Data mining applications using non-linear scientific methods - Ramakrishnan Arun (2008)
This thesis describes in detail two novel applications of the back-propagation neural network. In the first application, the neural network is viewed as a component extractor. Here, the network attempts to dynamically find the best indicators (through non-linear weighted averaging) that give a trading signal that matches as close as possible the perfect foresight. The results obtained after training the network on the Kuala Lampur Stock Exchange Composite Index are presented and discussed. In the second application, the network is used to forecast the modified regressed slopes of price returns. Thus, the ideas of both regression and neural networks are fruitfully combined. The forecasted value is used in a trading strategy that is reasonable and intuitive. The assumptions involved in using the regressed slope are inspected critically. Attention is given to performance. This is accomplished by means of the Sharpe Ratio by which ambiguity that may result from benchmarking a strategy against other indicators is avoided.