Let's say you are building a simple model (like the classroom examples) of trying to predict, given past information, if the stock goes up or down in the future. One could, like in classroom examples, turn this into a classification problem, and encode 1 if stock goes up, 0 if unchanged, and -1 if stock goes down.
When modeling such a model, in practice, would you:
Look at the ending prices of different fixed intervals, for example, 1 min, 1day, 2day, etc.. So if the stock price is higher exactly 1 min from now, you would encode this as a 1.
Or model so that at any point in time within 1min, 1day, 2day, the stock goes up in price. So let's say your time frame is 1min, and the stock goes up at the 30 second mark, but then goes back down and ends up negative, you would still encode it as a 1.
In the class room, we tend to do the former. But I would imagine in practice that this would lead to some datamining of the time period for the response - as what time period would you choose? The latter makes more practical sense as you just care if there's an opportunity to trade at a higher price. But, effectively, the time period of the response is different for each example - not sure how that would affect any of the assumptions and results of statistical/ML models.
Which would you say is more effective in practice?