Recurrent Embedding Kernel for Predicting Stock Daily Direction

Stock price movement is typically affected by a lot of hidden factors. Predicting stock price direction, especially short-term direction, is very challenging and consistently attracts researches. Deep recurrent neural networks, such as Long Short-Term Memory, typically outperform statistical time series models and traditional machine learning approaches with their mechanisms of learning to vectorize historical information. However, encoding entire history into a vector may unavoidably causes information loss regardless of memory learning and updating mechanisms, especially for those tasks where decisions need to be made on the current time point and similar historical time points are of great references to the decision making. In this paper, we propose a new deep architecture called Recurrent Embedding Kernel (REK) that can learn to make optimal decisions by referring to the entire history instead of just current memory vectors. Experimental results on multiple stock ETFs with different long-term trends show that REK outperforms RNN, LSTM, and GRU, on predicting daily price direction.