Looking forward to it, great job so far!

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I have a question about applying test sets to models. Although I planned to email you, but i found you here by random!

My master’s thesis in Finance was based on Easley et al.'s 2020 paper “Microstructure in the Machine Age” from the Review of Financial Studies. This paper used Random Forest (RF) to analyze financial market microstructures. The researchers created a RF-model with dataset of several microstructure indicator. Then evaluated which features were most significant in classifying the new unseen data (test-set). We do not look at prediction in this case, we look at the which features split the most effectively on the test-set.

In our tests, several indicators had high feature importance when creating the RF-model. But when applied to new, unseen data (the test set), the importance of these features often changed. Features that were crucial in the training phase were not as important for splitting new data, a pattern we consistently observed.

Easley et al. (2020) suggest that this occurs because financial microstructures are traditionally constructed on “in-sample” data, which has limited predictive power for new, unseen data.

I believe this finding can be important, as it can serve as an alternative way to back-testing. As backtesting does not make any sense for financial data, as all the random states in the market has already known.

I’m wondering if the Tsetlin Machine could be used to measure and analyze the same idea?

I actually stumbled over your research by accident when searching for a job. Pretty interesting model!

When is the Timeserie chapter for your book coming out?