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

被引:1
|
作者
Liu, Luxing [1 ]
Cai, Yufeng [1 ]
Wei, Yalu [1 ]
Jin, Hong [2 ]
Teng, Yin Pei [3 ]
机构
[1] Fujian Agr & Forestry Univ, Anxi Coll Tea Sci, Quanzhou 350002, Peoples R China
[2] Jiangxi Normal Univ, Business Sch, Nanchang 330022, Jiangxi, Peoples R China
[3] Fuzhou Univ Int Studies & Trade, Sch Finance, Fuzhou 350202, Peoples R China
关键词
Energy industry; earnings management; k-means algorithm; market performance; shares investment rating; NEURAL-NETWORK; PREDICTION; MODEL;
D O I
10.1142/S0219649222500769
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
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.
引用
收藏
页数:26
相关论文
共 50 条
  • [1] Can CSR reduce stock price crash risk? Evidence from China's energy industry
    Wu, Chia-Ming
    Hu, Jin-Li
    [J]. ENERGY POLICY, 2019, 128 : 505 - 518
  • [2] Sentiment contagion analysis of interacting investors: Evidence from China's stock forum
    Shi, Yong
    Tang, Ye-ran
    Long, Wen
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 523 : 246 - 259
  • [3] What are the benefits of attracting gambling investors? Evidence from stock splits in China
    Hu, Conghui
    Lin, Ji-Chai
    Liu, Yu-Jane
    [J]. JOURNAL OF CORPORATE FINANCE, 2022, 74
  • [4] Can stock market investors hedge energy risk? Evidence from Asia
    Batten, Jonathan A.
    Kinateder, Harald
    Szilagyi, Peter G.
    Wagner, Niklas F.
    [J]. ENERGY ECONOMICS, 2017, 66 : 559 - 570
  • [5] Can Foreign Institutional Investors Stimulate Environmental Innovation? Evidence From China's Stock Market Liberalization
    Wang, Zhongcheng
    Cheng, Yiheng
    Xue, Xinhong
    [J]. SAGE OPEN, 2024, 14 (02):
  • [6] Herding behavior in institutional investors: Evidence from China's stock market
    Zheng, Dazhi
    Li, Huimin
    Zhu, Xiaowei
    [J]. JOURNAL OF MULTINATIONAL FINANCIAL MANAGEMENT, 2015, 32 : 59 - 76
  • [7] Institutional Investors, Earnings Quality and Asset Liquidity: Evidence from China's Stock Market
    Kong, Dongmin
    Liu, Shasha
    Lu, Ting
    [J]. FRONTIERS OF BUSINESS RESEARCH IN CHINA, 2012, 6 (03) : 398 - 417
  • [8] Heterogeneous Beliefs, Institutional Investors and Stock Returns -Evidence from China
    Liu, Yan
    [J]. EURASIA JOURNAL OF MATHEMATICS SCIENCE AND TECHNOLOGY EDUCATION, 2017, 13 (12) : 7783 - 7790
  • [9] Foreign investors and stock price crash risk: Evidence from China
    Huang, Zhi-xiong
    Tang, Qi
    Huang, Siming
    [J]. ECONOMIC ANALYSIS AND POLICY, 2020, 68 : 210 - 223
  • [10] Foreign investors' trading and earnings in China's stock market --Evidence from Shanghai-Shenzhen Stock Connect
    Wang, Junkai
    Nie, Aoxiang
    Qi, Baoeli
    [J]. APPLIED ECONOMICS LETTERS, 2023,