Work

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

Year: 2022

Type: conference-paper

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.

Source: Procedia Computer Science

DOI: 10.1016/j.procs.2022.01.149

Authors: Wei Dai, Yuan An, Wen Long

Institutions: Chinese Academy of Sciences, University of Chinese Academy of Sciences

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: 面向金融市场的多源异构数据知识表示与应用研究, G8183588683