A novel approach for predicting trading signals of a stock market index
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2010
- Type: Text , Book chapter
- Relation: Forecasting models: Methods and applications p. 145-160
- Full Text: false
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An ensemble technique for multi class imbalanced problem using probabilistic neural networks
- Authors: Chandrasekara, N. , Tilakaratne, Chandima , Mammadov, Musa
- Date: 2018
- Type: Text , Journal article
- Relation: Advances and Applications in Statistics Vol. 53, no. 6 (2018), p. 647-658
- Full Text: false
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- Description: The class imbalanced problem is one of the major difficulties encountered by many researchers when using classification tools. Multi class problems are especially severe in this regard. The main objective of this study is to propose a suitable technique to handle multi class imbalanced problem. Probabilistic neural network (PNN) is used as the classification tool and the directional prediction of Australian, United States and Srilankan stock market indices is considered as the application. We propose an ensemble technique to handle multi class imbalanced problem that is called multi class undersampling based bagging (MCUB) technique. This is a new initiative that has not been considered in the literature to handle multi class imbalanced problem by employing PNN. The results obtained demonstrate that the proposed MCUB technique is capable of handling multi class imbalanced problem. Therefore, the PNN with the proposed ensemble technique can be used effectively in data classification. As a further study, other classification tools can be used to investigate the performance of the proposed MCUB technique in solving class imbalanced problems.
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
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Modified neural network algorithms for predicting trading signals of stock market indices
- Authors: Tilakaratne, Chandima , Mammadov, Musa , Morris, Sidney
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of Applied Mathematics and Decision Sciences Vol. 2009, no. (2009), p.
- Full Text: false
- Reviewed:
- Description: The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. 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 modified network algorithms are based on the structure of feed forward neural networks and a modified Ordinary Least Squares (OLSs) 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. These algorithms were employed to predict the trading signals of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.
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.
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
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
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