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Equity trend prediction with neural networks : an empirical analysis : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Computer Systems Engineering at Massey University, Albany, New Zealand
This thesis presents results of neural network based trend prediction for equity markets.
Despite a breadth of research which has focused on the prediction of various equity and
currency exchange markets, much has focused on the use of specific techniques in such
predictions. Few bodies of work have compared a wide range of equity market data
preprocessing and technical analysis techniques in creating a prediction model based on
feed-forward and recursive neural networks.
To achieve a broad-based prediction model, the work in this study was broken into three
distinct parts. Firstly, the neural networks goal is defined as finding whether a stock will
be higher or lower than the previous trading period. Subsequent to this, a variety of
input data scaling and network topologies are looked at. This includes the use of Self
Organising Maps (SOM) as a data classification method to limit neural network inputs
and training data requirements. Feed Forward and Elman networks of various topologies
are used to narrow down the best network combinations. The resulting simulation is a
neural network that can predict whether the next trading period will be, on average,
higher or lower than the current.
Secondly, the topology and preprocessing lessons learned during the first phase are
applied to two types of neural network. Technical analysis is applied to the input data in
an attempt to verify the usefulness of conventional stock indicators as inputs to neural
networks. The two types of networks trained are, for the purposes of this thesis, dubbed
indicator-predictive and price-predictive networks, meaning that technical analysis
inputs are used to predict the next trading days technical indicator or future stock price
Finally, a combined network is trained which takes the inputs from the price-predictive
networks in an attempt to gain better results. The hypothesis with this network is that
the combined neural network should learn which of its inputs are more indicative of a
stock price movement, and thus more accurately predict the future direction of the