This study is aimed at determining the future share net inflows and outflows by using the characteristics of Exchange Traded Funds (ETF) as variables in a data mining based analytic methodology. The relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. In order to explore the relationship between investor's perception of ETFs and subsequent net flows, this study is designed to shed light on the multifaceted linkages between fund characteristics and net flows. An international selection of 222 ETFs from one of the top three ETF providers is used in this study, of which fifteen attributes from each fund are used because they are likely to be contributors to fund inflows and outflows. Cross-Industry Standard Process for Data Mining (CRISP-DM) is used in this study accompanied with machine learning tools to develop a neural network which will forecast a positive or negative flow of net assets for ETFs.