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Tuesday, February 26, 2019

The Usefulness of Accounting Estimates for Predicting Cash Flows

The Usefulness of history Estimates for Predicting Cash Flows and Earnings Baruch Lev* New York University Siyi Li University of Illinois Theodore Sougiannis University of Illinois and ALBA January, 2009 * Contact instruction Baruch Lev (emailprotected nyu. edu), Stern School of Business, New York University, New York, NY 10012.The authors atomic spell 18 indebted to the editor and reviewers of the Review of account Studies for suggestions and guidance, and to Louis Chan, Ilia Dichev, John Hand, James Ohlson, shiva Rajgopal, and Stephen Ryan for helpful tittle-tattles, as well as to participants of seminars at A consequentlys University of political economy and Business, London Business School, Penn State University, Pur cod University, University of Illinois at Urbana-Champaign, University of Texas at D whateveras, Washington University in St.Louis, the joint ColumbiaNYU Seminar, the 16th Financial Economics and Accounting Conference, the 2006 abdominal aortic aneurysm FARS M idyear Meeting, and the 2008 AAA Annual Meeting. 1 ABSTRACT Estimates and projections argon insert in nigh pecuniary command items. These estimates authorisati altogether improve the relevancy of mo lootary learning by providing managers the blind drunks to convey to investors forward-looking, inside data (e. g. , on prospective collections from customers via the bad debt provision).On the some diametric hand, the fictional character of pecuniary discipline is compromised by (i) the increasing difficulty of making reliable reckons in a fastchanging, a good deal turbulent economy, and (ii) the frequent managerial defile of estimates to manipulate financial assureation. aban dod the ever-increasing prevalence of estimates in method of business relationship data, whether these opposing forces result in an improvement in the quality of financial tuition or not is among the most primal issues in accounting. We throw outvas in this piece of work he donatio n of accounting estimates embedded in accretions to the quality of financial schooling, as reflected by their recyclableness in the pret poleion of first step bullion time periods and moolah. Our extensive out-of- assay trys, reflecting some(prenominal) the statistical and stinting consequence of estimates, manoeuvre that accounting estimates beyond those in turning upper- geek letter items do not improve the cryion of gold periods. Estimates do, however, improve the foresight of adjoining years honorarium, though not of ulterior years realise. Our sparing significance tests corroborate that accounting estimates do not improve hard currency f custom or lolly harbingerion.We leave withdraw that the gain of accounting estimates to investors is limited, and pop the question suggestions for improving their benefit. 2 The Usefulness of Accounting Estimates For Predicting Cash Flows and Earnings 1. Introduction Financial statement information, be it labyr inthine spirit sheet items such(prenominal) as can property, plant and equipment, goodwill and early(a) intangibles, accounts receivable and inventories, or key income statement figures, such as r up to skilful awayues, subvention expense, in- do R&D, or the recently expensed employee song options, is by and oersizing establish on managerial estimates and projections.The economic condition of the enterprise and the consequences of its trading trading operations as portrayed by quarterly and matchless-year financial news reports be t presentfore an mingled and ever changing web of facts and conjectures, where the dividing telephone line between the ii is handsomely unknown to information mappingrs. With the incumbent move of accounting standard- trainters in the U. S. and afield toward increased fair- treasure heapment of summateitions and liabilities, the federal agency of estimates and projections in financial reports will notwithstanding increase.We a sk in this study what is the effect of the multitude of managerial estimates embedded in accounting data on the utilizableness of financial information? straightforward. The answer is far from On the one hand, estimates/projections atomic number 18 potenti whollyy rehearseful to investors because they be the primary opines for managers to convey credibly forward-looking proprietary information to investors1. Thus, for example, the bad debt provision, if estimated properly, informs investors on expected prospective coin in mixs from customers, restructuring charges predict early employee severance payments and plant closing costs, and the greatized portion of We say credibly in the principal(prenominal) because post Sarbanes-Oxley the star signs CEO and chief financial officer bring forth to certify that information contained in the periodic report fairly represents, in alone material respects, the financial condition and results of operations of the issuer 3 compute r softw atomic number 18 education costs (SFAS 86) informs investors about development projects that passed successfully technological feasibleness tests and ar accordingly expected to enhance proximo revenues and stipend. 2 This potential contri b atomic number 18lyion of managerial estimates to investors ssessment of prospective enterprise bullion flows underlies the oft-quoted statement by the Financial Accounting Standard Board (FASB) in its conceptual exemplar about the superiority of accumulations lollymostly ground on estimatesoer the life surfacely fact-based currency flows in predicting future enterprise ex tack flows cultivation about enterprise earnings based on accruals accounting in full general provides a better indication of an enterprises present and go on ability to generate favorable money flows than information limited to the financial aspects of silver in receipts and payments (FASB, 1978, p. IX).On the different hand, the contribution of esti mates to the return of financial information is counteracted by devil study factors (i) Objective difficulties. In the reliable vapourific and largely unpredictable business environment, due to fast-changing securities labor conditions (deregulation, privatization, emerging economies) and quick technological compounds, it is increasingly difficult for managers to make reliable projections of business events. Consider, for example, the estimated future ease up on bonus assetsa key component of the pension expense This estimate is essenti each(prenominal)y a fortune telling of the long- call doing of uppercase commercializes.Are managers better forecasters of market process than investors? 3 Or, reflect on the by and large large impairment charges of fixed assets and acquired intangibles (including goodwill) mandated by SFAS 121 and SFAS 142 The determination of these 2 Indeed, Aboody and Lev (1998) document a positive tie between hoodized softw atomic number 18 development costs and future earnings. 3 Consider, for example, the 2001 pension foot p arntages of trinity financial institutions, Merrill Lynch, banking company of NewYork, and Charles Schwab, which report the following estimates of the expected returns on pension assets 6. 60%, 10. 50%, and 9. 00%, respectively (Zion, 2002). The wide come out of estimates (6. 6%-10. 5%) of the long term setance of peachy markets reflects the inherently large uncertainty (unreliability) of the pension expense estimate. 4 charges requires managers to estimate future bills flows from tangible and intangible assets. In todays graduate(prenominal)ly competitive and contested markets the reliability of asset immediate payment flows regarded all over several(prenominal) years is obviously questionable.Accordingly, the accounting estimates and projections underlying financial information introduce a considerable and unknown gradation of noise, and by chance bias to financial information, cl early detracting from their usefulness. 4 (ii) Manipulation. Add to the above objective difficulties in generating reliable estimates the expected and frequently documented susceptibility of accounting estimates to managerial manipulation, and the consequent adverse impact of estimates on the usefulness of financial information becomes app atomic number 18nt.Given that it is very difficult to settle up with manipulators of estimateseven if an estimate turns out ex post to be far off the mark, it is virtually impossible to prove that ex ante the estimate was advisedly manipulatedthere atomic number 18 no effective disincentives for managers to manipulate accounting estimates. Indeed, umteen of the Securities and Exchange Commission (SEC) enforcement cases alleging financial reporting manipulation concern misuse of estimates underlying accruals (e. g. Dechow et al. , 1996). Thus, the impact of estimates underlying accounting measurement and reporting procedures on the usefulne ss of financial information is an open question, to be tryd in this study. The relevance of this examination cannot be overstated. Accounting estimates and projections underlie much of commonly pass judgment Accounting Principles (GAAP) and consume 4 A case in touch (Wall Street Journal, August 4, 2004, p. c1) Investors in Travelers subscribe to needed much than that ed umbrella protection from what has been raining on them since the company was spun out from Citigroup in early 2002. Late last month, St. Paul Travelers Cos. , announced what Morgan Stanley termed a blockbuster reserve charge of $1. 625 billion. The charge was about twice as large as analysts have been expecting. The insurer contends that the charge stems largely from the need to relinquish differing accounting treatments at the both companies Travelers and its acquisitionSt. Paul Cos. . It was just a reserve valuation adjustment, the company said.Sadly there seems to be teeny reason why Travelers executiv es didnt anticipate problems with St. Pauls restitution methodologies Mr. Benet Travelers chief financial officer saidwe recognized early on that there was a deviation in some of the methodologies to estimate reserves that would have to be addressed. (emphasis ours). Thus, disparate accounting methodologies utilize to estimate the same reserves, all approved by auditors, turn back a variance of $1. 625 billion. 5 most of standard-setters time and efforts.Just consider the major issues addressed by the FASB in recent yearsfinancial instruments, employee stock options, fixed assets and goodwill impairment, and the valuation of acquired intangibles, to name a fewall require major estimates and forecasts in the process of accounting measurement and reporting. If these and other accounting estimates do not contribute significantly to the usefulness of financial information, the efforts of accounting regulators, and even much seriously, the resources society devotes to the ge neration of estimates in the process of financial statement preparation and their auditing, be misdirected.Worse yet, if financial information users be led by the estimates-based accounting information to misallocate resources, an extra dead-weight cost is compel on society. We define and test the usefulness of estimates embedded in accrual earnings in terms of their ability to predict enterprise carrying into action. 5 This prophetical use of financial information is central to security digest and valuation and is withal a fundamental premise of the FASBs Conceptual Framework as indicated by the quote above. Future enterprise consummation is mainly reflected by property flows and earnings.Future bills flows are at the loading of asset and liabilities accounting valuation rules. Thus, for example, asset impairment (SFAS 144) is determined by expected exchange flows, and the useful lives of acquired intangibles (SFAS 142) are a function of future cash flows. More fundam entally, asset or enterprise cash flows are postulated by economic theory as the major determinants of their value. Given a certain ambiguity about the specialized definition of cash flows used by investors, we perform our tests with cardinal widelyused and frequently prescribed cash flow constructs cash from operations (CFO) and supererogatory cash flows (FCF).Much of prior related inquiry heightened on CFO. Free cash flows are central to 5 There are, of course, other uses of financial data, such as in contract arrangements, which are not aimed at predicting future enterprise execution of instrument. 6 numerous practitioners valuation sets (e. g. Brealey and Myers, 2003), and play an important role in explore in addition (e. g. , FCF is a primary shifting in the valuation constructs of Feltham and Ohlson, 1995). Cash flow expectancy is thus a predominant element of accounting measurements and practitioners valuation processes.Despite the prominence of cash flows in eco nomic asset valuation influences, there is no denying that many investors and analysts are victimization financial data to predict earnings. The underlying heuristics are somewhat obscured perhaps investors predict earnings first, and educe future cash flow estimates from the predicted earnings. In any case, earnings expectancy is prevalent in practice, and we therefore also examine the usefulness of accounting estimates for the divination of earnings, both operational and net income.The taper of this study is on accounting estimates, but many of the estimates underlying financial information are not dis tight-fittingd in the financial reports. 6 We, therefore, taper in this study on accruals, most of which are based on estimates. In cross, we distinguish between accruals which are largely un affected by estimates (changes in working detonator items, excluding inventorying), and accruals which are in the main based on estimates (most non-working capital accruals). This enables us to draw sharper inferences on the effect of estimates on the usefulness of financial information.We also analyze a small render of familys with data on ad hoc estimates which we split into pass off and non-recurring to separate noise (the non-recurring estimates) from information (the recurring estimates). Our empirical analysis is based on a stress of all non-financial Compustat unwaveringlys with the required dataranging from roughly 1,500 to 3,200 companies per yearand spanning the 6 For example, General Electric reports in its revenue recognition foot occupation that dis alike components of revenues derived from long-run projects are based on the estimated profitability of these projects.GE, however, does not break slash resume revenues into estimates and facts. 7 period 1988-2005. Our tests are leaded in three stages (1) In- exemplification, effort-by- constancy, soothsayings of future enterprise cash flows and earnings, based on (a) accredited cash flows only (the bench mark), (b) earnings, and (c) the set of cash flows, the change in working capital (excluding inventory), and various components of accruals based on estimates. present we follow the recidivateion procedures of Barth, Cram, and Nelson (2001) and find, on much recent data, results which are generally consistent with Barth et al.This is our departure point. (2) Out-of sample crocked specific predictions of future cash flows and earnings utilise the perseverance specific parameter estimates of the in-sample statistical regressions. The focus of this analysis is on the improvement in the quality of predictions brought about by the addition of estimates (accruals) to the predictors. We thus predict cash flow from operations, release cash flows, net income before extraordinary items, and in operation(p) income over various horizons one year ahead, second year ahead, accumulate devil years ahead, and fuse three years ahead.Our results show that accounting e stimates do not improve the prediction of future cash flows (both in operation(p) and surrender cash flows), compared with predictions based on accredited CFO and the change in working capital excluding inventory. However, accruals do improve bordering years prediction of net and operating income. Notably, cash flow predictions based on stream earnings only are significantly inferior to those generated by current CFO, contrary to Kim and Kross (2005). In our small sample analysis, n both recurring nor nonrecurring estimates improved significantly the predictions of either cash flows or earnings.The bottom lineaccounting estimates beyond those in working capital items (except inventory) do not improve the prediction of cash flows. 8 (3) Finally, we examine the economic significance of estimates. These tests complement stage two, which is based on the statistical significance of differences in the quality of alternative predictors. Since it is difficult to gauge economic significa nce from statistical significance, we perform various portfolio tests, where portfolios are constructed from predicted cash flows and earnings based on various predictors, some of which are based on estimates.The brachydactylous returns on these portfolios, generated by alternative predictors, are our gauge of economic significance. The focus here is on analyse the returns on portfolios constructed from predictions based on current cash flows only (the benchmark), with returns on portfolios constructed from predictions based on current earnings or current cash flows plus changes in working capital and estimates. The results from these tests generally corroborate the out-of-sample prediction tests.In practically all our portfolio tests the model that uses current operating cash flows only to predict firm performance generates higher unnatural returns than models which add estimates to the prediction process used for the portfolio formation, though most of these returns are insigni ficant. Furthermore, the portfolios constructed from predictions based on current cash flows only furnish abnormal returns with generally lower standard deviation than the alternative portfolios which include earnings or estimates among the predictors. We caution against sweeping conclusions.We examine the usefulness of accounting estimates in terms of predictive ability with respect to future firm performance. Accounting information is used for other goals too (contracting, national accounting), for which estimates may be useful. Furthermore, our prediction tests are based on fairly simple models. Users may be using different, more sophisticated models where estimates could prove to be useful. 9 Nevertheless, we believe that our findings draw attention to the significant vulnerability of financial information from the multitude of underlying estimates and projections, and to the urgent need for improving the eliability of estimates, on which we interpretation in the concluding section. The order of discussion is as follows variance 2 relates our findings to available research, and Section 3 outlines our research jut out. Section 4 describes our sample, and Section 5 reports our prediction tests. Section 6 informs on a onslaught of robustness checks, and Section 7 focuses on a subsample with an extended set of accounting estimates. Section 8 reports our portfolio (economic significance) tests, plot of ground Section 9 concludes the study. 2.Relation to functional Research Our study interfaces with several active research areas, and below we comment on the relation between our work and various representative studies. We are not familiar with empirical studies which assess the impact of accounting estimates on the edifyingness of financial information, but there is a substantial number of studies that examine the contribution of accruals to the prediction of future cash flows and other variables. These studies can be roughly classified into regression -based (in-sample) analyses, and out-of-sample prediction tests.An example of the former is the oecumenical work by Barth, Cram and Nelson (2001), who regress CFO on lagged value of CFO and components of accruals (primarily the changes in accounts receivable, inventories, and accounts payable, as well as derogation & amortization and other accruals). The authors report (p. 27) that apiece accrual component reflects different information relating to future cash flowsand is significant with the predicted sign in predicting future cash flows, additive to current cash flows. tincture that 10 predictive ability is assessed in this and similar studies by the significance of the estimated accruals coefficients and by the improvement inR 2. 7 An enkindle extension of the regression strand is provided by Subramanyam and Venkatachalam (2007) who examine the relative informative power of earnings and cash flows with respect to an ex post measure of the intrinsic value of paleness whic h uses Ohlsons (1995) equity valuation framework, based on accomplished determine of earnings and book value.The authors argue that such measurement of equity values avoids the necessity to assume capital market efficiency, as in Dechows (1994) study relating accruals to contemporaneous stock returns. Dechow documents a significant association between accruals and stock returns, but the implications of such association for market efficiency are challenged by Sloans (1996) findings of strong return reversals (market inefficiency) following extreme accruals.Subramanyam and Venkatachalam (2007) conclude that operating cash flows are more strongly associated with future cash flows than earnings, and that current earnings are more strongly associated with future earnings than cash flows. Regressing the ex-post equity measure on earnings and cash flows indicates that earnings exhibit a higher instructive power than cash flows. By and large, the in-sample regression studies suggest that accruals are associated with subsequent cash flows and contemporaneous equity values, a finding we largely update and corroborate in the initial stage of our analysis (Section 5. ). However, as is argued in Section 5. 1, in-sample regressions are not prediction tests, and may even provide misleading inferences concerning prediction power. We move, therefore, to out-of-sample tests. An early and innovative out-of-sample prediction test is dactyl (1994), who concludes from a sample of 50 companies with long historical data that cash flow is marginally superior to 7 Bowen et al. (1986) and Greenberg et al. (1986) perform similar regression-based, in-sample predictions. 11 earnings for short-term predictions and performs similar to earnings in long-term cash flow predictions.However, time-series and cross-section(a) out-of-sample short-term prediction tests by Lorek and Willinger (1996) and Kim and Kross (2005), respectively, show that current earnings predict more accurately future c ash flows than current cash flows do. Thus, a mixed picture emerges from the out-of-sample tests, calling for pass on research. Note also that most previous studies, in- and out-of-sample, focus on the prediction of cash from operations, despite the fact that release cash flows (a measure include in our tests) is frequently used by analysts and investors.Barth, Beaver, Hand and lubber (2005) provide an evoke perspective on the usefulness of accruals. Using the valuation framework of Feltham and Ohlson (1995, 1996), they examine the ability to predict equity value of various disaggregations of earnings immix earnings, cash flows and add accruals, as well as cash flows and quaternary major components of accruals. The prediction methodology is out-of-sample in a particular sense cross-sectional valuation models are run for distributively year (equity values regressed on contemporaneous earnings disaggregations), excluding each time a particular sample firm.The equity value of t hat firm is then predicted from the estimated coefficients of the models. Barth et al. (2005, p. 5) find present of some reduction in pixilated prediction errors from disaggregating earnings into cash flows and total accruals, and some additional reduction from disaggregating total accruals into its quatern major components medial prediction errors generally take over disaggregation of earnings only into cash flows and total accruals. Overall, these findings vary considerably by industry, and appear to indicate a more consistent success for the cash flows and total accruals model than for the cash flows and disaggregated accruals model. 8 8 Studies such as Bathke et al. (1989) and Lorek et al. (1993) also perform out-of-sample prediction tests. 12 The substantial body of research on the accruals anomaly initiated by Sloan (1996) is tangentially related to our study.This research establishes that accruals are often misinterpreted by investors large (small) accruals firms are cont emporaneously overvalued (undervalued) in capital markets, and these misvaluations are largely transposed within a couple of years. Notably, much of the accruals anomaly resides in small, light traded firms, which are unattractive to most institutional investors (Lev and Nissim, 2006), a fact that contributes significantly to the persistence of this anomaly. It is important to railway line that our focus in this study is different from the ccruals anomaly research we do not examine investors perceptions of accruals, and the consequences of such perceptions. Rather, we focus on the contribution of accruals and by implication of the embedded estimates to the primary role of financial informationassisting users in predicting future enterprise performance. The short-term market inefficiencies highlighted by the accruals anomaly are, of course, worth noting, but they do not inform much on the presumed role of accrualsto improve the prediction of enterprise performance.Stated different ly, slice extreme accruals are often mispriced contemporaneously by investors, a misperception correct fairly shortly there afterward, accounting accruals in general, prevalent in all(prenominal) financial report, may muted enhance the multi-year prediction of firm performance. It is this fundamental role of accruals and their underlying estimates that is the main theme of our study. The lack of convergence of the living accruals usefulness research makes it very difficult to draw firm conclusions.Some studies are in-sample, while others are out-of-sample some researchers relate accruals to contemporaneous returns or equity values whereas others to future values. Some predict cash flows while others predict equity values based on models using forecasted or realized residual earnings. Our main contribution to extant research is the focus on the estimates embedded in accruals and the provision of certain closure to the usefulness of 13 accruals issue. We distinguish between accrua ls which are largely based on facts and those primarily reflecting estimates, to focus on the usefulness of accounting estimates.Our main tests are out-of-sample predictions, replicating what most investors actually dopredict, with no ex post information (as implicitly assumed by in-sample studies), various versions of future earnings and cash flows. The richness of our predicted performance measures (two versions of earnings and two of cash flows), and the number of future periods examined (years t+1, t+2, and aggregate neighboring two years and next three years) enables us, we believe, to draw general conclusions about the contribution of estimates to firm performance rediction. Furthermore, our study is the first, we believe, to examine both the statistical and economic performance of accruals-based prediction models. Inferences from statistical significance are sometimes difficult to draw and generalize. Consider, for example, the Barth, Beaver, Hand and Landsman (2005, p. 5) conclusion we find evidence of some reduction in convey prediction errors from disaggregating earnings (emphasis ours). While definitely interesting, this conclusion leaves open the important question of how material is some reduction?Is it, for example, sufficiently large to support the current move of the FASB and IASB toward increased reliance on estimates in financial reports (fair value, stock option expensing, etc. )? Statistical significance coupled with economic significance, as provided below, allows for a more comprehensive evaluation of the evidence. 9 The focus on accounting estimates, the out-ofsample methodology, and the examination of both statistical and economic significance, all bringing certain closure to the research question, is our main contribution. 3. Research anatomy Examples of studies including economic significance tests are Ou and Penman (1989), Stober (1992), Abarbanell and Bushee (1998), and Piotroski (2000). 14 Our research design consists of three stages (a) in-sample association tests of cash flows (earnings) regressed on lagged values of these variables and accruals, (b) out-of-sample forecasts of cash flows (earnings) based on these variables and accruals and (c) calculation of hedge future excess returns on portfolios constructed from the out-of-sample predicted cash flows (earnings) in stage (b).We conduct the first stage as a link to and departure from previous research by estimating cross-sectional in-sample regressions as in the Barth, Cram and Nelson (2001) study (BCN hereafter). We use several prediction constructs, primarily to distinguish between accruals largely based on facts and those based on estimates. At one extreme of the accruals disaggregation we classify all the accruals in the operations section of the cash flow statement into working capital changes excluding inventory (? WC*) and the stay accruals, termed estimates (EST) EARNINGSCash from functional Capital Operations Change excluding (CFO) inventory (? WC*) Estimates (EST) ACCRUALS Working capital items with the exception of inventory, such as accounts payable and short-term vendible securities, are generally not materially impacted by managerial estimates,10 whereas 10 The accounts receivable change, net of the provision, is an exception, since it is subject to an estimate. But this estimate is include in our second accruals component, EST. 15 most of the remaining accruals are in fact pure estimates (e. g. , depreciation and amortization, bad debt provision, in-process R&D).At the other end of the accruals disaggregation we separate out the change in inventory (? INV) from the aggregate estimates (EST), effrontery the evidence (e. g. , Thomas and Zhang, 2002) that much of the accruals anomaly resides in inventory, probably due to intentional and unintentional misestimations of this item. We further break out depreciation and amortization (D&A) and deferred taxes (DT) from other estimates because the identification of these items is possible from Compustat data over the entire sample period. This disaggregation is depicted thus EARNINGS CFO WC* ( damaging inventory) ?Inventory (? INV) Dep. & Amortization (D&A) ACCRUALS Def. Taxes (DT) new(prenominal) estimates (EST*) The various components of accruals along with cash from operations (CFO),11 depicted in the two exhibits above are the free-lance variables in the estimation models underlying our in-sample predictions. We add to these variables the cash flow statement figure of capital expenditures (CAPEX), since the dependent variables in our models are future cash flows or earnings, which are generally affected by current investment (capital expenditures). We believe 11We measure CFO as in Barth et al. (2001), namely net cash flow from operating activities, adjust for the accrual portion of extraordinary items and discontinued operations. 16 that the addition of capital expenditures to the regressors improves the specification of the insample predic tion models, and sharpens our focus on the relative performance of the accruals components, our focus of study. Indeed, the capital expenditures variable is statistically significant in most of our yearly in-sample predictions models. 12 3. 1 Prediction tests Our prediction tests take the following general form.We predict two versions of cash flows (cash from operations and free cash flows) and two constructs of earnings (net income before extraordinary items and operating income) in years t+1 and t+2, as well as in aggregate years t+1 & t+2, and t+1 through and through t+3. To gain insight into the usefulness of estimates in predicting firm performance, we use pentad prediction models with increasing disaggregation of accruals (regressors) personate 1 current CFO onlythe benchmark model place 2 current net income (NI) only puzzle 3 current CFO and the change in working capital items excluding inventory (?WC*)namely, largely fact-based regressors puzzle 4 current CFO, the chang e in working capital items excluding inventory ? WC*, and total remaining accruals, largely based on estimates (EST) and fashion model 5 current CFO, the change in working capital items excluding inventory ? WC*, the change in inventories (? INV), depreciation & amortization (D&A), the change in deferred taxes (DT), and all other estimates (EST*)the most disaggregated model. The purpose is to examine whether the gradual addition of components of accruals 12 For robustness, we reran our predictions (report in instrument panel 3) without capital expenditures, and conclude that one of our inferences changes in the absence of capital expenditures. 17 estimates to current cash flows (the benchmark) improves the prediction of future cash flows or earnings. Increasing the disaggregation of accruals should, in general, enhance the quality of prediction (from model 1 to 5), since the individual accrual components are allowed to have different effects (multiples) on the predicted values. We examine model 2 because the predictor, earnings, is a summary accounting variable that has been extensively investigated for its information content and has been used in most prior studies (e. . BCN and Kim and Kross 2005). It is important to note that the cross-sectional estimates of the tail fin in-sample prediction models are obtained for 2-digit localize industry groups. These industry specific estimates make the implicit assumption of constancy of coefficients across firms clean tenable. We implement the second stage of our research design by using the industry specific estimated coefficients from each of the above five prediction models to exercise firm specific predicted values for cash from operations (CFO), free cash flows (FCF), net income (NI) and operating income (OI).We then calculate firm specific prediction errors as the difference between the actual and predicted values of each variable examined. The following examples of the prediction of free cash flows (FCF) w ill clarify our prediction procedures. A. Prediction of next years free cash flows, FCF (t+1) (a) bench mark model using CFO only (example for 1990) 1. Estimate cross-sectionally for each 2-digit industry the following regression FCF (89) = ? + ? CFO(88) + ? . , 2. Predict for each firm in a given 2-digit industry EFCF (90) = ? + ? CFO(89) using the previously determined industry specific estimated coefficients. . Determine prediction error for each firm in a given 2-digit industry EFCF (90) . FCF (90) 18 Here we predict 1990 free cash flows (EFCF(90) from current cash from operations, CFO (89) (and capital expenditures). First, for each 2-digit industry we regress cross-sectionally free cash flows of 1989 on CFO in 1988, and obtain the estimated coefficients ? and . ? Those coefficients are then used to predict firm specific free cash flows (EFCF) in 1990, using the firms actual CFO of 1989. Then, a firm specific prediction error is determined by comparing the firms actual 1990 F CF with the predicted one.The same procedure is repeated for every firm and sample year. (b) Restricted Estimates, form 4 (example for 1990) Estimate cross-sectionally for each 2-digit industry FCF (89) = ? + ? 1CFO(88) + ? 2? WC * (88) + ? 3EST (88) + ? . The subsequent prediction and error determinations are done as in (a) above. Here we predict 1990 free cash flows from CFO, ? WC* (change in working capital items excluding inventory), EST (estimates), and capital expenditures (not shown in the equation). First, a cross-sectional regression of 1989 free cash flows is run on the 1988 values of CFO, ? WC*, and EST, yielding coefficients ? ? 1, ? 2, and ? 3. Then, firm specific 1990 free cash flows are predicted, using the four industry specific estimated coefficients and the 1989 actual values of CFO, ? WC*, and EST. Finally, these 1990 FCF predictions are compared with the 1990 actual free cash flows to determine the prediction error. The same procedure is repeated for each firm a nd sample year. (c) Expanded Estimates, Model 5 (example for 1990) Estimate cross-sectionally for each 2-digit industry FCF (89) = ? + ? 1 CFO(88) + ? 2 ? WC * (88) + ? 3? INV (88) + ? 4 D & A(88) + ? 5 DT (88) + ? 6 EST * (88) + ? . 19The prediction and error determinations are done as in (a) above. Here we predict 1990 free cash flows from 1989 CFO, capital expenditures, and the disaggregated set of estimates (see second diagram at the beginning of this Section). Once more, we run by industry a cross-sectional regression of 1989 FCF on the 1988 values of the independent variables, estimating the ? and ? 1 ? 6 coefficients (and a ? 7 coefficient for 1988 capital expenditures). The firm-specific 1990 free cash flows are predicted using these industry specific coefficients and the actual values of the independent variables in 1989.Computation of the 1990 FCF prediction error follows. B. Prediction of year 2 free cash flows, FCF (t+2) bench mark Model (example for 1992) 1. Estimate c ross-sectionally by 2-digit industry FCF (90) = ? + ? 1CFO(88) + ? 2. Predict for each firm in a given 2-digit industry EFCF (92) = ? + ? 1CFO(90) 3. Prediction Error for each firm in a given 2-digit industry FCF (92) EFCF (92) This is the prediction of free cash flows in t+2. It follows the earlier procedure with one difference Here the cross-sectional estimate (first equation) and the forecast (second equation) involve a biennial lag (e. . , FCF in 1990 regressed on CFO of 1988). Same procedure is perform for each firm and sample year. The expanded prediction models incorporating disaggregated accruals follow steps (b) and (c), above. We also predict free cash flows for aggregate years t+1 plus t+2, and t+1 through t+3. These predictions are based on the procedures described above, except that aggregated future free cash flows are substituted for single year free cash flows as left-hand(a)-hand variables in the various models. The procedure demonstrate above for FCF is also us ed to predict cash from operations 20 CFO) in t+1, t+2, and aggregated future years, and to predict earnings in t+1, t+2 and aggregated future years. Two versions of earningsnet income before extraordinary items (NI) and operating income (OI)are predicted. The various prediction models for earnings are identical to those of free cash flows described above, except that earnings in t+1 and t+2 are substituted for FCF in those models. To summarize, we perform out-of-sample predictions of two versions of cash flows and two versions of earnings from current values of CFO, current values of NI, and CFO plus changes in working capital and various combinations of accruals.To evaluate the quality of the out-of-sample predictions, we compute summary measures of prediction errors derived from the firm- and year-specific estimated errors the mean and median write prediction errors indicating the bias in the forecasts, and the mean and median despotic prediction errors which hornswoggle from the sign of the error and indicate forecast accuracy. The firm-specific prediction error in a given year is computed as the realized value of cash flow or earnings deduction the predicted cash flow or earnings, divided by average total assets in year t. . 2 Portfolio analysis The third stage of our research design is motivated by Poon and Granger (2003, p. 491) who note Instead of striving to make some statistical inference, prediction model performance could be judged on some measures of economic significance. We interpret their statement as saying that we should not rely solely on the statistical significance of our prediction errors reckon in stage two but should also examine and perhaps even rely more on measures of economic significance.To gauge the economic significance of the contribution of estimates to the usefulness of financial information we perform a series of portfolio tests focusing on the incremental stock returns generated by the estimates-based prediction models . 21 Essentially, we use the out-of-sample predicted values of cash flows (CFO and FCF) and alternatively of earnings (NI and OI), obtained in the second stage of our analysis, to form portfolios.Specifically, for each sample year we glaring all firms (across all industries) on predicted firm-specific cash flows or earnings (four rankings, two for cash flows and two for earnings), scaled by average total assets in the end of year t. We then form ten portfolios from each annual ranking and compute risk-adjusted (size & book-to-market adjusted) returns from holding these portfolios over several future periods. In assessing the performance of the various predictors (CFO, NI, ? WC*, accruals of estimates), we primarily focus on a zero in-investment (hedge) system going long (investing) in the top ortfolio (the 10% of firms with the largest (scaled) predicted cash flows or earnings), and shorting (selling) the bottom portfolio (10% of firms with the lowest predicted cash flows or earn ings). The abnormal returns on these zeroinvestment portfolios indicate the economic contribution to investors of using accounting estimates as predictors. Thus, if estimates are useful to investors then portfolios constructed from predictions based on current cash flows and estimates-based accruals should consistently excel portfolios formed from predictions based on current cash flows only.It should be mention that if markets are efficient concerning the information in accrualsa big if, in light of Sloan (1996)and if investors select securities using procedures similar to our industry-based prediction models specified above, then our subsequent portfolio abnormal returns should be roughly zero. Our purpose in these portfolio tests, however, is not to examine market efficiency, rather to compare the performance of portfolio selection procedures with the estimates-based accruals against similar procedures without accruals (based on past cash flows only).We are thus focusing on the with- and without-accruals comparisons, being agnostic about market efficiency. Stated differently, the comparative abnormal hedge returns across the 22 five prediction models, rather than the statistical significance of those returns, is our focus of analysis. 4. Sample Selection and Descriptive Statistic We obtain accounting data from the 2006 Compustat annual industrial, full coverage, and research files, and use data from the statement of cash flows because Collins and Hribar (2002) suggest that such data are preferred to accruals derived from the balance sheet.Since reporting a statement of cash flows was mandated by SFAS 95 in 1987, our accounting data span the period 1988 to 2005. 13 In the in-sample regression analysis, each year from 1988 to 2004 is a predictor year (generating the independent variables) while each year from 1989 to 2005 is a predicted year (providing the dependent variables). Thus, 17 in-sample annual regressions are estimated for each industry. Our samp le selection procedure is as follows. We kill with 75,571 observations with values for NI, CFO, ? WC*, INV, D&A, DT, EST, EST* and CAPEX for the current year, year t, and for NI over a three-year horizon, t-1 to t+1. Firms with all fiscal year ends are included. We moderate for outliers by following the procedures in Barth et al. (2001). Thus, after eliminating the top and bottom one percentile of current NI and CFO we are left with 73,324 firm-year observations. By excluding observations with market value of equity or sales of less than $10 million, or with share prices below $1, to eliminate economically marginal firms, the number of observations decreases to 51,301.By deleting observations with studentized residuals greater than 3 or less than -3, we are left with 50,288 observations. Since we conduct industry-byindustry in-sample regression analysis we require each industry to have a minimum of 600 observations over the period 1988 to 2004. This criterion reduces the sample to its final size of 13 Valid statement of cash flows data for the year 1987 are available for a relatively small number of firms not becoming to do a meaningful industry-by-industry analysis. Thus, we do not use 1987 data. 23 41,124 observations.We obtain stock returns data for the portfolio analysis from the 2006 CRSP files. 14 prorogue 1 provides summary statistics (variables are scaled by average total assets) and a correlation matrix for out test variables. Panel A shows that depreciation and amortization (D&A) constitutes the bulk of the estimates underlying accruals (EST) The mean (median) of D&A is 0. 054 (0. 047), close to the mean (median) of EST, 0. 059 (0. 052). The mean of net estimates (EST*), excluding D&A and deferred taxes, is quite large, 0. 019, and is control mainly by large positive values, as the median value of 0. 04, Q1 of 0. 000 and Q3 of 0. 019 imply. CFO has the lowest while NI has the highest variability (standard deviations of 0. 129 versus 0. 149) amon g the various earnings and cash flow variables. In panel B all correlations are significant at the 5% level or better. We note the high negative correlations of our estimates variables, EST and EST*, with the income variables, NI and OI. However, the correlations of EST and EST* with both the cash flow variables, CFO and FCF, are much lower positive for EST and negative for EST*. 4 We repeated all of our analyses with a sample without any outlier removal, namely where we only require non- lacking values for the key variables, and at least 600 observations in each 2-digit SIC over the sample period 19882004. This sample consists of 65,178 observations and is substantially larger than the sample of 41,124 observations used in the analysis inform below. We find that for many industries the R-squares in the in-sample regressions are higher for the un-truncated data than for the truncated data.The forecast error results are essentially identical to the results from the truncated sample in terms of inferences but the errors are larger. The portfolio abnormal returns results exhibit similar patterns to the results from truncated data. Overall, the un-truncated data yield very similar results to those of the truncated data reported below. 24 5. verifiable Findings Prediction Tests 5. 1 Stage one In-sample Regressions Table 2 reports cross-sectional annual regressions, by industry, of CFO (cash from operations) on lagged values of CFO and earnings components (Model 5 in Section 3).The reported coefficient estimates for each industry are the means of the yearly coefficients over the 17 year period, 1988 to 2004. The significance of these mean coefficients is based on (nonreported) t-statistics calculated using the mean and standard errors of the 17 yearly coefficients, as in Fama and MacBeth (1973). We report the results for the CFO regressions so that they can be compared to the CFO results reported by BCN. The, in-sample regression results for FCF, NI and OI are ve ry similar to those reported in Table 2. It is evident that in each of the twenty-three ndustries in Table 2 the lagged CFO and ? WC* (change in working capital minus inventory) are highly significant. In the bulk of the industries, ? INV (inventory change) is also significant, as is D&A. However, DT (deferred taxes) and EST* (other accruals estimates) are significant for about half(a) of the industries only. These results are quite consistent with BCNs results reported in their Table 6, Panel B (note that the sum of our DT and EST* variables is the OTH variable in BCN). The fairly large R2s, ranging across industries from 0. 29 to 0. 71, are also consistent with the R2s reported by BCN.Thus, the BCN regression results over the period 1987 to 1996 hold well over our endless period, 1988-2004. Overall, the estimates indicate a strong association between CFO and lagged earnings components, raising expectations about strong out-of-sample performance as well. However, it is important to note that a regression analysis of a given variable on lagged values of that variable along with other data, as frequently conducted in accounting and finance research, is not a conclusive test of predictive ability. As noted in Poon and Grangers (2003, p. 25 92) survey In all forecast evaluations, it is important to distinguish in-sample and out-ofsample forecasts. In-sample forecast, which is based on parameters estimated using all data in the sample, implicitly assumes parameter estimates are stable through time. In practice, time variation of parameter estimates is a critical issue in forecasting. A good forecasting model should be one that can withstand the robustness of an out-of-sample test, a test design that is closer to reality. In our analyses of empirical findings we focus our attention on studies that implement out-of-sample forecasts. A dramatic example of misplaced inferences drawn on the basis of regression analysis has been recently provided by Goyal and Welch (2 007). Their focus is on the prediction of stock market returns based on a potpourri of variables suggested by prior studies (e. g. dividend yield, earnings-price ratio, book-to-market ratio), using in-sample regression models. After a comprehensive analysis, Goyal and Welch conclude that these models have predicted poorly both in-sample and out-of-sample for thirty years now these models seem unstable, as diagnosed by their out-of-sample predictions nd other statistics and these models would not have helped an investor with access only to available information to profitably time the market (Abstract). This important insight motivates our primary analysis which focuses on out-of-sample prediction tests. In the case of predicting stock returns, Goyal and Welchs concern, in-sample regression results are generally creaky and it is therefore not surprising that the out-of-sample predictions of Goyal and Welch perform poorly too.In contrast, in our case of predicting cash flows and earn ings, the in-sample regressions (Table 2) perform well, so, whether the more realistic out-of-sample predictions of cash flows and earnings perform equally well is an important empirical issue which we examine next. 26 5. 2 Stage two Out-of-sample Prediction Tests Table 3 summarizes our main out-of-sample prediction findings. Recall that we predict four key performance indicators cash from operations (CFO) free cash flows, delineate as CFO minus capital expenditures (FCF) net income before extraordinary items (NI) and operating income (OI).There are four prediction horizons next year, second year ahead, aggregate next two years, and aggregate next three years. Five prediction models are examined (they were discussed and demonstrated in Section 3), where the predictive (independent) variables are (1) CFO onlythe benchmark model, (2) NI only, (3) CFO and the annual change in working capital items excluding inventory (? WC*), (4) CFO plus the change in working capital items excluding inventory (? WC*), as well as the total remaining accruals (EST) which are largely estimates based, including the change in inventory, and (5) our most disaggregated model CFO, ?WC*, the change in inventories, depreciation and amortization, deferred taxes, and all remaining estimates. Current capital expenditure is included as an additional variable in each of the five models. We report in Table 3 four summary statistics for the prediction errors of our five models the pooled firm-specific mean overbearing error (MAER) of each of the five models, the pooled mean gestural error, or bias (MER), the mean R2s from annual regressions of firm-specific actual values of future cash flows or earnings on the corresponding predicted values, and the average over the years of Theils U-statistics. 5 We indicate with an ampersand (&), asterisk (*) or a hash () the pooled mean absolute prediction errors (MAER) which are significantly different 15 The reported Theils U-statistic is the average of t he yearly U-statistics. Theils U is defined as the square root of ?(actual-forecast)2/? (actual)2. The U statistic can range from zero to one, with zero implying a perfect forecast. Thus, models generating better predictions should have lower U statistics. 27 between Models 1 and 2, Models 1 and 3, and Models 3 and 4, and Models 3 and 5, respectively. 6 We have also computed the sample median signed errors, median absolute errors, and root mean square errors. Results from these indicators are very similar to those reported in Table 3 (we comment in the text on the occasional differences). Below are the main inferences we draw from Table 3, and additional analyses 1. Prediction of cash flows. Considering the prediction of cash from operations (CFO) and free cash flows (FCF)left two quadruples of columns in Table 3we note that the predictions derived from net income only (Model 2) are always significantly inferior to the predictions based on cash from operations only (Model 1).This is true across the four forecast horizons and the four error summary statistics. For example, in predicting one-year-ahead cash from operations (top left panel), the MAER, MER and Theils U are lower for Model 1 than for Model 2 (0. 056 vs. 0. 062, 0. 001 vs. 0. 003, and 0. 58 vs. 0. 64, respectively), while the R2 of Model 1 is higher than that of Model 2 (0. 46 vs. 0. 37). The difference in the MAERs is statistically significant, as indicted by the & sign. This pattern is evident across all eight panels reporting predictions of cash from operations and free cash flows for various horizons.Thus, for one- to three-year forecast horizons, current cash from operations is a better predictor of future cash from operations and free cash flows than current net income. This result is inconsistent with Kim and Kross (2005) findings that in one-year-ahead predictions of cash flows current earnings performs better than current cash flows. 17 16 All the absolute forecast errors (MAER) in Table 3 are statistically significant, with p-values of 0. 01 or better. The majority of the signed errors (MER) are also significant at p-values of 0. 1 or better, and many are statistically significant at least at p-values of 0. 05. The following signed errors are insignificant Model 1 in forecasting Years 12 CFO, Models 1 and 3 in forecasting Years 1-3 CFO, and Models 2, 4 and 5 in forecasting Years 1-3 OI. 17 It is important to note that Kim and Kross (2005) use balance sheet items to calculate cash from operations while we use statement of cash flows data. We were able to replicate the out-of-sample prediction results of Kim and 28Moving on to Model 3, (predictors CFO and the change in working capital items minus inventory), we note that the CFO and FCF predictions derived from current CFO only (Model 1) under-perform predictions based on current CFO and the change in working capital items excluding inventory, ? WC*. Thus, the mean absolute errors of Model 3 are significantly lower tha n those of Model 1 in all CFO and FCF panels, except in the FCF panel for the aggregate next three years horizon (bottom FCF panel). 18 The reported R2s and Theils U statistics also indicate the under-performance of Model 1 relative to Model 3.For example, in predicting one-yearahead cash from operations (top left panel), the MAER and Theils U are lower for Model 3 than for Model 1 (0. 054 vs. 0. 056, and 0. 56 vs. 0. 58, respectively), while the R2 of Model 3 is higher than that of Model 1 (0. 50 vs. 0. 46). Thus, for one- to three-year forecast horizons, the total change in working capital items excluding inventory is incrementally informative over current cash flows. This is relevant for our focus on the usefulness of accounting estimates, because the working capital items, excluding inventory, and with the exception of accounts receivable, are largely free of estimates.We now move to examine the contribution of accounting estimates to cash flow prediction. We do this by comparin g the performance of Models 4 and 5 to that of Model 3, where Model 3 becomes now our benchmark given its superior performance up to this point. We note that CFO and FCF predictions derived from Model 4 (based on CFO, the change in working capital items excluding inventory (? WC*), as well as all other accruals including the change in inventory) and Model 5 (based on CFO, ? WC*, the change in inventories, depreciation and amortization, Kross using balance sheet items for our sample period.Accordingly, the difference in the results between the two studies is due to the data used. As shown by Collins and Hribar (2002), the cash from operations, and accruals derivation from the statement of cash flows is preferable. 18 Note that despite the very small difference between the MAERs of Models 1 and 3, the mean differences are statistically significant at the 0. 05 level or better (see asterisks). 29 deferred taxes, and all remaining accruals) equally perform or under-perform the predictio ns from Model 3 (based on CFO and ?WC*). Specifically, the mean absolute errors of Model 3 are significantly lower than or equal to the mean absolute errors of Models 4 and 5 in all the CFO and FCF panels. Furthermore, the reported MERs, R2s and Theils U statistics are also consistent with the under-performance of Models 4 and 5 relative to Model 3. For example, in predicting one-year-ahead cash from operations (top left panel), the MAER, MER and Theils U for Model 3 are either equal to or lower than for Models 4 and 5 (0. 054 vs. 0. 054 and 0. 055 0. 001 vs. 0. 02 and 0. 002 and 0. 56 vs. 0. 57 and 0. 57, respectively), while the R2 of Model 3 is equal to or higher than the R2s of Models 4 and 5 (0. 50 vs. 0. 50 and 0. 49). Accordingly, we conclude that for one- to three-year forecast horizons the accounting estimates embedded in accruals, either as a lump sum or disaggregated, do not improve cash flow predictions over current cash from operations and the change in working capital (excluding inventory). 19 Conclusions Neither total earnings, nor disaggregated estimates-based accruals ystematically improve the prediction of cash flows (CFO or FCF) over the predictions based on current CFO and the change in working capital (excluding inventory). This finding is inconsistent with the FASBs conceptual stipulation that Information about enterprise earningsgenerally provides a better indication of an enterprises present and continuing ability to generate favorable cash flows than information limited to the financial aspects of cash receipts and payments (FASB, 1978, p. IX), though our data start ten years after this statement was issued 2. Prediction of earnings.The two quadruples of columns to the right of Table 3 report prediction performance statistics for net income (NI) and operating income (OI). Here, the 19 These inferences do not change when we examine median signed and absolute prediction errors (available on request). 30 predictions derived from net incom e (Model 2) significantly exceed those based on cash from operations only (Model 1), for the one-year-ahead forecasts. For example, in predicting next years operating income (top right panel), the MAER of Model 2 is significantly lower than that of Model 1 (0. 057 vs. 0. 061).The R2s and Theils Us confirm the stronger performance of Model 2, for one-year predictions. Interestingly, Model 2s predictions are significantly inferior to Model 1s in the two-years-ahead and aggregate next three years predictions (second and bottom NI and OI panels). For example, in predicting aggregate three-years-ahead operating income (bottom right panel), the MAER of Model 2 is significantly higher than that of Model 1 (0. 257 vs. 0. 253). Thus, for a one-year-ahead forecast horizon, current net income is a better predictor of future net income and operating income than current cash from operations. 0 Of the five models examined for earnings predictions, the best performer is Model 4 with three variabl es CFO, ? WC* (change in working capital excluding inventory), and EST (all other accruals)for all forecast horizons. Intriguingly, Model 5, where EST is disaggregated to several estimates-based accruals, is somewhat inferior to Model 4. Apparently, predicting from disaggregated accruals results in noisy forecasts. Conclusions Earnings is a better predictor of near-term earnings than cash flow.Accounting accruals, when disaggregated to working capital items and other accruals, improve further the prediction of operating and net income. No further improvement is achieved from a finer disaggregation of accruals. 6. Robustness Checks 1. How good are our prediction models? 20 Our prediction models are admittedly The median absolute errors are lower for Model 2 than for Model 1 in all NI and OI panels except in the bottom two panels (for the aggregate next two and three years horizons). 31 simplethey obviously abstract from many of the complexities of real life security analysis.Neverthe less, the R2s in Table 3derived from annual regressions of actual values (future cash flows or earnings) on predicted valuesare quite large. Thus, for example, for next years predictions (top panels of Table 3), the R2 range is 0. 33-0. 58. As expected, the R2s drop for second year predictions, yet they are still in the reasonable range of 0. 21-0. 37. Thus, despite their simplicity, our prediction models perform sensibly well. 2. Trimming extreme prediction errors. The results of Table 3 are after trimming the top 2% of the absolute forecast errors.We also computed prediction errors after trimming the top and bottom 1% of the forecast errors and without any trimming. The resulting patterns of prediction errors (not reported) are in both cases very similar to those of Table 3. As expected, Table 3 trimmed errors are substantially smaller than the non-trimmed errors, the R2s are larger, and the Theils U statistics are lower, yet our conclusions regarding the relative performance of the five models equally apply to the non-trimmed errors. substantially our inferences. 3. Classification by size of accruals.Since the estimates we examine are components of total accruals, we classified the sample firms into three groups, by the size of accruals, to check whether accruals size affects our findings. Specifically, for each sample year we bedded the firms by the size of total accruals (scaled by total assets), and then formed three groups the top 25% of firms (high accruals), the middle 50% (medium accruals), and the bottom 25% (low accruals). We then generated cash flow and earnings predictions for each of the three accruals g

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