Abbasi, E., Samavi, M. E., & Koosha, E. (2020). Performance evaluation of the technical analysis indicators in comparison with the buy and hold strategy in tehran stock exchange indices.
Advances in Mathematical Finance and Applications,
5(3), 285–301. [
in Persian] Doi:
10.22034/amfa.2020.1893194.1376
Abdi, M., & Najafi, A. (2018). Online Portfolio Selection Using Spectral Pattern Matching. Financial Engineering and Portfolio Management, 9(34), 175-192. [in Persian]
Arad, H., Kaviani, M., & Kaviani, M. (2024). Portfolio formation strategy using modified SVAM, P/CF, and P/S ratios in Tehran Stock Exchange. Strategic Research on Budgeting and Finance. [in Persian]
Azizi Ganzagh, H., & Jafari Samimi, A. (2022). Forecasting inflation in Iran with two approaches of econometrics and artificial neural network; Comparison of NARDL, NARX nonlinear models.
Journal of Econometric Modelling,
7(3), 39-68. [
in Persian] Doi:
10.22075/jem.2022.26727.1716
Ganzagh, H. A., Samimi, A. J., Elmi, Z. M., & Tehranchian, A. M. (2023). Comparing Inflation Forecasting Models in Iran: New Evidences from ARDL-D-LSTM Model.
Iranian Journal of Economic Research,
27(93), 149-176. [
in Persian] Doi:
10.22054/ijer.2022.63376.1037
Borodin, A., El-Yaniv, R., & Gogan, V. (2003). Can we learn to beat the best stock.
Advances in Neural Information Processing Systems,
16. Doi:
10.1613/jair.1336
Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the machines: Algorithmic trading in the foreign exchange market.
The Journal of Finance,
69(5), 2045–2084. Doi:
10.1111/jofi.12186
Cohen, G. (2022). Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies.
Mathematics,
10(18), 3302. Doi:
10.3390/math10183302
Cover, T. M., & Ordentlich, E. (1996). Universal portfolios with side information.
IEEE Transactions on Information Theory,
42(2), 348–363. Doi:
10.1109/18.485708
Dempster, M. A. H., & Leemans, V. (2006). An automated FX trading system using adaptive reinforcement learning.
Expert Systems with Applications,
30(3), 543–552. Doi:
10.1016/j.eswa.2005.10.012
Estrada, J. (2000).
The cost of equity in emerging markets: a downside risk approach. Doi:
10.2139/ssrn.249579
Girardi, G., & Ergün, A. T. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR.
Journal of Banking & Finance,
37(8), 3169–3180. Doi:
10.1016/j.jbankfin.2013.02.027
Ghasemiyeh, R., Sinaei, H., & Sahraei, S. (2023). Predicting liquidity in the Tehran Stock Exchange using learning models.
Strategic Research on Budgeting and Finance, 4(3), 11-29. [
in Persian] Dor:
20.1001.1.27171809.1402.4.3.1.5
Gordon, T. J. (1994). Cross-impact method (Vol. 4). American Council for the United Nations University.
Gouveia, A. N. C. (2020). Machine Learning Applications on Algorithmic Trading in the Foreign Exchange Market. Universidade NOVA de Lisboa (Portugal).
Györfi, L., Udina, F., & Walk, H. (2008).
Nonparametric nearest neighbor based empirical portfolio selection strategies. Doi:
10.1524/stnd.2008.0917
Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity?
The Journal of Finance,
66(1), 1–33. Doi:
10.1111/j.1540-6261.2010.01624.x
Jin, B. (2023). A Mean-VaR Based Deep Reinforcement Learning Framework for Practical Algorithmic Trading.
IEEE Access,
11, 28920–28933. Doi:
10.1109/ACCESS.2023.3259108
Kim, S. E., & Seo, I. W. (2015). Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers.
Journal of Hydro-Environment Research,
9(3), 325–339. Doi:
10.1016/j.jher.2014.09.006
Lin, T., Horne, B. G., & Giles, C. L. (1998). How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies.
Neural Networks,
11(5), 861–868. Doi:
10.1016/s0893-6080(98)00018-5
Mihatsch, O., & Neuneier, R. (2002). Risk-sensitive reinforcement learning.
Machine Learning,
49, 267–290. Doi:
10.48550/arXiv.1311.2097
Adabi firouzjaee B, Mehrara, M., & Mohammadi, S. (2016). Estimation and Evaluation of Tehran Stock Exchange Value at Risk Based on Window Simulation Method.
Journal of Economic Modeling Research,
7(23), 35-73. [
in Persian] Doi:
10.18869/acadpub.jemr.6.23.35
Mousavi Loletti, S. A., Ghanbari, H., & Mohammadi, O. (2024). Portfolio optimization using the semi-variance model with an emphasis on positive potential (Case study: Tehran Stock Exchange).
Strategic Research on Budgeting and Finance, 5(1), 57-78. [
in Persian] Doi:
20.1001.1.27171809.1403.5.1.3.0
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370. Doi: 10.2307/2938260
Neuneier, R. (1997). Enhancing Q-learning for optimal asset allocation. Advances in Neural Information Processing Systems, 10.
Rastegar, M. A., & Dastpak, M. (2018). Developing a High-Frequency Trading system with Dynamic Portfolio Management using Reinforcement Learning in Iran Stock Market.
Financial Research Journal,
20(1), 1-16. [
in Persian] Doi:
10.22059/jfr.2017.230613.1006415
Tang, S. (2022). Measurement and Management of Interest Rate Risk of Commercial Banks: Based on VaR-GARCH Model of a Case Study of SHIBOR. Scientific and Social Research, 4(1), 89–100.