Before revision:  While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, two major issues still exist in recent studies. First, the capture of long-range dependencies in time series is not sufficiently addressed. Second, the chaotic property of financial time series fundamentally lowers prediction performance.
After revision:  While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends.