Ontological Analysis For The Effect Of Insider Trading And Noise Trading On Movement Of Stock Price

Insider trading in stock market is the trading of a corporation's stock or other securities by individuals with potential access to non-public information about the company. It exists in almost all of the stock markets around the world and is recognized as an illegal action because it may cause the unstable development of the stock market, which is denoted by the serious deviation of stocks' true value and market value. Most of the countries in the world detected insider trading mainly through great fluctuations of the stock price. However, this is very difficult to operate because of the uncertainty of noise trading. Noise trading refers to a stock trader makes his or she investing decisions only by hearsay or rumor, and it has active and reactive effect on stock price. Therefore, the price of a stock, which is involved in insider trading, is determined not only by insider traders but also noise traders. Of course, we neglect the effect of other environment. Under their effect, the stock price may rise, fall or not change. Thus, in order to understand how they affect the stock price together and support the detection work of insider trading better, ontology based framework, for investigating the relationships between insider trading, noise trading and stock price fluctuation is proposed. Until now, there are no researches using ontology to explore how insider trading and noise trading affect a stock price, and this paper supports a new angle to recognize insider trading. Besides the parts of introduction and related work, this paper mainly contains three parts.The first part, we use a sub-language of Web Ontology language, OWL DL to provide a hierarchical framework. In this framework, there are primarily classes of insider trading, classes of noise trading, classes of stock price fluctuation, and relations between these classes. This part is a specification of the domain-specific knowledge.In the second part, a causal map is used to portray effects of different classes of insider trading and noise trading on stock price fluctuation. This map could be recognized as "cause-effect" form, which can be written as rules, using the OWL rules language. In the last part, three cases of insider trading, one's stock price rises, one falls and the other does not change are chose to evaluate the ontology. To complete the evaluation process, we use the ontology framework to elaborate one case. If the results from ontology correspond to their true change in stock market, the ontology is reasonable.The major contributions of this research is that the ontology helps understand the effect of insider trading and noise trading on stock price better, helps filter the effect of noise trading from great fluctuation of stock price, and therefore detect insider trading more effectively.