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Ah I understand now!! How about output? Is it generally to try to model a continuous variable (futureRet), or to a logit regression to model odds of a category (TopDecileRet)? If Y is cont, confused on how to trade a model, do you adjust your exposure as expctd y changs?
I exclusively try to predict continuous variables, generally either (excess) return over some period or (excess) return scaled by a volatility forecast
By “excess return” I mean return neutralised to any factors that you plan to hedge in your portfolio (eg market, sector or country exposure)
The typical “quant” way to trade it is to solve some kind of optimization problem with a linear alpha term (your forecast is the alpha), quadratic risk term, and penalties for transaction costs/market impact
Following this, another example is categorical to predict as a way to get around non-linear dynamics. E.g. if you have a factor implicated in extreme conditions, it may show up as a skew in returns (24hRet < 0) but not show up properly in linear regression.
So in this case simply checking for the categorical (a pretty naive parameter threshold of 0, not overfitting) will give you quicker insight into the value of the relationship
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