Category: M&A in Entertainment

Debunking the M&A Tidal Wave: Part V – Other Thoughts That Didn’t Make It In

Was I too strong in calling this series, “Debunking the M&A Tidal Wave in Media and Entertainment?

I don’t think so.

I want emphasize the “think” in that previous sentences. I’ve thought about this a lot. I mean, asked myself many, many, many times, “Wait, are you sure there won’t be a tidal wave surge in M&A now that the Justice Department lost in its AT&T battle? Even if M&A has been high the last few years, couldn’t it get higher? Are you implying you think M&A could go down in the future?”

Upon reflection—all that thinking, hat tip to iFanboy—and yes, I think I am appropriate in calling this a debunking. Certain narratives catch hold—coincidentally, like a tidal wave—and most everyone tends to repeat that narrative. The internet started this phenomena, but social media like Facebook and Twitter and Reddit amplify it.

To sum up all I’ve learned, here’s the brief history of M&A in media and entertainment:

– Media and entertainment (and communications/internet) has been consolidating since the 1980s, like all industry.
– After the recovery from the Great Recession, media and entertainment companies began consolidating again.
– By my numbers, the growth in M&A was somewhere between 8%-25% per year in total deal value (depending on the years you pick) and mega-deals (deals over $1 billion in value) increased from 8 in 2011 to 18 in 2017.
– These deals included both horizontal mergers (within the same industry), conglomerations expanding (conglomerates continuing to acquire new businesses) and vertical mergers (within different industries, including distributors such as cable or internet firms acquiring media and entertainment companies).
– Consolidation likely would have continued even if the Justice Department had won their lawsuit preventing the giant and horizontal AT&T-Time Warner Merger.

Phew. Sorry, I had to get that out.

Knowing what we know above, we can use that as our baseline. For any new information on M&A, we can update our priors, to use Nate Silver speak. Basically, if a new deal is announced or a current deal opposed by the government, we have a solid context to understand its impact on the larger M&A in media and entertainment (and communication/distributors/tech/social media) landscape.

For today, any time I dive super deep into a topic, I end up with a bunch of stray thoughts that don’t quite fit in any of my other articles. Today is the catch all round up of those pieces.

Other Thought 1: The Chaos of the Trump Administration

Ignoring the politics of the current administration—and how can you?—what does President Trump and Gary Cohn/Ajit Pai/Wilbur Ross mean for how entertainment companies conduct business? Really, the daily tweets and outrages for liberals or the perceived economic boom times for conservatives matter much less for how he had changed the regulatory environment for business.

But some of those Trump tweets matter. Like how he tweeted opposing the AT&T merger, praising the Disney-21st Century Fox merger and supporting Sinclair/Tribune. In each case, either base political calculations or personal relationships determined his support, not larger idealistic concerns (either free market or pro-consumer). In AT&T’s case, he hates CNN. In Disney’s case, he loves Rupert Murdoch, whose Fox News also supports him. In Sinclair, I think he knows he has a friendly voice supporting his policies.

Uncertainty is the key, along with the certainty that the key is winning Trump by pledging allegiance to him. That’s how companies can win in the short term, while America’s economy moves towards a rent-seeking/crony capitalist future that curtails economic growth. In the mean time, M&A will proceed apace as the key to execution means wooing Trumps favor. Before you decide to do a deal, think, how do we spin this to make Trump look good, while crossing our fingers Democrats don’t punish us for that?

Speaking of decisions…

Other Thought 2: What decisions can we make off this information?

Imagine you’re a major executive at a major studio, communications provider (cable, satellite or telco) or production company. Maybe you’re the boss or the head of his/her corporate strategy or business development team. The key question following a judge’s approval of the AT&T-Time Warner merger is: how should this influence our decision-making going forward?

Now, if you build it, they will come. In business, this means if you build a team with a mission, that team will recommend decisions in its interest. If you have a team devoted to assessing and executing mergers & acquisitions, they’ll probably recommend that you make a lot of mergers & acquisitions. That team—and its likely influential leader—would therefore recommend most CEOs be aggressive in their deal making. Hence, they probably read a lot into the AT&T merger decision as the green light for future mergers.

Ignore that team/leader.

If a merger with a competitor or supplier or other company makes sense economically, it probably made sense no matter which way this decision went. Now, it likely would change the probability on one line of the economic model—the line one the costs if the merger fails—but I would argue that would only apply to deals that almost exactly mimic Comcast-NBCU/AT&T-Time Warner style deals, meaning it would mainly apply to Comcast. And I don’t think Comcast CEO Brian Roberts has any desire to slow down his deal-making.

What about the information I’ve provided showing the huge surge in deal-making going on? Should this influence executives? Maybe.

The case would be strategic. If everyone around you is getting larger, then to continue to be able to negotiate with suppliers or be able to gain a presence in the marketplace, you may need size to compete. If you’re Discovery and Scripps, facing a world with shrinking cable subscribers, doubling the number of channels under negotiations may help keep your affiliate fees higher. Same with movie studios: Disney will be able to negotiate great revenue shares with theaters if they own 50% of the box office, so maybe you need size to compete with that.

But for that strategic case, I could trot out an early version of my “Theme 3: Strategy is Numbers!” At the end of the day, you don’t win in entertainment by simply being the largest player in the world. This isn’t a board game like Risk or Settlers of Cataan where simply being the biggest or lasting the longest means you win. You win by generating cash for your shareholders.

If the deals to get bigger end up costing you more in interest payments than they return in cash, then shareholders will lose money, even with the size. This is usually exacerbated by vertical deals, but all deals risk costing shareholders money. If anything, the frothier the M&A environment, the higher the prices paid, which increase the likelihood that deals don’t make economic sense.
In all, M&A in the larger sphere is interesting, but not determinative on the decisions you need to make as a decision-maker.

Other Thought 3: I’m even more skeptical of conclusions from the M&A data than I was before.

I can’t get over how noisy M&A data is. So noisy.

When you read sweeping conclusions in breathless reports about M&A, remember this. The biggest deals have a huge impact, but are really small in number, while the timing can fluctuate a lot over a given year, which can drastically change the conclusions. a lot, impacting any quarterly or yearly analysis.

The first impact of this uncertainty is to really hinder drawing trends underneath the data. Like say, “Oh look, the majority of deals are about developing innovation” or “achieving economics of scale” or that deals tend to be horizontal or vertical in nature. I’m also hesitant to point to explanations for why M&A is happening, besides that M&A is the natural state of industry in America.

Take the explanation that executives are fearful of the rise of Netflix, Youtube, and Amazon and disruptive business models. That’s a great narrative. But consider that Comcast tried to buy Disney in 2004, did buy NBC-Universal in 2010 and Disney has been buying big new businesses such as Pixar, Marvel, Lucasfilm and Maker Studios. If we tried to quantify this trend, we just couldn’t do it.

The second impact is I tend to look skeptically at any explanation that M&A is increasing year over year based on the most recent data. Again, it just won’t hold up to analytic scrutiny if you can move one or two deals and change the whole picture.

Other Thought 4: Explaining the M&A total number of deals in 2007/2008

In Part IV of this series, I pointed out that in 2007, the number of M&A deals exceeded 1,000. That’s huge, compared to the 800 or so deals we’ve averaged in most of the 2010s. Even though I dismissed this number when making my prediction, it doesn’t mean it doesn’t beg for an explanation.

I have two theories.

Theory 1: 2007 to 2008 was the peak of consolidation of bigger players of smaller mom and pop shops. Basically, this theory says in the 2000s small groups of cable providers, radio stations and independent broadcasters were swallowed up by larger groups. While we focus on the giants of cable, we forget that in the 2000s there were hundreds of cable companies, maybe even thousands. Yet a lot have been swallowed up by the larger players. Now, these deals were sometimes for cable providers as small as a few hundred thousand people, so they just didn’t rate a huge value. Now that they are gone, the number of deals has gone down, but the total value has gone up.

Theory 2: In the 2007 financial crisis, some businesses were divesting not merging. This makes more sense, and I believe one article mentioned this was happening, but honestly I don’t have the data to prove it. If I had Thomson-Reuters data, this is what I would definitely explore.
So I can’t prove either theory above, but they would make for interesting future study.

Other Thought 5: Horizontal versus vertical merger discussions were overblown.

One of the legal hot takes was that the Justice Department had typically ignored vertical mergers and this is what made the move against AT&T so bold. I have two counters.

First, the Justice Department didn’t do a great job of stopping horizontal mergers either.

In my data set, I see a ton of horizontal mergers that went through without scrutiny. In just 2016 and 2017, we saw announced horizontal mergers in broadcast (Sinclair and Tribune), theaters (Cineworld and Regal; Dalian Wanda with two theater chains), radio (Entercom and CBS Radio), conglomerate (Disney and 21st Century Fox), networks (Discovery and Scripps), and other cable/cellular companies, most of which passed scrutiny. So the Justice Department hasn’t done a great job stopping horizontal mergers, which makes the focus on vertical mergers…strange.

Second, the Justice Department has looked skeptical at vertical mergers. Namely, the Comcast-NBCUniversal deal, that it ultimately blessed with many conditions.

Overall, this is one of those data problems where we shouldn’t rely on the sparse data too much. There just aren’t a ton of examples and other factors may explain the conclusions better. Like say size. Many vertical deals just involve really small companies being acquired.

Honestly, just because this vertical deal was successful at trial doesn’t mean future deals will be as well. If cable and satellite companies keep increasing prices after deals clear approval—as both Comcast and AT&T have done—well a future government many decide that these deals aren’t great for consumers.

And wouldn’t that be something.

Debunking the M&A Tidal Wave: Part IV – Making My Predictions

One of the challenges of “big data” is that it is so…big. For any given subject, we have so many ways to measure things. I can pull one set of data to prove my point; you can take the same set of data and pull a different metric to prove your point.

Take gun violence: gun control advocates have their set of data and analysis showing how guns increase homicides, suicides and violence in general. Pro-second amendment folks have their own data proving their own points.

This applies even for something as innocuous as picking TV shows for a streaming platform. In one recommendation I authored, I counted over 1,400 numbers in one powerpoint presentation. How do we figure these issues out with so much data to choose from?

Well, I have a way. It’s unscientific, as far as I know, but it works for me.

Anytime we come across a significant issue with tons of metrics and variables and data, we can employ this method. I call it the “as many measurements as possible” approach, and I’d love to find out there are other more scientific ways to do this. Here’s how it works:

Take as many measurements as possible and determine if they support or nullify the issue under question. If the majority, super-majority or vast majority support the case, then the phenomena is probably real.

The point isn’t to take just one measurement as our gospel but all the measurements we can. If 9 of our 10 metrics indicate that a phenomena is real, then it probably is.

Take global warming/climate change. If you measure temperature, in 95 percent of measurements or ways to measure it, the climate is heating up. Sure a handful of scientists can find one or two ways to show the world isn’t heating up. Meanwhile, 99% of the rest of scientists measure the data in hundreds of different ways from daily highs increasing to the average temperature averaged over the year from city temperature to countryside to oceans and say, “Man, no matter how you measure this, this impact is real”.

Same with gun violence. Guns lead to increased gun homicides and suicides.

Same with stream video: a show that does well in total viewers probably has the most hours viewed and attracted the most new customers and gets the best customer reviews and so on.

I bring this up to put us in the right mind to do our last dive through the data on mergers & acquisitions. I clearly have a hypothesis that a “tidal wave” of M&A isn’t coming because the tide has been coming in for a while now. Like most of the last decade. But now we need the data to really show us what has been going on. As I can see it, we’ve set the terms, reviewed the narratives, gathered the data and now we need to ask the data what it sets. Since we have so much data on M&A and so many different ways to measure it, we could easily pick one or two metrics and have them change our minds. I’d rather apply the “as many measurements as possible” approach: interrogate the data in as many ways as possible and let the overwhelming conclusions, if they exist, be our guide.

So here are our two final questions to answer:

– What is the historic rate of M&A? (Partly answered in Part I.)
– Is that rate increasing, decreasing or can we tell?

The latter question in particular gets to basically the question at the heart of making this prediction. If M&A activity has been steady for most of the last decade, or if it has been increasing, then we should use that knowledge to make our predictions of future M&A activity.

Fortunately, M&A lends itself well to “as many measurements as possible” analysis. M&A can be measured by total number of deals by the size of deals by the types of industry or by percentage of concentration. So let’s look at as many metrics as we can.

Metrics in Opposition

We should start with evidence that M&A has either been low or decreasing over time. And there is one data point that makes this case:Slide11 Read More

Debunking the M&A Tidal Wave – Part III: Reviewing the Data

It never ceases to amaze me how much more there is to learn about this crazy industry. I call myself the “entertainment strategy guy” and things still surprise me. Take M&A (mergers and acquisitions) in entertainment & media.

For years, I thought I closely followed the trends of mergers and acquisitions and all that jazz.

Then, I started to rigorously answer the question from two weeks ago, “How much, if at all, will M&A activity decrease?”. Naturally, I turned to Google to look for big M&A deals. I tried to build myself a little table with every deal I could find. I kept finding deals I’d forgotten about. “Oh yeah, Lionsgate bought Starz!”

There has been a lot more M&A in entertainment then you’d think. It has been a constant flow since the recovery from the great recession. That’s what my unscientific table showed and what high level summaries from PwC (and others) show. And it genuinely surprised me how many deals I’d forgotten about.

Today, I’m going summarize what I saw in the data and the shape of it.

Gathering the Data: Part 1 – My Own Data

Here’s a snapshot of the table I started filling out and will use a bit today.

slide07.jpg

Why build a table myself in Excel? Well, it’s the easiest way to click on a few variables and sort the data to discover descriptive details yourself. One of my pet peeves in data analysis is when someone doesn’t actually own the data themselves, so they rely on someone else to draw conclusions. (Also, sorry for the compressed lines. This table violates my “rule of 8”. Usually tables should never have more than six columns, usually  6 is ideal.)

My process for gathering the data was as crude as it was simple: I googled “entertainment and media mergers and acquisition” and the year to find the biggest deals per year. I later used CrunchBase’s data set to find smaller deals. I would sort by company, starting with the studios and moving to distributors and such.

I really recommend at least trying to collect data yourself whenever possible. It’s harder and takes longer, but by doing it yourself, you force yourself to figure out which variables you want/need per data point. In this case, by looking myself, I learned some thing about M&A activity, and the data set in general. Even when I later switched to using PwC’s summarized data, I could use these insights to understand PwC’s conclusions.

For example, I learned how important the timing of a deal is. A lot of the articles covering M&A activity neglect to mention what they are tracking in their coverage. Is it when a deal was announced? (For many articles, yes.) But what if a deal doesn’t close? So you sort M&A activity by closed deals, but that could be skewed by how long deals take to close versus the year they started in. If you are trying to summarize the previous year’s M&A, well you’d leave out a lot of deals if you only track deals that close.

Could this effect the data? Absolutely. The AT&T-Time Warner could swing one year’s data by $85 billion dollars. The Comcast-NBCU merger swung various year by year totals by $35 billion. The failed Comcast-Time Warner Cable inflated a few years totals by $45+ billion before it was abandoned.

I also learned that trying to distinguish between “acquirer” versus “acquired/target” is touhg. Most deals are usually one company buying another. But sometimes two companies agree to merge, and it isn’t really an acquisition, so who is the acquirer versus the acquired/target? Other times a firm is buying a majority stake in a company it has partial ownership. These little distinctions and difference can plague data analysis when you try to capture them as variables.

What about the deal value? Again, this would seem like a relatively straightforward number, but it can change depending on how stock prices move over time. Or if a company has to raise it’s offer due to competitor or shareholder pressure. Sometimes, the numbers differ by billions, swinging the total deal value by 25% or more. I tried to use the higher number whenever possible. In my scan of the data through news reports, deals rarely got less expensive.

The last five variables were less about the nuts and bolts of the deal (who bought what for how much) and instead about providing some flavor. The pieces I thought would be the most useful for data analysis/business strategy were: the industries involved (network, radio, studio, cable, etc) for both parties, the “direction” (horizontal or vertical) since this came up a lot in the AT&T lawsuit, the status (to account for failed deals), and the stake of ownership. I assumed the last piece was to full ownership unless clarified. Also, in this case industry and direction were my own subjective opinions.

If I could add a piece, I’d add PwC’s description of the business purpose of the deal: consolidation, content, innovation, capabilities extension, or other/stake ownership.

Oh, and in the future I’d include “divestiture” as a final category. Not all deals are accretive and PwC/Thomson Reuter’s database tracks this. In down swings, companies spin off bad business units and ideally a good data set on M&A would tell you when that happens.

Gathering the Data: Part 2 – The PwC Data and Others

As I mentioned above, trying to collect all the information on M&A activity by myself was more time intensive then I thought. Let’s hope I can keep building it through the rest of the year to find additional insights.

In the mean time, I needed a better, quicker look. Fortunately, the good people at PwC using Thomson Reuter’s data were able to compile annual snapshots of M&A activity in the sector they called “media, entertainment, and communications” which I copied in my first post. I found every year’s study I could—in most cases using the articles on it—and compiled it into the table I ran in my last post. Here it is again for this who missed it:

3 Metrics MA Slide to updateI also found other articles about consolidation or M&A activity in other sub-disciplines in entertainment, again, usually through trade press articles. Take this chart from an article in Variety about M&A in TV production, which produced this table using IHS MarkIt data:

slide09.jpg

(Source: Variety/IHS Markit.)

In addition, I found articles about M&A in the Wall Street Journal, Hollywood Reporter and The New York Times. Where possible, I saved the numbers in the article to bolster my data. I’ve tried to provide links where possible, but I have so many I may save them for a future post.

Quality of the Data

So I have essentially two data sets at this point: my own from readings/capturing news articles and the PwC summaries. The question I had to ask myself—and you should be asking me—is how good do we think this data is?

Most people in data analysis miss this key step and it’s worth pausing to emphasize it. Just because you have data doesn’t mean it is any good. Do you see potential flaws that you should acknowledge? Or could cause you to throw out the data set? Do you see quirks in the data that signal bias? Always ask these questions of data (or ask your data scientists/consultants these questions).

From year to year and between data sets, M&A data on media, entertainment and communications (and I assume all industries) is plagued by discrepancies or opinions. The biggest unreliable variable was the timing of M&A deals. Announced deals by definition exceeded the number of deals that invariably closed. So every year’s annual report invariably lowered the previous year’s totals. Sort of like how GDP is invariably adjusted by the Commerce department in future reports. This can make each year seem like it exceeded the previous year’s totals, even if it just means that some announced deals won’t end up closing.

Just because we find flaws or inconsistencies doesn’t mean we have to throw the baby out with the bath water. The question is how much we need precision in this data. Since we’re looking for trends here, being off by a few days on when a deal was announced or closed won’t kill us. Same with being off several hundred million dollars in a price. Given that a few huge deals have the largest swings, being off by a few hundred million dollars won’t effect the larger trends. Even the trends for deals announcing or closing won’t effect the five year average of deals, for the most part. (Though, it helps if you keep you data consistent/apples-to-apples when possible.)

That said, I wouldn’t try to draw too many strong conclusions from the data set, given that it has inconsistencies. And two other issues I’ll discuss in the next section.

My self-made data set has one other HUGE flaw I don’t want to neglect: I made it by trying to find as many deals as possible so I missed a lot of deals. Rigorously reviewing the internet for deals isn’t a super reliable approach, which is why I opted mid-stream to change approaches to focus on high level summaries. I’d also add I mostly focused on US-based M&A, which is a mistake. These are global companies, but our focus naturally falls on places that speak our language. (Many companies had multiple Indian deals, but their total value pales in comparison to US-based deals.)

Initial Thoughts on the Data

So we have all these high level summaries and my table. What do we think of this data? What does it look like?

Summary: This is a noisy data set

Even if I had all of Thomson Reuters data at my disposal—I don’t—I’d still call this a “noisy” data set. Adopting The Signal and The Noise terminology, I mean that trying to draw conclusions about how individual variables impact the data set will be hard. Trying to draw precise predictions will be impossible.

Take years, for example. A year is a long time in business terms. But trying to draw conclusions about any given year’s M&A activity is fraught because deals could be categorized multiple ways. As we’ve seen, you could count the AT&T-Time Warner deal in 2016 or 2018 (or later if the appeal delays the deal further), which drastically impacts the value of the deals done in that year. Since timing could change the data set so much, we have to be careful drawing conclusions about any one year of deal-making. This is why I used the five year average to set our predictions.

Or take mega-deals. There are less than 18 in any given year. That’s a small data set. So trying to draw conclusions about mega-deals with our variables like “direction” or “industry” or “type of deal” is fraught. Or to be more precise, we can’t have statistical confidence in these conclusions.

Warning: Power-Law distribution amplifies the effects of small sample size.

This data set, and a lot of conclusions drawn from it, is power-law distributed. AT&T bought Time-Warner from an entertainment and media deal high of $85 billion dollars. And it was joined in 2016 by 15 other mega-deals of $1 billion or more. But according to PwC there were over 679 deals of any size in 2017. That means that the first two deals (Linked-In purchase by Microsoft being the second biggest deal I found) totaled $111 billion, so more than the other 677 deals that year.

As a side note, I love explaining “power-law distributions” to people. This type of distribution happens throughout entertainment. Power-law distributions mean that a small number of deals can have a huge impact on the data set, especially if you focus on the “average” without accounting for size. So if you’re counting/measuring impact by each deal equally (not weighted by value) you could miss a lot of trends.

Conclusion: We still need a prediction!

I know, I spent today just reviewing the data about M&A. As I’ve been editing this article, I’ve been asking myself a brutal question: is there enough meat on the bones for this article?

And you know what? I think there is. Every few months Deadline or The Hollywood Reporter or Variety publish an article summarizing M&A activity in media and entertainment. And it comes up on their podcasts. But trying to find an explainer or FAQ on where the data comes from? Good luck. It matters whether data sets are imprecise or noisy or flawed. And a lot of the reporting on M&A ignores that crucial context. Hopefully I provided that today.

I swear I’ll make a prediction tomorrow.