Precedent Transaction Analysis: Video Tutorial With Excel Examples

In this tutorial, you’ll learn about the Precedent Transaction Analysis, when it’s useful, how to conduct one using automated and manual data sources, and some of the disadvantages of this methodology.

The Precedent Transaction Analysis, along with Comparable Company Analysis and the DCF Model, is one of the “Big Three” valuation methodologies.

Of these three methodologies, it is also the most “optional” or “supplemental” one.

If you don’t have access to an expensive subscription service like Capital IQ or FactSet, it isn’t easy to find precedent transactions and gather the required data.

And even if you do have these services and perfect access to all the data, the analysis itself tends to produce more random results with a wider range of values than the other methodologies.

That said, you still need to know about the Precedent Transaction Analysis for interviews, case studies, and the job itself, so let’s start at the beginning:

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What is Precedent Transaction Analysis, and Is Precedent Transaction Analysis the Same as Comparable Company Analysis?

These two methodologies are related but different.

In a Comparable Company Analysis, you find publicly traded companies similar to the company you’re valuing, and you estimate your company’s value based on theirs.

For example, maybe your company’s Enterprise Value is currently $2,500, and it has Revenue of $1,000 and EBITDA of $200 for TEV / Revenue and TEV / EBITDA multiples of 2.5x and 12.5x, respectively.

Similar public companies trade at Revenue multiples of 2x and EBITDA multiples of 10x.

Therefore, since these other similar companies trade at lower multiples, your company may be overvalued currently.

Precedent Transaction Analysis is different because it’s based on companies that have been acquired rather than public companies that are still trading on the stock market.

For example, maybe you find a set of 10 similar, recent transactions in the industry of the company you’re valuing.

The acquirers in these transactions paid TEV / Revenue multiples between 2x and 3x and TEV / EBITDA multiples between 8x and 14x.

Since your company has Revenue of $1,000 and EBITDA of $200, its implied Enterprise Value based on Precedent Transactions is:

  • Implied Value from Revenue Multiples: $2,000 – $3,000
  • Implied Value from EBITDA Multiple: $1,600 – $2,800

Since the company’s Enterprise Value is currently $2,500, which is right in the middle of these ranges, we’d say it’s “appropriately valued.”

Precedent Transaction Analysis Examples in Excel (XLS)

You can get a few real examples of Precedent Transactions for different industries (steel manufacturing and biotech/pharmaceuticals) below:

Here’s the Steel Dynamics example:

Precedent Transaction Analysis - Steel Dynamics

And here’s the Jazz Pharmaceuticals example:

Jazz Pharmaceuticals - Precedent Transaction Analysis

The Rationale for Precedent Transaction Analysis (and its Drawbacks)

In theory, the Precedent Transaction Analysis should be more “grounded in reality” than the DCF Model or even the Comparable Company Analysis because it’s based on what companies have paid in real life for other companies.

In a DCF model, you could use ridiculous assumptions to get any output you want: maybe the company grows at 20% per year forever, or it goes from unprofitable to 30% profit margins in 2 years.

In other words, garbage in, garbage out.

But you can’t manipulate the data in the same way with Precedent Transactions because they’re based on historical records.

That said, the methodology does have some disadvantages.

It’s difficult and time-consuming to use without access to a paid database service like Capital IQ, it’s challenging to find deals that are truly “comparable,” and it tends to produce higher and more “random” values than the other valuation methodologies.

To value a company with Precedent Transactions, follow these steps:

  1. Find/select an appropriate set of transactions.
  2. Determine the metrics and multiples you want to use.
  3. Calculate the multiples for all the transactions.
  4. Apply the median or 25th/75th percentile multiples from the set to your company to estimate its Implied Equity Value and Enterprise Value (and Implied Share Price if your company is public).

Step 1: Find Precedent Transactions

The best option here is to use a service like Capital IQ, which lets you screen for Precedent Transactions based on financial, geographic, and industry criteria, as well as time:

Capital IQ Screen for Precedent Transactions

These criteria are always based on the Target company; the Acquirer’s financial profile doesn’t matter.

There are no universal or hard-and-fast rules on the screening criteria, so you should base them on the number of reasonably comparable deals you can find.

For example, if your initial criteria are:

  • Geography: France
  • Financial: Revenue above €1 billion
  • Industry: Chemicals
  • Dates: Past 3 years

And these criteria produce only 2 transactions, you need to make them broader.

You could do this by expanding the geography to “Europe” or “Western Europe” or going back 5-10 years rather than 3.

If one of these expansions produces 10 or 15 comparable transactions, you can stop there.

Ideally, you want at least 5-10 deals in the set, but probably not 30 or 50 deals.

A good upper limit might be ~20 transactions; if you go beyond that, many of the acquired companies will not be similar to the one you’re valuing.

As with Comparable Companies, you screen the deals like this because you want the acquired companies in the set to have similar risk/potential return profiles (i.e., similar Discount Rates).

If you do not have access to Capital IQ, FactSet, PrivCo, or other paid services, your best option is to find a recent deal in the sector, look for its Fairness Opinion, and see if that document includes a list of Precedent Transactions.

For example, if we’re valuing a steel company in the U.S., we could look up the name of a recent deal and limit the search to the site, as follows:

Fairness Opinion Search

The first result takes us to an SEC document with a Fairness Opinion provided by Moelis, which includes this set of Precedent Transactions:

Moelis - Precedent Transaction Analysis

We did not use this set because the target companies were global, and we prefer stricter geographic screens.

If you can’t find Fairness Opinions for recent deals, another option is to search for M&A reports for a specific industry, such as by Googling terms like: “[Industry Name] M&A reports multiples”

Manually Searching for Precedent Transactions

You should not spend much time on this exercise if you don’t have a paid data service.

If you can find a few deals or average/median multiples in 1 hour, great, but don’t spend hours or days searching for transactions when the DCF and Public Comps are easier and more useful.

Step 2: Pick the Metrics and Multiples to Include in a Precedent Transaction Analysis and Calculate Everything

As with Comparable Company Analysis, you want a mix of sales-based and profitability-based metrics, but you tend to focus on historical metrics and multiples here.

So, while you might find the Last Twelve Months (LTM) numbers and the projections over the next 1-2 years for the Public Comps, you would probably retrieve only the LTM numbers for the Precedent Transactions.

It’s difficult to find historical projections for acquired companies, and there may be consistency issues because it’s not always clear if these projections include expected synergies from the deal.

A good target might be the LTM Revenue and LTM EBITDA for each company, which you can combine with the Transaction Enterprise Value to calculate the multiples:

Valuation Multiples in Precedent Transactions

If you’re working in a specialized industry with different multiples, such as P / BV and P / E for banks or P / FFO and P / AFFO for REITs, you can also use those.

You could also add premiums for the acquired public companies that represent the offer price per share vs. the target’s pre-deal share price.

For example, if the Buyer paid $150 per share, and the Seller traded at $100 per share before the deal, that’s a 50% premium.

You could go back 1 day, 1 week, 1 month, or even use a 12-month average or a volume-weighted average price (VWAP) over a period to calculate these premiums.

We did this in the Jazz Pharmaceuticals valuation because we had the share price data for each acquired company:

Valuation Multiples and Premiums in Precedent Transactions

Premiums are not that useful because they’re always going to tell you that your company is worth more than its current share price (or recent average).

Unlike the valuation multiples and DCF, premiums cannot indicate that a company is currently overvalued, so they’re mostly used to support the specific offer price.

Step 3: Apply the Multiples or Premiums to Value the Company

You apply the median, 25th percentile, or 75th percentile multiples to your company’s financial stats to calculate its Implied Enterprise Value.

Then, you back into Implied Equity Value and the Implied Share Price to get a valuation range.

For example, we use this process with the Precedent Transaction EBITDA Multiples for Steel Dynamics:

Step 1: The company’s LTM EBITDA is $2.3 billion, and the 25th to 75th percentile multiples are 8.0x to 12.9x.

Therefore, the company’s Implied Enterprise Value is $18.5 billion to $30.1 billion.

Step 2: We add Cash and other non-core assets and subtract Debt and other funding sources to back into the Implied Equity Value.

For Steel Dynamics, we add around $1.1 billion of Cash and $25 million in NOLs and subtract $3.4 billion in Debt and $10 million in Noncontrolling Interests.

Step 3: Then, we divide the Implied Equity Value numbers by the company’s diluted share count, which is 211 million here.

Here’s the Excel formula for Steps 2 and 3 once we have the Implied Enterprise Values in Step 1:

Calculating the Implied Share Price in Precedent Transactions

Finally, we can plot the results on the football field valuation chart:

Valuation - Football Field Chart with Precedent Transaction Analysis

How to Do a Precedent Transaction Analysis Manually

In most cases, you rely on metrics and multiples from sources such as Capital IQ and Fairness Opinions in a Precedent Transaction Analysis.

But if you want, you could analyze the transactions manually and calculate the multiples for each one by yourself.

This manual approach is almost always a bad idea because it takes a huge amount of time and does not necessarily improve the results.

Unless you need to do this because your full-time job requires it, it’s complete overkill for interviews and case studies.

If you want to do a manual analysis, you’ll need the following documents:

Once you have all this, you calculate the Transaction Equity Value and Enterprise Value for the acquired company based on the Offer Price per Share and its Balance Sheet right before the announcement, as in the example for AstraZeneca / Alexion Pharmaceuticals below:

Calculating the Purchase Enterprise Value in a Precedent Transaction Analysis

Then, you calculate metrics such as the target company’s LTM EBITDA and Revenue based on their financial statements, including adjustments for non-recurring charges:

Non-Recurring Charges and the EBITDA Calculation in a Precedent Transaction Analysis

If you include premiums and projected numbers, you’ll have to spend time finding the data for those as well.

The bottom line is that doing this analysis manually creates a ton of extra work but rarely adds anything useful.

The metrics and multiples may be a bit more accurate, but you’ll be spending hours or days getting results that are ~1-5% better.

Finally, if some of the target companies in the set are private, good luck.

Private companies do not disclose their financial statements, so you’ll rely on press releases, deal announcements, and random Google searches, which means an Excel setup like the one above is impossible anyway.

The Trade-Offs and Disadvantages of the Precedent Transaction Analysis

Most textbooks, valuation guides, and Interview Guides say that Precedent Transactions tend to produce “higher valuations” than the Public Comps due to the control premium.

The control premium refers to how the Acquirer must pay a premium on top of the Target’s share price if it wants to move into a control position (i.e., own >= 50% of the shares).

For example, if a Target trades at $100 per share, why would it accept an offer to sell the entire company for $100 per share?

Shareholders could sell their shares individually for that price; they need a greater incentive to sell all their shares at once and give up all future upside in the Target.

However, reality doesn’t quite match the theory here because it’s more accurate to say that the Precedent Transactions produce a wider range of implied values than the other methodologies.

You can see this clearly if you look at the Steel Dynamics “football field” chart above.

This happens because companies do deals for different reasons, at different times, and in different market environments – and sometimes the data is wrong or incomplete.

So, if you look at deals from the past ~10 years, the valuation multiples might shift significantly over that time.

But if you limit your set to deals done in the past 1-2 years in an attempt to make them “comparable,” you may not find enough relevant transactions!

It’s a bit of a Catch-22, in which fixing or improving the analysis creates other problems.

The bottom line is that Precedent Transactions take more time and resources to find than Public Comps and often require access to a paid service like Capital IQ.

They don’t necessarily produce “better” results – just more variable ones – and you don’t need them for anything else in the analysis, such as the calculation of the Discount Rate.

Therefore, we view the Precedent Transaction Analysis as “supplemental” – it’s fine if you have the data, but don’t go crazy with it if you do not have a quick and easy way to find deals.

About Brian DeChesare

Brian DeChesare is the Founder of Mergers & Inquisitions and Breaking Into Wall Street. In his spare time, he enjoys lifting weights, running, traveling, obsessively watching TV shows, and defeating Sauron.