ETD Collection

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    American Option Pricing Using Computational Intelligence Methods
    (2006-03-22) Pires, Michael Maio
    An option is the right to buy or sell an underlying asset at a future date by fixing the price now. The field of option pricing produces a challenge because of the complexity with pricing American styled options which cannot be done by the Black-Scholes equations. Neural Networks and Machine Learning techniques are predictors based on past data and it is intuitive to believe that they can model American options as they are non-linear instruments. Call option data on the South African All Share Index (ALSI) was used for testing of the techniques. These two different techniques were compared. What was also done was the comparison of Bayesian techniques applied to both the techniques. What this provided was confidence levels for the predictions. The investigations showed that Machine Learning techniques out-performed Neural Networks. The investigations also showed that there is scope for work to be done to improve the model.
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    Image Shape Clasification Using Computational Intelligence and Object Orientation
    (2006-03-13) Machowski, Lukasz Antoni
    With the increase in complexity of modern software systems, there is a great demand for software engineering techniques. Calculation processes are becoming more and more complex, especially in the field of machine vision and computational intelligence. A suitable object oriented calculation process framework is developed in order to address this problem. To demonstrate the effectiveness of the framework, a simple shape classification system is implemented in C#. A suitable method for representing shapes of images is developed and it is used for classification by a neural network. Sets of real-world images of hands and automobiles are used to test the system. The performance of the object oriented system in C# is compared to a functional paradigm system in Matlab and it is found that object orientation is well suited to the later stages of machine vision while the functional approach is well suited to low level image processing tasks.
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    Optimal selection of stocks using computational intelligence methods
    (2006-02-08) Betechuoh, Brain Leke
    Various methods, mostly statistical in nature have been introduced for stock market modelling and prediction. These methods are, however, complex and difficult to manipulate. Computational intelligence facilitates this approach of predicting stocks due to its ability to accurately and intuitively learn complex patterns and characterise these patterns as simple equations. In this research, a methodology that uses neural networks and Bayesian framework to model stocks is developed. The NASDAQ all-share index was used as test data. A methodology to optimise the input time-window for stock prediction using neural networks was also devised. Polynomial approximation and reformulated Bayesian frameworks methodologies were investigated and implemented. A neural network based algorithm was then designed. The performance of this final algorithm was measured based on accuracy. The effect of simultaneous use of diverse neural network engines is also investigated. The test result and accuracy measurements are presented in the final part of this thesis. Key words: Neural Networks, Bayesian framework and Markov Chain Monte Carlo