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Analyzing the Advantage of Combination of Density Forecasts in Tehran Stock Exchange | ||
| 𝕴𝖓𝖙𝖊𝖗𝖓𝖆𝖙𝖎𝖔𝖓𝖆𝖑 𝕵𝖔𝖚𝖗𝖓𝖆𝖑 𝖔𝖋 𝕴𝖓𝖉𝖚𝖘𝖙𝖗𝖎𝖆𝖑 𝕰𝖑𝖊𝖈𝖙𝖗𝖔𝖓𝖎𝖈𝖘, 𝕮𝖔𝖓𝖙𝖗𝖔𝖑 𝖆𝖓𝖉 𝕺𝖕𝖙𝖎𝖒𝖎𝖟𝖆𝖙𝖎𝖔𝖓 | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 10 خرداد 1399 اصل مقاله (812.39 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2020.32870.1237 | ||
| نویسندگان | ||
S.Raheleh Shahrokhi 1؛ Hamid Khaloozadeh  2؛ HamidReza Momeni3
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| 1Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| 2Department of Systems and Control Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology | ||
| 3Department of Systems and Control, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده | ||
| Today, stock market plays a key role in the economy of any country and is considered as one of the growth indicators of any economy. Gaining the skills of gathering and analyzing data simultaneously, as well as using this knowledge in economic investigations, make time and precision factors to be the drawcard of any investor in competition with others. Therefore, having a predictive approach with the lowest degree of error will lead to smarter management of resources. Due to the complex and stochastic nature of the stock market, conventional forecasting approaches in this field have usually faced serious challenges, most notably losing the robustness when the data type changed over time. Moreover, by focusing on point forecasting, some useful statistical information about the objective random variable has been ignored inadvertently, undermining the prediction efficiency. The focus of this study is on density forecasting models which, unlike point forecasting, contain a description of uncertainty. Also, to take advantage of the diversity and robustness features of the combination, instead of an individual prediction, a combination of the density forecasting given by the different structures of ARMA, ANN, and RBF models is presented. In order to analyze the capabilities of these approaches in Tehran Stock Exchange (TSE), two basic methods of this category have been used to predict the price of MAPNA stock -one of the fifty active companies in this market- in the period 2012 to 2019. | ||
| کلیدواژه‌ها | ||
| Forecast Combination؛ Density Forecasting؛ Neural Networks؛ Simple Average؛ Stock Market | ||
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آمار تعداد مشاهده مقاله: 58 تعداد دریافت فایل اصل مقاله: 64 |
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