Mechanical Investing using machine learning initial observations.

Well, I told everyone last year that I was stepping back from continually trying to outperform the market
to just sit back and enjoy life. But I can’t help myself.
I had SIP historical data in a database and P123 started allowing downloads of historical data for a price.
Free open source very capable software like Scikit-learn has the tools to make predictions.
I’m still in the exploration stage experimenting with different features, different learning algorithms and
am not ready to put real money into the pot yet.

The practitioners of financial ML predictions have come up with methods to build ML models that
supposedly eliminate some of the unreliable post discovery problems MI screens have had in the past.
When we build a MI screen in 2024 we make a model based on what we already know about what
already happened.

ML evaluates a model differently. They first define an algorithm to take any data and predict the future
(like a neural network or many tree search). Then the break the historical data into shorter subsets. For
each subset you train the predictor model on typically 60 to 70% of the subset and test the results.
The model is judged on the results of all the train-test sequences where no train-test sequence has any
future data. A model that does well did well looking only at the data it had at that point in time.

My ML results are different from the results we are used to from screens. With screens we typically
trying to come up with a method using a very limited set of features that pick the top 5, 10 or 15 stocks
out of a universe of one or two thousand. ML regressions are designed to accurately predict both
winners and losers accurately. For ML I’ve been putting in 60 to 100 financial and momentum features
and letting the algorithms decide what works. Others are using 300 or more. My initial results trying for
the top 1% (15 out of 1500) give good but very inconsistent results. However top 5% is very good which
unfortunately, is a very large portfolio.

My initial impressions:
My ML is not yet up to the performance level other individuals (some obviously professional) are reporting.
My ML doesn’t do as well in the S&P500, probably because they are well evaluated by the big boys.
My ML does much better on mid-caps and even better on small caps.
My ML seems to especially outperform during the 2008 and covid dips. The down is slightly less and the
up is exceptionally good.

I’m still in the exploration stage experimenting with different features, different learning algorithms and
am not ready to put real money into the pot yet.
I haven’t got to the point where I model actual trades with turnover and friction.