Cognitive Learning Of Intelligence Systems Using Neural Networks: Evidence From The Australian Capital Markets

Tan, Joachim and Wong, Edward Sek (2002) Cognitive Learning Of Intelligence Systems Using Neural Networks: Evidence From The Australian Capital Markets. Asian Academy of Management Journal (AAMJ), 7 (2). pp. 1-9. ISSN 1394-2603

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Abstract

Artificial neural networks (ANNs) allow users to improve forecasts through pattern recognition. The purpose of this paper is to validate ANNs as a detection tool in four financial markets. This study investigates whether market inefficiencies exist using ANN as a model. It also investigates whether additional publicly available information can provide investors with a trading advantage. In finance, any forecasting advantage obtained through the use of publicly available information albeit internal or/and external market factors suggest inefficiencies in the financial markets. In this paper, we explore the efficiency of the United States, Japan, Hong Kong and Australia. In Australia, using the ASX 200 index, we demonstrate how the inclusion of external information to our ANN improves our forecasting. Our results show accounting for external market signals significantly improves forecasts of the ASX200 index by an additional 10 percent. This suggests the inclusion of publicly available information from other markets, can improve predictions and returns for investors.

Item Type:	Article
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management
Divisions:	Penerbit Universiti Sains Malaysia (USM Press) > Asian Academy of Management Journal (AAM)
Depositing User:	Mr Firdaus Mohamad
Date Deposited:	13 Jul 2017 08:03
Last Modified:	13 Jul 2017 08:03
URI:	http://eprints.usm.my/id/eprint/35603

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