Techniques in financial time series prediction
Stock markets around the world are affected by many highly correlated economic, political and even psychological factors. The interaction of these factors is in a very complex manner. The stock price time series usually contains the characteristics of high-noise and non-stationary, which ma...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/10356/45864 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Stock markets around the world are affected by many highly correlated economic,
political and even psychological factors. The interaction of these factors is in a very
complex manner. The stock price time series usually contains the characteristics of
high-noise and non-stationary, which makes classical statistical methods of price
prediction incompetent. It is thus necessary to adopt more advanced forecasting
techniques. Neural network is a type of nonlinear model that has proved to be
effective for time series prediction. Therefore, in this study, multi-layer feedforward
artificial neural network based on back-propagation algorithm is proposed and
MATLAB is used to implement the design and training of the neural network. The
model is trained with two years of historical data from January 2009 to December
2010 in order to predict the stock prices of General Electric. The accuracy of the
forecasting model is studied by calculating the difference between raw data and
simulation model results. The performance of the prediction model is evaluated using
two widely used statistical metrics. |
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