Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange
Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accura...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2024
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| Acceso en línea: | Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
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| _version_ | 1881508905181249536 |
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| author | Iftikhar, Hasnain Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_browse | Iftikhar, Hasnain Khan, Murad López Gonzales, Javier Linkolk Rodrigues, Paulo Canas Turpo Chaparro, Josue Edison |
| author_facet | Iftikhar, Hasnain Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/422 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4222026-10-30T16:14:37ZForecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchangeIftikhar, HasnainKhan, MuradTurpo Chaparro, Josue EdisonRodrigues, Paulo CanasLópez Gonzales, Javier LinkolkResearch articleforecasting stock pricesHodrick-Prescott filtertime series modelsfiltering-combination techniqueTraders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts.Articlehttps://doi.org/10.3934/math.202415910.3934/math.2024159https://hdl.handle.net/20.500.15037/422W4390538376enAIMS Mathematics2473-69882024-01-012026-06-13T08:13:14Z2026-06-13T08:13:14Z |
| spellingShingle | Research article forecasting stock prices Hodrick-Prescott filter time series models filtering-combination technique Iftikhar, Hasnain Khan, Murad Turpo Chaparro, Josue Edison Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title | Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title_full | Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title_fullStr | Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title_full_unstemmed | Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title_short | Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange |
| title_sort | forecasting stock prices using a novel filtering combination technique application to the pakistan stock exchange |
| topic | Research article forecasting stock prices Hodrick-Prescott filter time series models filtering-combination technique |
| topic_facet | Research article forecasting stock prices Hodrick-Prescott filter time series models filtering-combination technique |
| url | https://doi.org/10.3934/math.2024159 https://hdl.handle.net/20.500.15037/422 |
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