  Standard methods and theories in finance can be ill-equipped to capture
highly non-linear interactions in financial prediction problems based on
large-scale datasets, with deep learning offering a way to gain insights into
correlations in markets as complex systems. In this paper, we apply deep
learning to econometrically constructed gradients to learn and exploit lagged
correlations among S&P 500 stocks to compare model behaviour in stable and
volatile market environments, and under the exclusion of target stock
information for predictions. In order to measure the effect of time horizons,
we predict intraday and daily stock price movements in varying interval lengths
and gauge the complexity of the problem at hand with a modification of our
model architecture. Our findings show that accuracies, while remaining
significant and demonstrating the exploitability of lagged correlations in
stock markets, decrease with shorter prediction horizons. We discuss
implications for modern finance theory and our work's applicability as an
investigative tool for portfolio managers. Lastly, we show that our model's
performance is consistent in volatile markets by exposing it to the environment
of the recent financial crisis of 2007/2008.
