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What is the optimal input and target variables for forecasting with a deep neural network on daily stock/index data? More specifically I’m training a temporal convolutional network, but a more general answer is also appreciated.
Are normalized closing prices, daily returns, or nominal daily changes better inputs? For target variables, which is more interesting: nominal price, returns, or daily changes?
Are there any literature references on these topics?
There are hundreds of articles online about how to forecast daily close price using some variant of machine learning, most of the are total crap.
What is it exactly you want to predict? do you understand the dynamics of the problem at hand? machine learning is plain useless without understanding of the fundamentals, feature engineering is about knowing the problem and being able to dissect it in small piece of information for the ML algo to digest.
So ask yourself what is fundamentally driving your equity index? its stock components? their P/E ratios? the interest rates? the VIX index? the current momentum? the market regime? the investors levels of confidence?
Then ask yourself which data captures that? maybe P/C ratios would be a starting point? volumes? would daily points be sufficient is intraday needed?
