Monthly Archives: December 2011

The odds that you understand the economy better than POtUS are really, really low

The Internet has had innumerable salutary effects on society but one of its decidedly non-wondrous outgrowths is the bull market in half-baked, ill-informed snark. The Atlantic’s Megan McArdle detailed this beautifully over the weekend in if everyone Else is Such an idiot, How Come You’re Not Rich HERE, during which the author dismantles Forbes’ criticism of the coherent if ill-fated Netflix Qwickster strategy. Ms McArdle writes:

I don’t want to pick on [Forbes], particularly, because I’ve read some version of this lament about Netflix about a thousand times. And indeed, I completely agree that the Qwikster disaster was nothing short of debacletacular.

But how do we get from “that was a bad idea” to “Reed Hastings doesn’t understand what business he’s in?”  When Internet commentators see odd behavior that they don’t understand, why do they assume that the most parsimonious explanation is that management must be a bunch of drooling morons?

This tendency, to sit at a laptop and blamestorm without any type of detailed research, is the most destructive and lazy type of epidemic and politics is of course ground zero for the phenomenon. In terms of economic policy, a cursory examination of this morning’s media extends the accepted conclusion that both the Eurocrats and President Obama are the intellectual equivalent of drunken, “drooling morons.” Consider, though, how likely it is that you know more about the economic situation than they do. POTUS or Ms. Merkel have access to literally any information they need, including the attention of any global expert, (even professor Krugman at 3:00am if necessary, although I’d bet he’d be cranky) to respond to niggling questions. Add to this that the job security of said politicians is more dependent on economic factors than any other concern, it is a reasonable conclusion that to believe your comprehension of the situation is better than theirs is a colossal feat of arrogance.

To backtrack a couple thousand years, the general assumption regarding the fall of Rome is that the contemporary leadership was characterized by the same kind of ignorant as POTUS.  The historical record, however, clearly displays that the intelligentsia of the time was well aware of Rome’s failings, they just couldn’t figure out a way to fix them. Pointedly, the corruption of the Senate and political control by wealthy elite paved the way for the dissolution of democracy and the onset of empire.  They were not ignorant, contrary to popular belief, just overrun by socioeconomic trends too powerful to harness.

Returning to modernity, it is clear from McArdle’s post that Netflix CEO Reed Hastings was, with the spin-off strategy, attempting to address very real structural issues within his firm. The plan clearly ended up as an online Bay of Pigs, but the drivers behind it are completely explicable with a modicum of research. I would argue that the behavior of politicians is similarly intelligible, with electoral money-raising, pandering to constituents and poll-watching replacing rising digital streaming rights as major motivational culprits.

Labeling corporate or political celebrities “idiots” is to some extent to self-identify as part of the problem. Getting rid of these “idiots” will solve nothing, except to provide new vessels for sneering, self-congratulatory scorn. Focusing on the forces that makes these people appear to be morons, and widely publicizing these drivers to the point where they enter the general consciousness, actually provides an avenue for progress.

China and 1999

It occurred to me about a week ago that the global finance industry’s relationship with the China Story is a practical example of almost every trend discussed to date in this blog. The way to convey this was elusive but hopefully I’ve McGyvered up a structure this morning.

A few of things to keep in mind off the top.  One, consider the economic outlook for China as “in dispute” or “inconclusive” for the purposes of this post. My personal skepticism will become evident, but this is not a result of a personal dissection of macroeconomic factors underpinning the bull and bear cases. Also keep in mind that I am alleging no conspiracy on behalf of the industry. I am assuming sincerity behind all views. Third, a lot of the content below compares China-related sell side projections to those during the technology bubble and, having discussed the analytical limitations of this personal tendency in the previous post “Investor Anthropology”, I will ask you to keep this, my personal potential intellectual blind spot, in mind also.

Vendor Financing: How soon we forget. The fact that the technology bubble contained its own internal credit bubble is now frequently overlooked. Particularly in the telecom equipment space, corporate clients were encouraged to build monstrously large communications networks in order to avoid “falling behind” their competition. For CEOs the decision was made convenient by offers from the equipment companies to provide the financing for network construction. The unraveling of this process was central to the demise of the bubble as the lofty goals of new business models gave way to bankruptcy, ending debt repayment and severely impairing the balance sheets of former high-flying companies like Cisco and Nortel Networks.

There are, of course, limitations to the interpretation of China’s mass investment in Treasuries as the largest vendor-financing scheme in world history. However, the de facto result of current Chinese monetary policy does have similarities to the “here’s the money to buy my gear” pitch by 1990s telecom equipment manufacturers. The policy is designed to support manufacturing and the support of the greenback does improve the ability of Americans to buy Chinese exports.

Extrapolation: Tree Grows to Sky. Earlier this year, I involuntarily attended a client presentation on the growth and investment opportunities in China. About a third of the way through, a slide was displayed charting Industrial Production growth for China, Germany and the U.S. as a percentage of total global GDP over the past decade. Fair enough, very impressive Sino performance. A button was then clicked and these growth rates were extrapolated to 2035 when China was seen overtaking the U.S. My first reaction on seeing this was to sincerely hope that someone from the CFA Institute was in attendance so that the presenter wouldn’t leave the building with his charter. The list of potential hurdles to continued 10% annual GDP growth for China could, and have, resulted in 1,000 page tomes on Amazon. Economists and strategists frequently aren’t in the ballpark with their year over year forecasts, never mind 24 years out. In a straight line.

The prevalence of “tree grows to sky” forecasts for the technology/telecom sectors in the 90s barely needs explication. I talked about it HERE. All we really have to do is recall the terms “New Paradigm” and “information superhighway”.

The extrapolation of growth rates for an investment story takes on an extraordinary degree of momentum, for industry professionals and investors. For investors, Confirmation Bias becomes all encompassing. Portfolios are increasingly dominated by the investment theme, even if they solely consist of broad-based index funds, and everyone roots for their current investments with all the delusions that fandom implies.  Nervous investors that sell the trend early, and miss a subsequent rally, are derided by chest-thumping bulls.

Professionally, the pressure to remain bullish is intense. I was in the room in 1999 for a research meeting where the telecom analyst discussed the growing credit issues for one of the big companies. The analyst was asked how bad things could get and they hesitated, looked away and then said something like, “really, it’s a house of cards”. The Institutionally Salespeople immediately went aaaaaaapppppeshit. “YOU CANT SAY THAT! ALL MY CLIENTS HOLD THAT STOCK!”. It was loud and it was brutal and no one wanted to accept the possibility of a 180-degree turn in strategy.

Aside from this, capital markets staffing and compensation trends slowly distort in the direction of sustainable market trends. In the 90s, every firm needed a technology analyst and the poaching of analyst talent was constant and really, really expensive – seven digit guarantees were merely a starting point. In banking, the technology team worked day and night, constantly adding staff members to handle the deal flow.  As with any trend, the quality of deals declined dramatically near the end, in part because they could still get done but also because the larger banking team had to keep justifying the high levels of staffing by continuing to bring in revenue.

The China-related sector corollary is the mining and resource sectors and, to a lesser extent, global industrials. Analysts and bankers in these areas increasingly dominate the pay structure and staffing requirements. If Chinese economic growth slows dramatically at some point, it will take a long while before the heads of research departments will admit that the $8 million in annual guaranteed money they paid an oil analyst has basically gone down the drain. And, the head of the Oil and Gas banking team will fight to keep their staffing levels (with bullish outlooks) long after the business has dried up.  As with the tech bubble, these are the ways in which a sector-related bull markets become an institutional phenomenon, rather than simply a market call.

The Cockroach Theory or “I know, but story’s still intact”.  The vendor financing issues and the increasing number of late 1990s telecom bankruptcies formed good examples of the Cockroach Theory. Stock valuations were another although thankfully that is not an issue in the current market. (There are, however, market breadth-related factors that partially explain this. The 90s was an extraordinarily narrow market because of broader economic conditions).  It is always easy to identify cockroaches in hindsight.  It is also important to recognize that negative indicators that conflict with an embedded investment story are almost always ignored.

The China Story has its own increasing number of cockroaches. Regional government finances are atrocious and it is more or less consensus that the insolvency of the banking system was, despite government testimony to the contrary, merely papered over in 2004, not fixed. The Sino Forest debacle highlighted extensively dodgy accounting practices and more photos of newly built, empty neighborhoods are posted every week (or worse yet, poorly-constructed 20 story apartment buildings that have just fallen over).

In the end, this is clearly too big of an issue for a now-1000 word blog post, but I hope you see where I’m coming from. Dominant investment themes have characteristic effects both within the industry and on the psychology of investors. The dedication to, and reliance on these trends implies a degree of tunnel vision where potential hurdles are, consciously or not, avoided or ignored.  In the case of China, it is less important whether you believe the story is over or not – my favorite source on the subject, Beijing-based professor Michael Pettis, believes the economy will continue on its current path until 2013, fwiw – but that investors recognize when they become too invested in any specific outcome.





Pettis: How do we know that China is overinvesting?


Chovanec: Déjà Vu All Over Again

One-line charts and the inevitable failure of financial models

I am, admittedly, a bit of an irritable laborer and if you ask anyone I’ve worked with they will agree that my list of pet peeves is, like Slider’s Johnson, long and distinguished. Central among these is the (mis)use of  charts with only one line.  It moved up and/or down? That’s terrific. If you play with the scale on the Y axis, you can make it go up or down DRAMATICALLY, or make it look insignificant which, until you get another line on there, it actually is.

A chart showing the gold price, for example, is pointless without showing the DXY (US Trade Weighted $ index) so we can assess the extent to which the long-term inverse correlation of -0.85 is holding. And even that only gets you part of the way to learning anything because relationships like this regress to the mean at different speeds, or sometimes not at all.  An investor playing reversion to the mean on gold/DXY would need a legitimate time horizon of at least five years (currencies are notorious for ignoring fundamental factors for decades at a time – witness the strong US$ in the 90s), and even that’s assuming that nothing has structurally changed.

Every strategist, big name or no, has their minions running the exact same spreadsheets – whether economic factors, valuation attributions, sentiment or technicals.  The best strategists know which of these indicators to focus on at any given time, ie which “other line” to put on any asset price chart. Richard Bernstein (yes, him again) described his models as a cafeteria, where each day he’d grab a tray and pull down the data he felt most significant.

With this in mind it is not hard to see why most investment models fail. If, for instance, a model incorporates the fact Price/Book Value multiples has explained 15% of market performance over the past hundred years (I made that number up, btw), it will be significantly less useful in a technically-driven news market like the one we’re enjoying now. You can calculate the all the averages you want for attribution,  over any time period you want, but the second a model is built using them as constants, the model is destined for inevitable failure.

Dominant market drivers can be loosely categorized into three broad silos: Sentiment/Technical analysis (I consider technicals as a quantification of sentiment), Macro/Credit, and Valuation-oriented factors, each with a little bit of overlap.  The extent to which each factor determines overall market performance is far from constant. Overall S&P attribution can move from 65% technicals, 20% valuations, 15% macro to any combination of the three.  None of these numbers, in other words, will stay the same long enough to build a sustainably accurate model.

The above indicates why I believe that the “answers” to investing theory are much more likely to arise from studying biology, which incorporates constant adaptation, than physics. It also helps explain why retired engineers are by most accounts the worst possible type of broker clients. Almost uniformly, they believe that if they just keep working at it, the magic algo will appear. And, they have time to badger their FA every time a candidate formula doesn’t work, and they’re usually angry when they do.

Understanding the market begins is in my mind contingent with understanding the relationships, the “why” underpinning asset price movements and it is simply not possible to get any of these indications from one line price charts. Even with two lines, these relationships should be recognized as disposable – what worked yesterday may not work today. The data is best-used as a starting point, followed by investigations on the subjective factors underpinning correlation, which can provide guidance as to its persistence.  Its not easy, even if bull markets can make it appear so.


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