Research of daily stock closing price prediction for new energy companies in China

Qian Shen; Yifan Zhang; Jiale Xiao; Xuhua Dong; Zifei Lin; Qian Shen; Yifan Zhang; Jiale Xiao; Xuhua Dong; Zifei Lin

doi:10.3934/DSFE.2023002

Data Science in Finance and Economics

2023, Volume 3, Issue 1: 14-29. doi: 10.3934/DSFE.2023002

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Research article

Research of daily stock closing price prediction for new energy companies in China

1.
School of Statistics, Xi'an University of Finance and Economics, Xi'an 710100, China
2.
School of Mathematics, Xi'an University of Finance and Economics, Xi'an 710100, China

Received: 01 December 2022 Revised: 08 February 2023 Accepted: 14 February 2023 Published: 23 February 2023
JEL Codes: C22, C32, C45, C53

Nowadays, China is developing new energy industries to reduce carbon emissions to meet the challenge of world climate change, so investors can consider to invest in stocks of Chinese new energy companies to gain income. In order to study how to forecast stock closing prices of new energy companies in China, we have chosen 12 representative companies, and first used autoregressive univariate time series models to predict the trends of the stock closing prices in the next month. The results show that Seasonal Autoregressive Integrated Moving Average model has the best out-of-sample trend prediction effect. Second, we use multivariate time series forecasting models to predict the stock closing prices of each day through external variables. The results show that Temporal Convolutional Attention Neural Networks has the best effect of out-of-sample prediction. We recommend that investors who are interested in investing in new energy companies in China first use the Seasonal Autoregressive Integrated Moving Average model to predict the short-term stock closing price trend in the future, and then use the Temporal Convolutional Attention Neural Networks model to predict the stock closing price on the next day to decide whether to invest.
- stock price,
- Chinese new energy companies,
- time series prediction,
- neural network,
- automated machine learning
Citation: Qian Shen, Yifan Zhang, Jiale Xiao, Xuhua Dong, Zifei Lin. Research of daily stock closing price prediction for new energy companies in China[J]. Data Science in Finance and Economics, 2023, 3(1): 14-29. doi: 10.3934/DSFE.2023002

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Abstract

Nowadays, China is developing new energy industries to reduce carbon emissions to meet the challenge of world climate change, so investors can consider to invest in stocks of Chinese new energy companies to gain income. In order to study how to forecast stock closing prices of new energy companies in China, we have chosen 12 representative companies, and first used autoregressive univariate time series models to predict the trends of the stock closing prices in the next month. The results show that Seasonal Autoregressive Integrated Moving Average model has the best out-of-sample trend prediction effect. Second, we use multivariate time series forecasting models to predict the stock closing prices of each day through external variables. The results show that Temporal Convolutional Attention Neural Networks has the best effect of out-of-sample prediction. We recommend that investors who are interested in investing in new energy companies in China first use the Seasonal Autoregressive Integrated Moving Average model to predict the short-term stock closing price trend in the future, and then use the Temporal Convolutional Attention Neural Networks model to predict the stock closing price on the next day to decide whether to invest.

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