Cash flow forecasting by using simple and sophisticated models in Iranian companies

Document Type: Original Article


Assistant Prof., Department Of Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran.



Cash flow is one of the critical resources in the economic unit and the balance between available cash and cash needs is the most important factor in economic health. Since judgments of many stakeholders such as investors and shareholders about the position of the economic unit are based on liquidity situation, so predicting future cash flow is crucial. In this research, the impact of cash and accrual items on cash flow forecasts has been studied. Providing a proper model to predict operating cash flows and review some important characteristics of cash flow forecasting regression models, using a multilayer perceptron and determining the best model by using accrual regression model variables for predicting cash flows. For this purpose, 287 firms listed in Tehran Stock Exchange during 2008 to 2017 were studied; Linear and nonlinear regression, correlation coefficient and artificial neural network statistical methods have been used for data analysis and predictive power of powers was compared by using the sum of squared prediction error and coefficient of determination. Results showed that the accrual regression model can predict future cash flows better than other tested models and among corporate characteristics, the highest correlation belongs to sales volatility and firm size with accrual regression models. On the other hand, results of fitting different neural network models indicate that two structures with 8 and 11 hidden nodes are the best models to predict cash flows.


AL-Attar. A. and Hussain. S. (2004). Corporate data and future cash flow. Journal of Business Finance and Accounting, Vol. 31. Nos7-8. PP. 861-903.

Arnold. A., J., Clubb, C.D.B., Manson, S, and Wearing, R. T. (1991). The relationship between earning, funds flows and cash flows: evidence for the UK, Accounting and Business Research, Vol. 22, no. 85, PP. 13-19.

Arthur, N., Czernkowski. R. and Chen. M. (2007). The persistence of cash flow components into future earnings. Working paper, University of Sydney.

Barth, M.E., Cram, D. p. and Nelson, K. K. (2001). 'Accruals and the prediction of future cash flows. The Accounting Review, Vol. 76. No. 1, PP. 27-58.

Birt, J., Chalmers, K., Beal, D., Books, A., Byrne, S. and Oliver, J. (2008). Accounting business reporting for decision making, 2ndedn, John Wiley and Sons, Milton.

Bowen. R.M., Burgstahler, D. and Daley, L.A. 1986, 'Evidence on the relationships between earnings and various measures of cash flow', The Accounting Review, vol.61. no.4, pp. 713-25.

Cheng, A, Liu, C.S and Schaefer, T.F. (1997). Accounting accruals and incremental information content of earnings and cash flows operations. Advances in Accounting, Vol. 15, PP. 101-123.

Cheng,C.S.A. and Hollie, D. (2008). Do core and non-core cash flows from operations persist differently in predicting future cash flows? .Review of Quantitative finance and Accounting. Vol. 31, no1, PP. 29-53.

Chotkunakitti, p. (2005). Cash flows and accrual accounting in predicting future cash flow of Thai listed companies, PhD thesis, Southern Cross University.

Dechow, P.M. (1994). Accounting earnings and cash flows as measures of firm performance: the role of accounting accruals. Journal of Accounting and Economics, Vol. 18, no1, PP. 3-42.

Defond, M. and Hung, M. (2003). An empirical analysts cash flow forecasts. Journal of Accounting and Economics, Vol. 35, no1, PP. 73-100.

Ebaid, 1.E. (2011). Accruals and prediction of future cash flows: empirical evidence from an emerging marker. Management Research Review, Vol. 34, no7, PP. 1-32.

Elliott, B. and Elliott, J. (2007). Financial Accounting and Reporting Education, Essex.

Farshaadfar, S., Ng, C, and Brimble, M. (2008). The relative ability of earnings and cash flow data in forecasting future cash flows. Pacific accounting review, Vol. 20, no3, PP. 251-268.

Francis, R., and Eason, P. (2012). Accruals and the naïve out-of- sample prediction of operating cash flow. Advances in accounting, incorporating advances in international accounting, PP. 1-9.

Habib, A. (2010). Prediction of operating cash flow: future evidence from Australia. Australian accounting Review, Vol. 20, no2, P. 134.

IASB. (2001). Framework for the preparation and preparation of Financial Standards Board (IASB), London.

Janjani, R. (2015). Comparing US-GAAP and Tran-GAAP Operating cash flows to predict future cash flows. Journal of Financial Reporting and Accounting, 13(1), http://,1108/JFRA-06-2013-0047.

Kordestani, Gholamreza. (1995)."Ability to predict future cash flows and profits".Master's dissertation, TarbiatModarres University.

Kenneth S. Lorek, (2019), Trends in statistically based quarterly cash-flow prediction models. Journal of Accounting Forum,Volume 38, P. 145-151.

Linna Shi, HuaiZhang and JunGuo, (June 2014), Analyst cash flow forecasts and pricing of accruals, Advances in Accounting, Volume 30, Issue 1, Pages 95-105.
Lorek, K., &Willinger, G. (2008). Time-series properties and the predictive ability of quarterly cash-flows. Advances in accounting, 24. pp 65-70.

Lie, Q. (2006). Cash distribution and return, university of Michigan, pp 1-320.

Lorek, K., &Willinger, G. (2008). Time-series properties and the predictive ability of quarterly cash-flows. Advances in accounting, 24. pp 65-70.

Lorek, K., &Willinger, G. (2009). New evidence pertaining to the prediction of operating cash-flows. Review of Quantitative Finance Accounting, 32(1), 1-15.

Mcbeth, K.H. (1993). Forecasting operating cash flow: Evidence on the comparative predictive abilities of net income and operating cash flow from actual cash flow data. The Mid-Atlantic Journal of Business. Vol. 29, no2, PP. 173-87.

Mahdavi, Gh and Saberi, M. (2010). "Determine the optimal model of projected operating cash flows of companies listed in Tehran Stock Exchange" Journal of Accounting Advances, 2 (1) Row (58/3), 119-225.

Magmoud Abadi and Mansouri (2011). "The role of discretionary and non-discretionary accruals in predicting future cash, journal of research – Science financial Accounting

Mirfakhraddini, SeyedHeidar,; Moienaddin, Mahmoud,; Ebrahimpour, Alireza. (2009). "Compare the ability of cash flows and accruals to predict future cash flows". The Iranian Accounting and Auditing Review, 55:99-116.

Modarres, A. SianatiDeilami,Z. (2004). "The usage of multivariate time series model to forecast operating cash flows". The Iranian Accounting and Auditing Review, 10 (34): 77-110.

Nasser A. and Spear Mark Leis, (July–September 1997), Artificial neural networks and the accounting method choice in the oil and gas industry, Accounting, Management and Information Technologies, Volume 7, Issue 3, Pages 169-181.
Penham, S.H. and Yehuda, N. (2009). The Pricing of earnings and cash flow and the affirmation of accrual accounting. Review of Accounting Studies, Vol. 14, no4, PP. 453-479.

Pfeiffer, R.J., P.T. Elgers, M.H. Lo and L.L. Rees (1998). Additional evidence on the incremental information content of cash flow and accrual: the impact of errors in measuring market expectations. Accounting Review, Vol. 73, no3, PP. 373-385.

Rattachut Tangsucheeva and VittaldasPrabhu, (December 2014), Stochastic financial analytics for cashflow forecasting, International Journal of Production Economics, Volume 158, Pages 65-76.
Saghafi, A. Fadaie, H.R. (2007). "Choose an efficient model to forecast cash flows based on comparable models of companies listed in the Tehran Stock Exchange". The Iranian Accounting and Auditing Review, 14 (50): 3-24.

Saghafi, A. Sarraf, F. (2014). "A model to forecast cash flow in Iranian companies". Accounting Research, 6 (21): 1-26.

Sarraf, F. Saghafi, Ali; HassasYeganeh, Yahya,; Amiri, Maghsoud. (2013)."Linear and nonlinear regression models to estimate cash flows".Journal of accounting knowledge and management auditing, 2(8): 141-155.

Sarraf, Fatemeh. (2013). "Designing a model for predicting cash flow in Iranian companies". Ph.D. Thesis. AllamehTabataba'i University.

Saghafi, Ali.;Sarraf, Fatemeh.; AghabalaieBakhtiar, Hannaneh. (2015)."Application of artificial neural network in predicting future cash flow".Accounting Research, 3 (9): 63-80.

Shadi Farshadfar and RezaMonem, ( March 2013), Further Evidence on the Usefulness of Direct Method Cash Flow Components for Forecasting Future Cash Flows.The International Journal of Accounting, Volume 48, Issue 1, Pages 111-133.

Sebastian M.Blanc and ThomasSetzer, ( 16 June 2015), Analytical debiasing of corporate cash flow forecasts, European Journal of Operational Research, Volume 243, Issue 3, Pages 1004-1015.

Seng, D. (2006). Earnings versus cash flow as predictors of future cash flow: New Zealand evidence.Working paper, University of Otago.

Sharma, D.S. & Iselin, E.R. (2003). The relative relevance of cash flow and accrual information for solvency assessments: A multi-method approach. Journal of business Finance & Accounting, Vol.30, no7/8, PP 40-115.

Smith M. 1993, Neural. Net works for statistical Mode ling, New York.

Teoh, S.H., Welch, I. and Wong, T.J. (1998). Earnings management and the underformance of seasoned equity offerings. Journal of Financial Economics, Vol. 50, no1, PP 63-99.

Waldron, M.A. and Jordan, C.E. (2010), the comparative predictive abilities of accrual earnings and cash flows in periods of economic turbulence: the case of the it bubble',

Yousef iRadmandi, H. (2009). "Artificial Neural Networks", Faculty of Engineering, Qazvin, Iran.

Yoder. T.R. 2006, 'The incremental cash flow predictive ability of accruals models', PhD thesis, Pennsylvania State University.