An initial public offering (IPO) is a type of public offering in which a company’s shares are sold to institutional and individual investors. While the majority of studies on IPOs have focused on the efficiency of raising capital and price adequacy in IPOs, studies on portfolio allocation strategies for IPO stocks are relatively scarce. This paper develops a machine learning investment strategy for IPO stocks based on rough set theory and a genetic algorithm (GA-rough set theory). To reduce issues of information asymmetry, we use nonfinancial data that are publicly available to individual and institutional investors in the IPO process. Based on the rule sets generated from the training sets, we conduct 120 tests with various conditions involving the target days and the partition of the training and testing sets, and we find excess returns of the constructed portfolios compared to the benchmark portfolios. Investors in IPO stocks can formulate more efficient investment strategies using our system. In this sense, the system developed in this paper contributes to the efficiency of financial markets and helps achieve sustained economic growth.

The purpose of this study is to identify the environmental factors that affect the image of a school campus. To achieve this objective: the campuses of 28 four-year universities in Seoul, which has more colleges than any other area in Korea, were examined. A survey using campus maps showed the following. First, those in a college group with better campus images use a greater variety of places than those in the other group. Second, with regard to the places chosen as environmental factors that affect the campus image, the respondents gave the following answers. The respondents of the college group with better campus images reported that architecture, places of social interactions, and the natural environment affect the campus image, along with related activities. However, the respondents of the other group stated that visual aspects such as landmark buildings affect the campus image. Third, when the campus maps were analyzed, it was found that flows of traffic were longer at colleges with a better image, which indicates that: these colleges strive to boost the usability of the overall campus by designing various places to be used for various purposes. This finding demonstrates that the factors which affect campus images include not only visual aspects but also both social interactions and natural environments.