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dc.contributor.authorKazem, A.
dc.contributor.authorSharifi, E.
dc.contributor.authorHussain, Farookh Khadeer
dc.contributor.authorSaberi, Morteza
dc.contributor.authorHussain, Omar
dc.date.accessioned2017-01-30T12:52:06Z
dc.date.available2017-01-30T12:52:06Z
dc.date.created2012-12-13T20:00:31Z
dc.date.issued2012
dc.identifier.citationKazem, Ahmad and Sharifi, Ebrahim and Hussain, Farookh Khadeer and Saberi, Morteza and Hussain, Omar Khadeer. 2012. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing. 13 (2): pp. 947-958.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/26170
dc.identifier.doi10.1016/j.asoc.2012.09.024
dc.description.abstract

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).

dc.publisherElsevier
dc.subjectsupport vector regression
dc.subjectfirefly algorithm
dc.subjectstock market price forecasting
dc.subjectchaotic mapping
dc.titleSupport vector regression with chaos-based firefly algorithm for stock market price forecasting
dc.typeJournal Article
dcterms.source.volumeOct 2012
dcterms.source.startPage947
dcterms.source.endPage958
dcterms.source.issn15684946
dcterms.source.titleApplied Soft Computing
curtin.department
curtin.accessStatusFulltext not available


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