For Evil News Rides Fast,while Good News Baits Later In Stock Markets ?

As stated by British poet John Milton "For evil news rides fast,while good news baits later".Does this case exists in the stock markets? A good master of propagation characteristics of good information and bad information in the stock market can help investors and regulators make the best decision.However,most of the articles focus only on the relationships among markets from a micro perspective,which leads to a lack of systematic study in the whole market.Therefore,it is necessary to study the propagation characteristics of good information and bad information in the whole market systematically.Based on the China Securities Regulatory Commission industry classification criteria,stock data of Chinese financial listed companies is utilized to study the propagation characteristics of good information and bad information.Firstly,log return decomposition model is used to decompose the daily log return and extract good information series and bad information series of each stock.Secondly,the thesis constructs propagation network of good information and bad information via the linear Granger causality test model.Next,Dijkstra algorithm is used to find the shortest distance between each pair of nodes in the information propagation networks,and then construct Dijkstra network of good information and bad information.Finally,based on the above work,the author proposes four indicators including Number,Speed,Depth and Connectless to compare and analyze propagation characteristics of good information and bad information on the constructed Dijkstra networks.As revealed by the comparison results,among the listed Chinese financial firms,good information goes further to the other nodes in the network than the bad one.However,bad information propagates easier,faster and hasa high success rate than good information.Bad information propagates easier,faster and has a high success rate within the same cluster than between different clusters,while good information propagates farther within the same cluster than between different clusters.From the perspective of information propagation,the performance of log return decomposition is considered to be better than realized semi-variance method.