Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks

Abstract

e 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 timevarying evolution of the relationship, as well as the transmission characteristics under important cycles. us, 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 di erent cycles, sliding time window and complex network are applied. e 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 di erent cycles, the correlation has time-varying characteristics. As the cycle grows, the types of transmission patterns of the ve 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 nd that the series modes of uncorrelated or weakly positive correlation for ve 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. ese ndings can reveal the rules of the typical stock market in China's energy industry and help investors with di erent investment cycle preferences make sound decisions.

Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series

“…The transfer of a time-varying multivariate system to a complex network to analyse its evolution has been employed in a number of studies 13 , 36 , 40 , 61 , 62 . Although the exact methodology for this transfer varies, the general approach can be seen as similar.…”

“…The previously mentioned studies employing complex networks for the evolution of multivariate systems by Jiang et al, Yu et al, Qi et al, Dong et al, and Yu et al 13 , 36 , 40 , 61 , 62 also subsequently analyse their constructed networks. This analysis takes a number of forms but primarily it determines properties and metrics of the network that can then be used to describe the evolution of the original system.…”

“…This analysis takes a number of forms but primarily it determines properties and metrics of the network that can then be used to describe the evolution of the original system. This analysis can be characterised as the following types: determination of the degree of the nodes, the distribution of this degree, and the comparison of this to standard network models, such as a scale free network 13 , 36 , 40 , 61 ; the distribution of edge weights (frequency), comparison to power law distribution, and total level of self-loops within the network (i.e. when a causality pattern does not change over a time step) 13 , 36 .…”

“…when a causality pattern does not change over a time step) 13 , 36 . Common network methods are used, such as the closeness of the network 40 , and the betweenness of nodes 36 , 40 , 61 , 62 . Clustering is also often employed for more in-depth analysis of the behaviour of the system, for example modularity-based methods 13 , 64 .…”

“…These complex network approaches to discovering behaviour have been employed by a number of researchers 9 , 12 , 13 , 15 , 36 – 40 , demonstrating their usage in the field. However, there still exists opportunity for expansion within these techniques 13 , 33 .…”