Friday, April 19, 2013

Data Snow Blindness

I am taking a break from everything else that’s going on today to tell the story of an error in data analysis and presentation, seriously affecting the strength of the argument being made, that to me is jaw-dropping – not the error itself, but people’s reaction to its being pointed out.  And that reaction is:
Nothing.
Seriously.  Nothing.  Nothing by commenters on either blog where the original analysis and reactions is prominently mentioned (Weisenthal and Krugman).  Nothing by the bloggers themselves. No change in the analysis as presented on the blog.  Dead silence. Debate continues to be waged based on the uncorrected blog data.
I have racked my brain as to why this might be.  The best reason I can think of is that most people are so focused on the “bright” parts of the data as presented, the fact that the two measures over time involved show a clear relationship, that they ignore the fact that one of the two measures is not quite the correct one to use.  It is, if you will, an example of blindness like the blindness caused by trying to look ahead into a landscape of snow reflecting the sun fiercely – the well-known phenomenon of “snow blindness.”  Data snow blindness.  And, as in the case the other snow blindness, you become truly blind – you simply don’t notice the information indicating that the analysis is off.
The Story
The story begins when Weisenthal reacts to an ongoing debate on the merits of investment in gold by doing a couple of graphs comparing the value of the S&P 500 index over time (1979 and 2007 to now) to the value of gold in the markets ($/troy ounce). His point – a valid one – is that even with recent fluctuations in gold’s price, investment in stocks outperforms investment in gold over time.
Now, as I found out in doing my post-MIT-Sloan-School-of-Management research on investment theory for my own use, it turns out that the S&P 500 is a price-only measure.  That is, the typical value of the S&P 500 that everyone quotes does not include the value of dividends that the companies issue over time, and it doesn’t include the reinvestment of those dividends.  As far as I can tell from observations over the past 15 years as well as from previous data, the dividends themselves average about 2.3% per year, and the reinvestment adds another 0.1% per year (the “geometric mean” of returns, the correct way to measure, suggests that at least until about 2007 (a period of maybe 90 years), the growth in the S&P 500 was about 10.8% per year, which meant that reinvestment of dividends over the course of a year would yield about 2.3% times 10.8% times ½ [to reflect the fact that dividends don’t all occur at the start of the year], or about 0.12%).
If you don’t believe my assessment, go look at the S&P web page where they report S&P 500 values and returns over time.  Above the regular report is a measure of “total return.”  If you look at the description of that return, you find that “total return” does indeed compute return with dividends included (it appears that dividend reinvestment is also included, but I’m not sure about that).  They do it over a much longer period than a year, so at this point the TR index is almost twice the regular S&P 500 index.
If you add in this adjustment, the result of the analysis changes significantly.  In Weisenthal’s original graph (made so that 2013 represents 1), the ratio of S&P 500 to gold price goes from about 0.1 in 1979 to 1 in 2013. Accepting the same starting point, my computed ratio goes from about 0.1 in 1979 to 2.12 in 2013.  A sharp drop in the ratio (reflecting dubious “flight to safety”) from 2007 to 2009 becomes a drop from about 6 to 2, not a drop from 5 to 0.7. It hasn’t been flat or dropping since then; it’s been climbing by about 6%.  Stocks don’t just beat gold over long periods of time – they beat gold over the short and medium term pretty consistently, and over the long term by a huge amount – try 21.2 times as much.
So I posted this in a comment in Weisenthal’s blog and, getting no response, in Krugman’s blog.  As noted, no one took notice (I would have posted my comments in caps, but it’s not netiquette to scream).  In fact, the debate in the comments proceeded as if the original Weisenthal graphs were the issue – is the government understating inflation data (from “gold bugs”) and therefore there is another gold price surge to come once that becomes clear?  Is the advantage of stocks over gold clear enough, or would an even further surge erase it?
Data snow blindness.
Implications For Y’All
At this point, I have to distinguish this from other sources of problematic analyses that have happened recently – e.g., the Reinhart-Rogoff controversy in economics (which apparently revolved partly around a coding error) and the London-trader miscomputation of risk (partially a human Excel miscalculation).  Those are not really a problem of not noticing that one of your sets of data points is not capturing well what you want it to capture – and they have been exhaustively investigated and debated.
For another example of data snow blindness, I’d like to go back to investments again – the idea of 401(k)s (also applicable to IRAs).  What no one seems to be pointing out is that the expense ratios in those 401(k)s are quite high – I believe they’re still above 2%.  If your employer isn’t paying into your 401(k), this means that you must balance the gain 20 years from now in lower taxes when you cash them out with the loss you get from not sticking an equivalent amount in a Vanguard S&P 500 index fund with an expense ratio of 0.1%.  Even if you pay zero taxes when you cash out, somewhere over a 10-20-year dividing line, you may well lose money on your IRA/401(k) compared to the alternative.  And that’s true of 401(k) bond investments as well (vs. bond index funds). Or so the data suggests – but no one seems to notice this enough to discuss it.
Here’s a few more:  stock risk vs. bonds and everything else. If you have your money in a US S&P 500 index fund, what is your risk?  Over any 20-year period, the stock market as a whole has outperformed any other investment – including inflation.  But what if the stock market collapses drastically and stays collapsed?  If you think about it, that would mean that the US government has collapsed, since it’s the government that insures the banks underpinning economic investment by various mechanisms. So the risk of collapse of the stock market’s 500 largest members is pretty much the same as the risk of the US collapsing – in which case, without that government backing, your money is likely to be worthless (and your gold coins). So why are you “diversifying” beyond the stock market, again?  If you’re planning to start drawing it down or keeping it level within the next 20 years, then some amount of, say, a bond index fund or inflation-protected securities (TIPS) that will keep up with inflation is fine; but the reason for doing anything beyond that is not as clear as it might seem.  Data snow blindness.
How about stock investment returns? Today mutual fund companies compare their results to the S&P 500 – is that the regular S&P 500 or the total return one?  Do they include their expense ratios – above 2% until recently, now (afaik) around 1.5% -- and do you compare them to the Vanguard 0.1% and the Fidelity Spartan 0.2% (plus withholding a bit in cash, which right now earns effectively zero)?  There’s a reason why those index funds outperform around 70% of all other stock investments over a 10-year period, and probably close to 90% over a 30-year-period.
In other words, the real implication of data snow blindness is that it is probably hitting you right in the wallet right now – not necessarily yours, since everyone else seems to be doing it too. Or almost everyone else … Gee, I wonder why the Vanguard S&P 500 index fund is one of the two most popular stock investments today?
Anyway, please think about it.  Me, I’m going to go off and check myself for further signs of data snow blindness. 

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