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Research topic selection, empirical research, and model optimization
时间:2016-06-07 11:27    点击:   所属单位:网络与信息安全学院
讲座名称 Research topic selection, empirical research, and model optimization
讲座时间 2016-06-12 09:30:00
讲座地点 北校区新科技楼 1012会议室
讲座人 Prof. Jianyu Ma
讲座人介绍

 Jianyu Ma received his Ph.D.in Business Administration (Finance) from University of Texas Pan American. He is an Associate Professor (tenured) in Finance at Robert Morris University, Adjunct full professor of Graduate School at University of Maryland University College. He has 30+ publications and presentations as first author. His research appears in International Journal of Revenue Management, International Journal of Commerce and Management, International Journal of Business, International Journal of Business  and Systems Research, among others. His current research interests include mergers and acquisitions, international investment, and local government taxation. He is an editorial review board member of International Journal of Revenue Management. He served as President-Elect for 25th International Conference on the Pacific Rim Management (2015), the annual conference of Association for Chinese Management Educators (ACME). Currently, he serves as President of ACME.

 

讲座内容
The lecture introduces common practice in the process of selecting a research topic and discusses advantages and disadvantages of empirical research method. The lecture also addresses the issue regarding model optimization and includes critiques of a research paper. The paper discusses model selection for mergers and acquisitions analysis in Asian emerging markets. We extract a data set of mergers and acquisitions from Asian emerging markets and examine the distribution of the stock returns for the acquiring firm and the corresponding market portfolio in each deal. Non-normal distribution of the returns appears in the test of most deals. We use two robust regressions and a nonparametric statistic test to examine the efficacy of the standard OLS market model. The traditional methods of measuring abnormal returns around event windows may be flawed. The robust regressions, Huber regression M-estimator and bootstrapping quantile regression, provide better and higher estimation of abnormal returns.
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