If you’ve been with me for a while, you’ve probably heard me say that I spend a lot of time reading. While much of it focuses on markets and investing, I also make a point of reading outside finance. Some of the best ideas come from people solving completely different problems.
Recently, I read an article by James Sethna at Cornell University. He posed a simple question: Why is science possible?
Sethna’s research examines complex systems. Whether scientists are studying cells, ecosystems, or economies, they’re confronted with countless variables. Yet useful models don’t attempt to account for every detail. Instead, they isolate the relationships that have the greatest influence on the outcome they’ re trying to understand.
Investors face the same challenge.
It’s easy to believe that better decisions come from gathering more information. Another research report. Another analyst’s opinion. Another interview. Eventually, you have more information than you can effectively use.
I’ve learned that more information doesn’t always improve a decision. Often, it simply makes the decision more difficult.
That realization shaped my own research.
I wasn’t trying to build an indicator that explained everything happening in the market. I wanted to identify one measurable condition that consistently appeared before the types of opportunities I wanted to trade.
After years of testing, I came to the conclusion that that condition was fear.
More specifically, I wanted to measure periods when fear became excessive and identify the point where that fear began to recede. Those observations became the foundation of the ITV indicator, and the trading process I have been sharing with my readers for over a decade.
That process also explains why I don’t chase headlines. Every trading day brings another explanation for what just happened and another prediction about what comes next. Some will prove accurate. Many won’ t. None of them determine whether I have a trade.
My decisions come from a process that was established long before today’s headlines appeared.
Trusting a system doesn’t mean ignoring new information. It means knowing the difference between information that changes the setup and information that simply competes for your attention.
Sethna’s research reminded me that every useful model is selective by design. It doesn’t become valuable because it includes everything. It becomes valuable because it consistently identifies what matters.
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