Cash flow disaggregation and prediction of cash flow

Abstract

Listen

Translate article

PurposeThe purpose of this paper is to investigate the incremental information content of estimates of cash flow components in predicting future cash flows.Design/methodology/approachThe authors examine whether the models incorporating components of operating cash flow from income statements and balance sheets using the direct method are associated with smaller prediction errors than the models incorporating core and non-core cash flow.FindingsUsing data from US and UK firms and multiple regression analysis, the authors find that around 60 per cent of a current year’s cash flow will persist into the next period’s cash flows, and that income statement and balance sheet variables persist similarly. The explanatory power and predictive ability of disaggregated cash flow models are superior to that of an aggregated model, and further disaggregating previously applied core and non-core cash flows provides incremental information about income statement and balance sheet items that enhances prediction of future cash flows. Disaggregated models and their components produce lower out-of-sample prediction errors than an aggregated model.Research limitations/implicationsThis study improves our appreciation of the behaviour of cash flow components and confirms the need for detailed cash flow information in accordance with the articulation of financial statements.Practical implicationsThe findings are relevant to investors and analysts in predicting future cash flows and to regulators with respect to disclosure requirements and recommendations.Social implicationsThe findings are also relevant to financial statement users interested in better predicting a firm’s future cash flows and thereby, its firm’s value.Originality/valueThis paper contributes to the existing literature by further disaggregating cash flow items into their underlying items from income statements and balance sheets.