I am writing my masters thesis and am using LSTMs for daily stock return prediction. So far I am only predicting numerical values but will soon explore a classification style problem and predict whether it will go up or down each day.
I have explored several scenarios
- A single LSTM using as input only the past 50 days return data
- A stacked (2 layers) using as input only the past 50 days return data
The results are not great for either (and I didn't expect them to be). So I tried some feature engineering using 3 day MA, 5 day MA, 10 day MA, 25 day MA, 50 day MA of the daily returns as well as the actual daily return, meaning I have 6 input features. All other variables are kept constant yet the model now overfits (see the training and test loss plots below). Does anyone have any ideas why this may be?