Welcome back to Data Driven, the podcast where we explore the big
ideas in data science, AI, and all things data
engineering. Today, we're diving into the complex world of
mergers and acquisitions where data meets corporate strategy
and not always in the friendliest way. With us are 2 top tier
experts who know this landscape inside and out, Baruch Lev,
professor emeritus from NYU, and Phong Gu, professor of
accounting at the University of Buffalo. We're going to unpack why
up to 75% of mergers fail and how to spot the ones
that succeed. Buckle up. It's data driven insight at its
finest.
Hello, and welcome to Data Driven, the podcast where we explore the
emerging fields of data science, artificial intelligence, and, of course, data
engineering. Today, we're gonna talk about a branch of, I
guess, applied analytics, where we analyze how
mergers and acquisition data, goes through. And with us, we
have 2 esteemed guests today. It's not every day we have 2 guests.
So I'm gonna read the bio of 1, and Andy will
read the bio of the other guest. With us today is Baruch
Lev, a professor emeritus at NYU Stern
School of Business, where he taught and conducted research on mergers and
acquisitions for decades. He worked formally at
UC Berkeley and University of Chicago. His work has been widely cited
in academic and professional circles,
and with over 63,000 Google Google Scholar
citations. He's a leading authority in corporate finance and valuation.
And also with us is Feng Gu. He's a professor
of accounting at the University of Buffalo and has extensive
experience in analyzing the financial aspects of corporate
acquisitions. His research focuses on the economic
consequences of corporate decisions and has been
published in top tier academic journals. Welcome
gents. Thank you. Thank you for having
us. Yeah. Thank you for the invitation.
Yeah. No problem. We're we're always great to to to have you here. And
and part of our listeners are wondering, hey, I thought this was a data science
podcast. And and I would say that if you are
having an IT career, not just a data career or any career, you are
gonna be impacted at some point along, by a merger and or acquisition.
Sure. And I don't have a lot I don't know about you, Andy.
I don't have a lot of fond memories of them all working out. It's
always been a change, and, you know, change change is always,
brings challenges. Yes. And I'm sure these gentlemen,
study those challenges and have a lot to share with our audience,
and us. You work for a large company in
IT. I own a small boutique consulting
company that that provides data engineering and and and
similar services. So I'm excited to learn what you got going
on. In case someone wants to acquire, my
company. And I'm sure you're keeping an eye on this, Frank,
in case someone wants to merge with yours. Well
and, again, I wanna be clear. The current company I work
for, I joined post IBM acquisition. Right? So all of these horror
stories are actually the worst merger I was ever privy
to was, as an employee, was, well, I guess I can say
it now, the Bankers Trust Deutsche Bank acquisition,
which, Deutsche Bank being a German company,
when they passed out and and Bankers Trust was an American company. When they passed
out the cards announcing the merger or celebrating the merger, the
I speak German, so the English sides called it a merger.
The German side used the word Uber Nemen, which means
takeover. That's yeah. I know just enough Latin to
pick that up. Which was, which I thought was
interesting because that's basically what it was. So to when I talk about my
merger horror stories, I'm not talking about where I am now. This is 20 years
back. And, the other thing as a
customer, when the the the companies
I use have merged, I've not really been a happy customer. I think Sirius
XM, XM Radio was a much better
satellite radio provider than Sirius XM is. And that's just my
opinion. That's not the opinion of anyone else. My wife seems to
enjoy it, but it is what it is. So what really excited me about this
so before our listeners start, like, what the heck are we gonna talk about? These
guys are gonna bring data to the table, and that's why I'm excited to have
them there. So I'm gonna get off my soapbox because people don't wanna hear us
banging on. They wanna hear you guys.
So starting with the data side, we
have probably the largest sample of
mergers and acquisitions ever assembled.
We have a sample of 40,000 mergers and
acquisitions worldwide,
spending over the last 40 years. And
on this huge sample, we
have developed a quite sophisticated
statistical model, multivariate
statistical model with 43 variables
to identify statistically,
the attributes,
the factors that contribute to success
and failure of companies.
Excuse me, of mergers and acquisitions. So, basically,
the entire work that we did, which is summarized in the book, of
course, is heavily data
driven. It's also supported by
other study, which are always
data, driven large sample studies
of specific issues of mergers and acquisitions
that, we didn't examine.
So, we combine all of this
to a set of of
observations and recommendations of
why 70 to 75%
of all mergers fail
fail to achieve sales growth, fail
to achieve synergies in in cost
of sales efficiencies, failed to maintain
the share price of the buying,
companies. It's an amazing number that
surprises most most people who
see it. That that is a large
number, and I'm kinda shocked to learn that. I would have
thought that, you know, it would have been on the positive side of
that that 5050 mark that, that the
mergers and acquisitions succeeded, and there were benefits enjoyed
by all. But it sounds like what you're saying is no about 3
quarters of those fail on some or, you know, some or maybe
all, of those desired outcomes. Yeah. I'm
actually not surprised. I had heard that statistic before and kind of based on
based on my anecdotal kind of personal experience, I think that that sounds reasonable.
But the question I have is if if it if the situation is so
bad, a lot of questions, How do they how do these companies convince their
respective boards to take the buyout? Is it just a,
how did how do they pull that off?
The way to an acquisition is
usually a failure of the acquiring
company. Sales slow
down, earnings turn to
losses, market share is lost,
and everything gets excited,
particularly investors who are, of course, losing money
and influential investors who have a a
big say on company. Directors
are are looking, and the
call gets out of we have to
do something big. And, usually, the
something big is a big acquisition.
And that's how that's how that's the usual
way of getting, to this.
Managers, are optimists.
Lots of psychological studies have shown that
managers are much above average
optimists. Some of them are overoptimists.
They may be they may be aware that many
most, m and a, fail, but they
are convinced that they will make it.
And they are convincing their board of directors and
sometimes even shareholders to, support it.
Yeah. So the persuasion and
the pressure to acquire also come from
frequently, investment bankers,
financial analysts, and consultants. These people, of course,
say, you know, have obtained financial benefits
from, completed deals. They always pressure
the acquiring company to by pointing out, hey. This is a good
deal for you, and we can help you, you know, go through
this and clear all the hurdles and everything will work
out fine. And, so this is really the
best decision for you to make. They're really
play these consultants and investment bankers really play a very
important role in convincing, both sides of the
acquisition to complete the deal as soon as possible.
Gotcha. That sounds like sorry. Go ahead. I
just want to say in conclusion, you know,
some, m and a proposals are being
rejected. Not everything is accepted. Just
recently, an Israeli company
got, an acquisition
proposal from no less than Google for
$23,000,000,000. Goodness. After
after consideration, they, they rejected it. So
not everything is accepted. But
many, many acquisition strongly
supported by the CEO are indeed
accepted. Well, it
sounds like there's financial incentive, for the
people around the process for the process to
conclude? Because I imagine they don't get paid unless the
acquisition goes through. Correct? Yes. And there are
also there are also quite large, incentives
for managers for concluding the deal.
A recent study showed that,
many managers get, acquisition bonuses
between $5,15,000,000.
Got it. And that's for concluding the deal, not
for succeeding, but for just
concluding the deal. Wow. And,
we have we have in the book, we show statistics,
which I've never seen anywhere else, that,
serial acquirers,
their tenure is 4 to 5 years
longer than CEOs that
don't acquire or acquire just few companies.
My guess is that, directors are
somehow satisfied with very active CEO
who try to change the course of the company,
let them acquire our companies, and then they give them,
more time to to somehow
somehow, complete the complete the deal and complete
the integration. But I was,
someone someone just recently asked me, what surprised you most? One
of the things that surprised me most in researching the
book was this 4, 5 year,
10 year edge of serial
acquirer CEOs, irrespective
of the success of the mergers.
Yeah. And this difference of CEO tenure by 4
to 5 years is obtained after we have
controlled for other contributors to CEO tenure, like
corporate performance and other important factors. So in
other words, our conclusion basically says with everything else equal,
if you make a series of acquisitions, your
CEO tenure is going to be extended by 4 to 5
years on average, which is really a
long, long extension. Acquisitions are almost,
tenure insurers or CEOs.
So it sounds like the, the,
incentives are a little bit lopsided.
Yeah. Definitely are from all sides. As Frank mentioned,
the, commission hungry, investment bankers, and
consultants benefit from the deal.
CEOs benefit from, the deal. The only
one who paid the price are the shareholders. And and
many times, employees are being fired.
Customers, suppliers,
suffer. Mergers have an
overall effect on the entire economy
on the Which I think this. Yeah. Which I think, like, begs the question,
like, if you play this out long enough,
more people lose than win. And, like, what's the effect of this in
the global economy? Because a lot of during times of
uncertainty, a lot of the star performers leave because they're not sure
what's gonna happen to them. Yeah. Right? Because usually
usually, the the acquiring company tends to keep more of their people.
What and and and I think that's probably a different game if, you know, if
a if an £800 gorilla buys a small start up. I think that's one type
of dynamic. But if you have kind of these 2 industry
titans that buy each other, right, something more
akin to Deutsche Bank and Bankers Trust, right,
there's probably a lot of because they see each one of them sees
sees each other well, one side sees itself as a peer and the other
sees it as superior itself as superior. And that's gotta lead
to all kinds of weird personal interdynamics.
Yeah. You're perfectly right. I
mean, acquisition of large
targets relative to the size of the acquiring
company are almost, a recipe for
failure. We analyze in the book the examples,
several years ago of Sprint acquiring
Nextel. That's the 3rd and the
5th, at the at the time. The 3rd and the 5th,
wireless operators. This was they
were about the same size. Sprint was a little
larger. This was an unmitigated,
disaster, the whole thing. They
they completely failed in in,
merging the employees.
They even they even kept the separate headquarters of
the 2 companies and the separate operating
systems. Customers will ask, do you want to
join the operating system of Nextel or
Sprint? I mean, huge churn,
huge desertion of customers,
and then the whole thing, collapsed.
Yeah. Acquisition of large
targets are very, very difficult to
integrate. And you indicated most of the reasons, with your
example of Deutsche Bank. Right. Right. And I'm a former
Nextel customer. Same. And I was not
I think I think the Sprint acquisition could have been worse. But if that's your
metric, it could have been worse as from a customer's point of view. Yeah.
I I suppose based on the numbers you're telling me, it could have been worse.
It sounds like a pretty good pretty soft pretty safe outcome.
I'm doing the low bar symbol. If you're watching the video, you could see that.
But I'm the bar is down here for could have been worse.
Frank, you asked about how this type of deals may
affect employees of the target versus the acquiring company.
I I think it's a great question. In the research for this
book, we spent a lot of time looking into how
acquisition deals may affect, employees.
And we did look at, the reaction from the target
company's employees, and we find that as soon as the news of
mergers acquisition comes out, a growing number
of target company's employees decide to leave the company. And
this happened even before the merger, gets
completed. So they learn from their experience
or maybe from your experience involved in this 2 large bank merger
that the target employees always get, you
know, relatively unfair share in the post
acquisition termination, for the purpose of
creating synergies, cost savings, and so on. So
on average, mergers acquisitions have not been
friendly to employees. We're documenting one chapter of our
books, the loss of job positions on
average is about 5 to 7%
of the combined entities workforce, which is a
significant number. Yeah. You know, it sounds a little low
when you put it that way. 5 to 7% doesn't sound like a lot. But
I can imagine, you know, in these, you know, in in Frank's
Bank, acquisition scenario.
Yeah. That's, you know, that's across thousands of
employees. Yeah. That can be a large number of
people. Well, there was also the rock stars. You know?
Yeah. I don't know how it is now, but back then, you know, Wall Street
was very aggressive about getting you know, they would basically go to the top
trader at, let's say, BT Bankers Trust, and say, hey.
We know you're feeling a bit uncertain now. Why don't you have a conversation with
us? Right? And you can you you'll make more you'll make,
like, 20% more or twice as much and bring anyone you want over
to. Right? So the I suspect the numbers are actually higher,
but the published numbers in terms of layoffs are probably 5 to
7%. But I think the star performers,
I think you kinda lose the star performers almost right away. Right?
Yeah. Yeah. You're you're perfectly right. That that's what
economists call moral hazard, which
means the employees employees you lose. It's not
just a matter of numbers. You lose the best
employees, those with the best alternative
outside, and you are left with those without
any or very attractive alternatives. So the
degradation of the work workforce is much
more serious than just the numbers. Yeah. Yeah. You
know, I have an experience like that too. I I just, for some reason,
it escaped me earlier, but I was a manager
at Unisys, and I was managing the, the data
engineering team. We called it the ETL for extract,
transform, and load, team. There were about 40 people who
were a combination of full time workers and
then an extended collection of subcontractors.
And we went through a merger and I'll spill the beans on this one too
with Molina Healthcare that was headquartered in,
out in California. And
we had some of that. In fact, my my boss who was a
director, he was a fantastic example of this,
definitely a high performer, published 5 books,
a known entity in the data field, and just an excellent,
leader in my opinion. In
the ramp up to the merger,
or it actually was an acquisition. In the ramp up to that, he
when he got wind of it, he began putting out feelers,
for, you know, making a move to another company. And eventually,
he did. And this was excuse me. His move, him
leaving was a huge hit to the company, a
huge loss. And he did this months before the deal
was concluded, like a full quarter ahead of time. And does that I'm
curious. Does that count in the 5 to 7%? Would his
leaving count in that, or would you would it be post acquisition?
In in some cases, it it is included. In other
cases, it it may not be included. It all depends on the
relative timing of acquisition announcement versus k. The
fiscal year end. Because as you probably know,
companies don't disclose the number of employees all the time. I
think right now, they, you know, provide this number once a year in
their annual report. So there's always some discrepancy,
in the number of in the exact number of employees, you know,
between fiscal year end and, the announcement of the
acquisition. Gotcha. But on average, it should be, you know,
around that number. You mentioned the importance of
losing key talent. Frank also made the key point here. We
completely agree with you. Actually, in one of the chapters in your
book, we have a graph showing, clear
evidence, supporting this effect of
losing talent. We document that after the acquisition is
completed, 2 to 3 years down the road, there is a
clear pattern of declining employee productivity.
So that's normally a sign of losing key talent.
You know, you know, you have lost the most important human capital
component of your combined workforce, and there's no way,
your workforce productivity is gonna be as strong as they used to
be. So that's clearly a consequence,
on the on the employee side after mergers, acquisitions are
completed. So I wanna mention we're recording this on the 18th
October 2024, and the book is
named the m and a, m ampersand a,
failure trap. And the subtitle is why most
mergers and acquisitions fail and how the few succeed.
And that book is due out according to Amazon today.
They're projecting November 15th. So a little less than a
month from now is when that book is due to be available. Is that accurate
as far as you know? Yes. Excellent.
Now I'm gonna buy the book. So I wanna know more.
Thank you. Thank you, William. Yes. We have one say
say 1. Yes. We
made it. Make it 2.
Your order is going to be the most special one because it's the first one.
And, and since you bought the book, you can all, you can
also give us, high recommendation.
Okay. As for And we'll do that. Yeah. Yeah. Well, both Frank and I, you
may not know this, but Frank and I are published. We've written I Frank, you've
written a couple. Right? Couple 3?
3. 3. Yeah. Mhmm. And I've been involved
either as the sole author or a member of a team for 14.
But I started way before Frank to be fair. That's a great
number. Well, it warms my
heart to hear smart people say that, but I have to share. I
have to share that it has way more to do with insomnia than
intelligence. Just just so you know.
That's even more incredible.
I I recall holding, my my youngest is
17 years old now. But when he was a baby, I did
that year. I wrote 2 at the same time. I just wrote chapters in a
book on a team, just a few chapters, but I'll never do that again.
And I haven't since. But I was holding him and had, you know,
my arm had his head in my arm here and holding the bottle, feeding
him at, like, 2 AM. And I'm typing on the laptop with
my other hand. True story.
That's quite a story. Yeah. This looks like an an amazing
book. I've yeah. I'm a data, you know, a data weenie, being a
data engineer, and I've worked around financial data of all my career.
What we did at Unisys was Medicaid, driven data.
And so you get a lot of finance in there. So we get it you
know, we dabbled in that part of it, and there's just so much financial
data out there. And I've seen so many ways to analyze it
and then ways to, you know, not intentionally,
but misanalyze it. You you look at the data,
an old story intentionally and intentionally. Well, I imagine there's some
intent. I was trying to be nice, Frank. But
I have an old story that I share with data engineers. It's
not, you know, it's not a real life story, but it's an analogy of
the misapplication of thinking that sometimes goes along
with this. It's kind of a, you know, getting the cart before the horse or
miss you know, misunderstanding cause and effect. And
the analogy that I use is, if you analyze
the altitudes of aircraft in flight, you
will find that the altitudes drop as they near
an output sorry, an output, an airport, and everybody says,
well, duh. And I'm like, so one conclusion
you could draw from that is in placing airports, someone
did an analysis of this data and said, where the craft are
lowest, we'll build an airport there. And we all know that's not
true. You know? But Yeah. Yeah. That happens. That kind of thinking
happens a lot in analysis. And I'm wondering if that
kind of mistaken analysis, if mistaken cause and effect
plays into some of the thinking early on. Is
that any of that leading to the 75%
failure or failure to achieve result rate?
There are lots of studies that are done by particularly done by,
consultants, and they are based on,
simple correlations. For example,
companies, high on the ranking of,
ESG, made it through the COVID
disaster better than, others. Gotcha.
I, with a group of, other researchers,
rather than looking at just the correlation between
ESG and success, we used a big
model that looked at, that had
representation of the industry, other
variables there. Turns out that,
most of these high up on the ranking of,
ESG, were high-tech companies.
They were extreme they were extremely successful as we
know, many of them. Yeah. And this,
of course, was reflected in share prices and profits
and others. And they also had the means
to contribute to the community and do other things
that those who rank companies on on ESG
like. So this is a this is a
clear example in statistics of the
missing correlated variable. The variable
that that really went in was the
industry of, of, this. And and and these
these, people who just ran the simple correlation,
missed it. That's why we built we built a
humongous model of 43 variables
that attempts to take everything into account.
And then when when one variable
indicates success or failure, for example, in your
case of Deutsche Bank, we have a variable
of foreign acquisition. This variable
comes out after the estimation with a negative
coefficient, meaning it detracts. All the
all other things equal, it detracts from the acquisition,
success. So we can say with with,
fair certainty that,
this is indeed a contributing factor because we accounted
for, for most of the others. Yeah.
Yeah. Brooke is absolutely right about, the special care we
take to ensure that we're not just documenting simple
correlation. We're actually, you know, the identifying
the cause and effect relationship, In most
of all performance related variables, we
make very careful adjustment for industry average
performance, at the same time. So this removes a
lot of confounding factors from our analysis and gives
us a lot of confidence in the validity of our results.
That makes perfect sense. And I can see, and
you've got the word trap in the title of your book. I can see the
trap of, you know, making
a correlation, which is a valid thing. It's a valid point in my example
about the planes and the airports. It's a valid example.
Apparently, you know, what you're sharing with me is you're seeing this, and somebody just
picking up and focusing on a single correlation
and making that the driving metric. And
that that makes perfect sense. And I as you were explaining that,
both of you, I thought of, books I've read
about Warren Buffett's, and his partner, and I can't
nobody remembers his well, it's Charlie. Charlie Munger. Charlie Munger. Right.
Him and Charlie work together, and they look at the fundamentals. And they
just over and over again, they just pour through probably all
of the things that y'all are recommending, you know, for
people who are interested in a merger or an acquisition. You probably recommended
the same stuff. It's, you know, the fundamentals of
what makes a business, you know, stable. And as you
mentioned, Baruch, about, Deutsche Bank, that
foreign acquisitions number, that's not something I would have thought of. But,
you know, if it's stored in a data table somewhere, then I'd I'd look at
the data, of course. Mhmm. But it's not I'm not a business mind.
I am a I'm an engineer, for better or worse. As someone who
lived through it, like, there definitely was a lot of disconnect between American business
culture and German business culture. Like, it was a very That makes sense. It was
I mean, it was a massive disconnect. You know? Yeah. The joke we had at
the time, I think Chrysler was bought by Mercedes or Daimler Group that year.
Daimler. Around that same time. And the joke was, thank God that that happened
because we would be the biggest cross Atlantic disaster.
You know, everybody was so focused on we were a distant
second compared to what's going on there. And that, I mean, if you Chrysler's never
really recovered from that. Well, the the joke I heard about that
is, you know, how do they pronounce Daimler Chrysler in Germany?
And it was they call it Daimler. Yeah. It's slightly Chrysler is slightly
yeah. It's true, though. Like and, you know, one card says take over, and the
other side of the card in English says merger. Right? Like, it's it's it's,
you know, a lot of people had a good laugh at
that, but I mean, there was a lot of truth to that. And also too,
like, there's a funny meme going around about this, where it was a
professor basically saying a 100% of the people who don't understand
the difference between causation and correlation will die.
That's a good meme. Yes. I'll have to dig it up and and reshare
it. This was this was,
many, many years ago, and I took it to University of
Chicago, a statistics course. One of the
first example in the first class was,
the instructor showing a very high correlation between
lung cancer and living in,
Arizona. No way. Of course of
course, the correlation is there, but that's not the causation.
Arizona's weather is very good for the lungs. And that's
why lung patients are going to a
result. Oh. So, the causation is
exactly the opposite direction than what the
correlation seems to show. Yeah. His his next
example is that more people die in hospitals than at
home, which means that which means that hospitals
are extremely dangerous to people. I have to try to
avoid try to avoid them. That's those are
really good examples. And I I one of the examples I read a
long time ago, I was gonna say it was from the it may have been
from World War 2, but I'm not a 100% positive of that.
But there were aircraft engaged in combat,
and they wanted to reinforce aircraft to make them survive, you
know, the engagements better. And since they were
pointing out, the bullet holes are showing up in these patterns, and they
noticed that, you know, there's some here and there's some that we need to reinforce
those areas. And someone thankfully pointed out that, wait, these planes
are making it back. We need to put the reinforcement where
the where the bullet holes are not. You know? So
yeah. Survivor bias. Right? I think that's That's yeah. Yeah. That's
it. That's true. But, yeah, great examples.
So you have to be careful with analyzing data,
particularly in our case, and that's
straight, into the topic of your,
of your, podcast. Mhmm.
I let I let, Feng briefly
describe the many databases sources
that we use and converge,
to get this kind of a sample and statistical model.
Yeah. Yeah. So this is, really, the most
important part about how we did our research to write this
book. Everything, as Brooke mentioned earlier, is data driven.
Our main conclusions are supported by, you know,
analysis using large sample, not just a couple of,
case studies, some anecdotal evidence. No. To reach
that level, we pull data
from a large number of sources starting
from a mainstream mergers acquisition database,
which gives a lot of details about both the acquiring company and
a target company, the time of the announcement,
the terms of the deal, and other interesting
details like exactly what the the acquiring company CEO
said about, his or her expectations
for the forthcoming acquisition and so on. So we
use that as the starting point to,
collect as much data as needed. As Brooke mentioned,
you know, we try to avoid simple correlation kind
of scenario. So, in addition to industry,
level adjustment, we also look at entire
history of the acquiring company and the target company, you know,
3 to 5 years before they get to the point of making a
deal. Try to understand the circumstances of the acquisition.
And then that is completed by
using financial statement data, which is obtained
from the company's financial statements, across multiple
years, both before the acquisition and after the acquisition.
Of course, stock price, information plays a huge role in
understanding, both investors' immediate
reaction to the acquisition news, and the performance
of the combined entity after the acquisition is
completed over several years down the road. Not just a couple of
months, not just 1 year. We actually track, 3 to 4
years after the acquisition is completed in
order to obtain, a more robust and a
consistent view of how the value of the company has been
affected by the acquisition, is that value creation or
value destruction? Alright. I also mentioned earlier
about, you know, employee turnover. You asked you
made a lot of good points about how mergers acquisition may
affect, employees, not just everyday employee, but also
key talent, of each organization. So
we obtained very detailed employee turnover data
from a database that is, I think, based
on LinkedIn, information. So the original source is
LinkedIn, which is probably, the most
comprehensive database nowadays on employee
turnover, very detailed real time employee turnover, not
just, you know, once a quarter, once a year kind of information.
So, we had very detailed,
you know, in details a very detailed data
on the trend of employee turnover. We look at it month by
month to see exactly, how employees
decide to stay or leave, once
the merger news, comes out. So that gives
you a snapshot of, the variety of databases we
use, to, you know, conduct our analysis
and then to provide our evidence. It's it's really a very,
very comprehensive process. But you mentioned
LinkedIn, and, I'm pretty sure the grain
of their, to and from dates of employment, That
that is a monthly drain that that they store that data in. That's
something a data engineer would pick up on. But I I
love the way you're describing how you acquired your data
and, you know, in that it was a very
macro process. You were looking at as many companies as you could
find. I like that part of it. I like the time span that
you applied going 3 to 4 years after the merger acquisition
occurred. It it really reminds me I mean, I'm more excited about
reading the book now because it reminds me of the business books that
I learned the most from. And I I won't mention the other books,
but there's only a handful of them that take that approach.
And I I think it bodes well for the success of your book.
So I'm I'm curious how, if how
and if you, encountered data
that you either decided was out of bounds?
Did you did you have limits on that? Did you run into
any data quality issues?
Yeah. In some cases, because we require the post
acquisition performance information to be available for
3 to 4 years after the acquisition. You know,
some companies don't survive that long. Actually, we have seen
cases where the acquiring company later on, became
too weak and eventually being acquired by other company.
So those cases were probably not fully captured.
We also don't have full information on some of the
private targets. We don't know everything about their
performance, before the acquisition like sales,
profitability, and so on. And, of course, these private targets
don't even have stock price information. So you
can't see how investors react, the investor of the
target company reacts to the news of acquisition. You can't even
measure, this frequently used metric called,
acquisition premium. You know, in in case of, a publicly
traded company acquiring another publicly traded company, you
can easily measure this acquisition premium
by comparing the stock price of the target before
the acquisition use, with the deal,
the the the acquisition price that the acquiring company decides to pay.
But in the case of a private target, you really cannot do that
because, you know, they don't have stock treated, on the open
market. So we had to be creative. Brooke
and I developed a measure relating the acquisition price
to the sales number of the target, which
is actually very useful information because this
allows us to get around this private target issue and
make the metric much more comparable. And we
actually developed a lot of insights from using this, different
measure of acquisition premium. Cool.
That's interesting. That's interesting. I like the fact that you take a data
driven approach to this. Right? Because you listen to Bloomberg or whatever, they always
show the rah rah. Look how great this merger is
gonna be. It makes sense in this point of view. And if you're lucky,
maybe they'll spend 10 seconds on, like, the detractors of it and things like that.
But, you know, looking at this data all up, like,
it it seems that and also think, too, the other thing to
double click on is, if it's a private company, it's probably
going to be way smaller. So I think a bigger fish eating a smaller fish
is less likely to have indigestion, so to speak.
Whereas if 2 big fish eat each other,
there there's a lot of territorial fighting.
Yeah. That's that's exactly, what Brooke mentioned earlier.
Acquisition of a larger target is much more difficult to succeed
because the integration process can become very contentious.
Fight of egos and, a lot of, you
know, emotional issues can get into the way to
prevent the integration to be fully successful.
Right. That Right. That makes sense. And it it gives me hope as a,
you know, as a smaller company that maybe one day someone will come
along. And I keep up with a touch of newsletters on this, not
not a lot. I really didn't start looking into it until we started approaching,
the 10 year mark. And one of the things that
shocked me was the size of of companies.
And and when I talk about the size, I mean, how small
companies are, revenue wise. I mean,
I I saw one newsletter that was talking that a
I don't know how big of a segment this is for targets of
acquisition, but they were half a1000000 to a1000000 and a half in gross
sales. And that was shocking to me. I was like, I would be thinking they
were looking at 10, 20,000,000, you know, size companies.
But according to this one newsletter, it
was a hot thing, you know, going after companies that that size
in revenue. And I was shocked. Can you
still hear me? Yeah. I can still hear you. No problem. We you
disappeared a little on the video, but Yeah. Because I I I got the
phone call. No. I wonder. But if you if you can hear
me, that's that's okay. Yeah. That's good. We can hear you. Strong.
Yeah. Yeah. Yeah. So so speaking of small
acquisitions, what you said is exactly chewing
some specialty sectors. Like, in our book, we mentioned
large pharmaceutical companies acquiring much, much
smaller, biotech firms in order to beef up their
product pipeline. You know, the smaller size of
this target is really misleading, you know, when you mentioned
sales because, these are basically start up companies
and they focus on developing technology.
Especially if you look at the earnings, many of them don't have profit for
decades. But that doesn't mean they're not valuable. We
actually have some cases showing that a large pharmaceutical
companies are often willing to pay a very high premium to
acquire these, startup biotech firms because they see the value
there. So, you know, acquisitions coming all color
and shades. It's it's a huge phenomenon no matter
what type of industry you look at, not just in tech industries. If you
look at some of the highly matured industry like food,
energy, Every year, you see large and small
deals all the time. So that's that's what really, you know,
interest Brooke and I when we decide to,
write a book on this topic because it's ubiquitous
and affects everybody, not just shareholders, affects
employees. In some cases, affects consumers,
customers as well because, you know, a merged company
may decide to increase price in order to show,
the value of the acquisition. Right? Or decrease their
services or downgrade their services. Move one
of the levers on the seesaw there. Yeah. Yeah.
Yeah. We have we have, on this point, we have
a a brief chapter in the book, titled
killer acquisitions. And these are the cases.
Yeah. And we give examples. These are the cases in which
the acquisition is made, basically,
to kill the target in this case too. I've heard of
that. Yeah. Yeah. The most the most probably the most
the most famous case is Visa,
trying to acquire Visa Visa debit,
not Visa credit. Visa debit, which has
a huge market share. I think they have 70% of,
all the all the US market in this case. And here
comes, a small start up, which
is much more efficient in obtaining data,
linking to customers and things like this. Mhmm.
And they, they try to, they try to
acquire this company, with the with
the clear it was. It it came out in an email from the
CEO with a clear intention to basically,
terminate the, the product. The whole
thing the whole thing was litigated by Department of
Justice and then Visa retreated.
But, we quote a study on the pharmaceutical
industry, a very, very in-depth,
study that, that
track the products of the acquired company
match with the products of the buying,
company, they concluded about 70%
of acquisition in the pharmaceutical industry,
killer acquisitions. Because if you look after
the acquisition, all of a sudden, you see that the product
of the of the target disappears.
And Gotcha. What are what are regulators' thoughts on that?
Like, I imagine that Very, very negative.
Very negative. In this case, of course, of pharmaceuticals, it
affects health of people. Right. Yeah. It
it harms it harms, innovation.
And, this this is this is an interesting chapter.
Killer acquisitions. I'm so looking forward on
its own. Right? I'm I'm definitely looking forward to this.
November 18th, you say? It's 15th. 15th. 15th, 15th. November
15th, according to Amazon. Oh, no. Actually, now it just changed.
I am not making this up. 13th is what I'm seeing now. It's nice. Oh,
nice. Okay. 3 to 6. I maybe I misread it before. I thought you said
13, but it says 13 now. That's the, the date given by the
publisher. Yeah. So the the book is now being,
I believe, produced in the last, phase of
production. And then at the end of the month, we'll leave the warehouse. And
around 13th November, it will be available for shipping. Yeah.
Very cool. And then What I'll do what I'll do is I
will put the link to the Amazon, page for
the book. I'll put that on a calendar note and schedule it for, like, 5
AM or something on 13th. And so I can go over and buy it right
away. You you you really wanna be the first one to order. I
well, I won't order it, but I'll buy it first once it's once it's released.
I would I don't do the preorder so often because and what I'll do is
I'll check probably on 10th to see if it's available by Kindle because I
know sometimes they send those out a little earlier. Oh, yeah. That's true. And, yeah,
I'll grab it then for sure. But, yeah, those If you're going to buy
it, what are you going to do with the baby? No. That's true.
That's a good thought. I don't I don't think the baby's a baby anymore.
No. He's he's driving now. So
that's a good good point, though, Baruch. Thanks. Thanks for reminding me. I need
to stay on top of that sort of stuff, and I need all the help
I could get. Yeah. We think we
ran out of time for questions, but that's fine. I think this was an exciting
conversation that I think explains a lot of what we're seeing in in
our careers where we we start one company. You're also starting to see a
pattern of, you know, let's say Microsoft buying LinkedIn. LinkedIn has
largely been left alone. Yeah. Yeah. Yeah.
Well, they were doing a lot right to start with. Right. Right. Right.
Right. I think that's that's an interesting thing is that, you know,
smart companies, they know if it's if it's big enough and it's doing the right
thing on its own Mhmm. Leave them alone. Yeah. That's that's
that's the story of Google and YouTube. Yeah.
Yeah. Yeah. Yeah. Yeah. I'm here in the country. I I live out in
the woods in Virginia, and we say if it ain't broke, don't fix it.
Yeah. But on on the other hand May I may I
mention one thing that, didn't come up with, in
the discussion? We developed in this
book and with about a large chapter to
it something which I think is really unique,
and that's a a 10 factor
scorecard for acquisitions. Nice.
Everyone knows that, lending decisions, credit
card decisions, largely made by looking at
at, the credit scores of people,
we developed, based on the 10 most
influential variables of our model,
we developed a very easy to use,
friendly to use scorecard that, you
can you can before the acquisition, you
can get a a a the likelihood
of success of this acquisition, a percentage,
which will indicate the likelihood of success.
I guess that this would be very useful, both
to managers in somehow early
screening of several acquisition candidates
and to, investors who are
often asked to vote on acquisitions without
any information. Mhmm. So this
this, acquisition scope, is something
which is, really unique to our book. Yeah.
Yeah. And I would say as a entrepreneur who, you know, wouldn't
mind somebody sweeping in and acquiring the company, this could
help me improve my score. Yes. You know, make me a more
attractive target for acquisition. Yep. You know, I'm not not saying
any of my customers listen. I'm not selling. Yeah. But,
well, we'll let you know if that happens. But the, but,
yeah, I mean, it's a an I think all around, that's just great.
And I look again, one more reason to look forward to the book coming out.
Awesome. Cool. Well, this has been a great yeah. Yeah. This is
great. I'm I'm really glad we got into this, and you've answered a lot of
my questions about how acquisitions get
approved, who wins, and who loses. Usually, it's the employees and the
customers, and and who wins. And turns
out that the people calling the shots are the winners. Funny how that works. I
know it technically speaking, it's a correlation. But
I see what you did there, Frank. You see what I did there? Where can
people find out more about the book? Do you have a does the web the
book have a website, or do you guys have LinkedIn
or anything? Not yet. Maybe maybe
maybe we should, create it. Okay. Yeah. Amazon
Amazon gives, a short
description of the book. Okay. Mhmm.
And and the endorsement. We have some great endorsement,
about this book. And, yeah. But may maybe the
first, place to go is really Amazon and get
the description of the book. Awesome. It is the
m ampersandamanda failure trap.
That's Got it. We'll make sure to put a link in the show notes.
And anything else, Andy? No, sir.
Alright. Well, with that, well, let's And that's a wrap for today's episode
of data driven. We hope you enjoyed this deep dive into the
data behind mergers and acquisitions, whether it's a friendly merger or
an Uber name and take over. A huge thank you to our
guests, Baruch Lev and Foam Gu, for their fascinating
insights. If you've ever wondered why so many mergers
fail, now you know data doesn't lie. Be sure to check out
their upcoming book, the m and a failure trap, for even more
data driven revelations. As always, thanks for tuning
in. Don't forget to subscribe, leave a review, and
join us next time for more data centric discussions.
Cheers.