پیش بینی نقدشوندگی در بورس اوراق بهادار تهران با استفاده از مدل های یادگیری

 

Abrishamgir Malmiri, M. K. (2019). Investigating the impact of the corporate governance mechanism on the liquidity of the shares of companies admitted to the Tehran Stock Exchange. Master's Thesis, Department of Accounting, Imam Javad Institute of Higher Education, Yazd. (in persian)

Farjami, M. (2013). The impact of stock price synchronicity and volatilities of stock return on stock liquidity. Master's Thesis, Department of Accounting, Isfahan University, Faculty of Administrative and Economic Sciences, Isfahan.(in persian)

 

Fallah pour, N. (2020). Investigating the effect of systematic liquidity risk on stock returns in Tehran Stock Exchange. Master's thesis, Department of Management, Shahid Chamran University, Faculty of Economics and Social Sciences, Ahvaz. (in persian)

Filsaraei, M., Arian Tabar, A. & Bakhtiari, F. (2023). Examining the role of lack of transparency of financial information and audit quality on the relationship between aggressive taxation and stock price concurrency. Budget and Finance Strategic Research Journal, 3(4), 89-126. (in persian)   

Guerra, P., Castelli, M., & Côrte-Real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74, 175-187.                                     

Hadi Dolabi, N. (2018). A study of stock liquidity in Tehran Stock Exchange using market microstructure elements. Master's Thesis, System Management and Productivity Department, Tarbiat Modares University, Technical and Engineering Faculty, Tehran. (in persian)

Heydari Zare, B. and Kordloi, H. (2009). Stock price prediction using artificial neural network. Management Quarterly, 17, 56-49. (in persian)

Jeanne, O., & Sandri, D. (2023). Global financial cycle and liquidity management. Journal of International Economics, 103736.

                         

Khang, P.Q., Hernes, M., Kuziak, K., Rot, A., Gryncewicz, W. (2020). Liquidity prediction on Vietnamese stock market using deep learning. International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 24, 2050-2058.

Luque Raya, I. M., & Luque Raya, P. (2023). Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond. European Journal of Management and Business Economics.

Mark, L., Sandra, M. .(2009). Liquidity and capital structur. Journal of Financial Markets, 12, 611-644.

 

Masjed Mousavi, M. S. (2010). Investigating the relationship between asset liquidity and stock liquidity in companies listed on the Tehran Stock Exchange. Master's thesis, Department of Financial Management, University of Economic Sciences, Faculty of Financial Sciences, Tehran. (in persian)

 

Nikpey Pesyan, V., Reza Zadeh, A., Ahmadi Nejad, H. & Ahmadvand, Sh. (2022). Investigating the non-linear relationship between the stock market and Iran's economic growth (the autoregression model approach with a distributive break), Budget and Finance Strategic Research Journal, 3 (10), 113-151. (in persian)

Shariffar, A.; Khalili Iraqi, M.; Raeisi Vanani, I. and Falah, M. (2021). Evaluation and validation of deep learning optimal architecture in stock price forecasting (LSTM algorithm approach). Financial Engineering and Securities Management Quarterly, 48, 370-348. (in persian)                                                   

 

Vazan, M. (2020). Deep learning: principles, concepts and approaches. Tehran: Miaad Andisheh Publications. (in persian)