Weekly Signal & Noise Filter
Ālea iacta est
For those that fell asleep in during their high school Latin class, or skipped Classics altogether in university, “alea iacta est” translates to “the die is cast”.
Attributed to Julius Cesar in 49 BC, when he led his army across the Rubicon River in Northern Italy, the classical lexicon refers to things or events that have happened and cannot be changed back.
It is also a descriptor of current Fed monetary policy, as the over-tightening has arrived.
I. Contextualizing the Fed’s Framework
For new readers, the idea of a rapid, disinflationary-induced overtightening has been discussed in prior commentary.
First, in various Substack posts throughout November and December.
Secondly, in a series of posts on the private @PbEconomix feed on the social media app, X (formerly Twitter), back in mid-November and throughout December.
The evolution of the disinflationary information surface has mirrored the Kübler-Ross model of the five stages of grief:
Denial: Clinging to the idea that a December hike was a realistic possibility as late as October 2023.
Anger: Anchoring to year-over year measures of inflation instead of 3-month and 6-month trends.
Bargaining: The hiking cycle is over, but rate cut expectations are over-blown, in terms of both timing and scope.
Depression: Confusion over the appropriate forward monetary policy stance.
Acceptance: Cuts are a thing, and differences of opinions are now measured in months.
The evolutionary path of policy expectations is best illustrated by the changing probabilities of the pending cut cycle.
A month ago, the odds of a 25bps cut in January were 4%. They are 16.5% now.
A month ago, the odds of a 25bps cut in March were 41.5%. They are 72.8% now.
A month ago, the odds of a 25bps in May were 49.6%. They are 15.6% now.
What we are seeing is the market adjusting its expectations by pulling forward the timing of rate cuts.
These probabilities are fluid and will continue to shift with the evolutionary path of the incoming data.
It is much easier to contextualize the Fed’s monetary policy framework and use it to front run their policy reaction function, than predicting the evolutionary path of incoming data.