scholarly journals Jumping Risk Communities in the Energy Industry: An Empirical Analysis Based on Time-Varying Complex Networks

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Hui Wang ◽  
Lili Jiang ◽  
Hongjun Duan ◽  
Yifeng Wang ◽  
Yichen Jiang ◽  
...  

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.

2017 ◽  
Vol 9 (2) ◽  
pp. 189
Author(s):  
Yue Liu ◽  
Jingqiu Wu

In China, the main profit of the energy industry is traditional energy sources, the proportion of traditional energy companies take on a high number. However, China has been putting forward green economy, with strongly support of national policy, the new energy enterprises emerge in an endlessly stream, the businesses involved in new energy economy profit a lot and that everyone is better off, which leads to a relatively strong upward tendency for new energy stocks. Therefore, based on such a fierce competition in the energy industry, it is necessary to know if the relevance of the new energy stock and traditional energy stock is positive or negative. This thesis is based on a combination of correlation analysis and regression analysis, analyze the correlation of new energy stock and traditional energy stock, and the sub-sectors of new energy, do research on stock investment strategy through the analysis of convergence. We firstly use SPSS to carry out correlation analysis on stock price, quantitatively illustrate the relationship between the two kinds of stocks, and then calculate the correlation coefficient, determine its correlation strength, at last linear regression analysis by SPSS, and summarize a functional relationship for the stock.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hongduo Cao ◽  
Tiantian Lin ◽  
Ying Li ◽  
Hanyu Zhang

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. Firstly, we propose a new pattern network construction method for multivariate stock time series. The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks. It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K-nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next-day volatility patterns of a single stock. The results show that the optimal models corresponding to the two algorithms can be found through cross-validation and search methods, respectively. The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%. In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Yajie Qi ◽  
Huajiao Li ◽  
Sui Guo ◽  
Sida Feng

The relationship between investor attention and stock prices has been a topic of interest in economics. Previous studies have shown that the correlation relationship between the two changes with time. However, there are few studies to explore the time-varying evolution of the relationship, as well as the transmission characteristics under important cycles. Thus, this paper is dedicated to discover the dynamic transmission characteristics of the correlation between investor attention and stock price. We selected the typical stocks of China’s energy industry, PetroChina and Sinopec, as the research objects, as they occupy a large market share and are representative. And the transaction data and attention data are used to build investor attention indicator. In order to reproduce the dynamic transmission process of correlation at different cycles, sliding time window and complex network are applied. The results show that PetroChina and Sinopec stocks have a weakly negative correlation between investor attention and stock price from 2017 to 2018. However, from the perspective of different cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the five consecutive days of correlation between the two become less, but the transmission intensity between the modes increases and the transition becomes more regular and inclined. In addition, by mining the important transmission modes and main transmission paths under important periods, we find that the series modes of uncorrelated or weakly positive correlation for five consecutive days dominate the transition of modes in the networks. Also, the closed loop formed by these two important modes and related modes is the main transmission path. These findings can reveal the rules of the typical stock market in China’s energy industry and help investors with different investment cycle preferences make sound decisions.


Author(s):  
Mirosław Wasilewski ◽  
Marta Juszczyk

The aim of the study was to investigate the investors’ opinions concerning the usefulness of behavioral factors for investment decisions. The research was carried out in the group of 100 investors, using the services of five brokerages with a long history of operation. The results of the research show that people’s psychological conditions and sentiment in the stock market play an important role in the decision-making process of investors in the capital market. The importance of this factor increased with the length of the investment period. The emotional states of people and their psychological conditions affect the stock price volatility. However, the complexity of the determinants of stock prices makes the market value of stocks can be affected by many factors at the same time and investors seem aware of this.


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