Al-Attar, A, M., and Maali, B, M.,(2017). The Effect Of Earnings Quality On The Predictability Of Accruals And Cash Flow Models In Forecasting Future Cash Flows, The Journal of Developing Areas, Tennessee State University College of Business, 51(2(, p. 45-58, Doi.10.1353/jda.2017.0030.
Arnedo, L., Lizarraga, F., and Sanchez, S. (2012). The role of accounting accruals for the prediction of future cash flows: evidence from Spain, journal of the spanish economic, springer, 3(4), p.499-520, Doi.org/10.1007/s13209-011-0070-7.
Asadi, Gh., and Naghdi, S.(2018). Designing and Formulating the Forecasting Model of Economic Growth by Accounting Approach, Journal of Accounting Knowledge, 9(34), P. 39 -63. Doi.10.22103/JAK.2018.11095.2524.
Badavar nahndi, Y., Pakmaram, A., and ghaderi, Gh. (2019). The Impact of Financial Reporting Quality on Interaction between Agency Costs and Speed of Adjustment of Stock Price, Journal of Accounting Advances, 10(2), P. 31 -60. Doi.10.220499/JAA.2018.30097.1726.
Choi, W ,Han, S, Hwan, S. J., and Kang, T . (2015). CEO's Operating Ability and the Association between Accruals and Future Cash Flows, Journal of Business Finance and Accounting, 42(5-6), p. 619-634. https://Doi.org/10.1111/jbfa.12118.
Etemadi, H.,Azar, A., and Baghaee, V.(2012). Application of Neural Networks In Corporate’s Proﬁtability Prediction, Journal of Accounting Knowledge , 3 (10), p.51-70. Doi.10.22103/JAK.2012.444.
Farshadfar, S., and Monem, R. (2017). Further evidence of the relationship between accruals and future cash flows, Accounting and Finance, 59(1), p.143–176, Doi.org/10.1111/acfi.12260.
Hamidian, M., Mohhamadzadeh Moghadam, M,B., Naghdi, S. and Esmaeili, J.(2018). Dividend Policy Prediction by Multivariable and Univariate Neural Network Models, Journal of Investment Knowledge, 7(26), P. 169 -184.
Heydar pour, F., Arabi, M., and Gannad, M. (2017). The Effect of Short-term, Medium, and Long-term Time Horizons on the Prediction of Future Cash Flows: A Comparative Study of the Ability of Operating Earnings and Cash Flows, Journal of Financial Management Strategy, 4(4), P. 107 -127.Doi. 10.22051/JFM.2017.9337.1077.
Kenneth S. Lorek.(2019). Trends in statistically based quarterly cash-flow prediction models, Journal of Accounting Forum, 38, p.145-151. https://doi.org/10.1016/j.accfor.2013.10.006.
Khakrah Kahnamouei, M., and Khakrah Kahnamouei, T. (2017). Providing a Model to Predict Future Cash Flow Using Neural Networks on the Pharmaceutical and Chemical Industries of Tehran Stock Market, International Journal of Accounting and Financial Reporting, 7(1), p.213-226. Doi: https://doi.org/10.5296/ijafr.v7i1.10822.
Khoshhal Dastjerdi, J., and Hosseini, S.M. (2010). Application of Artificial Neural Network in Climatic Elements Simulation and Drought Cycle Predication (Case Study: Isfahan Province), Geography and Environmental Planning, 21(3), p. 107-120.
Kumar, P. C., and Walia, E.(2006). Cash Forecasting: An Application of Artificial Neural Networks in Finance, International Journal of Computer Science and Applications, 3(1), p. 61 – 77. Doi.10.22619/ijcsa.2018.100120.
Larson, C., Sloan, R., and Zha Gied, J.(2018). Defining, measuring, and modeling accruals: a guide for researchers, Review of Accounting Studies, 2018, 23(3), p.827-871, Doi: 10.1007/s11142-018-9457-z.
Li, Y., Mountinho, L, A., Opong, K., Pang, Y.(2015). Cash flow forecasting for South African firms, Review of Development Finance, 5, p.24-33. Doi:10.1016/j.rdf.2014.11.001.
Mahdavi, Gh. and Saberi, M.(2010). Determining the Optimal Model for the Prediction of Operating Cash Flow of Companies Listed in Tehran Stock Exchange, Journal of Accounting Advances, 2 (1), p.199-225. Doi. 10.22099/JAA.210.3435.
Pang, Y.(2015). The design of dynamic and nonlinear models in cash flow prediction, PhD thesis, University of Glasgow, Scotland, https://eleanor.lib.gla.ac.uk/record=b3130051.
Pang, Y., Oponga, k., Moutinhoa, L., and Lib, Y. (2015). Cash Flow Prediction Using a Grey-Box Model, Proceedings of the 21st International Conference on Automation and Computing, University of Strathclyde, Glasgow, UK, 11-12 September 2015.
Roozbacksh , N., Rezaiepajand, P., and Najari, M. (2013). prediction of the current cash flows using artificial neural network in Tehran Stock Exchange, the first international conference on political epic(with an approach to the middle east revolutions) and economic epic (with an approach to management and accounting),Rudehen, azad university of rudehen branch. https://www.civilica.com/Paper-ICPEEE01-ICPEEE01_1780.html.
Sarraf, F. (2019). Cash flow forecasting by using simple and sophisticated models in Iranian companies, Iranian Journal of Finance, 3(1), p.24-52. Doi. 10.22034/IJF.2020.202650.1071.
Saghafi, A., Sarraf ,F., and Aghabalaei Bakhtiar, H.(2015). The Application of Artificial Neural Network in Predicting Future Cash Flows, journal of accounting reviwe, 3(9), p.63-80. Doi. 10.22055/JIAR.2016.12725.
Sarraf, F., and Saghafi, A. (2014). A Model for Cash Flow forecasting in Iranian Companies, Accounting Research, 6(21), p. 1-26.
Shubita, M, F.(2013). Accruals and Cash Flows a Case of Jordan, Interdisciplinary Journal of Contemporary Research in Business, 5, p.428-441.
Yarifard, R., Karmanj, J., Nematjoo, H., and Ebrahimi, M. (2016). Forecast of cash flows in the listed firms of Tehran Stock Exchange, journal of management, 27(107), p.47-57.