Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning

Qiang Gao

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2004A hybrid ARIMA and support vector machines model in stock price forecasting 2010Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2014Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction 2018Deep learning-based feature engineering for stock price movement prediction 2016Stock prediction using deep learning

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