| Summary: | 碩士 === 國立臺灣大學 === 資訊管理研究所 === 84 === With the progress of information technology, it is more and
more popular to use IT in the real life. This study is to
forecast TAIWAN stock market. Two methods are used in this
study; one is back-propagation neural network, the other is
statistical multiple regression. In addition, two decision
support systems are also designed to trade stocks
automatically. There are 18 companies sampled in this study.
Ten out of 32 technical indicators are selected. These 10
indicators are fixed inputs of all neural network and
statistical models. Each method has three kinds of models. One
is to forecast the fluctuation of stock prices in 6 days, one
is to forecast the fluctuation in 12 days, and the last is to
forecast the fluctuation in 18 days. Using these models, this
study computes the accuracy rate of forecasting and performance
of trading simulation. Results reveal that one can forecast
TAIWAN stock prices with certain accuracy. Back-propagation is
better than multiple regression which is better than buy-and-
hold strategy. The most profitable model in this study is to
trade stock gradually by neural network model using the 18-days-
later prices as target variables.
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