What Can Cluster Analysis Offer Stock Investors? Evidence from the China’s Energy Industry

Luxing Liu, Yufeng Cai, Yalu Wei, Hong Jin and Yin Pei Teng
Additional contact information
Luxing Liu: Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 350002, P. R. China
Yufeng Cai: Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 350002, P. R. China
Yalu Wei: Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 350002, P. R. China
Hong Jin: ��Business School of Jiangxi Normal University, Nanchang 330022, P. R. China
Yin Pei Teng: ��School of Finance, Fuzhou University of International Studies and Trade, Fuzhou 350202, P. R. China

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 02, 1-26

Abstract: China is one of the world’s major producers and consumers of energy. The investment value of China’s energy industry has attracted the attention of investors at home and abroad. Few studies, however, have specifically investigated investment ratings in China’s traditional energy industry. This study, therefore, uses scientific analysis methods to help investors measure the investment value and returns of China’s energy industry. From the perspectives of market performance and earnings management, we select factors that influence stock value evaluation indicators and undertake an empirical analysis using financial statement data for 2020 from the Wind database. Based on a factor analysis of the main financial indicators (e.g. amplitude, turnover rate, gross profit margin of sales, growth rate of operating revenue), we obtain five main factors: stock market performance, trading heat, profit quality, profit scale, and profit potential. The k-means algorithm in Python is then used to analyse 56 stocks in China’s energy industry, and we divide their investment ratings into six grades: risk stocks, prudent holding, undetermined class, hold rating, ordinary rating, and buy rating. By identifying the group characteristics of different types of stocks, this study can provide a decision-making basis for investors while also having reference value for research institutions, financial departments, and government departments.

Keywords: Energy industry; earnings management; k-means algorithm; market performance; shares investment rating (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219649222500769
Access to full text is restricted to subscribers

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:wsi:jikmxx:v:22:y:2023:i:02:n:s0219649222500769

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219649222500769

Access Statistics for this article

Journal of Information & Knowledge Management (JIKM) is currently edited by Professor Suliman Hawamdeh

More articles in Journal of Information & Knowledge Management (JIKM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().