Wednesdays with Wenzel: Early Edition
To get you ready for Money Supply release
Today, the Federal Reserve will release Money Supply numbers for September 2021. I like to break those down each month in the style that the late, great Austrian School trained Robert Wenzel used. Usually that is my most popular post. Here’s last month’s look. Of course, I’m not the only one who does this in the RW tradition, so I recommend you look at this fella’s work too.
In preparation for today’s release, it’s a good time to kick off a new weekly tradition that I hope you really enjoy. I’m going to call it Wednesdays with Wenzel.
Each Wednesday going forward, I plan to drop a little knowledge from Robert Wenzel and discuss what I like and don’t like about it. My primary sources will be his three books,
The Fed Flunks
Foundations of Private Property Society Theory
Problems with Modern Monetary Theory
plus selected posts from his website archives (Target Liberty and Economic Policy Journal), notes from his Daily Alert newsletter, and finally tidbits from his video series (on economics and investing).
Of course, it’s only Tuesday, but I wanted to get you the money supply numbers tomorrow coupled with a preview of what the series will look like.
So without a further ado, here’s an excerpt from The Fed Flunks that will be especially relevant ahead of tomorrow’s money supply release:
The data I use to determine where we are in the boom-bust business cycles is largely money supply data… The primary driver, according to [Austrian Business Cycle Theory], of the business cycle is central bank manipulation of the money supply.
The central bank creates money, but that money does not fall into hands of everyone at the same time. Because the money is created via banks, it is the capital good sectors, such as the stock market and real estate, that generally see the newly created money first, and thus it should not come as a surprise that it is the stock market and real estate market that benefit most from the boom phase of the business cycle. And, further, when money growth is slowed, that is during the bust phase of the business cycle, the impact is harshest on the same capital goods sectors, e.g. the stock market crashes but not the price of French fries or sodas.
This is a basic introduction to ABCT and to Cantillon effects, where newly created money does not raise all prices at once. One might ask why the real estate market does not crash with the stock market every time. Tom Woods does a good job of explaining constraints on the real estate market that reduce speculative activity and the incentives that were created which fueled the last bubble in his book Meltdown. The housing industry is clearly affected by the boom-bust cycle, but the structure of housing economy is vastly different than the stock market.
The money supply growth numbers that I calculate are based on the raw data that can be found at … the Federal Reserve web site. Specifically, I use the data found in the H.6 release…
I use the 13-week averaged quarterly data, provided at H.6, as a rough guide as to when money supply changes have been in effect long enough to have an impact on the economy. I feel compelled to emphasize that the quarterly measure is just a rough rule of thumb that I have developed as a result of monitoring the money supply data for decades….
Specifically here’s what I do: I go to Table 2 of the H.6 release and go to column 10, which is the M2 13-week, non-seasonally adjusted measure of M2. I calculate the percentage change in money growth by taking the earliest week in the data and most current week and determining the percentage change from the earliest week to the current week. That gives me the quarterly change in money growth. I then raise the rate exponentially by the power of 4 to get an annualized number.
The tables have moved around some, as has the way the data is presented, and also how frequently the Fed updates it, oh and how M2 is measured in the data, since the print of this book. But you get the idea.
I make a slight tweak on the calculation. I use the current week minus the same week in the last quarter, which is one week older than the way Wenzel was calculating it. I also use an exponential moving average that is 7 weeks of data. I think this will highlight trends quicker. Then I add a separate graph that looks at 26 week changes with a 13 week average to help see both trends.
Finally, let’s get Robert’s take on seasonally adjusted numbers
I use non-seasonally adjusted data… since I want to know the exact amount of money out in the system available to bid up prices…. You don’t dress based on seasonally adjusted weather…
I love that line.
That’s a good place to close up this edition of Wednesdays with Wenzel. I’ll see you later today with money supply data.
Let’s take a look at our current Liberty Wellness Challenge portfolio, along with another great fasting tip and stretching routine.
An update on UNG’s dramatic price rise yesterday
