A Volume-Aware Positional Attention-Based Recurrent Neural Network for Stock Index Prediction

Abstract

With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as recurrent neural networks (RNNs) and attention mechanisms to capture the complex patterns hidden in stock market trends. Most existing approaches to this task employ an attention mechanism that primarily relies on the information extracted from input features but fails to consider the other important factors (e.g., trading volume and position), which can potentially enhance these attention-based approaches. Motivated by the observation, we extend the attention mechanism with features needed for stock performance prediction in this article. Specifically, we propose a volume-aware positional attentionbased recurrent neural network (VPA-RNN) for this task. First, we propose a generic method of adding position awareness to the attention mechanism. Next, the trading volume is incorporated into the original attention distribution to form a revised distribution. To evaluate the effectiveness of VPA-RNN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed VPA-RNN can significantly outperform several existing highly competitive methods.