Majestic Research Blog

Several years ago I was having a conversation with one of our clients: a buy-side firm that takes the same creative approach to unearthing and analyzing unique data as we do, and has been doing so since well before Majestic was founded.  The particular person I was talking to is a brilliant computer scientist whom I have the utmost respect for, and who has demonstrated on numerous times a profound ability to understand (at least from a technology perspective) how the world is evolving.  What he mentioned to me took me aback as it struck at the core of what we were trying to build.  He said, “Don’t you think all or most of this data we are working so hard to gather and acquire will be readily available to anyone in the not-so-distant future?”  Given much of our value proposition is largely focused on the uniqueness and proprietary nature of our information, it is easy to see why his remark had me concerned.  Fast-forward to today, and there is ample evidence that he was correct in his thinking, and loads of data seem headed on a path to becoming a commodity, but it turns out that with respect to Majestic I was wrong to be so apprehensive.

Commodity businesses are challenging ones.  When all you are competing on is price, margins are difficult to maintain and price wars can wreak havoc on a company’s value.  Just look at the history of flash memory, Website hosting, or what is currently occurring with telecom service providers.  Once consumers focus solely on price it becomes a fight to the bottom.  Ultimately, commodity providers need to layer something on top of the commodity product to differentiate themselves.  For instance, consider the crowded space of solid-state drives (SSDs), which many people speculate will grow tremendously into notebooks and enterprise computers over the next couple of years.  While the NAND chips inside the SSD might be considered the commodity, the controller that manages the memory can make a world of performance difference and allow a company to gain a tremendous edge.  So the layers on top of the commodity become the differentiators.

Back in the early days of Majestic, we thought it was imperative to demand exclusivity around all of the data deals that we signed, and we routinely paid a high premium for such exclusivity borne from a fear that without the exclusivity we would lose a considerable amount of our unique value proposition.  Over time, however, it became apparent that the value we added on top of the data through our analysis, triangulation across multiple datasets (including those we gather internally), and ability to distill insight from and bring life and color to the data in a way that was most relevant to our clients became our true value proposition and was also something that other companies would be challenged to replicate.  We realized that it was our ability to answer our clients’ most challenging questions, identify large points of inflection early, and solve our clients’ problems through our triangulation across many datasets of both the proprietary and non-proprietary sort that is what we were being paid for by the majority of our clients.

We therefore decided to reserve exclusivity covenants only for special cases where we determined the data was just too unique and value-added not to do so.  But data that is truly unique or difficult to access is getting harder and harder to find, thus shifting the value from the data to the process and end-product.  Companies that make their primary business simply packaging, providing a front-end into, and reselling data and information may find themselves in trouble.

Data and information are freeing themselves and disseminating at a frantic pace, and the concept of paying large or even very small amounts of money for information is becoming less and less appetizing.  Just ask the newspaper publishers.  As the raw data itself becomes more and more readily available and easily accessibly across numerous platforms, the value will shift from being a provider or platform to simply deliver the data to being a platform whereby the maximum value is extracted from large amounts of data.  Let’s take a look at some examples that illustrate this point.

Consider XBRL.  XBRL is a markup language (a child of XML), meant to standardize the way businesses report data.  Importantly, it is an open standard, free of any license fees.  With respect to financial data, an SEC mandate went into effect nearly a year ago requiring companies to “tag” financial data in XBRL, and there are many related initiatives currently underway, including a bill in Congress that would require XBRL tagging for all bailout requests and expenditures, as well as a bill meant to accelerate acceptance and broader use of interactive data.  Ultimately, in the not-too-distant future, nearly every business metric for public companies will be readily and freely available to anyone whether they want to use the data for their own work or build an application on top of the data in much the same way so many programmers have created brilliant and useful apps for the iPhone and now for Android phones.  Here is a rudimentary example and starting block of what can ultimately be accomplished–a not-for-profit project called Freerisk.  A few other companies looking to build businesses around making data open, free, or highly affordable can be found here,  here, and even Amazon is in the mix, as can be seen here.

So are data businesses becoming commodity businesses?  Not just yet, but it is beginning to happen, and I believe the trends here will accelerate, and this will ultimately happen quickly.  When it does, companies that make their livelihood selling in raw form large amounts of data will see their margins severely compressed–as with flash memory, the consumer will focus only on price.  That is what happens in commodity businesses.  The value will transfer to business models that leverage creativity, analysis, and the ability to drive insight from information in scalable ways.  Just as Apple knew that its hardware edge in the mobile space would not be long-lived so it developed the app store by opening up its SDK in a way that shook up the industry and will allow it to maintain its edge into the foreseeable future, so too must information companies think of creative ways to protect their platforms.

Consider two titans in the information business, Thomson Reuters and Bloomberg.  Bloomberg recently took an amazing initiative that should have received a lot more publicity than it did by opening up its proprietary security codes to anyone.  The interface can be found here.  In the meantime, Reuters was recently questioned by the European Commission on terms of use relating to its own proprietary codes (the RIC codes), and S&P is currently being investigated by the EC as to whether its codes breach any European antitrust laws.  So while the competitors are questioned and investigated, Bloomberg, by opening the codes up to everyone, will have lots of people creating tremendous value–and saving untold amounts of time–for lots of other people, and the codes will become more and more pervasive as a result.  In fact, they will likely become the currency that will be a large one of the many ways Bloomberg will benefit from this decision.  I expect Bloomberg’s bold initiative will be a paradigm shifter for the space, as others will be forced to ultimately scramble in response.  But for now Bloomberg is the one that seems to “get it,” and it will be interesting to see what Bloomberg’s next move is.

As far as Majestic goes, we will continue our mission of unearthing new and novel data sets whose analysis can drive important investment and business decisions, and we will continue to increase our focus on knowing which datasets to triangulate across to answer different types of questions, and how best to present the insight.  As more and more data becomes available to us and to the public at large, we will understand better than anyone else how to combine it with other datasets to answer the questions that drive investment and business decisions.  A tremendous amount of future value will be in both identifying the signals and filtering out the noise, and we’ve invested over 7 years in doing just that.  Our triangulation, testing and filtering process is the analog to the controller of flash memory.  I used to listen to arguments that packaging and reselling the raw content again and again to multiple different parties was the only way to get true value from the business, and I worried that what we were focusing on might not effectively scale.  Ironically, what once troubled me as not effectively scalable I now think of as providing us the opportunity to become the market leader of firms that can add value in the burgeoning world of pervasive and ubiquitous data–a world that is coming faster than most people think.

In Hans Christian Andersen’s wonderful tale, “The Emperor’s New Clothes,” a couple of weavers promise the Emperor a new suit of clothes made from the most magnificent cloth imaginable. Beautiful and elaborate, the clothes would also have the special trait of being invisible to anyone who was stupid or not fit to view its splendor.  The weavers continued to get paid more and more as they described to all of the Emperor’s officials who came by to check on the progress all of the intricate details and exquisite colors.  While the officials saw nothing, none spoke up for fear of being stupid or unfit. Eventually, the Emperor himself sung the glorious praises of his new clothes that he could not even see lest he expose himself as unfit to be Emperor.  Finally, during a great procession, the Emperor paraded himself naked through town with all the people extolling and cheering the virtues of his splendid new clothes until suddenly a little child shouted out, “But he hasn’t got anything on!” This led to whispers, and eventually all the people were saying, “He hasn’t got anything on!”

Lately, we’ve been reading a lot of bullish rhetoric about gold. Much smarter investors (and likely smarter people) than myself such as David Einhorn, Paul Tudor Jones, and John Paulson have each made outsized, well-publicized bets on the rise in the price of gold, and several hedge funds have even created share classes denominated in gold. The world’s largest gold producer, Barrick Gold Corp., recently announced it had eliminated all of its gold hedges in order to gain as much leverage as possible to the price of gold. Current broad sentiment on gold has been extremely bullish by almost any measure as can be seen here, and central banks have been big buyers as well.  All of this has me thinking.

For nearly 15 years I worked for the brilliant market prognosticator Dr. Marty Zweig, where I was Co-Director of Research and did considerable work on our various models that determined asset allocations, etc. One of our core models was our Sentiment Model, which was contrarian in nature and would look for cases of extreme sentiment in one direction as a great opportunity to exploit a move in the opposite direction. A right of passage for any young analyst starting at Zweig was reading and grading on a very bearish to very bullish scale a multitude of newsletters to identify periods when the so-called “experts” were all aligned in one direction (they were nearly always wrong). The approach is remarkably intuitive (bearish sentiment means lots of cash on the outside that could move in on any positive news and vice-versa) – to learn more you can buy his terrific book, “Winning on Wall Street”. The model worked particular well at the poles. It is difficult to argue that we are not at or rapidly approaching a pole with respect to bullish gold sentiment. Witness the following chart showing daily volumes for SPDR Gold Shares (and the SPDR Gold Trust as a result has accumulated more metal than Switzerland’s central bank):

But sentiment (and 5000 years of history, for that matter) aside, I still can’t rationalize why in today’s world so many people, hedge fund managers (I know, I know – they’re people too), central banks, etc., would want to own large amounts of gold. I’ve talked to some colleagues whose investing acumen I respect, have read some articles and have done some research, but I still find myself wondering.

Many talk about gold as an inflation hedge, and while empirical evidence can be interpreted both for and against its effectiveness as a hedge – a challenging exercise since the price of gold here remained fixed at $20.67 for about 100 years and then $35 from 1934 up until the 70s – I would suspect that in this age of more and more sophisticated contingent claims wouldn’t there be much more precise and less speculative ways to hedge inflation such as these.

The argument of gold as a currency is a little more interesting. Central banks have been buying gold of late and may indeed become net buyers in the near future as the dollar by comparison appears more and more to be an inferior holding. So while gold no longer backs currencies, it is still regularly used to diversify fiat currency holdings. But as gold prices, already quite high, continue higher wouldn’t buying gold become incrementally less appealing? And how does a country with a reserve the size of, say, China’s attempt to diversify in any meaningful way into a supply-constrained asset without impacting the asset to such a degree that it loses the majority of its appeal?

The supply constraint argument may be the most compelling. There are only about 160,000 tonnes of gold that have ever been removed from the earth and this number is expanding at only 1.6% per annum. Money, by contrast, is expanding at a much greater rate right now as money will begin to lose its purchasing power. The supply-side argument makes enough sense in as much as I can see that current gold supply is limited and growing at a far slower rate than money supply, but it’s the whole demand thing that is a bit ambiguous to me since most of the demand appears to be driven by the constrained supply in what becomes something of a virtuous circle: a dynamic being driven in part by a lot of the rhetoric currently extolling gold’s virtues.

This gives me a great idea. If the supply constraint is really the driving force, why don’t we create a vehicle that removes this whole sticky demand and utility thing altogether? We’ll call the units Supply Constrained Assets Minus Any Real Utility or SCAMARUs.

Initially, we’ll only create 160,000 units and the only characteristics these units need to have is that the float can only grow by 1.5% a year maximum and that they oughtn’t be hard since hard assets tend to have some real utility, and we want to keep these babies pure and devoid of any worthwhile use vs. the current hard assets that people currently use to hedge inflation. For instance, steel is quite hard and that comes in handy with respect to building cars, infrastructure, skyscrapers, bridges, appliances, etc. Gold is also hard and gold can be used for fine jewelry, and (pausing…), and, hmmm… well there’s NFL superstar Chris Johnson’s teeth and this gorgeous Vertu cell phone, and… come to think of it, maybe my SCAMARU idea is not so differentiated after all. Wait – I did think of one other thing… The Emperor’s New Clothes were weaved from gold!!! Where’s that little boy when you need him?

This is my first post on a blog that will discuss and explore issues surrounding Majestic Research, equity and market research of both the traditional and non-traditional sort, and data analysis for decision making in general.  Majestic Research was founded seven years ago by me and by Seth Goldstein in an effort to build the first equity research firm driven entirely by analysis layered on top of large amounts of proprietary empirical data from non-inside sources.

Much fuss is being made about Bill Belichick’s decision to go for a first down from his own 28-yard line late in the game on Sunday.  A decision that may have cost him the game (although there is no way of knowing what the result would have been had he punted).  Many talking heads, including radio announcers, sports journalists, current and former players and coaches, my cab driver yesterday morning, and Patriots fans everywhere have lambasted the decision with adjectives such as “idiotic,” “unthinkable,” “feeble-brained,” “uninformed,” and several unprintable on this blog being thrown around generously.  Of course, what every one of these talking heads fails to do is back up their claims with anything other than “common sense” or “anyone who knows football knows…” while every statistical analysis shows unequivocally that Bill Belichick made absolutely the right decision if the measure is the decision that gave him the highest likelihood of winning the game.  It isn’t even close.  In fact, the statistical analyses I have looked at, such as here, actually don’t tell the whole story, since the likelihood of the Colts scoring after a punt is even higher than what is stated, given they would be going for it on every fourth down as compared to a typical series from which the statistic is derived.  So his decision is even more correct than the fine folks at advancednflstats.com have determined.

Now, as a lifelong Steelers fan, I have no love for Bill Belichick.  He is a cheater, a poor sport, and a bully, and I will take a coach like Mike Tomlin over Bill Belichick every time. That said, I have always had more respect for his coaching ability and his willingness to think unconventionally and rely on statistics over conventional wisdom in an effort to win than for any other coach in the game.

As CEO of a firm whose (often unconventional and counter-to-consensus) analyses and insights on industries, companies, and products are driven entirely on analysis of data, I have tremendous empathy for him right now given all of the abuse being thrown his way.  From time to time, our analyses will lead to flawed conclusions; perhaps there was sampling error, or bias in the data, or the data is not a perfect proxy for what we are trying to measure.  I like to think that we get it right more than any other firm precisely because we remove the emotion and human bias and focus strictly on the data, but still, no data is perfect.  Most of our clients understand this and are more interested in trying to understand the “how” to explain the delta between our information and reality than anything else.  But we will always have a couple of clients who will ask how they can possibly trust us going forward, who claim, “Majestic was wrong” (if it wasn’t human error – I would counter that the data was wrong), or spew some adjectives and nouns at us that fall in the unprintable category mentioned above, or in very rare instances even cancel their subscription.  The thing is, just as I would expect Bill Belichick to make the same decision if he found himself in the same circumstance precisely because he made the “right” decision, and it just didn’t work out for him, I would expect our analysts to come to the same conclusion that may have led us astray in the past if the data looks the same, as long as we haven’t learned anything new about the data that would suggest otherwise.  As well, I would expect our salespeople to push the information just as hard as the last time.

If we are correct 80% to 90% of the time, we are doing an excellent job.  Casinos make a lot of money (although not as much as they used to, as can be seen in one of our excellent gaming reports) by only being right slightly over 50% of the time.  The thing is, many people are unable to make the distinction between a correct decision that doesn’t work out and an incorrect decision, and often times this failure will hurt them in the future.  In fact, when it comes to making investments, I would argue that nearly every successful trader does not succumb to this flawed way of thinking and that is precisely what makes her successful when compared to, perhaps, an equally analytical trader who does suffer from this gambler’s fallacy.

So, I commend Bill Belichick on making the clear correct decision in a situation that required some courage and chutzpah (and while my hatred of the Pats makes me glad it didn’t work out, the mathematician in me also makes me a little sad – okay not that sad), and if he was still married, I wonder what he would have said to his wife when he got home.  Because now that I think about it, back when I was covering consumer technology, there were certainly a couple of occasions when a company would report not exactly in line with what I was representing from our data and I would come home clearly upset.  When my wife asked me, “What is the matter?” I’m quite sure I answered, “I got it wrong.”