Impactful messaging: Elite sentiment in Chinese new energy vehicle vs machine learning perspective
Xingyue Gong and
Guozhu Jia
Finance Research Letters, 2023, vol. 57, issue C
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.
Keywords: Elite sentiment; Machine learning; New energy vehicle; CEEMDAN; Prediction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612323006232
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323006232
DOI: 10.1016/j.frl.2023.104251
Access Statistics for this article
Finance Research Letters is currently edited by R. Gençay
More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().