I am trying to construct a dataset to apply MLP in forecasting financial returns. The main idea is that I want to predict future equity returns (1 month ahead, but the horizon can vary, just to give an idea) using fundamental data.
My dataset is made of n features for N companies. The main issue is that the features are not recorded at the same point in time, so that some of them would be a monthly time series and some other a daily time series. I am trying to understand the general way to approach this problem because it's my first ML application.
What should I do if my features are time series of data that differs in this sense? I have red a lot of publications, but no one gets in deeper about how the dataset is composed (before train/test splitting). Can someone recommend some useful source?
Suppose I have a variable amount of companies over 10 years of monthly data, mainly because some of them doesn't exist anymore, but it is important for the problem of survivorship bias.
For each of these N companies I have n features that represents fundamentals of these companies. I would be able to regress the monthly return, so I am trying to solve a supervised learning problem.
What I cannot understand is the feasibility of the problem using a simple Multilayer perceptron instead a more complex structure like RNN or even LSTM. I wouldn't want to switch to one of these more complex architectures for some specific reason related to the project I am building.
Probably I am missing the reasoning behind the training of the network. How should I provide input data to the architectures in order to perform backpropagation and GD? The only thing I am sure is that I should shift the monthly return series of a time period to recast the problem as supervised.