The Investment Strategy Decision Support System Applying Rule-based Neural Network and Trading Rules Extracted from Qualified Foreign Institutional Investors by Data Mining

Abstract

In the stock market, investors can invest in stocks to obtain profit. But there are many individual persons without professional investment knowledge about the stock market. It is difficult for them to choose stocks with high returns. After they have chosen the good stocks, it is also difficult for them to choose the time for selling or buying the stock. So, most of investors would lose money in investing stocks. The purpose of this study is to propose a Decision Support System, DSS, which could support investors to invest in the stock market. The DSS could recommend investors which stocks may have high returns, and recommend the appropriate time to buy or sell the stocks. For the knowledge acquirement of DSS, we would apply data mining technique to relative electronics stocks information owned by Qualified Foreign Institutional Investors (QFIIs) to obtain the QFII’s investment knowledge. The QFII’s investment knowledge is composed of many trade rules in stock market investment. For the timing of buying or selling stocks, the different trade rules may provide different significance. So, in different time, each trade rule has different reference weight. We would use rule-based neural network to train each trade rule to obtain reference weight. In this way, we expect to support investors to invest in the stock market to obtain high returns or to decrease damage.