I am training a neural network with time dependent financial data. In order to avoid overfitting I would like to stop the training at the point where my neural network stops improving on a set of validation data, different from the training data.
My question is how to best divide my data in training and validation data? And should validation data be randomly selected or be taken all from the end of the sample?
I am trying to predict stock prices based on a window of past prices, and if I randomly select data for validation could this lead to a information leakage?