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Stock Return Forecasting Using Dynamic Nonlinear Methods: Parametric and Nonparametric Modeling | ||
| فصلنامه مطالعات اقتصادی کاربردی ایران | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 09 شهریور 1404 | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22084/aes.2025.31371.3815 | ||
| نویسندگان | ||
| سیداحسان حسینی دوست* 1؛ محمدحسن فطرس2 | ||
| 1استادیار گروه اقتصاد، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی سینا | ||
| 2استاد گروه اقتصاد، دانشکده علوم اقتصادی و اجتماعی، دانشگاه بوعلی سینا، همدان، ایران | ||
| چکیده | ||
| Accurate stock market forecasting is a challenging and complex problem for the market analysts and decision makers. During the past decades accuracy of different methods are examined yet there is no consensus on optimum forecasting method. In this regard, the main objective of present study is to investigate eligibility of nonlinear time series, such as exponential smoothing and regime-switching models beside Box-Jenkins scheme in forecasting of stock return time series. Data set consist of daily observations of Apple and Microsoft corporations as of 2024 to 2025. Moreover, due to the overwhelming application of Artificial Intelligence methods in computation, Both of the in-sample and out-sample forecasting are carried out and performance of models is evaluated using standard error criteria. Findings indicated that the behavior of the return series for the both of the corporations are chaotic and nonlinear methods are appropriate in modeling. The exponential smoothing method outperformed the developed SETAR-GARCH and ARIMA-EGARCH procedures in terms of the majority of error criteria in the both of in-sample and out-sample forecasting. However, the MLP has outweighed the ES model based on every calculated error criteria. The estimated S-statistic of Diebold-Mariano test confirmed results of the forecasting in favor of the MLP method. This finding suggests application of the dynamic nonparametric methods in modeling and forecasting of the selected time series. Implication of such finding recommends use of dynamic nonlinear and nonparametric methods in financial series prediction. | ||
| کلیدواژه‌ها | ||
| Stock Return Forecasting؛ Chaos Testing؛ Parametric and nonparametric methods؛ Dynamic Nonlinear Modeling؛ AI Approach | ||
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آمار تعداد مشاهده مقاله: 15 |
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