We introduced a variety of complex network models to study a stock market. We observe a change in the complex network structure and properties to detect the influence of financial crises on the stock market. The scale-free complex network model was introduced and used to detect the sectors of the stock market utilising the community structure. The same sign of the volatility method was introduced to identify the connection between the nodes of a weighted network. The complex network shows a capacity to clearly detect the response of stock markets to political events (Brexit and the 2016 US elections) and examine the propagation of these crises.
|Qualification||Doctor of Philosophy|
|Award date||14 Oct 2019|
|Publication status||Unpublished - 2019|