Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning
This work provides an investigative tool for portfolio managers to analyze correlations in stable and volatile market environments, though it is incremental as it applies deep learning to an existing econometric approach.
The paper tackled the problem of predicting intraday and daily stock price movements using deep learning to exploit lagged correlations among S&P 500 stocks, finding that accuracies decrease with shorter prediction horizons while remaining significant, and the model performed consistently even during the 2007/2008 financial crisis.
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.