Narrow Results

There exists a widely held belief that informed investors manipulate stock prices prior to seasoned equity offerings (SEO). Contrary to this assertion, a model is developed, which demonstrates there is significant evidence that informed investors not to manipulate trading prior to a SEO. Furthermore, there is an arguement that informed investors to trade the stock in the same direction indicated by their private information. In addition, the model is consistent with previous empirical evidence. Previous literature heavily relies on the Gerard and Nanda (1993) model. The model allows for more than one informed investors, whereas Gerard and Nanda de facto allows for only one. This model setting is not only more realistic to the real world, but also dramatically reverses its conclusion that there exists manipulative trading. It also indicated that following Securities and Exchange Commission (SEC) Rule 10b-21 and Rule 105, whose intention is to curb this manipulation, the SEO discount will change in either direction. Thus previous literature delineating methodology of utilizing the SEO discount change to test for the existence of manipulative trading is not well grounded. The model also predicts that undervalued firms tend to disclose more information in order to improve the stock price informativeness, whereas overvalued firms tend to do the contrary.

Cancer is a leading cause of mortality worldwide and the major exhausting factor for social resources in healthcare, medical treatment, and the loss of working force. Therefore, developing cancer therapy methods and appropriate prognosis or assessment for cancer therapies are of critical importance. Due to the high cost in exploration and assessment of cancer therapy methods, mathematical modeling of the immune system is viewed as a potentially powerful tool in the development of improved treatment regimens and prediction of disease progression. In the present work, several general principles in mathematical modeling of immune–tumor interactions and cancer therapies are summarized first. Secondly, the acquisition of the parameter values and model calibration are discussed according to mathematical techniques in qualitative analysis. Moreover, various therapy strategies are tested on the constructed mathematical model, from which constructive suggestions for developing new clinical treatment methods are provided. Additionally, some general guidance for new therapies are also discussed by analyzing the sensitivity of the system parameters. In the end, we also discuss essential difficulties in building the mathematical model for cancer patients.