The GDB Cup: Applying 'Real World' Financial Data Mining in an Academic Setting

摘要

Financial data mining models is an appealing, but difficult task. Data miners are certainly motivated by the prospect of discovering a financial "Holy Grail." However, designing and implementing a model poses many intellectual challenges. These include securing and cleaning data; acquiring a sufficient amount of financial domain knowledge; bounding the complexity of the problem; and properly validating results. Financial data mining in an academic context is especially difficult due to the student's limited financial domain knowledge and the relatively short period (one semester) for building a financial model. This paper describes an application of a financial data mining project, called the GDB Cup, within an academic setting and discusses various "lessons learned" from assigning the GDB Cup over three different semesters. Case study results are presented.