IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v57y2023ics1544612323006232.html
   My bibliography  Save this article

Impactful messaging: Elite sentiment in Chinese new energy vehicle vs machine learning perspective

Author

Listed:
  • Gong, Xingyue
  • Jia, Guozhu

Abstract

Elite messaging is sensitive to framing decisions and shaping public sentiment. This study examines the influence of elite sentiment on the pricing of the stock index of China's new energy vehicles (NEVs). To construct elite sentiment indexes for China's NEVs, data from a highly active online elite forum were collected and a series of machine learning models were utilized to generate predictions. The results demonstrate that the elite sentiment index has a pronounced predictive impact on stock index prices. Furthermore, incorporating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method can further enhance the baseline model's predictive ability.

Suggested Citation

  • Gong, Xingyue & Jia, Guozhu, 2023. "Impactful messaging: Elite sentiment in Chinese new energy vehicle vs machine learning perspective," Finance Research Letters, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323006232
    DOI: 10.1016/j.frl.2023.104251
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323006232
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    2. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    3. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    4. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    5. Akhtar, Shumi & Faff, Robert & Oliver, Barry & Subrahmanyam, Avanidhar, 2013. "Reprint of: Stock salience and the asymmetric market effect of consumer sentiment news," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4488-4500.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    2. Mimoun Benali & Lahboub Karima, 2024. "Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 594-602, March.
    3. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    4. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    5. Hongcheng Ding & Xuanze Zhao & Zixiao Jiang & Shamsul Nahar Abdullah & Deshinta Arrova Dewi, 2024. "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods," Papers 2408.13214, arXiv.org.
    6. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    7. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    8. Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).
    9. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    10. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    11. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    12. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    13. Snezana Eminidou & Marios Zachariadis & Elena Andreou, 2020. "Inflation Expectations and Monetary Policy Surprises," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 306-339, January.
    14. Gong, Xu & Chen, Liqiang & Lin, Boqiang, 2020. "Analyzing dynamic impacts of different oil shocks on oil price," Energy, Elsevier, vol. 198(C).
    15. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    16. Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
    17. Zhaoshuai Pan & Zhaozhi Zhang & Dong Che, 2023. "Exploring Primary Aluminum Consumption: New Perspectives from Hybrid CEEMDAN-S-Curve Model," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    18. Xiao, Xiang & Hua, Xia & Qin, Kexin, 2024. "A self-attention based cross-sectional return forecasting model with evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 62(PA).
    19. Wang, Zhe & Teng, Yin-Pei & Wu, Shuzhao & Liu, Yuxiang & Liu, Xianchang, 2023. "Geopolitical risk, financial system and natural resources extraction: Evidence from China," Resources Policy, Elsevier, vol. 82(C).
    20. Bangzhu Zhu & Shujiao Ma & Rui Xie & Julien Chevallier & Yi-Ming Wei, 2018. "Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 105-121, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323006232. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.