| Summary: | 碩士 === 國立交通大學 === 電機與控制工程學系 === 86 === It has been extensively discussed on mechanical trading
methods applied in financial time series. In this thesis, we try
to fit and forecast the twenty-one stocks from the Taiwan stock
market by linear regression and neural networks approaches,
respectively, and then compare their profits. In linear
regression approach we use the statistical hypothesis test to
form a simple trading rule, and apply this rule to the price,
value and volume of each stock to compare their profits. In
neural network approach we filter out the noise on the price by
wavelet transform to approximate the price trend more accurate.
In the simulation results, the linear regression approach has
higher profit than the neural network approach due to its poor
forecastability. Finally, we provide a simple criterion to
survey a new trading method and suggestions to improve the two
approaches in the future.
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