| Summary: | 碩士 === 國立臺北大學 === 國際財務金融碩士在職專班 === 99 === The study is using macroeconomic variables to forecast Taiwan transportation stocks returns through the application of back-propagation network approach. The period of study span is from the beginning of 2000 to September of 2010 with quarterly information. In total of 2,236 data, the model contains stock returns and net sales growth ratios of 18 listed transportation companies as well as other 16 microeconomics indices.
Start with stepwise regression to select the most related variables combination. Following with artificial neural computing and genetic algorithm for testing and calculation, the predicted hit ratio (PHIT) of external validity is the tool for performance measurement. In addition, moving windows method is adopted to examine the forecast result when the training periods are tested and then moving forward to next period for testing. Finally, two investment strategies are built up based on the best sequence of prediction and see whether the approach can lead to increment of yield rate.
The empirical study shows:
1. The result reveals a significant effect on the transportation stock returns by lagging one to four periods from most of the selected macroeconomic variables.
2. Under best scenario of training period, the average PHIT among all of the transportation stocks is up to 60.80%
3. If go down to company level, some of the companies’ PHIT are better than average result. There are 4 companies’ PHIT reach to 72.22%.
4. According to the best model result, we can develop a simulation through the investment strategies. The outcome shows the long position of strategy 1 defeats the other. In the testing period of 18 quarters, the best company shows an outstanding performance in return of 155%. In addition, most of the individual stock returns apparently beat the result of TAIEX in the same period.
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