A double-layer neural network framework for high-frequency forecasting

Nowadays, machine trading contributes significantly to activities in the equity market, and forecasting market movement under high-frequency scenario has become an important topic in finance. A key challenge in high-frequency market forecasting is modeling the dependency structure among stocks and business sectors, with their high dimensionality and the requirement of computational efficiency. As a group of powerful models, neural networks (NNs) have been used to capture the complex structure in many studies. However, most existing applications of NNs only focus on forecasting with daily or monthly data, not with minute-level data that usually contains more noises. In this article, we propose a novel double-layer neural (DNN) network for high-frequency forecasting, with links specially designed to capture dependence structures among stock returns within different business sectors. Various important technical indicators are also included at different layers of the DNN framework. Our model framework allows update over time to achieve the best goodness-of-fit with the most recent data. The model performance is tested based on 100 stocks with the largest capitals from the S&P 500. The results show that the proposed framework outperforms benchmark methods in terms of the prediction accuracy and returns. Our method will help in financial analysis and trading strategy designs.