“INFORMS Journal on Computing”, a UT DALLAS LIST journal, accepted “A Commonsense Knowledge-enabled Textual Analysis Approach for Financial Market Surveillance”, a research paper jointly completed by Visiting Assistant Professor Chen Kun and Professor Wang Huaiqing of the Department of Financial Mathematics and Financial Engineering together with the City University of Hong Kong and the Hong Kong Securities and Futures Commission. Chen Kun is a co-first author and SUSTC is a co-first author affiliation.

Market surveillance systems (MSSs) are increasingly used to monitor trading activities in financial markets to maintain market integrity. Existing MSSs primarily focus on statistical analysis of market activity data and largely ignore textual market information, including, but not limited to, news reports and various social media. As suggested by both theoretical explorations in finance and prevailing market surveillance practice, unstructured market information holds major yet underexplored opportunities for surveillance. In this study, the researchers propose a news analysis approach with the help of commonsense knowledge to assess the risk of suspicious transactions identified in market activity analysis. Our approach explicitly models semantic relations between transactions and news articles and provides semantic references to words in news articles.

The UT DALLAS LIST includes 24 top business school magazines. It covers finance, management, marketing and other disciplines, and is an important basis for business school rankings. “INFORMS Journal on Computing” was first published in 1989 as a quarterly. As of 2014, according to the collating information of the National Natural Science Foundation of China (NSFC), a total of seven papers with Chinese scholars as main authors (first author or corresponding author) have been published, and it is listed as a top journal in the fields of management science and engineering and business administration.