A Forecasting Model of Dynamic Grey Rough Set and its Application on Stock Selection

The main purpose of paper is to establish a system, which combines rough set and grey theory. This model is used to let the time-serial, season-serial or regular data have the dynamic trend concepts by grey prediction, then, select the data sets with trend value through rough set screening system. It mainly is applied for a portfolio prediction in the stock market. Our study first predicts each listed company´s attributes of condition and decision-making by grey prediction, secondly groups their attributes by K-means grouping tools, then filters and categorizes the groups with the classified capacity of rough set for uncertain and non-sufficient information and selects the stock portfolio. And then we evaluate the company shares from the portfolio according to their past EPS and ROE and elect the better ones again. Finally, the selected companies are arranged in order with grey relation and determine the weight of each share in the portfolio according to it. The experimental result in Taiwan: during five years (2000-2004), the average annual rate of return was 38.1%. The portfolio determined by the model overran the market dramatically