Yesterday, the monthly CPI (inflation) numbers were released, and there couldn’t have been a more perfect case study for me to demonstrate what Signal 2 Noise is all about. Let us start with the basics.
All of my charts and tables are home-grown in Python by me. I am not using any third-party software services but rather taking full control of the data. Hopefully, my coding skills can match my vision and speed needs. I am mentioning this because I can see I need to add more space to the right margin and put data labels on the mean. Monthly inflation came in hotter than expected, at 0.44%.
Most people make the assumption that higher inflation will lead to delays in the Fed reducing interest rates, which is normally interpreted by the market as bad news. The opposite, in fact, occurred last night with a strong rally. Now let us get back to the headline, and let me explain why this is such a perfect case study.
We all know that the Fed has a 2% inflation target for core inflation. A lesser-known fact is that the 2% isn’t based on the Labour Department’s report released yesterday. Instead, the Fed’s goal is measured against a separate index called the personal-consumption expenditures price index (PCE), which is maintained by the Commerce Department, and their February report is to be released on the 29th of March.
When I read the headline this morning, the first thing I did was run an analysis on what the performance in the SP500 would look like if you only invested when core inflation was below 2% versus above 2%. Spoiler alert: the results are far more noisy than you would have thought. Perhaps I can share my findings with the Bloomberg journalist.
In the above analysis, investing in the SP500 when core inflation is below 2% results in a cumulative return of 783%.
In the above analysis, investing in the SP500 when core inflation is above 2% results in a cumulative return of 1224%.
This is exactly what I am trying to do in the newsletter. I want to separate the logical noise from the signal with data-driven research.