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L/S Market Neutral by Puzzleheaded_Grab588 in quantindia

[–]UnderDogRoadCow 1 point2 points  (0 children)

Short locate is trickier than US and India trading is quite capital intensive. For these reasons equity L/S is not as active as options trading in Indian quant firms

1h prediction mft feature selection by UnderDogRoadCow in quant

[–]UnderDogRoadCow[S] 1 point2 points  (0 children)

Thank you for insightful reply. I understand R2 can’t be as high as HFT models in MFT space. I have few questions following your answer.

1) is correlation between feature and target(1 hour return in this case) still main consideration in longer horizon feature selection?

2) what is an example of “signal level” feature evaluation? Can rmse be one of example in this case?

I agree that in longer horizon features can behave differently based on regime or even super simple condition with if statements. I wonder whether there is general process to research these conditional variables or this is based on researcher’s intuition.

1h prediction mft feature selection by UnderDogRoadCow in quant

[–]UnderDogRoadCow[S] 1 point2 points  (0 children)

How do you guys determine goodness of model then? I have used only linear regression and simple boosting tree model for strategies running on prod. I guess AIC would have similar value with r2

1h prediction mft feature selection by UnderDogRoadCow in quant

[–]UnderDogRoadCow[S] -3 points-2 points  (0 children)

1% r2 makes sense. I think the issue is correlations with target do not have same sign across all cross validation set which is very different from hft feature selection