Predicting trading signals of stock market indices using neural networks
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2008
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Auckland 1 December 2008 through 5 December 2008 Vol. 5360 LNAI, p. 522-531
- Full Text: false
- Description: The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, hold and sell, of stock market indices. Most commonly used classification techniques are not suitable to predict trading signals when the distribution of the actual trading signals, among theses three classes, is imbalanced. In this paper, new algorithms were developed based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLS) error function. An adjustment relating to the contribution from the historical data used for training the networks, and the penalization of incorrectly classified trading signals were accounted for when modifying the OLS function. A global optimization algorithm was employed to train these networks. The algorithms developed in this study were employed to predict the trading signals of day (t+1) of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions. © 2008 Springer Berlin Heidelberg.
Predicting trading signals of the All Share Price Index Using a modified neural network algorithm
- Authors: Tilakaratne, Chandima , Tissera, J.H.D.S.P , Mammadov, Musa
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 9th International Information Technology Conference; 28th-29th October, 2008, Colombo , Sri Lanka
- Full Text: false
- Reviewed:
- Description: 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.
Effectiveness of using quantified intermarket influence for predicting trading signals of stock markets
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Data Mining and Analytics 2007: Sixth Australasian Data Mining Conference, AusDM 2007 Vol. 70, p. 171-179
- Full Text:
- Reviewed:
Quantification of intermarket influence on the Australian All Ordinary Index based on optimization techniques
- Authors: Tilakaratne, Chandima , Morris, Sidney , Mammadov, Musa , Hurst, Cameron
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at CTAC 2006: The 13th Biennial Computational Techniques and Applications Conference p. 42-49
- Full Text:
- Reviewed:
Quantification of intermarket influence based on the global optimization and its application for stock market prediction
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Hurst, Cameron
- Date: 2006
- Type: Text , Conference paper
- Relation: Paper presented at Integrating AI and Data Mining, 1st International Workshop Proceedings, Hobart, Tasmania : 4th - 5th December, 2006
- Full Text: false
- Reviewed:
- Description: This study investigates how intermarket influences can be used to help the prediction of the direction (up or down) of the next day's close price of the Australian All Ordinary Index (AORD). First, intermarket influences from the potential influential markets on the AORD are quantified by assigning weights for all influential markets. The weights were defined as a solution to an optimization problem which aims to maximise rank correlation between the current day's relative return of the AORD and the weighted sum of lagged relative returns of the potential influential markets. Then, the next day's relative return of the AORD is predicted by applying the neural networks as a classifier. Two different scenarios were compared: 1) using the current day's relative returns of different sets of influential markets as separate inputs; and, 2) using only the weighted sum of these relative returns as a "combined market". The results revealed that the second approach provides better predictions in all cases. This shows the effectiveness of the proposed approach for quantifying intermarket influences and the potential of using the "weighted combined markets" for the prediction
- Description: E1
- Description: 2003001609