Neural Network method is a technique that is heavily researched and used in applications
within the engineering field for various purposes ranging from process
control to biomedical applications. The success of Neural Networks (NN) in engineering
applications, e.g. object tracking and face recognition has motivated its
application to the finance industry. In the financial industry, time series data is
used to model economic variables. As a result, finance researchers, portfolio managers
and stockbrokers have taken interest in applying NN to model non-linear
problems they face in their practice. NN facilitates the approach of predicting
stocks due to its ability to accurately and intuitively learn complex patterns and
characterizes these patterns as simple equations. In this research, a methodology
that uses fractal theory and NN framework to model the stock market behavior
is proposed and developed. The time series analysis is carried out using the
proposed approach with application to modelling the Dow Jones Average Index’s
future directional movement. A methodology to establish self-similarity of time
series and long memory effects that result in classifying the time series signal as
persistent, random or non-persistent using the rescaled range analysis technique is
developed. A linear regression technique is used for the estimation of the required
parameters and an incremental online NN algorithm is implemented to predict
the directional movement of the stock. An iterative fractal analysis technique is
used to select the required signal intervals using the approximated parameters.
The selected data is later combined to form a signal of interest and then pass it
to the ensemble of classifiers. The classifiers are modelled using a neural network
based algorithm. The performance of the final algorithm is measured based on
accuracy of predicting the direction of movement and also on the algorithm’s
confidence in its decision-making. The improvement within the final algorithm
is easily assessed by comparing results from two different models in which the
first model is implemented without fractal analysis and the second model is implemented
with the aid of a strong fractal analysis technique. The results of the
first NN model were published in the Lecture Notes in Computer Science 2006
by Springer. The second NN model incorporated a fractal theory technique.
The results from this model shows a great deal of improvement when classifying
the next day’s stock direction of movement. A summary of these results were
submitted to the Australian Joint Conference on Artificial Intelligence 2006 for
publishing. Limitations on the sample size, including problems encountered with
the proposed approach are also outlined in the next sections. This document also
outlines recommendations that can be implemented as further steps to advance
and improve the proposed approach for future work.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/3898 |
| Date | 10 October 2007 |
| Creators | Lunga, Wadzanai Dalton |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 556871 bytes, application/pdf, application/pdf |
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