Inductive reasoning in trading may be the preferred method for developing systems. One reason, as argued by Doyne Farmer in Cracking Wall Street, is due to Local Predictability. The concept is based in chaos theory, and references the idea that there may be an underlying order in the short term that enables prediction of events that are not equally predictable, if at all, in the long term.

Farmer was a founder of Prediction Company, which was purchased by UBS. There is an interesting presentation Prediction Company created a few years back, titled The Business of Model Based Trading. Both the article and the attached .pdf are worth a quick read.

In the referenced article, there are a number of analogies and layman interpretations of the interaction between physics, math, and finance. Below are two passages that I enjoyed:

He likes to use a favorite example when explaining the anatomy of a prediction. “Here, catch this!” he says, tossing you a ball. You grab it. “You know how you caught that?” he asks. “By prediction.” Farmer contends you have a model in your head of how baseballs fly. You could predict the trajectory of a high fly using Newton’s classic equation f=ma, but your brain doesn’t stock up on elementary physics equations. Rather, it builds a model directly from experiential data. A baseball player watches a thousand baseballs come off a bat, and a thousand times lifts his gloved hand, and a thousand times adjusts his guess with his mitt. Without his knowing how, his brain gradually compiles a model of where the ball lands — a model almost as good as f=ma, but not as generalized. It’s based entirely on a collection of hand-eye data from past catches.

And:

While running from lions, or investing in stocks, the tiniest edge over raw luck is significant.

I’ve completed about half of the book on Einstein, and it is very apparent that he prefers deductive reasoning over inductive reasoning. Simons, in 2000, made the opposite argument regarding his Medallion Fund:

We don’t start with models. We start with data. We don’t have any preconceived notions. We look for things that can be replicated thousands of times. A trouble with convergence trading is that you don’t have a time scale. You say that eventually things will come together. Well, when is eventually?

This identifies a divide between the methods of trading strategy creation.

First, you have those who believe that trading methods should be created through the analysis of existing characteristics. Behavioral finance and much of market microstructure theory are based upon this type of reasoning.

Second, you have those who believe that any pattern or anomaly derived from data is just as substantiated. Medallion’s historic returns are based upon this type of reasoning.

I thought this was interesting after reviewing ASTA, which seems to have capabilities of supporting both approaches. Perhaps, the solution to the question of which method is more appropriate is based upon what the trader believes they are looking for. Einstein was looking for underlying laws, and from a prior post we already know that Simons doesn’t believe in underlying laws in the financial marketplace, even if the principles that underpin his systems don’t change.

[Update] This post brings to mind a newsletter I received a number of months back from Mike at Breakout Futures. You can access the main article here. He provides simple code for TradeStation that will develop automated systems for the user, based upon the type of price pattern system the trader dictates.

While I haven’t used this type of system, I think it warrants further review with caution - it is likely similar in concept, although more rudimentary, to those systems developed at aiQUANT and Neural Market Trends.

Personally, I have used deductive reasoning to develop frameworks for money management methods, and then tested data in a similar, inductive method, to create the details.