This research employs five predicting variables to implementing the market timing strategy. These five variables are E/P1, E/P2, B/M, CP and GM. The investment performances of market timing under a variety of investment horizons are examined. There are four different forecasting horizons, which are one-month, three-month, six-month, and twelve-month investment horizons. Both the linear approach and artificial neural networks are employed to forecasting the market. The artificial neural network is employed with a view to capture the non-linearity property embedded in the market. The results are summarized as follows. (1) Both the linearity and nonlinear approaches are able to outperform the market. According to the results of Cumby-Modest test, they do have the market timing ability. (2) In the simple regression models, the performance of CP is relatively well compared to those of other variables. (3) The correct prediction rate increases as the investment horizon increases. (4) The performance of the expanding window approach is on average inferior to that of the moving window approach. (5) In the simulations of timing abilities over the period of May, 1991 to December, 1997. The multiple regression models has the best performance for the cases of one-month, three-month, and six-month investment horizons. On the other hand, BP(1) has the best performance for the case of one-year investment horizon. Contents Chapter 1 Introduction ……………………………………… 1 1.1 Background……………………………………………………………. 1 1.2 Motivations and objectives…………………………………………….3 1.3 Thesis organization ………………………………………………….. 4 Chapter 2 Literature Review…………………………………6 2.1 Previous studies on market timing……………………………………. 6 2.2 Predicting variables…………………………………………………… 8 2.3 Artificial Neural Networks……………………………………………10 2.4 Back Propagation Neural Networks…………………………………..11 2.5 Applications of ANNs to financial fields………………….………….12 Chapter 3 Data and Methodology……………………….….15 3.1 Data………………………………………………………………..….15 3.2 Linear approaches to implementing market timing strategy……….…18 3.3 ANNs to implementing market timing strategy…………..…………..23 Chapter 4 Results on Timing Performance……………..…26 4.1 Performance of linear approach………………………………………26 4.2 Performance of ANNs………………………………………………...38 4.3 Performance evaluation……………………………………………….39 Chapter 5 Summary…………………………………………54 5.1 Conclusions……………………………………………………….….54 5.2 Future works…………………………………………………………55 Appendix……………………………………………………..56 References……………………………………………………57 / This research employs five predicting variables to implementing the market timing strategy. These five variables are E/P1, E/P2, B/M, CP and GM. The investment performances of market timing under a variety of investment horizons are examined. There are four different forecasting horizons, which are one-month, three-month, six-month, and twelve-month investment horizons. Both the linear approach and artificial neural networks are employed to forecasting the market. The artificial neural network is employed with a view to capture the non-linearity property embedded in the market. The results are summarized as follows. (1) Both the linearity and nonlinear approaches are able to outperform the market. According to the results of Cumby-Modest test, they do have the market timing ability. (2) In the simple regression models, the performance of CP is relatively well compared to those of other variables. (3) The correct prediction rate increases as the investment horizon increases. (4) The performance of the expanding window approach is on average inferior to that of the moving window approach. (5) In the simulations of timing abilities over the period of May, 1991 to December, 1997. The multiple regression models has the best performance for the cases of one-month, three-month, and six-month investment horizons. On the other hand, BP(1) has the best performance for the case of one-year investment horizon.