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Macro economic data for machine-learning models

I was wondering if anyone has an opinion on whether one can feed in macro-economic data, such as inflation, into ML models using the nominal 'levels' or should differences be used instead? I know with prices one needs to input the 'returns' (moves), but I wasn't sure about macro economic data levels versus moves.... Thanks for your thoughts!

2 responses

Hey Tanya,

It all depends on your specific hypothesis or what types of hypotheses you want the ML method to suggest. One of the main reasons you use returns over prices is that they're more likely to be stationary, but there are certainly times when having the prices as an input to the model with or without returns may make more sense. Likewise what preprocessing you do on the macro-economic data largely depends on what kinds of relationships you want to find with your ML model. Personally I like to use ML algorithms in research to find hypotheses and then test them out of sample. Then you can use the hypotheses as trading models once they're confirmed. You can also use them as online predictors but that tends to be a bit more finnicky. I would run the models in research with both the level data and differences as feature inputs and see which they like to use more.

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thanks! I think that's quite a neat explanation - like you said, sometimes we might want to feed in levels rather than differences... for example, I've read a few papers where they feed volatility levels rather than differences.