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The purpose of this paper is to investigate the incremental information content of estimates of cash flow components in predicting future cash flows.

The 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.

Using 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.

This 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.

The findings are relevant to investors and analysts in predicting future cash flows and to regulators with respect to disclosure requirements and recommendations.

The findings are also relevant to financial statement users interested in better predicting a firm’s future cash flows and thereby, its firm’s value.

This paper contributes to the existing literature by further disaggregating cash flow items into their underlying items from income statements and balance sheets.

The purpose of this paper is to examine the comparative abilities of current period cash flows and earnings (and its components) to predict one‐year‐ahead cash flow from operations in Egypt.

The study uses the cash flow prediction models developed by Barth, Cram, and Nelson to examine the predictive abilities of earnings and cash flows for future cash flows. The first set of prediction models uses cross‐sectional regression to compare the predictive abilities of cash flows and aggregate earnings for one‐year‐ahead cash flow from operations. The second set of prediction models tests whether disaggregating earnings into cash flows and the major components of accruals enhances the predictive ability of earnings for one‐year‐ahead cash flow from operations.

The findings of the study reveal that aggregate earnings have superior predictive ability than cash flows for future cash flows. Also, the results reveal that disaggregating accruals into major components – changes in accounts receivable and payable, and in inventory, depreciation, amortization, and other accruals – significantly enhances predictive ability of earnings.

The study provides empirical evidence on the superiority of earnings in predicting future cash flows. The findings of the study should be considered in explaining the results of value relevance research Egypt. However, owing to relatively small sample size, given the thinness of the Egyptian capital market, these findings should be interpreted with caution.

The paper contributes to the limited body of research on the superiority of earnings and cash flows in predicting future cash flows by examining the predictive abilities of earnings and cash flows for future cash flows in Egypt as one of many emerging markets.

Provides a comprehensive, critical review of failure prediction with cash flow information since Beaver (1966); and tabulates the methods and cash flow variables used, and the results produced. Describes the literature as “inconsistent and inconclusive” and discusses possible reasons why, e.g. the measurement and diversity of cash flows, lack of model validation, multicollinearity etc. Points out the importance of cash to solvency and dividend payouts; and the limitations it places on creative accounting. Summarizes the reasons for previous inconsistencies and considers possibilities for further research.

The purpose of this paper is to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The study uses the list of CRISIL NSE Index (CNX) Nifty Shariah Index companies as its sample for a period of 10 years for conducting the analysis. The study utilizes the cash flow prediction models to examine the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows.

The study report that contrary to Financial Accounting Standard Board assertion, current cash flows have superior predictive ability of next period cash flows than current aggregate earnings in case of Shariah-compliant companies in India. The results further show that there are no gains from decomposing earnings into accruals and cash flows in predicting future cash flows. There is no increase in explanatory power (measured by adjusted R2) when aggregate earnings are disaggregated into accruals and cash flows to predict next period cash flows.

The empirical findings of the study will enable the Shariah compliant investors to understand the role of current earnings (and its components) and cash flows in predicting next period cash flows in case of Shariah-compliant companies in India.

To the best of author’s knowledge, this is the first study which examines the relative predictive abilities of current earnings (and its components) and cash flows for next period cash flows in case of Shariah-compliant companies in India.

The purpose of this paper is to investigate whether financial analyst coverage affects the dissemination of disclosed operating lease information into cash flow predictions and stock prices.

The difference in lease expense between capital/finance lease and operating lease reporting is estimated based on the approach in Hsieh and Su (2015). This difference is referred to as the earnings impact from operating lease capitalization and is only available from footnotes. The authors then include the level of financial analyst following in a cash flow model to study its impact on the cash flow predictive value of the earnings impact. Similarly, the level of financial analyst following is inserted in an earnings-return model to assess the effect of analyst coverage on the association between contemporaneous stock returns and earnings impact.

The authors find that the cash flow predictive value of the earnings impact shifts to the interaction between analyst coverage and the earnings impact, suggesting that the decision-usefulness of the earnings impact is conditioned on the level of analyst following. Nevertheless, the authors find that the earnings impact continues to have explanatory value for the contemporaneous stock returns, while the interaction between analyst coverage and the earnings impact does not. This finding suggests that the earnings impact is already fully reflected in stock prices regardless of analyst following.

Since the estimation of the earnings impact from reporting operating leases as capital leases is based on the method developed by Imhoff et al. (1991), the results and inferences are thus constrained by the validity of the method.

The authors find that financial analyst activities accelerate the incorporation of the earnings impact from operating lease capitalization in cash flow predictions, but it does not promote the impounding of the earnings impact into stock prices. This finding suggests that financial analysts' influence on the dissemination of the earnings impact hinges on the type of economic activity, and failing to consider the financial analyst following in studying the cash flow predictive value of the earnings impact would obscure the findings.

The authors extend the findings of prior research that financial analysts' activities promote the incorporation of firm-specific information into stock prices by investigating the impact of financial analysts on the dissemination of disclosed operating lease information.

The main objective of this paper is to compare the ability of US-generally accepted accounting principles (GAAP) operating cash flows versus Iran-GAAP operating cash flows in predicting future cash flows.

The sample comprises 240 firms (1,200 firm-years) during the period from 2004 to 2008 for which operating cash flows and other variables are available. Cross-sectional and panel data regression models are used in testing the hypotheses.

This study finds that operating cash flows based on Iran-GAAP are no more effective in predicting future cash flows than those based on USA-GAAP, and the predictive ability of the model is improved by adding the earnings accrual components to the operating cash flows.

The study suggests that the Iranian accounting standard setting committee recommends that the statement of cash flows be prepared based on the three-category model instead of the five-category model in an attempt to converge with the International Financial Reporting Standards. Consistent with Financial Accounting Standards Board and financial analyst recommendations, the results reveal that earnings are a better predictor than cash flows from operations.

Banks, financial statement users, and accounting standard setters have long disagreed on the informativeness of banks’ statements of cash flows (SCFs) and there is a lack of relevant evidence in the literature. This paper examines the informativeness of the SCFs of U.S. commercial banks in two settings where SCFs are purported to be useful. The first analysis tests the incremental value relevance of banks’ SCFs beyond income statements and balance sheets and compares bank's SCFs with those of industrial firms. We find that banks’ SCFs have limited incremental value relevance, and are much less value relevant than industrial firms’ SCFs. The second analysis examines and finds no distress-predictive power of banks’ SCFs, especially in the presence of standard distress predictors. Overall, our results are consistent with the view that banks’ SCFs have limited informativeness.

Business failures during the economic recession of 2008‐2010 years were unusually high in the USA. The purpose of this paper is to build a new model to predict business failure, using mostly cash flow statement based measures as predictor variables and discriminant analysis technique.

The authors' data matrix consisted of 100 firms and seven predictor variables. A total of 50 “failed” firms were matched with 50 non‐failed firms according to Standard Industrial Classification (SIC) code and size. Financial statement data for the year prior to failed year were pulled from COMPUSTAT database. Seven predictor variables were selected, namely Operating cash flow divided by current liabilities, Cash flow coverage of interest, Operating cash flow margin, Operating cash flow return on total assets, Earning quality, Quick ratio and Three‐year sales growth. The SPSS‐19 software was used to perform discriminant analysis (DA).

The DA model classified 83.3 percent of original grouped cases correctly. The cross‐validated approach (jackknife or leave‐one‐out method) correctly classified 79.5 percent of cases. The chi‐square test of Wilks' lambda was significant at 0.000 level which means the model as a whole performed very well in predicting business failure.

This study is unique in many respects. First, the sample companies are not industry specific. They come from more than 20 different two‐digit SIC codes, which means the authors' model is very generic in nature. Second, the seven predictor variables (financial ratios) they selected are logically justified; these are not an outcome of step‐wise procedure. Third, most of the predictor variables use operating cash flow information from the cash flow statement. Fourth, all the failed firms in the authors' test sample are from the most recent, 2008‐2010, period.

The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cash‐flow liquidity in forecasting. However, a great challenge for contracting firm to manage his multiproject cash flow when large and multiple construction projects are involved (manipulate large amount of resources, e.g. labour, plant, material, cost, etc.). In such cases, the complexity of the problem, hence the constraints involved, renders most existing regular optimization techniques computationally intractable within reasonable time frames. This limit inhibits the ability of contracting firms to complete construction projects at maximum efficiency through efficient utilization of resources among projects. Recently, artificial neural networks have demonstrated its strength in solving many optimization problems efficiently. In this regard a novel recurrent‐neural‐network model that integrates multi‐objective linear programming and neural network (MOLPNN) techniques has been developed. The model was applied to a relatively large contracting company running 10 projects concurrently in Hong Kong. The case study verified the feasibility and applicability of the MOLPNN to the defined problem. A comparison undertaken of two optimal schedules (i.e. risk‐avoiding scheme A and risk‐seeking scheme B) of cash flow based on the decision maker's preference is described in this paper.