This paper presents results of neural network based trend prediction for equity markets.
Raw equity exchange data is pre-processed before being fed into a series of neural
networks. The use of Self Organising Maps (SOM) is investigated as a data classification
method to limit neural network inputs and training data requirements. The resulting primary
simulation is a neural network that can prediction whether the next trading period will be,
on average, higher or lower than the current. Combinations of pre-processing and feature
extracting SOM’s are investigated to determine the more optimal system configuration.
Halliday, R. (2004), Equity trend prediction with neural networks, Research Letters in the Information and Mathematical Sciences, 6, 15-29