I have several timeseries datasets of stock data, with fundamental indicators. I would like to build a model that selects stocks for buy and hold.
I understand that to perform this task I have two options:
Train a model for each stock: This way, I understand that it is the most practical, however, the amount of data for each model will be very reduced (Each dataset has less than 1000 lines).
Putting all the data together in a single dataset: I didn't find anything on the internet to support this idea, however, I understand that the model would be more robust and would have a much larger amount of data to be trained.
So, what would be the correct way to perform this type of analysis? Any of you would suggest another way?
Thannk you in advance!