Cognitive Learning Of Intelligence Systems Using Neural Networks: Evidence From The Australian Capital Markets
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 wheth...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Academy of Management (AAM)
2002
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| Subjects: | |
| Online Access: | http://eprints.usm.my/35603/ http://eprints.usm.my/35603/1/AAMJ_7-2-2.pdf |
| Summary: | 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. |
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