Malone’s moves: a chess analogy

John Malone, one of the eight CEOs mentioned in my post about the Outsiders book, is featured in Cable Cowboy, a book that tries to describe how he built his cable empire and, in the process, compounded the stock of his cable company TCI by an astounding 30.3% per year for over 25 years.

Written by Mark Robichaux, a Wall Street journalist who covered several of Malone’s deals over the years, the book provides some behind-the-scenes color around his myriad cable and content deals.

But it does not, at least to my satisfaction, explain exactly what did Malone did that was so different from other CEOs.  He was clearly very smart and well educated – he graduated from Yale University with a B.A. in Electrical Engineering and Economics as a Phi Beta Kappa and National Merit scholar, and obtained an M.S in Electrical Engineering from an NYU program at Bell Labs as well as a Ph.D. in Operations Research from Johns Hopkins.

And the book describes how Malone learned about cable directly from some of its early pioneers. He was clearly good at financial engineering and pioneered many of the techniques used by private equity firms today (aggressive use of debt leverage). He could do this because he was early in realizing that cable revenues were reliably recurring, like a utility (but unregulated!), so it could be used to raise a lot of debt inexpensively. He also systematically maximized the tax benefits of financing his cable assets in this manner. I think he may have been somewhat lucky to not get wiped out at various points in his career while operating with a high level of leverage.

But beyond these operational strengths, I think he was particularly good at multiplying value via his deal-making. He was perpetually buying and selling various cable and content assets but its not obvious how all that wheeling and dealing actually creates value.

An analogy struck me while reading about some of his deals: the concept of trading small advantages in chess.

Lets say you start with a pawn sacrifice in the opening to get a move advantage. As time passes, unless one plays forcefully, this temporal advantage can quickly dissipate. So good players often convert this into a positional advantage if the opportunity presents itself. Positional advantage is more structural and hence robust. Later in the end-game it can, in turn, be converted into another kind of advantage – a passed pawn or perhaps a sacrifice to get an attack on the opponent’s king, etc. Thus there is a constant trading of advantages, from transient to permanent ones, depending upon the board situation. And a skilled player can usually translate this kind of trading of small advantages into a win.

I get the sense that Malone was very good at doing something equivalent in his business deals.

Using his deep knowledge of the cable industry, he could sense when a cable asset became available at an attractive price. He had a good sense of the intrinsic value of the cash-flows of cable assets. He would opportunistically buy such a mispriced asset even if it was not what he ultimately wanted (e.g. not in a region where he was building a roll-up of cable assets). Just like in chess you collect a small advantage when you can, even when its not a mating attack on the opposite king. You do that to get something to trade with.

Then he would patiently wait, sometimes for years, before an opportunity came to sell this asset, which would usually have appreciated by then (since he bought it when it was distressed). He would use the cash from this sale to then turn around and buy an asset that he really wanted all along. Or buy back shares in TCI if they were undervalued.

Thus he avoided overpaying for premium assets – the downfall of many of his competitors who were pursuing so-called “strategic M&A”on the advice of their investment bankers.

An example of his patience was evident in how he waited to but content assets (programming) until he had enough scale from his rolling up a bunch of regional cable providers. Once he had enough scale in cable distribution, he was in a strong negotiating position to acquire content assets on favorable terms.

And, since he could distribute the content to more subscribers than his competition, he was able to net more cash flow from his content assets. He would then leverage this additional cash-flow by raising more debt and buying more cable subscribers. And so on. This kind of virtuous cycle (more subscribers -> more content -> more subscribers) with increasing returns to scale can indeed explain compounded returns of 30% per year for more than two decades.

He was able to get to this point by systematically trading one advantageous deal into another, like a master chess player, thus multiplying overall value (in other words, by multiplying what economists call gains from trade).

The book triumphantly ends by describing how he is crowned his career by finally selling TCI to AT&T, once again opportunistically, when he judged that they were paying an attractive price for it.

And then he is supposed to have retired. 

Except he did not!

I think Malone is still playing this game, only this time in Europe, even in his seventies!

A company he chairs and controls, Liberty Global, is well along the way to owning a cable franchise that dominates Europe. There are significant economies of scale in doing so in such a dense and contiguous geographical area – just think of a cable truck being able to efficiently serve neighboring regions vs. one that services installations scattered all over the map.

And yet Liberty Global also owns some assets in Chile, completely disconnected from Europe! This fact was puzzling me when I was initially analyzing the company until the chess analogy came to mind. I now suspect Malone bought the Chilean cable opportunistically, when they were available for cheap, knowing full well that he will trade them later for what he really wanted – assets in the dense areas of Europe.

Indeed, recently the Liberty CEO is now talking about selling the Chilean cable assets and is in the process of buying more cable in Netherlands that is contiguous with their other European cable units.

In another repeat of the TCI playbook, Liberty Global is only now going about acquiring content in Europe. They have begun by making some small investments recently (e.g. a small position in ITV), but clearly waited until they had rolled up enough distribution muscle before they did so. At this point they are already the largest cable company (by number of subscribers) in Europe and thus clearly can get very attractive terms from any content producer there. And just like TCI, they can then monetize the acquired content better than others since they have the largest number of subscribers.

As Yogi Berra said, its deja vu all over again.


Disclosure: I am long Liberty Global (LBTYA) in various personal and professional portfolios.





Why is this “mate in 3” so hard?

I like chess puzzles and if you are like me you know that “mate in 3” can have only a limited number of solutions and usually can be solved within, say, 10 to 15 minutes (master level players will of course be much faster). However, the following puzzle turned out to be much more tricky, at least to me (I am only a club level player). Before going on, give it a try:

Black to move and mate in 3 moves:


If you can’t wait for the answer, you can see it fully described here by Joe Wiesenthal, the prolific economics editor of Business Insider. As he says:

“the eminent chess player and commenter Susan Polgar posted on her blog the following:

Black to move and checkmate in 3. Please no computer analysis. This is a very cool checkmate. Try to find it for yourself.

Now chess problems where you’re asked to just mate in 3 moves aren’t typically all that hard, so my curiosity was piqued by the fact that Polgar said not to use a computer. Obviously it couldn’t be that simple if you’re tempted to use a computer to solve a 3 move chess problem.

And it wasn’t! In fact I spent the evening looking at it yesterday without getting it.”

But now I am intrigued – exactly why is this elegant little puzzle so surprisingly hard?

And that I find to be an even more interesting puzzle, one of human psychology. I think a hint can be obtained by trying to solve an entirely different, almost trivial, problem in arithmetic. Try this:

A bat and a ball cost $1.10. The bat costs $1.00 more than the ball.

How much does the ball cost?

If you answered, like most people, 10 cents then you are wrong!

The correct answer, which will be immediately obvious upon reflection, is 5 cents (since $1.05-$0.05 = $1.00).

This problem comes from the research of Shane Frederick, a collaborator of Daniel Kahneman, one of the world’s top cognitive psychologists. Frederick’s paper, “Cognitive Reflection and Decision Making” (Journal of Economic Perspectives, Volume 19, Number 4, (2005) pp 25–42) describes what might be happening in such problems.

Here are two more arithmetic problems from the paper:

If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?

In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?

If you said 100 minutes for the widgets or 24 days for the lake, then once again you got the wrong answer. But you are not alone – a surprising majority of students from elite universities, including maths and physics types, get these elementary problems wrong (data in the paper cited above and various follow-up papers).

So what is going on?

Our brains can be thought of as consisting of two separate but interacting systems. As Kahneman explains in his brilliant Nobel lecture:

“The operations of System 1 are fast, automatic, effortless, associative, and difficult to control or modify.

The operations of System 2 are slower, serial, effortful, and deliberately controlled; they are also relatively
flexible and potentially rule-governed.”

System 1 can be thought of as the intuitive system and System 2 as the reflective system – what we normally call “thinking”. Obviously, neurons are firing in both cases, but System 1 feels so effortless that most people don’t realize the massive extent of neural processing involved in, say, seeing that we are looking at a chair, since such acts of perception are accomplished by the fast System 1.

It is likely that System 1 has “hardwired” the critical processing that our ancestors needed frequently, like perceiving objects and making very quick (“intuitive”) judgments. It is basically a pattern recognizer. But it can also learn new things after sufficient repetitions – it is where our habits reside.

System 2 is more flexible and algorithmic computer in its style, albeit a very slow computer. The overall executive control also is a part of System 2.

As the Frederick paper explains, the three arithmetic problems are

“easy” in the sense that their solution is easily understood when explained, yet reaching the correct answer often requires the suppression of an erroneous answer that springs “impulsively” to mind.

And that is exactly what I guess  is going on with the chess puzzle above.

Every chess puzzle lover “knows” that a discovered check pattern is often the heart of many a pretty mating sequence. And sure enough, there is a very seductive discovered (and, indeed, double!) check available on the second move after the obvious (and correct) rook check at first move.

So our intuitive pattern detector jumps to the conclusion that this discovered check just has to be part of any solution. It leads us down the proverbial garden-path and we tend to waste of a lot of time on this dead-end.

The solution to the chess puzzle finally emerges only after we have somehow (hours later in my case) managed to suppress this discovered-check. After that happened me – hours later – I finally focused on the fact that the white king is completely locked-in after the rook check on the first move. And after this “aha” moment, finding the two moves by the black bishop that deliver the coup-de-grace was not too hard.

Of course a computer would solve this problem in milliseconds, since the search tree is so small. And the computer, in a sense, is all logical system 2 with no intuitive system 1 to mislead it!

Unlike the arithmetic problem, there has been no research on this chess puzzle to my knowledge, so this explanation of why this simple enough mate is so hard for humans is just my best guess at this point.

Just in case this discussion makes System 1 appear dumb, it is worth keeping in mind that artificial intelligence programs still cannot come anywhere close to being able to perceive patterns that are trivial for humans. It is also the likely source of creative insights and the two systems together are responsible for all the glories of human achievement.

Incidentally, if you enjoy this sort of stuff, Kahneman’s Thinking, Fast and Slow is a true masterpiece and easily one of the best books I have read in the last few years.