Support vector regression with chaos-based firefly algorithm for stock market price forecasting
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 forecastin...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/26170 |
| _version_ | 1848751909054185472 |
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| author | Kazem, A. Sharifi, E. Hussain, Farookh Khadeer Saberi, Morteza Hussain, Omar |
| author_facet | Kazem, A. Sharifi, E. Hussain, Farookh Khadeer Saberi, Morteza Hussain, Omar |
| author_sort | Kazem, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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). |
| first_indexed | 2025-11-14T08:00:13Z |
| format | Journal Article |
| id | curtin-20.500.11937-26170 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:00:13Z |
| publishDate | 2012 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-261702017-09-13T15:52:27Z Support vector regression with chaos-based firefly algorithm for stock market price forecasting Kazem, A. Sharifi, E. Hussain, Farookh Khadeer Saberi, Morteza Hussain, Omar support vector regression firefly algorithm stock market price forecasting chaotic mapping 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). 2012 Journal Article http://hdl.handle.net/20.500.11937/26170 10.1016/j.asoc.2012.09.024 Elsevier restricted |
| spellingShingle | support vector regression firefly algorithm stock market price forecasting chaotic mapping Kazem, A. Sharifi, E. Hussain, Farookh Khadeer Saberi, Morteza Hussain, Omar Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title | Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title_full | Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title_fullStr | Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title_full_unstemmed | Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title_short | Support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| title_sort | support vector regression with chaos-based firefly algorithm for stock market price forecasting |
| topic | support vector regression firefly algorithm stock market price forecasting chaotic mapping |
| url | http://hdl.handle.net/20.500.11937/26170 |