Brainstorms: Thoughts On The Degree Of Reflexivity In Regimes
How does volatility and reflexivity work across different assets in different regimes
A while back I read this great book on complexity economics called The Origin Of Wealth. I had already read a number of books on nonlinear dynamics, chaos theory, and complexity but this one really brought a tangible expression to theoretical concepts. I started going down the rabbit hole of reading papers and building models with presuppositions that were more centered on these types of ideas.
Now you might think, I don’t have an advanced mathematics or science degree, how am I going to benefit from these types of books? In my mind, these concepts put words and numbers to the things you learn intuitively in life. The problem is, if you grow up in a very safe environment without danger or risk-taking, you don’t understand how chaos, unpredictability, and complexity function.
Find a friend who actively works in some type of first responder or combat situation and pick their brain. You’ll see how much overlap there is. I don’t talk much about my upbringing but there wasn’t anything safe about it. Having specific experiences growing up frames the presuppositions you use to view the world.
One of the interesting ideas The Origin of Wealth Talks about is feedback loops or reflexivity. This is something that Soros talks about in his books but the Origin Of Wealth provides a much more comprehensive picture.
A feedback loop occurs when the input for one system (or variable) is the output for another system (or variable). This is distcintion from a correlation which only measures how closely inputs are tracking with one another. Correlation analysis can reflect a feedback loop but just because two variables correlate doesn’t mean they are in a feedback loop.
As you know, I touched on this idea of reflexivity in the macro report:
Here is one of the charts from the macro report. The key thing to notice is not simply if there is a positive or negative feedback relationship between two variables but also the DEGREE.
For example, during 2020, stocks and bonds were in a feedback loop where bonds when up as stocks went down. The opposite occurred in 2022. However, there is an entire spectrum in between that you need to navigate. This is where you need to be mapping the degree of feedback and also the TYPE of feedback (i.e. stocks up + bonds up / stocks up + bonds down / stocks down + bonds down / stocks down + bonds up).
Quantifying these dynamics can be incredibly useful for identifying signal-to-noise. Let’s dig into how these function across price action today:
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