Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Sep 26;10(19):e38560.
doi: 10.1016/j.heliyon.2024.e38560. eCollection 2024 Oct 15.

Forecasting the volatility of educational firms based on HAR model and LSTM models considering sentiment and educational policy

Xuefan Li  1 , Donghua Li  2 , Yuxiang Cheng  3 , Wen Li  4
Affiliations

Forecasting the volatility of educational firms based on HAR model and LSTM models considering sentiment and educational policy

Xuefan Li et al. Heliyon. .

Abstract

This study aims to investigate the impact of sentiment and policy on the volatility of educational stock prices by using HAR (Heterogeneous Auto Regressive) and LSTM (Long Short-Term Memory) models. We construct a weighted educational index volatility composed of nine publicly traded educational companies from the Shenzhen Stock Exchange and Shanghai Stock Exchange, and analyze the impact of sentiment and policy variables on the volatility of educational stock prices. We use OLS regression models and LSTM prediction models to analyze the data by developing various of models to investigate the impact of sentiment, education policies and their intersection effect. The empirical results show that the sentiment index and policy index have significant impacts on different time horizons of educational stock price volatility. The LSTM model confirms the effectiveness of including sentiment and policy variables in predicting educational stock price volatility. These findings carry several practical implications, particularly for investors, education-listed companies, and policymakers. And this study contributes to the literature by providing new evidence on the impact of sentiment and policy on the volatility of educational stock prices and by demonstrating the usefulness of combining HAR and LSTM models in predicting stock price volatility.

Keywords: Education stock volatility prediction; HAR model; Investor sentiment; LSTM model; Political policy.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Daily price volatility dynamics of educational index.
Fig. 2
Fig. 2
Five minutes high-frequency price dynamics of educational companies.
Fig. 3
Fig. 3
General structure and recurrent module display of LSTM.
Fig. 4
Fig. 4
Comparison of actual daily volatility and in-sample predicted daily volatility.
Fig. 5
Fig. 5
Comparison of actual weekly volatility and in-sample predicted weekly volatility.
Fig. 6
Fig. 6
Comparison of actual monthly volatility and in-sample predicted monthly volatility.
Fig. 7
Fig. 7
Forecasting for future 15 daily volatility.
Fig. 8
Fig. 8
Forecasting for future 15 weekly volatility.
Fig. 9
Fig. 9
Forecasting for future 15 monthly volatility.
See this image and copyright information in PMC

References

    1. Berger D. Investor sentiment: a retail trader activity approach. Rev. Account. Finance. 2022;21(2):61–82.
    1. Gong X., Zhang W., Wang J., Wang C. Investor sentiment and stock volatility: new evidence. Int. Rev. Financ. Anal. 2022;80 - PMC - PubMed
    1. Andrew A., Salisbury A. The educational experiences of Indian children during COVID-19. Econ. Educ. Rev. 2023;97 doi: 10.1016/j.econedurev.2023.102478. - DOI
    1. Huang J., Xuedi P.I., Huang Z. Understanding the complexity of teacher professional learning in the context of China's" double reduction" policy. Educ. Sci. Theor. Pract. 2022;22(2):194–209.
    1. Liu Z., Duan X., Cheng H., Liu Z., Li P., Zhang Y. Empowering high-quality development of the Chinese sports education market in light of the “double reduction” policy: a hybrid SWOT-AHP analysis. Sustainability. 2023;15(3):2107.

LinkOut - more resources