پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 25 |
| تعداد شماره‌ها | 954 |
| تعداد مقالات | 7,830 |
| تعداد مشاهده مقاله | 13,144,314 |
| تعداد دریافت فایل اصل مقاله | 9,311,925 |
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 | ||
|
سایر فایل های مرتبط با مقاله
|
||
| مراجع | ||
|
AbdolMaliki, A.H; Hamidian, M; & Baghani, A. (2019). Investigating the presence of fractal characteristics in the price and returns of Tehran Stock Exchange companies using the non-linear ARIFMA model. Financial Engineering and Protfolio Management, 11(44), 207-226. (In Persian)
Ahn, K; Choi, M. Y; Dai, B; Sohn, S; & Yang, B. (2018). Modeling stock return distributions with a quantum harmonic oscillator. EPL (Europhysics Letters), 120(3), 1-8.
Alaminos, D; salas, B.M; & and Fernandez-gamez, M.A. (2022). Forecasting stock market crashes via real-time recession probabilities: a quantum computing approach. Fractalsvol. 30, no. 05, 2240162 (2022).
Alexio, H; Akram, U; & Sui, Y. (2022). The impact of macroeconomic indicators on US stock returns. Asia Pacific Journal of Marketing and Logistics, (March). Doi: 10.1108/APJML-05-2018-0191.
Anatolyev, S; & Barunik, J. (2019). Forecasting dynamic return distributions based on ordered binary choice. International Journal of Forecasting, 35(3), 823-835.
Armen, S. A; Anvari, E; & Rocky Kianpur, S. (2022). Modeling the dynamic index of financial conditions and investigating its effect on the predictability of Iranian stock returns. Journal of Asset Management and Financing, 10(1), 47-72. (In Persian)
Azevedo, V; & Christopher, H. (2022). Enhancing stock market anomalies with machine learning. Review of Quantitative Finance and Accounting, 60(11), 195–230.
Baaquie, B. E. (2018). Quantum Field Theory for Economics and Finance, Cambridge University Press, www.cambridge.org .
Ban, G.Y; El Karoui, N; & Lim, A. (2018). Machine learning and portfolio optimization. Manag Science, 64(3), 1136–1154.
Barak, S; Arjmand, A; & Ortobelli, S. (2017). Fusion of multiple diverse predictors in stock market. Information Fusion, 36, 90-102.
Basak, S; Kar, S; Saha, S; Khaidem, L; & Dey, S.R. (2019). Predicting the direction of stock market prices using tree-based classifiers. North am J Econ Financ, 47,552–567.
Bodnar, T; Mazur, S; & Okhrin, Y. (2017). Bayesian estimation of the global minimum variance portfolio. Eur J Oper Res, 256(1), 292–307.
Cao, L; & Tay, F. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Networks, 14(6), 1506–1518.
Chinco, A; Clark-Joseph, A.D; & Ye, M. (2019). Sparse signals in the cross-section of returns. J Financ, 74(1), 449–492.
Cubadda, G; Grassi, S; & Guardabascio, B. (2022). The Time-Varying Multivariate Autoregressive Index Model. ArXiv: 2201.07069 [Econ].
Daubechies, I. (1992). Ten lectures on wavelets, CBMS-NSF Regional Conference Series in Applied Mathematics, 61, Philadelphia: SIAM.
Dichtl, H; Drobetz, W; & Otto, T. (2023). Forecasting Stock Market Crashes via Machine Learning. Journal Financial Stability, 65.
Drachal, K. (2016). Forecasting spot oil price in a dynamic model averaging framework have the determinants changed over time? Energy Economics, 60, 35-46.
Dunis, C.L; Laws, J; & Evans, B. (2008). Trading futures spread portfolios: applications of higher order and recurrent networks. Eur J Financ, 14(6), 503–521.
El Ghourabi, M; Nani, A; & Gammoudi, I. (2020). A value‐at‐risk computation based on heavy‐tailed distribution for dynamic conditional score models. International Journal of Finance & Economics, 26(3), DOI:10.1002/ijfe.1934.
Fischer, T; & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res, 270(2), 654–669.
Gu, S; Kelly, B; & Xiu, D. (2020a). Autoencoder asset pricing models. J Econom: 1–22.
Gupta, R; Hammoudeh, S.h; Kim, W.J; & Simo -Kengne, B.D. (2014). Forecasting China’s foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty. North American Journal of Economics and Finance, 28, 170 -189.
Haven, E. (2019). The Quantum Formalism in Social Science: A Brief Excursion. Springer Nature Switzerland AG, 116–123.
Heaton, J.B; Polson, N.G; & Witte, J.H. (2017). Deep learning for finance: deep portfolios. Appl Stoch Model Bus Ind, 33(1), 3–12.
Hosseininia, P; Fattahi, Sh; & Soheili, K. (2022). Investigating Iran's Financial Markets Influenceability on the Global Economy Using Continuous Wavelet Transform. Journal of Financial Management Strategy, 10(4), 31-54. (In Persian)
Huang, W; Nakamori, Y; & Wang, S.Y. (2005). Forecasting stock market movement direction with support vector machine. Comput Oper Res, 32(10), 2513–2522.
Jeon, S; Hong, B; & Chang, V. (2017). Pattern graph tracking-based stock price prediction using big data. Future Generation Computer Systems.
Juhro, M; & Iyke, B. (2019). Monetary policy and financial conditions in Indonesia. Bulletin of Monetary Economics and Banking, 21(3), 283– 302.
Kim, H; & Han, S. T. (2016). The enhanced classification for the stock index prediction. Procedia Computer Science, 91, 284-286.
Koop, G; & Korobilis, D. (2013). A New Index of Financial Conditions. University of Glasgow. Adam Smith Business School, Gilbert Scott building, Glasgow.
Koop, G; McIntyre, S; Mitchell, J; & Poon, A. (2020). Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970. Journal of Applied Econometrics, 35(2), 176-197.
Korobilis, D. (2013). Assessing the transmission of monetary policy shocks using time -varying parameter dynamic factor models. Oxford Bulletin of Economics and Statistics, 75,157-179.
Li, J; & Jiang, Y. (2022). Recent Advances of Dynamic Model Averaging Theory and Its Application in Econometrics. Journal of Financial Risk Management, 11, 740-756.
Liu, D; Song, Y; & Chen, D. (2022). Time-varying comparison of the effectiveness of China’s price-and quantity-based monetary policy tools: an empirical analysis based on the TVP-FA-S-VAR model. Journal of the Asia Pacific Economy, 1-23.
Mohammadi, T; Neisy, A.S; Abdullah Milany, M; & Havaj, S. (2017). The application of transitory fashions model in estimating the fundamental and temporary value of Tehran stock exchange stock returns: integration of space-state approach and Markov transition regime. Iranian Journal of Economic Researchis, 23(75), 1-20. (In Persian)
Moody, J; & Saffell. M. (2001). Learning to trade via direct reinforcement. IEEE Trans Neural Networks, 12(4):875–889.
Ohwadua. O. E, & Ogunfiditimi. F.O. (2018). A Quantum Finance Model for Technical Analysis in the Stock Market. International Journal of Engineering Inventions, 7(2), 7-12.
Pedro G. G; & Flavio A. Z. (2020). Measuring Systemic Risk via GAS models and Extreme Value Theory: Revisiting the 2007 Financial Crisis. Finance Research Letters, 181(11), 23-37.
Qiao, R; Weike, C; & Qiao, Y. (2022). Prediction of stock return by LSTM neural network, Applied Artificial Intelligence. An International Journal, 36(1).
Qin, Q; Wang, Q.G; Li, J; & Ge, S.S. (2013). Linear and nonlinear trading models with gradient boosted random forests and application to Singapore stock market. J Intell Learn Syst Appl, 5(1), 1–10.
Rahnama Rodposhti, F; & Dehaghi klantary, M. (2013). Multifractal models in financial sciences: their origin, characteristics and applications. Financial Knowledge of Securities Analysis, 7(24), 25-47. (In Persian)
Ren, R; Wu, D.D; & Liu, T. (2019). Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Syst J, 13(1), 760–770.
Rostami, M; & Makiyan, S. N. (2022). Tehran Stock Exchange returns Forecasting: Comparison of Bayesian, Exponential Smoothing and Box Jenkins approaches. Iranian Journal of Economic Research, 27(91), 189-221. (In Persian)
Samavi, M. E; Niko Maram, H; Madan Chi Zaj, M; & Yaqubnejad, A. (2020). Modeling and forecasting the return distribution of the total index of the Iranian capital market and Bitcoin cryptocurrency with the time-varying GAS method. Financial Knowledge of Securities Analysis, 15(55), 1-14. (In Persian)
Saraf, F; Nasiri, Z; Tanhayi, M. R; Emamverdi, Q; & Najafi Moghadam, A. (2022). Predicting fluctuations in Tehran Stock Exchange indices through a Harmonic Quantum Oscillator model. Accounting and auditing research. In press. (In Persian).
Segnon, M; Gupta, R; & Wilfling, B. (2023). Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks. International Journal of Forecasting, 1(39).
Sojoodi, S; & Mousavi, F. (2022). Comparison of the Seven-Factor Model with the Capital Assets Pricing Model and the Fama and French Three-Factor Model to Predict the Expected Returns of Stock in the Tehran Stock Exchange. Journal of Financial Management Strategy, 10(4), 1-30. (In Persian).
Torrence, C; & Compo, G. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79: 61–78.
Vaziri Kurdestani, J; Farid, D; Nazemi Ardakani, M; & Hosseini Bamakan, S. M. (2023). Evaluation of PSO-BiLSTM method for stock price forecasting using stock price time series data (Case study: Iran Stock Exchange and OTC stock). Financial Management Strategy, 10(4), 125-150. (In Persian).
Zhang, Z; Zohren, S; & Stephen, R. (2020) Deep reinforcement learning for trading. J Financ Data Sci, 2(2), 25–40. | ||
|
آمار تعداد مشاهده مقاله: 299 تعداد دریافت فایل اصل مقاله: 39 |
||
