How Far from Normal are We?
As I have mentioned, I have been hard at work on my book and am approaching completion of the raw manuscript. The title of the book is What’s Wrong with Money?: The Biggest Bubble of All – and How to Invest with it in Mind, and if you would like to be on the notification list to receive an email when the book is published, simply send an email to WWWM@enduringinvestments.com. Even better, you can pre-order it already, even though it’s not due out until later this year or early next year.
Yesterday, I finished up the draft of the second section, which is the “where are we now” section (there are three sections in total, and I am part-way through the “investing” section). I really enjoyed writing the following section and I think the charts are fun. So I thought I would include a snippet of Chapter 9 here for you:
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If a length of steel is flexed, it is impossible to know exactly when it will fail. We can, however, figure out when that critical point is approaching, and estimate the probabilities of structural failure for a given load. These are just probabilities, and of course such an estimate depends on our knowledge of the structural properties of the piece of steel.
With economies and financial markets, the science has not yet advanced enough for us to say that we know the “structural properties” of economies and markets. And yet, we can measure the stress markets are under by measuring departures from normalcy and make observations about the degree of risk.
Didier Sornette wrote a book in 2003 called Why Stock Markets Crash: Critical Events in Complex Financial Systems.[i] It is a terrific read for anyone interested in studying these questions and exploring the developing science of critical points in financial markets. His work goes a long way towards explaining why it is so easy to identify a bubble and yet so hard to predict the timing of its demise.
So in that spirit, let us look at a few pictures that illuminate the degree of “departures from normalcy” in which economies and markets currently are. Figure 9.6 shows the nice relationship between the increase in GDP-adjusted money supply (M/Q from Figure 3.1) and the increase in the price level (P) over the nice, regular, period between 1962 and 1992. I’ve added to this plot a dot representing the latest ten year period, and (for fun) a dot representing the ten years ending in the heat of the stock market bubble in 1999. Do we appear to be out of normalcy?
Figure 9.6: Compounded money growth versus compounded inflation, 10-year periods
Figure 9.7 shows the relationship between stocks and spot commodity prices, as represented by the S&P 500 and the Bloomberg Commodity Index. The curve is from 1991 to 2007, excluding the period around the equity bubble (1998-2002). The two dots show the current point, and the point from December 1999. Do we appear out of normalcy?
Figure 9.7: Stocks versus spot commodity prices
Let’s try one more. Figure 9.8 shows the same commodity index, but this time against the money supply. It makes sense that spot commodity over time should move more or less in relation to the aggregate amount of money in circulation. The relative prices of two items are at least somewhat related to their relative scarcities. We will trade a lot of sand for one diamond, because there’s a lot of sand and very few diamonds. But if diamonds suddenly rained down from the sky for some reason, the price of diamonds relative to sand would plummet. We would see this as a decline in the dollar price of diamonds relative to the dollar price of sand, which would presumably be stable, but the dollar in such a case plays only the role of a “unit of account” to compare these two assets. The price of diamonds falls, in dollars, because there are lots more diamonds and no change in the amount of dollars. But if the positions were reversed, and there were lots more dollars, then the price of dollars should fall relative to the price of diamonds. In this case, dollars have been raining from the sky and yet their price relative to commodities has not fallen – that is, the nominal price of commodities has not risen, as we would have expected. Figure 9.8 shows that the price of money, relative to hard assets like physical commodities, may be in the greatest bubble it has ever been in. And since, unlike stocks and unlike real estate, everybody holds money, this may be the biggest bubble of them all.
Figure 9.8: Commodity prices versus money supply
All three of these figures – and I could have chosen many others – show a highly-flexed economy and highly-flexed markets. A break in this steel bar is almost assured; the only question is when.
Moreover, while we hear so much today about the “coming deflationary depression,” I have to say that with the quantity of reserves in the system and the direction in which the monetary pictures are flexed, there is in my opinion as much chance of a deflationary outcome as I have of being appointed Prime Minister of Egypt.
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[i] Sornette, Didier, Why Stock Markets Crash: Critical Events in Complex Financial Systems, Princeton University Press, 2003.
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Something weird about the last two curves (9.7 and 9.8): they look to me like they asymptotically vertical before they even far enough to the right to meet the red dots at _any_ value on the y axis. It can’t be right that commodity prices have to be infinite for the spx to read 2100. Simple dimensional analysis suggest that if there is a relationship between those numbers, it would have to be linear.
Interesting point, and a great reminder about why high R-squareds aren’t automatically worth their weight in … whatever you weigh R-squareds in. Both of those curves are curves not because that’s what the functional form SHOULD be, in some sense – I have no natural feeling what it should be but linear isn’t a bad guess. Or loglinear.
With 9.7 my suspicion is that it is linear, with commodities prices being too high when the S&P was 500-1000. Then the 1000-1500 area, which looks pretty linear to me, is probably roughly right, and the 500-1000 area shows commodities too high for the level of stocks. Which would make sense when stocks were out of favor in an environment still prone to worry a lot about inflation. Similar answer for 9.8, except that the x axis probably ought to be M/Q, rather than M, which would flatten out the chart.
Good comments. I might make some corrections to the regression lines on the basis of your comments. Thanks!
right, sorry. 9.8 should probably be log-linear. and yes, its quite possible that you have the wrong variable on one of the axes of 9.7. good thought.
glad to be of (even a tiny bit of) help!
chart 9.7 & 9.8 correlations are (I think) based on/implying a steady M2>M3 multiplier. Just conjecture but I have a feeling that part of the massive growth in M0/M1/M2 has been ‘absorbed’ by a reduction in M3&M4 and hence the correlation breaks i.e. regime shift. That said if the multiplier starts going up again & velocity picks up………..
Not really making that assumption, but see the discussion in the comment above. I don’t pay much attention to the multipliers because they are inherently unstable especially in a regime of abundant liquidity (assuming a stable multiplier was what got people in trouble with hyperinflation predictions when base money exploded, but there was never any reason to think it would be stable if the Fed was paying banks not to lend and bank reserves aren’t in the transactional money supply so they don’t affect commerce). M2 is what M2 is, and we really don’t know much about the drivers of the multipliers. Now, if we could PREDICT them…
But you’re exactly right about the what-if, and that’s the biggest fear I have (well, the biggest fear about money growth. My biggest overall fear is that when the Fed starts tightening, money velocity will go back up, as it usually does when rates rise). We just don’t know how stable the M0->M2 multiplier is. Probably banks are somewhat capital constrained even when they aren’t reserves constrained, but if THAT is the case then lending should be going up pretty quickly now because bank capital is much improved…
I understand the above but the point I was trying to make is that I think that taking M2 to correlate commodity or inflation indexes which has a good historical correlation might not be relevant at present as TOTAL money supply or substitutes (whether its called M3, M4 or L (like in Japan)) is not rising to the same degree (for now). What the actual multiplier is isn’t relevant for the correlation but commodities & inflation correlated to M3 are not as out of line as correlations vs M0/M1/M2 I suspect. As you say once rates start rising and velocity (or the multiplier) start going things will look very different. As a matter of interest have you got the above graphs correlated vs the wider monetary aggregates? I have a feeling GC/PL & others will only start making new highs once those start going…btw I agree 110% with the conclusion but I think the supplanting of monetary aggregates explains why the current data point is so far from the M2 correlation. Either way I am short fiat money so hope the data point re-correlates at some point.
As an aside I think the concentration of assets especially M0/M1/M2 in the hands of the few reduces multipliers & especially velocity which might be another reason for the data point and the break in the correlation.
Kind Regards
JR
Maybe, but since the Fed no longer produces the higher aggregates it is hard to tell what they are doing.