اسدی، مسعود؛ میربرگکار، سید مظفر و چیرانی، ابراهیم (۱۴۰۱). ارائه یک مدل شبکه عصبی برای پیشبینی سود شرکتهای پذیرفته شده در بورس اوراق بهادار تهران و مقایسه دقت آن با الگوهای HDZ و ARIMA.
مجله حسابداری مدیریت، ۱۵(۵۴)، 33-47.
DOI: 10.30495/jma.2022.21080
بالونژادنوری، روزبه و شهیدی، آمنه (1404). بررسی رابطه غیرخطی مستقیم و معکوس نرخ ارز و بازدهی بازار سهام در ایران.
مجله توسعه و سرمایه، 10(2)، 125-146.
DOI: 10.22103/jdc.2024.23602.1474
تسلیمپور، علی؛ عسگرزاده، غلامرضا؛ قالمق، کریم و نصیری، هومن (1404). پیشبینی شاخص قیمت و بازده نقدی بورس اوراق بهادار تهران (TEDPIX) با استفاده از مدل شبکه عصبی NARX.
فصلنامه مدیریت دارایی و تأمین مالی، 14(1)، 48–21.
DOI: 10.22108/amf.2025.143461.1940
حیدری، مهدی و امیری، حمیدرضا (1401). بررسی توان الگوهای مبتنی بر هوش مصنوعی در پیشبینی روند قیمت سهام بورس اوراق بهادار تهران.
پژوهشهای مالی، 24(4)، 623–602.
DOI: 10.22059/frj.2022.320064.1007149
کاویانی، میثم؛ فخرحسینی، سید فخرالدین و حسنپور، امین (1404). تحلیل مقایسهای پویایی شاخصهای بازار سرمایه تهران با استفاده از مدل خودرگرسیونی در چارچوب رگرسیون کوانتایل.
توسعه و سرمایه، مقالات آماده انتشار.
DOI: 10.22103/jdc.2025.25330.1561
سیف، سمیرا؛ جمشیدی نوید، بابک؛ قنبری، مهرداد و اسماعیلپور، منصور (1400). پیشبینی روند بازار سهام ایران با استفاده از نوساننمای موج الیوت و شاخص قدرت نسبی.
پژوهشهای مالی، 23(1)، 157–134.
DOI: 10.22059/frj.2020.310664.1007072
شریففر، امیر؛ خلیلیعرقی، مریم؛ رئیسی ونانی، ایمان و فلاحشمس، میرفیض (1401). کاربرد معماریهای یادگیری عمیق در پیشبینی قیمت سهام: رویکرد شبکه عصبی پیچشی.
فصلنامه مدیریت دارایی و تأمین مالی، 10(3)، 20–1.
DOI: 10.22108/amf.2022.129205.1673
صمدی، سعید؛ شریفی، علیمراد؛ احمدزاده، عزیز و خانزادی، آزاد (1388). بررسی امکان جانشینی بین نهادهی انرژی با سرمایه در بخش فلزات اساسی.
فصلنامه تحقیقات اقتصادی، 44(4)، 129-155.
https://jte.ut.ac.ir/article_20344.html
عسگری، منصور (1399). عوامل مؤثر بر اشتغال در صنایع کارخانهای کشور.
مجله توسعه و سرمایه, 5(2)، 47-65.
DOI: 10.22103/jdc.2020.11705.1040
فخاری، حسین؛ ولیپور خطیر، محمدولی و موسوی، سیده مائده (1396). ارزیابی عملکرد شبکههای عصبی بیزین و لونبرگ- مارکوارت در مقایسه با الگوهای کلاسیک در پیشبینی قیمت سهام شرکتهای سرمایهگذاری.
پژوهشهای مالی، 19(2)، 318–299.
DOI: 10.22059/jfr.2017.214203.1006264
یراقی، مرضیه و ربیعی، اعظم (1399). مروری و مقایسه الگوریتمهای شبکه عصبی بازگشتی عمیق LSTM و GRU در مدلسازی دادههای سری زمانی نرخ ارز.
علوم محاسباتی، 5(4)، 50–40.
https://civilica.com/doc/1901544
References
Al-Ameer, M., Hammad, W., Ismail, A., & Hamdan, A. (2018). The relationship of gold price with the stock market: The case of Frankfurt Stock Exchange.
International Journal of Energy Economics and Policy, 8(5), 357–371.
https://www.econjournals.com/index.php/ijeep/article/view/6720
Anwer, Z., Azmi, W., & Ramadili Mohd, S. M. (2019). How do Islamic equities respond to monetary actions?
International Journal of Emerging Markets, 14(4), 503–522.
https://doi.org/10.1108/IJOEM-11-2017-0459
Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques—Part II: Soft computing methods.
Expert Systems with Applications, 36(3), 5932–5941.
10.1016/j.eswa.2008.07.006
Bermúdez Delgado, N. A., Bermúdez Delgado, E., & Saucedo, E. (2018). The relationship between oil prices, the stock market, and the exchange rate: Evidence from Mexico.
The North American Journal of Economics and Finance, 45, 266–275.
https://doi.org/10.1016/j.najef.2017.10.006
Asadi, M., Mirbargkar, S. M., & Chirani, E. (2022). Presenting a neural network model for predicting the profits of companies listed on the Tehran Stock Exchange and comparing its accuracy with HDZ and ARIMA models.
Management Accounting Journal, 15(54), 33–47.
https://doi.org/10.30495/jma.2022.21080 [In Persian].
Asgari, M. (2021). Factors affecting employment in manufacturing industries of Iran.
Journal of Development and Capital, 5(2), 47–65.
https://doi.org/10.22103/jdc.2020.11705.1040 [In Persian].
Balounejad Nouri, R., & Shahidi, A. (2025). Investigating the direct and inverse nonlinear relationship between exchange rate and stock market return in Iran.
Journal of Development and Capital, 10(2), 125–146.
https://doi.org/10.22103/jdc.2024.23602.1474 [In Persian].
Barakat, M. R., Elgazzar, S. H., & Hanafy, K. M. (2015). Impact of macroeconomic variables on stock markets: Evidence from emerging markets.
International Journal of Economics and Finance, 8(1), 195–207.
https://doi.org/10.5539/ijef.v8n1p195
Brown, M. S., Pelosi, M. J., & Dirska, H. (2013). Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks. In
International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 27–41). Springer.
https://doi.org/10.1007/978-3-642-39712-7_3
Chen, X., Wei, L., & Xu, J. (2022). House price prediction using LSTM.
arXiv.
10.48550/arXiv.1709.08432
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734.
https://doi.org/10.3115/v1/D14-1179
Dahir, A. M., Mahat, F., Ab Razak, N. H., & Bany-Ariffin, A. N. (2018). Revisiting the dynamic relationship between exchange rates and stock prices in BRICS countries: A wavelet analysis.
Borsa Istanbul Review, 18(2), 101–113.
https://doi.org/10.1016/j.bir.2017.10.001
Etale, L. M., & Eze, G. P. (2019). Analysing stock market reaction to macroeconomic variables: Evidence from Nigerian Stock Exchange (NSE).
Global Journal of Arts, Humanities and Social Sciences, 7(3), 14–28.
https://eajournals.org/wp-content/uploads/Analysing.pdf
Fakhari, H., Valipour Khateer, M., & Mousavi, S. M. (2017). Evaluating the performance of Bayesian and Levenberg–Marquardt neural networks compared to classical models in predicting stock prices of investment companies.
Financial Research, 19(2), 299–318.
10.22059/jfr.2017.214203.1006264 [In Persian].
Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction.
Neurocomputing, 172, 446–452.
https://doi.org/10.1016/j.neucom.2015.03.100
Gui, J., & Wu, X. (2022). Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model. In
Proceedings of the International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022) (Vol. 12330, pp. 577–584). SPIE.
10.1117/12.2647506
Hansson, F., & Rostami, J. (2019).
Time series forecasting of house prices: An evaluation of a support vector machine and a recurrent neural network with LSTM cells (Bachelor's thesis, Uppsala University).
https://www.maklarstatistik.se/wp-content/uploads/kandidatuppsats2019.pdf
Heydari, M., & Amiri, H. R. (2022). Investigating the power of artificial intelligence-based models in predicting stock price trends of the Tehran Stock Exchange.
Financial Research, 24(4), 602–623.
https://doi.org/10.22059/frj.2022.320064.1007149 [In Persian].
Hyde, S. (2007). The response of industry stock returns to market, exchange rate, and interest rate risks.
Managerial Finance, 33(9), 693–709.
https://doi.org/10.1108/03074350710776244
Jain, A., & Biswal, P. C. (2016). Dynamic linkages among oil price, gold price, exchange rate, and stock market in India.
Resources Policy, 49, 179–185.
https://doi.org/10.1016/j.resourpol.2016.06.001
Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015). An empirical exploration of recurrent network architectures. In
Proceedings of the 32nd International Conference on Machine Learning (pp. 2342–2350). PMLR.
https://proceedings.mlr.press/v37/jozefowicz15.html
Kamalov, F. (2020). Forecasting significant stock price changes using neural networks.
Neural Computing and Applications, 32(23), 17655–17667.
https://doi.org/10.1007/s00521-020-04942-3
Kaviani, M., Fakhrehosseini, S. F., & Hasanpour, A. (2025). A comparative analysis of the dynamics of Tehran stock market indexes using an autoregressive model within the quantile regression framework.
Journal of Development and Capital. Advance online publication.
10.22103/jdc.2025.25330.1561 [In Persian].
Lee, J. W., & Brahmasrene, T. (2018). An exploration of dynamical relationships between macroeconomic variables and stock prices in Korea.
Journal of Asian Finance, Economics and Business, 5(3), 7–17.
https://doi.org/10.13106/jafeb.2018.vol5.no3.7
Malkiel, B. G. (2003). The efficient market hypothesis and its critics.
Journal of Economic Perspectives, 17(1), 59–82.
https://doi.org/10.1257/089533003321164958
Megaravalli, A. V., & Sampagnaro, G. (2018). Macroeconomic indicators and their impact on stock markets in Asian 3: A pooled mean group approach.
Cogent Economics & Finance, 6(1), Article 1432450.
https://doi.org/10.1080/23322039.2018.1432450
Samadi, S., Sharifi, A., Ahmadzadeh, A., & Khanzadi, A. (2010). Substitutability between energy and capital in basic metals manufacturing.
Journal of Economic Research, 44(4), 129–155.
jte.ut.ac.ir/article_20344.html [In Persian].
Seif, S., Jamshidi Navid, B., Ghanbari, M., & Esmaeilpour, M. (2021). Predicting Iran's stock market trend using the Elliott Wave oscillator and the Relative Strength Index.
Financial Research, 23(1), 134–157.
https://doi.org/10.22059/frj.2020.310664.1007072 [In Persian].
Shariffar, A., Khaliliaraghi, M., Raeesi Vanani, I., & Fallahshams, M. (2022). Application of deep learning architectures in stock price forecasting: A convolutional neural network approach.
Journal of Asset Management and Financing, 10(3), 1–20.
https://doi.org/10.22108/amf.2022.129205.1673 [In Persian].
Shiller, R. J. (2017). Narrative economics.
American Economic Review, 107(4), 967–1004.
10.1257/aer.107.4.967
Kuang, S. (2023). A comparison of linear regression, LSTM model and ARIMA model in predicting stock price: A case study of HSBC stock price.
BCP Business & Management, 44, 478–488.
10.54691/bcpbm.v44i.4858
Singh, N. P., & Sharma, S. (2018). Phase-wise analysis of dynamic relationship among gold, crude oil, U.S. dollar, and stock market.
Journal of Advances in Management Research, 15(4), 480–499.
10.1108/JAMR-12-2017-0124
Song, Y. (2018).
Stock trend prediction: Based on machine learning methods (Doctoral dissertation, University of California, Los Angeles). ProQuest.
https://www.proquest.com/docview/2031586457
Taslimpour, A., Askarzadeh, G., Ghalmegh, K., & Nasiri, H. (2026). Forecasting the Tehran Stock Exchange Dividend and Price Index (TEDPIX) using a NARX neural network model.
Journal of Asset Management and Financing, 14(1), 21–48.
https://doi.org/10.22108/amf.2025.143461.1940 [In Persian].
Tiryaki, A., Ceylan, R., & Erdogan, L. (2019). Asymmetric effects of industrial production, money supply, and exchange rate changes on stock returns in Turkey.
Applied Economics, 51(20), 2143–2154.
https://doi.org/10.1080/00036846.2018.1540850
Verma, R. K., & Bansal, R. (2021). Impact of macroeconomic variables on the performance of stock exchange: A systematic review.
International Journal of Emerging Markets, 16(7), 1291–1329.
10.1108/IJOEM-11-2019-0993
Vidal, A., & Kristjanpoller, W. (2020). Gold volatility prediction using a CNN-LSTM approach. Expert Systems with Applications, 157, Article 113481. https://doi.org/10.1016/j.eswa.2020.113481
Yaraqi, M., & Rabiei, A. (2020). A review and comparison of deep recurrent neural network algorithms LSTM and GRU in modeling exchange rate time-series data.
Computational Sciences, 5(4), 40–50.
https://search.isc.ac/dl/search/defaultta.aspx?DTC=8&DC=1328953 [In Persian].
