educate any banks participating in providing financial leverage for the deal.

What the investor is really looking for is high-quality, detailed, proprietary alpha data on the company itself and the industry it operates in, including the company’s competitors. And generally they get this from two places:

• From-the-horse’s mouth insights from senior experts in the sector;

• Alternative data from data providers. (I’ll go into examples of this at a later date. But I’m sure I have piqued your interest in just who or what these might be!)

This takes us back to the piece I wrote earlier this month on the convergence of the Expert Network sector and the Private Company data market.

My overall feeling now, some weeks later, and after speaking to a number of Expert Network industry insiders, PE investors, and experts, is that this is extremely likely to happen for a number of reasons:

• There is a lot of wasted time and resource currently by both the Expert Networks in order to find the experts for clients to speak to, and the experts themselves in signing up for specific projects;

• Expert Networks account managers likely do not fully understand the industry they have been asked to bring together experts to provide insights about;

• Expert Networks do not necessarily procure the correct experts for the clients to talk to;

• The highest value data is data which is the most proprietary;

• So there’s an incentive to combine data and insights into a highly organised and proprietary data source.

I realise yet again that this points me to provide you with detailed insights into AlphaSense and Tegus - the hybrid Expert Network/Data & Analytics providers in the market who seem most advanced towards this. It may well be time for me to provide you with such an analysis in a future piece.

Let me know by taking part in the poll below if this is of interest:

But back to the matter at hand - the use of this information by Private Equity companies.

It is clear that the information received from these sources of alpha can make or break a deal for a professional investor. It is must-have decision-making private company data. This distinguishes it from the mass of Company Data & Analytics providers, who serve up a mix of financial information from the same fundamental providers and scraped publicly-available data, layered over with a taxonomy - often claiming that AI provides the intelligence layer.

Private Equity firms shy away from such providers at the critical decision-making point in the investment process. Generic data is not useful at this juncture - the information has to be of the highest quality. And PE firms are not afraid to spend large sums of money to obtain it.

As we have discussed before, data tends to flow to where money is. In this part of the market we can see this from the growth of Expert Networks. Expect the Data & Analytics providers to follow suit.

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Some things I’ve found interesting in the last week:

• Interesting article from Byrne Hobart at Capital Gains on the Alternative Data market

• Jason DeRise
  interviews Don D'Amico of Glacier Networks on the implications for web scraping of the verdict in the Meta vs Bright Network case.

• LinkedIn post by Michael Rhodes on AltData which flags that “in 2023, the alternative data market expanded from $3.23bn to $4.74bn (a growth rate of 47%) and is expected to hit $19bn by 2027.”

• Article in The Verge about how Robots.txt is being used, abused and ignored in the age of AI looking for LLM training datasets.