Clarence N. W. Tan

A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

A study of the parameters of a backpropagation stock price prediction model

Reports an empirical study of an artificial neural network which implements an experimental backpropagation stock price prediction model. A backpropagation neural net stock prediction model was constructed to test its prediction capability. The parameters were varied and the corresponding predictive results were recorded. The parameters studied in this research were the learning rate, momentum, number of neurons in the hidden layer, activation function and input noise. The artificial neural network model has been treated by many as a black box that takes inputs to produce a desired output. This research attempts to study the behavior of this black box when its parameters are altered.<<ETX>>

Incorporating artificial neural networks into a rule-based financial trading system

This paper discusses the incorporation of artificial neural networks (ANNs) into a rule-based financial trading system to enhance and improve trading profitability. It discusses the advantages of artificial neural nets over traditional rule-based-only systems. It also introduces an ideal ANN rule-based financial trading system that can incorporate technical, fundamental and chart analyses into the financial trading decision-making process, which may improve the probability of trading success. Each of the analysis methods is discussed in the context of its incorporation into an ANN-based system.<<ETX>>

Trading a NYSE-stock with a simple artificial neural network-based financial trading system

Reports hypothetical trading results of a New York Stock Exchange (NYSE) listed stock over a period of two years using an artificial neural network (ANN) based financial trading system. The system was designed, constructed and tested for its ability to predict stock prices and more importantly increase trading profit. This system is still at a preliminary stage and many of the parameters effect on the ANN have not been fully explored yet. However, this simple system has provided insight into the design of a successful ANN-based financial trading system, as the results have been quite encouraging.<<ETX>>