- Title
- Predicting trading signals of the All Share Price Index Using a modified neural network algorithm
- Creator
- Tilakaratne, Chandima; Tissera, J.H.D.S.P; Mammadov, Musa
- Date
- 2008
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/104015
- Identifier
- vital:11011
- Abstract
- This study predicts whether it is best to buy, hold or sell shares (trading signals) of the All Share Price Index (ASPI) of the Colombo Stock Exchange, using a modified neural network (NN) algorithm. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is imbalanced. The structure of this modified neural network is same as that of feedforward neural networks. This algorithm minimises a modified Ordinary Least Squares (OLS) error function. An adjustment relating to the contribution from the historical data used for training the networks, and penalisation of incorrectly classified trading signals were accounted for, when modifying the OLS function. A global optimization algorithm was employed to train these networks. Results obtained were satisfactory.
- Relation
- Proceedings of the 9th International Information Technology Conference; 28th-29th October, 2008, Colombo , Sri Lanka
- Rights
- Coyright uknown
- Rights
- This metadata is freely available under a CCO license
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