Year: 2010
Type: dissertation
Abstract: The weak form of Efficient Market hypothesis (EMH) states that it is impossible to forecast the future \nprice of an asset based on the information contained in the historical prices of an asset. This means \nthat the market behaves as a random walk and as a result makes forecasting impossible. Furthermore, \nfinancial forecasting is a difficult task due to the intrinsic complexity of the financial system. The \nobjective of this work was to use artificial intelligence (AI) techniques to model and predict the future \nprice of a stock market index. Three artificial intelligence techniques, namely, neural networks (NN), \nsupport vector machines and neuro-fuzzy systems are implemented in forecasting the future price of \na stock market index based on its historical price information. Artificial intelligence techniques have \nthe ability to take into consideration financial system complexities and they are used as financial \ntime series forecasting tools. Two techniques are used to benchmark the AI techniques, namely, \nAutoregressive Moving Average (ARMA) which is linear modelling technique and random walk (RW) \ntechnique. The experimentation was performed on data obtained from the Johannesburg Stock \nExchange. The data used was a series of past closing prices of the All Share Index. The results \nshowed that the three techniques have the ability to predict the future price of the Index with \nan acceptable accuracy. All three artificial intelligence techniques outperformed the linear model. \nHowever, the random walk method outperfomed all the other techniques. These techniques show an \nability to predict the future price however, because of the transaction costs of trading in the market, \nit is not possible to show that the three techniques can disprove the weak form of market efficiency. \nThe results show that the ranking of performances support vector machines, neuro-fuzzy systems, \nmultilayer perceptron neural networks is dependent on the accuracy measure used.
Author: Lufuno Marwala
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W105798426