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Here is the last article related to our discussion of observations by the financial media with only a handful of observations, statistics, and time series data.  The goal here is to provide an actual example to see what some of the pitfalls are.  Prior to starting that discussion, I wanted to provide links to the first and second articles:

The first article laid the groundwork for the idea that there are many misuses of statistics and related items which appear most everyday in the print and television financial media.  Here is a link:

https://latticeworkwealth.com/2019/11/11/breakthrough-drugs-statistics-and-anecdotes-investing/

The second article focused more on time series data and using the normal distribution to make conclusions and predictions about the financial markets.  As promised, I will be posting a more detailed mathematical article as a supplement.  However, the point of this article is only to make you aware of what to look for in general.  You do not need to feel the need to get very granular.  The audience that really wants more information has contacted me offline and is very small.  Here is a link:

https://latticeworkwealth.com/2019/11/20/breakthrough-drugs-statistics-and-anecdotes-time-series-statistics/

Now let’s begin our journey to sum up these two articles by using the specific example of a “yield curve inversion”.  First, what exactly is the yield curve?  Okay, we are going to keep this explanation simple.  The point of this article is not to become an expert on bond yields.  The yield curve is simply the interest rate (referred to as “coupon” in the financial jargon) of bonds at certain maturities.  For U.S. Treasury issues, you normally look at the interest rate on one-month, three-month, and six-month  U.S. Treasury Bills.  Then you add in one-year, two-year, three-year, five-year, seven-year, and ten-year U.S. Treasury Notes.  And finally, you have the thirty-year Treasury Bonds (otherwise referred to as the “long bond” in financial jargon).  Why bills, notes, and bond?  It is simply a naming convention for all U.S. Treasury debt less than twelve-months is a bill, between one-year and ten-years is a note, and anything greater than ten-years is a bond.  Once you know all those interest rates, you draw a line that connects all of those interest rates from one-month U.S. Treasury Bills all the way to thirty-year U.S. Treasury Bonds.

Why do people focus on this?  Well, first, you would expect that interest rates for one-month bills to be lower than thirty-year bonds.  Think of it like this:  if your friend borrowed $20 and was going to pay you back at the end of the week or in three years.  What interest rate would you charge him/her?  Now a totally altruistic person would say nothing.  But let’s say you are trying to teach your kids the value of money.  Most people would charge a greater amount of interest for three years compared to one week.  The U.S. Treasury debt market works very similarly.  People who loan the government money for one month normally demand a lower interest rate than those people who are going to have to wait thirty years to get their money back.  When the economy is growing normally, the yield curve is called steep.  It goes from lower interest rates and gradually moves higher.  But that is not the only shape of the yield curve possible.

The other two are flat and inverted.  A flat yield curve simply means that interest rates all along the various maturities are pretty much the same.  Now, as our article will shift to, an inverted yield curve means that closer maturities actually have a higher interest rate than the very long-term maturities.  Why does this happen?  Well, most economists and financial professionals will tell you that the economy is slowing down and a recession is coming.  Why?  The last 7-8 recessions were preceded by a yield curve inversion.  Let’s take a look at the yield curve over time by comparing two-year U.S. Treasury Notes with ten-year U.S. Treasury Notes.  Keep in mind that we are taking a look at the difference between the two.  A number that is positive means that interest rates are higher for ten-year bonds and a negative number means just the opposite.

Here is a daily comparison from June 1, 1976 through November 6, 2019:

Daily Spreads - All Data

Here is the same comparison but on a monthly basis:

Monthly Spreads - All Data

I used a graph of the month difference (“spread”) to smooth out some of the volatility.  Now if you remember your economic history, you will notice that there are negative “spreads” that occur prior to a downturn in the U.S. economy.  Let’s focus on the yield curve inversion prior to the Financial Crisis.  As you can see, the yield curve was inverted at various times over the course of 2006 to 2008.  It took approximately two years from the yield curve inversion before the Financial Crisis hit in full force in September 2008.  Because this pattern has occurred before, economists and financial professionals appearing on television or writing articles have pointed to the yield curve inversion just recently.

But you should take a closer look at the latest inversion of the yield curve.  It is only a small difference and only lasted for a short period of time.  I will blow it up to investigate and will show November 1, 2018 through October 31, 2019:

Daily Spreads - 2018 to 2019

I had to use a one-year timeframe to even be able to get the difference in interest rates to show up.  So, for a period during August 2019 and September 2019,  there were a plethora of financial markets’ articles and television commentators who talked about how soon a recession would take place in the U.S. economy.  In fact, there were days when over 25% of the day’s coverage of financial market news focused only on this yield curve inversion.  Now, will the U.S. economy go into recession in the next 12-24 months?  Well, that is still an open question.  The main point is that the financial news media focus on things that have similar patterns for only a brief period of time.  Even worse though, financial “experts” who know very little about the bond market and economics start making predictions.  And, as I have said many times in the past, the financial news media rarely, if ever, invites guests back or has another article written about how wrong they were.

Lastly, you will sometimes here people say that there is a 30% chance that the U.S. economy will enter a recession in the next 12-24 months.  Where does that percentage come from?  Oftentimes, it is a “best guess”.  Unless you hear that same financial professional talk about a probity econometric model that came up with that percentage of recession probability, you should take the comment with a “grain of salt”.  Trust me though, most financial professionals are not running probit models when they tell you their opinion on this matter (related to an inverted yield curve or due to another topics/event).  In the supplemental article that is forthcoming, I will actually discuss a panel probit model that the Bank of International Settlements (BIS) just ran to look at the phenomenon of yield curve inversion preceding a recession in an economy.  It is not that the percentages derived are “correct” per se.  The important point is that they are not “pulled out of a hat” by someone.

I hope that this series of articles has been helpful in covering this important topic.  The main takeaway is that, whenever you hear or read about a financial market prediction, you should always look to see how many examples (observations) are being used.  If it is less than 30, you should not take it very seriously at all.  Additionally, any time series data that is trending upward or downward cannot be used to talk about the financial markets.  Remember you need to first-difference the time series data or adjust it in some other manner.  Why?  Otherwise, there may be correlations between two or more time series that just are not really there because the trend dominates.  (Please refer to the second article for more information in this regard).  So, please be more aware and skeptical of what you hear or read.  It is not that the information/prediction is totally wrong.  The salient thing is that it should be based on sound statistics and mathematics.