Predicting the Australian Stock Market Index Using Neural Networks Exploiting Dynamical Swings and Intermarket Influences

Abstract

This paper presents a computational approach for predicting the Australian stock market index -AORD using multi-layer feed-forward neural networks from the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets, and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.

An empirical methodology for developing stockmarket trading systems using artificial neural networks

“…Thus, there appears little benefit in determining the optimal timeframe at any point in time, as it could not be relied on to hold, thus reducing the expected lifetime of a trading strategy, and adding additional risk (see for example, Balsara et al [58]). Published work exists which describes cycles already found in the Australian stockmarket, such as a 6-day cycle discovered by Pan et al [59], however, this was discovered in the AORD index, and there is no reason to expect it would hold for individual stocks, each with its own individual characteristics. For example, it could be expected that certain stocks had radically different cycle lengths, such as bank stocks following the economic cycle, resource stocks following the strength of the industrial cycle in other countries, etc.…”

Stock price forecasting model analysis based on the wavelet neural network and the data cleaning technology

“…The riotous neural system is utilized to take in the non-straight stochastic and disordered examples in the stock framework and estimate another list with previous records. The legitimacy of the plan is investigated hypothetically, and the recreation results demonstrate that it has a decent execution [14,15].…”

Business Model Design An Evaluation of Paper-based and Computer-Aided Canvases

“…In (Pan, Tilakaratne, Yearwood, 2005) is presented a computational approach for predicting the Australian stock market index -AORD using multi-layer feed-forward neural networks from the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis.…”