Investment Decision Making Using FGP: A Case Study

Financial investment decision making is extremely difficult due to the complexity of the domain. Many factors could influence the change of share prices. FGP (Financial Genetic Programming) is a genetic programming based forecasting system, which is designed to help users evaluate impact of factors and explore their interactions in relation to future prices. Users channel into FGP factors that they believe are relevant to the prediction. Examples of such factors may include fundamental factors such as "price-earning ratio", "inflation rate" or/and technical factors such as "5-days moving average", "63-days trading range breakout", etc. FGP uses the power of genetic programming to generate decision trees through combination of technical rules with self-adjusted thresholds. In earlier papers, we have reported how FGP used well-known technical analysis rules to make investment decisions. This paper tests the versatility of FGP by testing it on shorter-term investment decisions. To evaluate FGP more thoroughly, we also compare it with C4.5, a well-known machine learning classifier system. We used six and a half years’ daily closing price of the Dow Jones Industrial Average (DJIA) index for training and over three and half years’ data for testing and obtained favourable results for FGP.