M.Comm. / The availability of large amounts of information and increases in computing power have facilitated the use of more sophisticated and effective technologies to analyse financial markets. The use of neural networks for financial time series forecasting has recently received increased attention. Neural networks are good at pattern recognition, generalisation and trend prediction. They can learn to predict next week's Dow Jones or flaws in concrete. Traditional methods used to analyse financial markets include technical and fundamental analysis. These methods have inherent shortcomings, which include bad timing of trading signals generated, and non-continuous data on which analysis is based. The purpose of the study was to create a tool with which to forecast financial time series on the Johannesburg Stock Exchange (JSE). The forecasted time series information was used to generate trading signals. A study of the building blocks of neural networks was done before the neural network was designed. The design of the neural network included data choice, data collection, calculations, data pre-processing and the determination of neural network parameters. The neural network was trained and tested with information from the financial sector of the JSE. The neural network was trained to predict share prices 4 days in advance with a Multiple Layer Feedforward Network (MLFN). The mean square error on the test set was 0.000930, with all test data values scaled between 0.1 - 0.9 and a sample size of 160. The prediction results were tested with a trading system, which generated a trade yielding 20 % return in 22 days. The neural network generated excellent results by predicting prices in advance. This enables better timing of trades and efficient use of capital. However, it was found that the price movement on the test set within the 4-day prediction period seldom exceeded the cost of trades, resulting in only one trade over a 5-month period for one security. This should not be a problem if all securities on the JSE are analysed for profitable trades. An additional neural network could also be designed to predict price movements further ahead, say 8 days, to assist the 4-day prediction
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:2548 |
| Date | 16 August 2012 |
| Creators | De Villiers, J. |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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