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پیش‌بینی بازده سهام مبتنی بررویکرد مدل‌های میانگین‌گیری بیزین؛ کوانتوم مالی و تحلیل موجک پیوسته | ||
| راهبرد مدیریت مالی | ||
| مقاله 8، دوره 13، شماره 1 - شماره پیاپی 48، فروردین 1404، صفحه 167-192 اصل مقاله (733.95 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2024.43067.2794 | ||
| نویسندگان | ||
| فاطمه صراف* 1؛ زهرا نصیری2؛ محمد رضا تنهایی3؛ قدرت الله امام وردی4؛ علی نجفی مقدم5 | ||
| 1دانشیار، گروه حسابداری، دانشکده اقتصاد و حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران. | ||
| 2دانشجوی دکتری،گروه حسابداری، دانشکده اقتصاد و حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 3استاد تمام، گروه فیزیک، واحد فیروزکوه، دانشگاه آزاد اسلامی، تهران، ایران. | ||
| 4استادیار،گروه علوم اقتصادی، دانشکده اقتصاد و حسابداری، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران. | ||
| 5استادیار، گروه حسابداری، دانشکده اقتصاد و حسابداری، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران. | ||
| چکیده | ||
| مدل‌های خطی با توجه به عدم استخراج صحیح شکل توزیع شرطی داده‌ها؛ عدم ثبت رفتار پویای توزیع شرطی داده‌ها؛ وجود فرض‌های محدود‌‌کننده خلاف واقعیت؛ توانایی مناسبی جهت پیش‌بینی بازدهی در دنیای امروز را ندارند. هدف اصلی پژوهش حاضر رفع ابهام در تعیین مدل مناسب جهت پیش‌بینی بازدهی سهام در بازه‌های زمانی مختلف در بازار سرمایه تهران است. این پژوهش از نوع کاربردی می‌باشد. نمونه پژوهش حاضر بازار بورس اوراق بهادار تهران در بازه زمانی 1/07/1397 تا 1/07/1401 با داده‌های روزانه است. مدل‌سازی بازدهی سهام با استفاده از 8 دسته از الگوهای 1-کلاسیک یا ساختاری، 2-رگرسیون‌های غیرساختاری؛ 3-رگرسیون‌های بیزین پارامتر متغیر زمان، 4-مدل‌های تبدیل موجک گسسته و پیوسته، 5-رویکردهای فراابتکاری، 6- شبکه عصبی مصنوعی ساده و عمیق 7-دیفرانسیل تصادفی 8- کوانتوم مالی صورت گرفته است. بر اساس نتایج در بازه زمانی کوتاه‌مدت 1 روزه، مدل‌های میانگین‌گیری بیزین؛ در میان مدت 16 روزه مدل‌های کوانتوم مالی و در بلندمدت 32 روزه مدل‌های موجک پیوسته از دقت بالاتری برخوردار بودند. بر اساس یافته‌های پژوهش می‌توان اذعان داشت برای پیش‌بینی بازدهی سهام لازم است در بازه‌های زمانی مختلف از مدل‌های مختلفی بهره گرفته شود و استفاده از رویکردی یکسان موجب کاهش دقت در بازدهی سهام خواهد شد. | ||
| کلیدواژه‌ها | ||
| بازدهی سهام؛ کوانتوم مالی؛ میانگین‌گیری بیزین؛ موجک | ||
| عنوان مقاله [English] | ||
| Forecasting of Stock Returns based on the Approach of Bayesian Models Averaging; Quantum Finance and Continuous Wavelet Analysis | ||
| نویسندگان [English] | ||
| Fatemeh Sarraf1؛ Zahra Nasiri2؛ Mohammad Reza Tanhayi3؛ Qudratullah Emam verdi4؛ Ali Najafi aghdam5 | ||
| 1Assosiate Prof, Department of Accounting, Faculty of Economics and Accounting, South Tehran Branch, Islamic Azad University,Tehran, Iran. | ||
| 2Ph.D. Student, Department of Accounting, Faculty of Economics and Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| 3Prof ,Department of Physics, Firoozkooh Branch, Islamic Azad University, Tehran, Iran. | ||
| 4Assistant Prof, Department of Economic Sciences, Faculty of Economics and Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| 5Assistant Prof, Department of Accounting, Faculty of Economics and Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| چکیده [English] | ||
| Linear models due to the lack of correct extraction of the shape of the conditional distribution of data; Failure to record the dynamic behavior of the conditional distribution of data; the existence of limiting assumptions contrary to reality; They do not have the proper ability to predict returns in today's world. The main goal of the current research is to resolve the ambiguity in determining the appropriate model for forecasting stock returns in Tehran capital market in different time frames. This research is of an applied type. The time domain of the data used in this research is daily data from 2018/9/23 to 2022/09/23. To predict and model stock returns in this research from 8 categories of estimation models 1- Classical or Structural, 2- Non-Structural regressions; 3- Time-varying Parameter Bayesian regressions, 4- Discrete Wavelet transform and Continuous Wavelet transform models, 5- Metaheuristic Approaches, 6- Simple and Deep Artificial Neural Networks approaches, 7- Stochastic differential, 8- Financial quantum were investigated. Based on the results in the short term of 1 day, Bayesian averaging models; In the medium term of 16 days, financial quantum models and in the long term of 32 days, continuous wave models had higher accuracy. Based on the finding of research, it can be acknowledged that in order to predict stock returns, it is necessary to use different models in different time frames, and using the same approach will reduce the accuracy of Predicting stock returns. | ||
| کلیدواژه‌ها [English] | ||
| Stock Returns, Financial Quantum, Bayesian Averaging, Wavelet | ||
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