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.