Optimizing the Output of Long Short-Term Memory Cell for High-Frequency Forecasting in Financial Markets

Abstract

High-frequency trading (HFT) requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in HFT. A well-documented and tested method that considers these time irregularities is a type of recurrent neural network (NN), named long short-term memory (LSTM) NN. This type of NN is formed based on cells that perform sequential and stale calculations via gates and states without knowing whether their order, within the cell, is optimal. In this article, we propose a revised and real-time adjusted LSTM cell that selects the best gate or state as its final output. Our cell is running under a shallow topology, has a minimal look-back period, and is trained online. This revised cell achieves lower forecasting error compared to other recurrent NNs (RNNs) for online HFT forecasting tasks such as the limit order book (LOB) mid-price (MP) prediction as it has been tested on two high-liquid U.S. and two less-liquid Nordic stocks.