Price change prediction of Ultra high frequency financial data based on temporal convolutional network

Wei Dai · Yuan An · Wen Long

39 citations0/0 files storedProcedia Computer Science

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

Abstract

Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2018Deep learning models for bankruptcy prediction using textual disclosures 2020Portfolio optimization with return prediction using deep learning and machine learning 2020Gold volatility prediction using a CNN-LSTM approach 2006Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest 2020Forecasting daily stock trend using multi-filter feature selection and deep learning 2015Modelling high-frequency limit order book dynamics with support vector machines

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2025A Dual Output Temporal Convolutional Network With Attention Architecture for Stock Price Prediction and Risk Assessment

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