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Let's say you want to model the next day price returns for a set of US equities large cap ETFs (a relatively homogenous group). Would you model all the ETFs as a single, 15 years data set, or each ETF individually? Each ETF would have around 15*252 = 3780 unique data points.

I know, it's a general question that is relative to the predictors used, the strategy goal and other things. But what are the plus and minuses of each approach?

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Model them individualy and as a group. When you model them as a group you are essentially building a stock index that you can compare the performance of individual stocks to and can then calculate a subgroup beta for each stock. You can also calculate a beta coefficient for the group as a whole to the wider market.

Since I assume that you are modeling them with the intention of trading on the prediction output, you need to have a sense of the variance of each stock due to the discrete nature of placing trades. You cannot enter a single trade that buys the whole group, unless the group is already a fund of funds ETF.

To build an index you neeed to include all of the data points anyway. 3780 unique points is simple in post 2007 Excel 64 bit model. A solution with a SQL database in any popular programing language will also make 3780 points seem trivial.

The challenge will be to ensure you normalize price movements for stock splits, dividends, etc over such a long time period.

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