Decision Support in Mutual Fund Investment Based on Internet Derived Data

Abstract: Data mining is a set of methods for extracting previously unknown, incomprehensible, and unactionable information from a large database, and using it to make critical business decisions. Data mining is often used in the knowledge discovery process to distinguish previously unknown relationships and patterns within data. Specifically, it is applied to a large database. In many cases, the data sets found on the World Wide Web are derived or processed data that usually can be used for decision support directly. An investment company without a fixed capitalization that purchases shares in numerous enterprises and issues its own shares for public sale provides a mutual fund. The investment profit of mutual funds is evenly distributed to each shareholder. This type of financial company or non-profit organization is also called an open-ended investment company or organization. The stock prices in the market are dynamic fluctuating every moment. The information recorded and published today is outdated for tomorrows market. Therefore, the Internet is an ideal place to record stock market activities and to provide updated information for its audiences. In this paper, we study mining derived data from the Internet for decision support in mutual fund investment.