- Title
- Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences
- Creator
- Pan, Heping; Tilakaratne, Chandima; Yearwood, John
- Date
- 2005
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/57814
- Identifier
- vital:686
- Identifier
- ISSN:1443-458X
- Abstract
- This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front 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.; C1
- Publisher
- Australian Computer Society
- Relation
- Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
- Rights
- Copyright Australian Computer Society (uploading privileges were granted by permission of the Australian Computer Society Inc)
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; Computer science; Information systems; Software engineering; Stock market prediction; Financial time series; Neural networks; Feature selection; Correlation; Variance reduction; Overtraining
- Full Text
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