A novel hybrid framework for forecasting stock indices based on the nonlinear time series models
This study presents a new hybrid forecasting system to enhance the accuracy and efficiency of predicting stock market trends. To do this, the proposed involves several steps. Firstly, the closed stock index price time series is preprocessed to address missing values, variance stabilization, nonnorma...
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| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2025
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881508904227045376 |
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| author | Iftikhar, Hasnain Khan, Faridoon Torres Armas, Elías A. Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_browse | Iftikhar, Hasnain Khan, Faridoon López Gonzales, Javier Linkolk Rodrigues, Paulo Canas Torres Armas, Elías A. |
| author_facet | Iftikhar, Hasnain Khan, Faridoon Torres Armas, Elías A. Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | This study presents a new hybrid forecasting system to enhance the accuracy and efficiency of predicting stock market trends. To do this, the proposed involves several steps. Firstly, the closed stock index price time series is preprocessed to address missing values, variance stabilization, nonnormality, and nonstationarity. Second, the stock index closing prices are processed and filtered into a nonlinear long-term trend series and a stochastic series using three proposed filters and a benchmark filter. Third, the filtered series are estimated using the nonlinear and neural network autoregressive models. Fourth, the residual from both the fitted models is extracted, and a new series is obtained. The new residual series is forecasted using the autoregressive condition heterogeneity model. Finally, the forecasts from each model are combined to get the final estimates. The results indicate that the proposed final hybrid model produced the most accurate and efficient comparison with the baseline models. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/402 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4022026-10-30T16:14:36ZA novel hybrid framework for forecasting stock indices based on the nonlinear time series modelsIftikhar, HasnainKhan, FaridoonTorres Armas, Elías A.Rodrigues, Paulo CanasLópez Gonzales, Javier LinkolkStatistics, generalProbability and Statistics in Computer ScienceProbability Theory and Stochastic ProcessesEconomic Theory/Quantitative Economics/Mathematical MethodsThis study presents a new hybrid forecasting system to enhance the accuracy and efficiency of predicting stock market trends. To do this, the proposed involves several steps. Firstly, the closed stock index price time series is preprocessed to address missing values, variance stabilization, nonnormality, and nonstationarity. Second, the stock index closing prices are processed and filtered into a nonlinear long-term trend series and a stochastic series using three proposed filters and a benchmark filter. Third, the filtered series are estimated using the nonlinear and neural network autoregressive models. Fourth, the residual from both the fitted models is extracted, and a new series is obtained. The new residual series is forecasted using the autoregressive condition heterogeneity model. Finally, the forecasts from each model are combined to get the final estimates. The results indicate that the proposed final hybrid model produced the most accurate and efficient comparison with the baseline models.Article10.1007/s00180-025-01614-5https://hdl.handle.net/20.500.15037/402W4408338798enComputational Statistics0943-40621613-96582025-03-112026-06-13T08:12:47Z2026-06-13T08:12:47Z |
| spellingShingle | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods Iftikhar, Hasnain Khan, Faridoon Torres Armas, Elías A. Rodrigues, Paulo Canas López Gonzales, Javier Linkolk A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title | A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title_full | A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title_fullStr | A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title_full_unstemmed | A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title_short | A novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| title_sort | novel hybrid framework for forecasting stock indices based on the nonlinear time series models |
| topic | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods |
| topic_facet | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods |
| url | https://hdl.handle.net/20.500.15037/402 |
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