Effects The of Financial Statement and Informational Complexity on Cash Flow Forecasts
Leslie Hodder Patrick E. Hopkins David A. Wood Kelley School of Business Indiana University Bloomington, IN 47405-1701
December 23, 2006 Email: [e-mail], [e-mail], & [e-mail]. We thank the International Association for Accounting Education and Research (IAAER) and the KPMG and University of Illinois Business Measurement Research Program for their generous research support. This project also benefited from funding provided by the Kelley School of Business and BKD LLP and the research assistance of Yvonne Lee. We thank Mary Barth and Katherine Schipper for their suggestions during the development of this project and Kris Allee, Vicki Dickinson, Susan Keenan, Laureen Maines, Mark Nelson, Derek Oler, Kenny Reynolds, Mike Staub, Mike Tiller, participants at the IAAER Reporting Financial Performance workshops in Bordeaux and New York City, and participants in the workshops at the University of Florida, Indiana University, New York University, and the 10th World Congress of Accounting Educators in Istanbul, Turkey for their comments.
Abstract We characterize the operating-activities section of the indirect-approach statement of cash flows as backwards because it presents reconciling adjustments in a way that is opposite from the intuitively appealing, future-oriented, Conceptual Framework definitions of assets, liabilities and the accruals process. We propose that the reversed-accruals orientation required in the currently mandated indirect-approach statement of cash flows is unnecessarily complex, causing increased cash-flow forecast error and dispersion. We also predict that the mixed pattern (i.e., +/, /+) of operating cash flows and operating accruals reported by most companies also impedes investors ability to learn the time-series properties of cash flows and accruals. We conduct a carefully controlled experiment and find that (1) cash-flow forecasts have lower forecast error and dispersion when the indirect-approach statement of cash flows starts with operating cash flows and adds changes in accruals to arrive at net income and (2) cash-flow forecasts have lower forecast error and dispersion when the cash flows and accruals are of the same sign (i.e., +/+,/) with the sign-based difference attenuated in the forward-oriented statement of cash flows. We also conduct a quasi-experiment to test our mixed-sign versus same-sign hypotheses using an archival sample of publicly available Value Line cash-flow forecasts. We find that Value Line analysts cash-flow forecasts exhibit the same pattern of forecast error as documented in our experiment.
The Effect of Financial Statement and Informational Complexity on Cash Flow Forecasts I. Introduction In this study, we investigate whether the currently required structure of the operatingactivities section of the indirect-approach statement of cash flows impedes investors ability to learn the time series properties of operating cash flows and operating accruals. We propose that the current structure of the indirect-approach statement of cash flows, which starts with reported earnings and reverses changes in non-cash and non-operating items to arrive at operating cash flows, presents information in a way that is opposite of the intuitive, future-oriented perspective taken in the Conceptual Framework definitions of assets (Financial Accounting Standards Board (FASB) 1985, para. 25), liabilities (para. 35), and the accruals process (para. 141). Prior research suggests that information presentation that does not conform to the semantic, sequential or causal structure of a data-generation process can negatively influence humans ability to learn the parameters of forecast-relevant data-generating processes (Luft and Shields 2001). Thus, we predict that the reverse-process format, currently required in the indirect-approach statement of cash flows, interferes with financial-statement users learning, resulting in systematically higher cash-flow forecast errors and greater forecast dispersion. We also investigate whether the offsetting (i.e., mixed sign) pattern of operating cashflows and operating accruals information reported by most companies also impedes investors ability to learn the time series properties of operating cash flows and operating accruals.1 Prior research suggests that this offsetting pattern of positive and negative forecast-model inputs will likely lead to increased incidence of forecast errors over the errors observed for same-sign
Our analysis shows that in the years 2003-2004, 68 percent of firms in the Compustat Annual database have positive operating cash flows and negative changes in current operating net assets.
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inputs. Overall, we predict that same-sign operating cash flows and operating accruals information will result in lower forecast errors and dispersion than mixed-sign operating cash flow and operating accruals information. However, related to our format-related predictions, we expect that presenting cash flow and operating-accruals-change information in a more accrualsprocess-congruent (i.e., forward) format will result in improved learning of the model parameters, causing lower forecast errors and dispersion. We test these predictions in a computerized experiment in which 50 second-year business graduate students, with prior training in financial forecasting and residual-income valuation models, sequentially forecast year-two cash flows from operations (CFO) for 16 different companies after receiving year-one CFO and year-one changes in non-cash, current net operating assets (to help exposition, we refer to this change as CNOA). After each of the 16 judgments, we provide participants with cumulative feedback including the inputs to their forecast tasks (i.e., year-one CFO and CNOA for each preceding forecast) and accuracy-related information for each preceding forecast (i.e., the matched set of actual year-two CFO, their forecasts of year-two CFO, and the percentage forecast error). For each of the companies, we provide half of the participants with highly summarized indirect-approach statement of cash flows information in the currently mandated reverse order (i.e., net income CNOA = CFO), and we provide the other half with the same information in forward order (i.e., CFO + CNOA = NI). We also counterbalance the signs of the year-one CFO and CNOA information, so that each subject receives each of the following year-one CFO/CNOA sign combinations four times: +/+, +/, /+, /.2
We also counterbalance the order of the companies by rotating the companies and by reversing each of the rotated orders. This counterbalancing scheme controls for order effects and also results in all of the companies appearing with equal frequency across the 16-company forecast sequence.
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Consistent with our predictions, we find that the underlying structure of the operating section of the indirect-approach statement of cash flows significantly affects the extent to which participants learn the time-series model that generates year-two CFO. Compared to the forwardorder format for the indirect approach statement of cash flows, we find that forecast errors and forecast dispersion are significantly higher under the currently mandated reverse-order format. In addition, we find that mixed-sign forecast inputs lead to significantly higher cash-flow forecast errors and forecast dispersion across both forecast conditions, but that the forecast errors from the mixed-sign inputs are worst in the currently mandated reverse-order indirect approach statement of cash flows. In recent years, financial analysts have published forecasts of CFO with increasing frequency (DeFond and Hung (2003). Motivated by our theoretical development and experimental findings, we also conduct a quasi-experimental analysis designed to determine whether analysts published CFO forecasts exhibit a pattern of forecast errors that is consistent with the errors observed in our experiment. Because all publicly traded non-real-estate and nonfinancial US companies must prepare a reverse-order indirect approach statements of cash flows, we cannot test with archival data whether the forward-order indirect-approach statement of cash flows yields lower forecast errors. However, publicly available CFO and CNOA data exhibit sufficient variability to allow a quasi-experimental test of our mixed-sign hypotheses. As we predict, after controlling for economic factors previously associated with analysts forecast accuracy, we find that our sample of Value Line CFO forecasts exhibit significantly higher yeartwo forecast errors when year-one CFO and year-one CNOA have mixed signs (i.e., +/, /+ versus +/+, /).
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Although we focus on cash-flow forecasts, our study is related to prior research suggesting that analysts and investors systematically ignore forecast-relevant information contained in publicly released performance statements (i.e., statements of income, statements of cash flows). For example, Elgers, Lo, and Richardson (2003) and Bradshaw, Richardson, and Sloan (2001) provide evidence that sell side analysts earnings forecasts do not sufficiently reflect the transitory nature of current-period accruals, resulting in predictable patterns of earnings-forecast errors. These studies demonstrate that current-period analysts earningsforecast errors are positively related to prior-period earnings accruals (i.e., difference between operating cash flows and earnings. Overall, these results are consistent with financial-statement users systematic information-processing inefficiency when combining two types of performance information (i.e., cash flow and accruals) contained in the statements of cash flows for publicly listed US companies.3 Our experimental results suggest that two features of the indirect-approach statement of cash flows reporting regimefinancial-statement structure and information configurationcan significantly affect financial-statement users ability to detect time-series patterns in reported accounting information. The conceptual frameworks of the United States Financial Accounting Standards Board (FASB) and the International Accounting Standards Board (IASB) identify understandability as a necessary qualitative characteristic of financial information. Financialstatement structures that are more understandable should increase comprehension of the time-
Statement of Financial Accounting Standards (SFAS) No. 95 (Financial Accounting Standards Board [FASB] 1987) allows companies to prepare the statement of cash flows using the (1) indirect approach or (2) the direct approach with indirect approach information also reported. Thus, some form of the accruals-based-income-to-cashflows reconciliation in the indirect approach is provided by all companies. According to the American Institute of Certified Public Accountants (2005), 592 out of 600 companies (98.7%) only report cash flows from operations using the indirect approach.
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series properties (e.g., persistence) of reported performance information, leading to higherquality financial forecasts. Whether understandability is improved through mandated, economy-wide reformatting of the primary financial-statements (e.g., SFAS 130) or via user-level decision aids (c.f. Bonner 1999) depends on the perceived costs and benefits of each. Our analysis provides evidence on a potential benefit of statement reformatting (i.e., improved investor understanding of time-series properties of performance measures). Asset pricing theory indicates that market efficiency increases as the level of available information increases and as investor knowledge becomes more homogenous (Easley and Ohara, 2004). Thus, financial disclosure that is equally accessible to more- and less-sophisticated investors can increase market efficiency, even if the number of value-relevant facts reported in the financial statements is unchanged. We organize the remaining paper in the following way. In the next section, we discuss current cash-flow reporting and, after considering relevant judgment and decision making research, make our research predictions. We describe our experiment and results in Sections III and IV. We describe our quasi-experimental analysis of Value Line cash-flow forecasts in Section V and provide a summary and conclusion in Section VI. II. Theory and Predictions Financial-statement analysis and forecasting is an iterative, evaluative process through which financial-statement users engage in information discovery, expectation formation, hypothesis testing, and conditional prediction. During this process, financial-statement users attempt to understand patterns and relations in reported company-specific information, often with the goal of identifying the portion of performance that is expected to recur in future periods. Prior research suggests that the format of information presented in financial statements can 5
influence the way company-specific information is processed and used by novices (e.g.., Maines and McDaniel 2000) and experts (e.g., Hirst, Hopkins and Wahlen 2004). Thus, financialstatement format also likely affects financial-statement users time-series-pattern learning and resulting financial-forecasting judgments. An important structural feature of the currently prescribed format for the indirectapproach statement of cash flows is its reverse orientation; that is, increases in CNOA are reversed to reconcile current-period earnings to current-period CFO. For example, an increase in an operating asset account, like accounts receivable, is subtracted from earnings to arrive at CFO. Likewise, an increase in an operating liability account, like accounts payable, is added to earnings to arrive at CFO. However, the valence of each of these adjustments is opposite (i.e., reverse) from the conditional expected future cash-flow effects of each item (i.e., compared to current period CFO, we expect additional future cash flows for larger accounts receivable balances and additional future cash outflows for larger accounts payable balances). Indeed, the logic underlying the operating-section adjustments in the indirect-approach statement of cash flows occasionally leads our most gifted teaching colleagues to implore confused students and executives to simply memorize the reverse relation because the reverse-accruals logic is difficult.4 As modeled in Dechow, Kothari, and Watts (1998), Barth, Cram, and Nelson (2001) and Francis and Smith (2005), current-period CNOA (i.e., or current-period accruals) are positively associated with future cash flows. The positive, inter-temporally dependent relation between current-period accruals and future cash flows conforms to the accrual-accounting
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The books we reviewed often provide little discussion of accruals-process congruent future-period cash flow effects, and instead offer highly detailed comprehensive lists of the operating net asset accounts along with individual instructions for whether an increase or decrease in each account is added to, or subtracted from, earnings to arrive at CFO.
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perspective offered in most introductory and intermediate accounting courses (e.g., Phillips, Libby, and Libby 2005; Revsine, Collins, and Johnson 2004) and provides an intuitive and parsimonious explanation for the empirical observation that current period earnings (which includes the current-period CNOA) explain more variation in next periods operating cash flows than current-period operating cash flows (Dechow, et al. 1998; Barth, et al. 2001). Further, the definitions of assets and liabilities in Statement of Financial Accounting Concepts No. 6 (FASB 1985) suggest that the relation between current-period change in operating accruals and future cash flows is positive and causal. Psychology research offers some insights into the likely effects of reverse-presentation of information in financial-information comprehension and performance forecasting tasks. At its most basic level, the process of using financial statements to forecast future performance is one in which financial statement users learn probabilistic relations between dependent and independent variables and make out-of-sample predictions of a future-value-correlated measure, like future earnings or operating cash flows. A robust finding in prior psychology research is that learning and subsequent judgment accuracy are greater for prediction scenarios with a positive relation between independent and dependent variables than for prediction scenarios with a statistically equivalent negative relation (Naylor and Clark 1968; Deane, Hammond, and Summers 1972; Brehmer 1973, 1974, 1979; Brehmer, Kuylenstierna, and Liljergren 1974). Interestingly, positive relations (e.g., y = a + bx) in multiple-cue probabilistic learning tasks result in superior learning and judgment performance for tasks in which individuals have no prior knowledge of the signs of the model parameters (i.e., they must learn the sign and weight of parameters based solely on the data) and for tasks in which individuals are provided with pre-experiment information about the signs of the parameters. Although, Muchinski and 7
Dudycha (1975) and Sniezek (1986) provide some evidence that learning and judgment accuracy improves in context-specific tasks with labeled variables, the settings in which prior research demonstrates improved negative-relation learning are fairly straight-forward (e.g., predicting college grades based on high-school grades in related and unrelated subjects). While these context-related findings provide some support for financial-statement users having the ability to adjust for the negative-accruals presentation format in the indirect-approach statement of cash flows, we believe the use of cash flow and accruals information in forecasting is sufficiently complex to mitigate the potential benefits of contextual familiarity. Thus, we propose that the currently implemented indirect-approach structure for the statement of cash flows will impede learning of persistence of the CFO and CNOA components of year-one earnings, leading to higher levels of year-two forecast errors and higher levels of year-two forecast dispersion. More formally, we propose the following hypotheses (in alternative form): H1A: Investors year-two operating-cash-flow forecast errors will be higher when increases (decreases) in year-one operating net assets are presented as negative (positive) reconciling items in an earnings-to-cash-flows reconciliation, as compared to the opposite presentation in a cash-flows-to-earnings presentation. Investors year-two operating-cash-flow forecast dispersion will be higher when increases (decreases) in year-one operating net assets are presented as negative (positive) reconciling items in an earnings-to-cash-flows reconciliation, as compared to the opposite presentation in a cash-flows-to-earnings presentation. Prior research also suggests that CFO and CNOA typically have a +/ mixed sign configuration (e.g., Dechow, et al. (1998) report average values of approximately 1.63 per share for CFO and -0.50 per share for CNOA while Sloan (1996) reports this configuration for 7 out of 10 deciles). Given the cognitive complexity of learning a historical trend of year-one CFO, yearone CNOA, and year-two CFO, and of forecasting out-of-sample CFO, mixed-sign variation in model inputs to correctly generate one-period-out forecast judgments will further overload the 8
H1B:
processing capacity of individuals. In particular, humans quickly reach computational capacity in complex tasks (like trend learning), leaving little additional mental capacity for manipulation of data or complex calculations (Miller 1956). Further, even in high-stakes real-world settings (e.g., investing), individuals will attempt to use schematic problem representations and take computational shortcuts (Hong, Stein and Yu 2005; Simon 1982). This leads us to the following (alternative form) predictions about the effect of mixed-sign versus same-sign inputs into cash flow forecasts. H2A: Compared to forecasts made when year-one changes in operating net assets and year-one operating cash flows have the same sign, investors year-two operating-cash-flow forecast errors will be higher when year-one changes in operating net assets and year-one operating cash flows have different signs. Compared to forecasts made when year-one changes in operating net assets and year-one operating cash flows have the same sign, investors year-two operating-cash-flow forecast dispersion will be higher when year-one changes in operating net assets and year-one operating cash flows have different signs. III. Experimental Design We investigate the effects of cash-flow statement format and forecast-relevant information configuration (i.e., sign of model inputs) in a 2 x 2 mixed-design experiment (format is the between-subjects factor varied at two levels: forward versus reverse; sign is the withinsubjects factor varied at two levels: mixed versus same). Fifty second-year masters-level business students participated in our experiment. Masters-level business students are appropriate participants for two reasons. First, our theory and hypotheses are related to a general psychology of inference and pattern learning in a financial-forecasting setting. Because we have no ex ante reason to believe that general forecasting experience will eliminate the effects of format or input-sign configuration on forecast accuracy or dispersion, we decided to avoid unnecessarily consuming limited available analyst 9
H2B:
resources. Second, Elliott et al. (2007) find that second-year masters students (i.e., similar to our participants) are suitable proxies for nonprofessional investors, an important constituent group for accounting standard setters and regulators. As indicated in Table 1, participants previously completed 10 (standard deviation = 4) accounting and finance courses, with one of those courses a three-credit hour MBA-level financial-statement-analysis course that included an emphasis on residual-income valuation and financial forecasting. Participants had, on average, 14.5 (standard deviation = 30) months of prior work experience. As indicated in Table 1, we found no significant differences (all p-values > 0.10) between treatment groups for demographic information, including current degree program, prior work experience, accounting/finance classes taken, the number of participants who plan to buy or sell equity securities, or the number of times participants previously performed fundamental analysis on the financial information of publicly traded companies.5 Overall, our participants appear to be knowledgeable about the relation between cash flows and accrual accounting and have experience in financial forecasting. Procedure for Experiment Participants completed the experiment during a single monitored session in a computer lab. Computer administration allows us to control the flow of information to participants, monitor the amount of time participants spent viewing individual screens, and to collect processrelated data (e.g., the number of times participants viewed previous screens or the feedback pages). In addition, computer administration allows us to prevent participants from changing their forecasts after learning the actual outcomes. At the beginning of the experiment session, we provided participants with written and oral instructions describing how to access the
We present a subset of demographic information in Table 1. None of the demographic question (tabulated and untabulated) yielded significant differences across treatment conditions (all p-values > 0.10).
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computerized experiment, how to enter their responses during the session, how to receive compensation upon completion of the experiment, and prohibited forms of assistance during the experiment (e.g., computational aids, like Microsoft Excel). Participants completed a series of pre-experiment questions designed to gauge their accounting knowledge, their perceived difficulty of the accounting knowledge questions, and their perceptions of the time series relation between Year1 CFO, Year1 CNOA, and Year2 CFO. After finishing the pre-experimental-treatment questions, participants began the experimental task. The experimental task consisted of viewing Year1 CFO and Year1 CNOA information for 16 different companies and predicting Year2 CFO for those companies. Figure 1 illustrates the primary computer screens for operationalizing the forward-order and reverse-order formats. We intentionally incorporate a highly simplified statement of cash flows into our experiment because we wish to conduct the cleanest possible test of cash-flow-statement format effects. Including additional real-world features (e.g., multiple line items of reconciling information) will uniformly increase the complexity of the time-series learning task. However, we could identify no theoretical reason to expect increased complexity to attenuate the hypothesized differential effect of format on time-series learning. However, an increase in complexity would certainly increase the noise in our data, thereby also increasing the likelihood of making a Type II error in our inferences. In the Reverse Order (RO) condition, participants view three lines of information on the Statement of Cash Flows screen, in the following order: YEAR1 Operating Earnings (Year1 OE) YEAR1 (Increase) Decrease in Non-Cash Operating Net Assets (Year1 CNOA) and YEAR1 Cash Flows from Operations (Year1 CFO). The actual amount of Year1 OE is
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masked by a black box.6 When ready, participants proceed to the Judgment screen on which they provide their estimate of Year2 CFO. After participants enter a forecast amount, they are taken to a screen that includes only their Year2 CFO forecast and the actual Year2 CFO realization. When ready, the subject can proceed to the Feedback screen that presents cumulative, comprehensive summary information that includes Year1 CFO, Year1 CNOA, Year2 actual CFO, their Year2 Forecast of CFO, and their percentage forecast error for each of their previous forecasts. Our second between-subjects condition, Forward Order (FO), differed from RO in two ways. First, the Statement of Cash Flows screen includes company data in the following order (i.e., opposite of the reverse order): Year1 CFO, Year1 Increase (Decrease) in NOA, and Year1 OE (again blackened out). Second, the Feedback screen includes cumulative, comprehensive summary information that includes the same information as the RO condition, except it is presented in the same order as Statement of Cash Flows screen for the FO condition. After completing their forecasts for each of the 16 companies, participants completed several post-experiment questions designed to elicit perceived task difficulty and judgment confidence. Participants were asked to assign weights to the values of CFO and NOA based on how these characteristics related to future CFO in the last few predictions they made. Finally, participants were asked to supply answers to several demographic questions. Upon completing the experiment, participants left the computer lab and were compensated based on the absolute value of their forecast errors. Compensation rates were determined based on average forecast errors of students who pre-tested the experiment. All
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Providing an explicit amount for OE introduces a potential confounding factor into the experiment. OE is a summation of Year1 CFO and CNOA and, therefore, represents redundant information. If subjects believe OE was more relevant to forecasting Year2 CFO, they can compute it by summing the Year1 CFO and NCOA.
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participants were eligible to earn between $6 and $20, with actual payoffs spanning that range, and a mean payoff of $13. Company Data Included in the Experiment Materials Information for 500 companies was generated through a simulation based on Barth et al. (2001). Specifically, Year2 CFO was modeled as a linear function of Year1 CFO and Year1 CNOA. Panel A of Table 1 provides the parameters of the generating function. We seeded a zero-mean, normally distributed error term into the simulation to add a small amount of noise to the subsequent realization of CFO. We chose 16 firms from the model-generated observations. We intentionally selected companies to obtain a balanced design across all possible combinations of positive and negative operating cash flow and operating asset changes (+/+, +/, /+, /), crossed with randomly assigned positive and negative model errors. In Panel B of Table 1, we present the task information for the sixteen firms we include in our experiment. Panel C of Table 1 presents descriptive statistics for the 16 selected firms. Although selection of sample firms was not random, parameters estimated from regressing sample values of Year2 CFO on Year1 CFO and Year1 CNOA do not differ from theoretical model parameters.7
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Across the two conditions, we presented the 16 firms in eight unique sequences. Four of the sequences were based on a four-firm rotation. Specifically, the firm randomly assigned as Firm 1 in Sequence 1, is Firm 5 in Sequence 2, Firm 9 in Sequence 3 and Firm 13 in Sequence 4. The remaining four sequences are an exact reversal of Sequences 1 through 4. Therefore, the firm randomly assigned as Firm 1 in Sequence 1 is Firm 16 in Sequence 5, Firm 12 in Sequence 6, Firm 8 in Sequence 7 and Firm 4 in Sequence 8. These eight unique sequences assures that every firm appears with equal frequency in Trials 1-4, 5-8, 9-12, and 13-16 This also results in equal representation of the positive and negative operating cash flow and operating asset changes (+/+, +/, /+, /) across the trials in each condition.
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IV. Results of the Experiment Pre-Experiment Knowledge Questions Prior to beginning the forecasting task, participants completed an accruals knowledge test, and assessed the difficulty of the test. Participants were required to successfully complete the knowledge test before they could proceed to the 16-company forecasting task. Table 2, Panel B indicates that the almost all participants in both treatment groups correctly answered the required test questions on the first attempt. Participants rated these questions as relatively easy with a combined mean difficulty rating of 4.61 on a 15-point scale, increasing in perceived difficulty. Perceived difficulty of the tutorial task did not differ across groups (Z = -0.73 and Z = -0.95, both p-values >0.10). Overall, the results reported in Panels A and B suggest that random assignment of participants across conditions successfully distributed knowledge and abilities across the two treatment groups. Panel C of Table 2 presents participants post-experiment self-assessments of forecasttask difficulty and forecasting confidence. Neither the participants perceived difficulty of the forecast task (Z = 0.55, p-value >0.10) nor their forecast-related confidence levels differed across conditions (Z = 0.55, p-value >0.10) despite the differential treatments. These results suggest that participants perceptions of task difficulty and participants confidence in their forecasting skills are independent of their assigned treatments. Hypothesis Tests Late-Round Forecasts Table 3, Panel A reports participants median and mean forecast accuracy statistics, along with standard deviations of participants forecast judgments (i.e., our proxy for forecast dispersion), separately presented for the crossed combinations of format (two levels: reverse
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order, forward order), input sign (two levels: mixed, same) and forecast judgment timing (two levels: first half, last half).8 Before discussing the fully interacted analysis of the complete data set, we first examine whether our predicted forecast-error and dispersion effects occur in the later rounds (i.e., last half) of the experimental trials. We first test for differences in the late-round subset of data because we did not provide estimation-relevant information prior to the experiment (i.e., participants will learn the model parameters after receiving trial-by-trial feedback) and we made no explicit ex-ante predictions of differences in participants pre-experiment forecasts. While not definitive, significant differences in later rounds suggest that the predicted effects are fairly robust and are more likely to occur in natural settings. Consistent with hypothesis H1A, we find that in the last half of the experimental trials the median forecast errors of participants in the reverse-order condition are significantly higher than those of participants in the forward-order condition (see shaded boxes in Table 3, Panel A; 0.29 compared to 0.17; F = 11.86; p-value = 0.001; one-tailed; comparison statistic untabulated).9 Consistent with hypothesis H1B, we find that in the last half of the experimental trials, the dispersion in forecast errors was significantly greater for participants in the reverse-order condition than for participants in the forward-order condition (see shaded boxes in Table 3, Panel
We focus our discussion on median forecast accuracy statistics to mitigate the influence of extreme observations on readers inferences. In addition, given that our dispersion hypotheses predict differences in variance across treatment conditions (i.e., violation of a distributional assumption in parametric tests), we report all of our primary inferences on the basis nonparametric (i.e., rank-transformed) tests. As usual, parametric tests yielded similar inferences, with more extreme test statistics and related p-values. 9 Each individual gave multiple responses (i.e., the within-subject responses are not independent from each other), so we conduct a repeated-measure ANOVA on the rank-transformed dependent variable to compare medians. Because conventional parametric tests of differences in sample variance assume normal distributions, we also test differences in dispersion by conducting a repeated-measure ANOVA on a rank-transformed variability measure. Our rank transformed variability measure is generalized from the nonparametric test for differences in dispersion suggested by Fligner and Killeen (1976) (as modified by Conover et al. 1981). For this test, the dependent measure is computed by transforming the absolute value of the difference between each observation and its corresponding cell median. The transformation used is -1(0.5+i/2(N+1)) where -1 represents the inverse standard normal distribution function, i is the ranked absolute value, and N is the combined sample size of all experimental cells.
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A; 1.70 compared to 1.07; F = 28.44; p-value <0.001; one-tailed; comparison statistic untabulated). Interestingly, we observe larger errors and higher dispersion in the reverse-order condition despite the investment of equivalent amounts of time by participants in both groups.10 This result suggests that improvements in accuracy associated with the forward-order treatment are due to lower task complexity rather than greater effort by forward-order participants. We also find, in the last half of the experimental trials, that the median forecast errors for mixed-sign trials are significantly higher than those same sign trials (see unshaded boxes in Table 3, Panel A; 0.25 compared to 0.19; F = 3.72, p-value <0.027; one-tailed; comparison statistic untabulated), thereby supporting our prediction in H2A. With respect to hypothesis H2B, we find that in the last half of the experimental trials, the dispersion in forecast errors was significantly greater for mixed sign trials than for same sign trials (see unshaded boxes in Table 3, Panel A; 1.95 compared to 0.46; F = 24.68, p-value <0.001; one-tailed; comparison statistic untabulated). Taken together, these results suggest that the all of our predicted effects survive in the later rounds of cash flow forecasts, after all participants had equal opportunity to learn the parameters of the data-generating model. Hypothesis Tests All Forecast Judgments We extend these analyses to examine differences over the entire range of participants responses. To further test our forecast-error-related research hypotheses, we conduct a repeatedmeasures analysis of variance (ANOVA) on the rank-transformed forecast errors and we report that analysis in Table 3, panel B. To further test our dispersion-related research hypotheses, we conduct a repeated-measures analysis of variance (ANOVA) on a rank-transformed variability
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In prior psychology studies, a common proxy for effort is the amount of time spent on a given judgment task. In the present study, the average amount of time spent per forecast did not differ across any of the sample partitions (all p-values > 0.10).
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measure and we report that analysis in Table 3, panel C. Panels A and B of Figure 2 provide graphic representation of the interactions from these two respective analyses. Consistent with hypothesis H1A, the significant main effect for Method indicates that, across all forecasts provided by participants, median forecast errors are significantly greater in the reverse-order condition than in the forward-order condition (0.30 compared to 0.22; F = 8.66, p-value < 0.01). Consistent with hypothesis H1B, the significant main effect for Method indicates that dispersion is higher in the reverse-order condition (standard deviation of 2.26 compared to 1.43; F = 29.97, p-value < 0.01). To explore the learning process in each treatment, Table 3, Panel A presents forecast errors and dispersion statistics for the first and last eight forecast trials. The significant main effect for Half in Table 3, Panel B indicates that median forecast errors are significantly greater in the first eight trials than in the last eight trials (0.36 compared to 0.22; F = 12.18, pvalue < 0.01). Consistent with differential learning evidenced by participants across the two methods, forward order showed a significant decline in median forecast errors over the last eight trials compared to the first eight (0.37 versus 0.17), while the forecast error remaining statistically flat in the reverse order condition ( 0.35 versus 0.29) (see middle figure of Panel A, Figure 2 for graphical presentation). This differential rate of learning across the two methods is confirmed by the significant Method x Half interaction term reported in Panel B of Table 3 (F = 5.36, p-value = 0.02). Table 3, Panels B and C also presents the results of tests that compare participants accuracy and dispersion across same-versus-mixed signs of CNOA and CFO. Hypotheses H2A and H2B predict that forecast error and dispersion increase with task complexity. In our cashflow forecasting context, we propose that task complexity increases when data inputs have 17
conflicting signs. Consistent with hypothesis H2A, the significant main effect for Mixed in Table 3, Panel B (untabulated medians are 0.36 compared to 0.22; F = 21.41, p-value < 0.01) indicates that forecast errors are higher for company-observations of mixed signs compared to same-sign company-observations. Consistent with hypothesis H2B, the significant main effect for Method indicates that dispersion is higher for company-observations with mixed signs (standard deviation of 2.59 compared to 0.47; F = 32.67, p-value < 0.01). An alternative way to think about our research hypotheses is to compare the model weights implicitly evidenced by the 16 trials of forecasts provided by participants. As reported in Table 4, we extracted implicit subject model weights for CFO and NCOA by regressing subject forecasts of future cash flows on current levels of CFO and NCOA. The mean (median) weight placed on CFO by the forward order participants is 0.90 (0.91) compared to 0.96 (0.91) for the reverse order condition. Although the means differ, the identical medians confirm that the difference is not statistically significant. Therefore, it appears that participants in both conditions underweight CFO by the same amount relative to the theoretical model weight. The insignificant difference in subject model weights on CFO is not surprising given that CFO was displayed with the same sign in both conditions. Interesting differences across conditions are revealed by the subject weights on CNOA. Specifically, our evidence shows that participants robustly underweight CNOA in the reverse order condition (mean weight of 0.41 compared to the theoretical value of 0.55) while participants in the forward-order condition assign an average weight of 0.50 to CNOA, which does not statistically differ from the theoretical value. This pattern of results is even more pronounced for median implied CNOA weights.
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This pattern of results is consistent with reverse order participants failing to fully adjust for the sign reversal inherent in the reverse order presentation.11 Cross-subject dispersion is also much higher in the reverse order condition. Implied model weights for CFO in the reverse order condition range from 0.13 to 2.88 (standard deviation of 0.45) compared to the implied CFO model weights in the forward order condition, which range from 0.26 to 1.29 (standard deviation of 0.25). The relatively high dispersion across participants in the reverse order condition is also apparent in participants weights on CNOA which range from -0.37 to 2.06 (standard deviation of 0.44). In contrast, subject weights on CNOA in the forward order condition range from 0.05 to 1.24 (standard deviation of 0.23). These results provide additional support for hypothesis H1B that dispersion is higher among participants in the reverse-order condition. V. Quasi-Experiment with Value Line Cash Flow Forecasts Quasi-Experimental Design In this section, we provide evidence on the effects of informational complexity in the context of passively observed cash flow forecast data collected from the Value Line Investment Survey. The Value Line Investment Survey is a comprehensive source of information and ratings on approximately 1,700 publicly-traded companies compiled by over 90 independent analysts. Each company report contains, among other things, Value Line's proprietary performance ranks and financial forecasts for the coming 1 to 5 years. We hand-collect cash flow forecast and actual data from company reports, published monthly over the period 20032005. We exclude from our sample firms in the banking and insurance industries because measures of operating accruals for these firms are not comparable to those in other industries.
11
Recall that increases (decreases) in operating assets are displayed as negative (positive) numbers in the reconciliation of income to CFO. Subjects evaluating the reverse-order format must translate a number subtracted in arriving at CFO in the current period into an increase in future CFO.
19
We further limit our analysis to calendar-year firms for which cash flow forecasts are available as of April 30 of each year. We choose April 30 as a cutoff date for forecasts to allow sufficient time for analysts to incorporate into their current year forecasts previous cash flow information contained in annual reports published during the first quarter. We collect realized (actual) cash flows from subsequently published Value Line reports to ensure that predicted and realized cash flows are reported on consistent measurement bases. We also collect Value Lines proprietary PREDICTABILITY and STABILITY indexes for each firm. All other financial statement data is collected from Compustat by individually cross-referencing company names and stock tickers in Compustat to those provided by Value Line. Our data collection procedure results in 893 firm-year observations with sufficient data to complete our analyses.12 In contrast to our experimental research design, we are unable to control (by assignment) factors likely to influence forecast accuracy that may also be correlated with our proxy for informational complexity. Specifically, in our quasi-experiment we cannot impose a consistent cash-flow-generating process across conditions and across companies. Therefore, we must augment our archival research design with proxies that control for differences in firms underlying data-generating processes that may affect the predictability of future cash flows.13 We use the life-cycle construct developed by Dickinson (2006) to control for firm attributes that affect the generation of future cash flows. Dickinson (2006) shows that firms life-cycle stages are systematically associated with future levels of profitability and cash flows. Extending prior research, she develops a proxy based on patterns of firms cash flows to identify
Forecasts made in 2003 and 2004 are paired with actual realizations reported in 2004 and 2005, respectively. We are also unable to control through randomization or assignment, factors associated with analysts that may influence forecast accuracy. Therefore, we must rely on an assumption either that a) Value Line analysts are equally competent at their forecasting tasks, or b) that if competency differs across Value Line analysts, less competent analysts are not systematically assigned to firms with greater informational complexity. We have insufficient analyst data to test either of these assumptions.
13
12
20
five life-cycle stages (Startup, Growth, Mature, Shake-out and Decline). The jointly determined pattern of operating, financing, and investing cash flows represents firms strategies and capacities for obtaining and using resources. Dickinson (2006) defines Startup firms as those with negative cash from operations (CFO), negative cash from investing (CFI), and positive cash from financing (CFF). This pattern reflects the initial investment necessary to bring profitable ideas to market. Startup firms have higher information risk and experience greater operating variance across industries. Growth firms also have negative CFI and positive CFF, reflecting continuing investment. However, growth firms have strongly positive CFO as they realize monopoly rents that accrue to innovators. Growth firms experience increasing profit margins, although variance is high. Mature firms have positive CFO and negative CFI, but CFF is negative, reflecting return of capital to investors. Mature firms have the most predictable profitability and cash flows. In contrast, as their businesses contract, Decline firms have negative CFO, positive CFI and negative CFF. Dickinson (2006) classifies all other patterns as Shake-out firms. Shake-out firms have variable patterns of cash flows that reflect either rejuvenation through structural change, or progression to Decline. Because life-cycle stage influences firms cash and profit-generating processes, we estimate model (1), controlling for life-cycle STAGE and other variables thought to influence forecast accuracy.
ERRORt = 0 + 1MIXEDt 1 + 2,i STAGEi ,t 1 + 3 ASSET _ GROWTH t 1 + 4 RANK _ | INCOME t 1 | + 5 RANK _ | CNOAt 1 | + 6CFOt 1 + 7 PREDICTABI LITYt 1 + 8 STABILITYt 1 + 9CFOt 5,t + 10CNOAt 5,t + 11SIZEt 1 + 12 PROFITABIL ITYt 5,t + 13, j INDUSTRY j ,t 1 + t
j =1 12 i =1 4
(1)
21
ERRORt is the absolute value of the difference between the actual cash flow per share reported by Value Line for a given year and the forecasted cash flow per share predicted by Value Line analysts as of April 30 of that year. We divide the difference by the absolute value of the actual operating cash flow per share for the year (CFO). We use the absolute value of the ERROR because our primary interest is in relative magnitude of forecast errors, rather than the sign. We include MIXEDt-1 as an explanatory variable to proxy for informational complexity. MIXEDt-1 is set to 1 when CFO and changes in operating assets (CNOA) are of different signs (positive CFO and negative CNOA, or negative CFO and positive CNOA). We predict a positive coefficient on MIXEDt-1. We include dummy variables to represent four of the five lifecycle stages (Start-up, Growth, Shake-out, and Decline). Because the coefficients capture the effect on ERROR relative to mature firms, we predict positive signs for each of the four STAGE variables consistent with these firms having more variable operations. We augment the regression with ASSET_GROWTH because high growth firms may experience structural shifts in their operating and information environments that render forecasts more difficult and less accurate. We expect the coefficient on GROWTH to be positive. We compute GROWTH as the change in (ASSETSt assets ASSETSt-1)/ASSETSt-1 ). Prior research finds that relatively extreme realizations of income or accruals are less likely to recur (e.g. Freeman and Tse 1992; Sloan 1996). If analysts fail to consider mean-reverting tendencies of extreme performance realizations, then forecast errors will be higher following such realizations. We include RANK_|INCOMEt-1| and RANK_|CNOAt-1| to control for these hypothesized effects. RANK_|INCOMEt-1| is the decile rank of the absolute value of income before extraordinary items divided by assets. RANK_|CNOAt-1| is the decile rank of the absolute value of the change in net operating assets (CNOA). We compute CNOA as the difference between 22
INCOME and CFO, divided by ASSETS. We rank the absolute values of INCOME and CNOA by year, within the population of Compustat firms. This identifies with higher ranks those observations falling within the extreme positive or negative portions of the cross-sectional distribution. We expect positive coefficients on each of these rank variables. We also include the prior period reported realization of CFO, deflated by assets, to control for potential differences in firm life cycles. Because firms with relatively high levels of operating cash flows are likely to have more stable operations, we predict a positive coefficient on CFOt-1. Value Line reports two proprietary measures related to forecasting. The first is a predictability index that measures the reliability of forecasts based on the stability of year-to-year comparisons. The index ranges from 5 to 100, where 5 represents the lowest level of predictability and 100 the highest. We expect a negative coefficient on PREDICTABILITYt-1. The second proprietary measure is the price stability indexa measure of the stability of the stocks price. This index also ranges from 5 to 100, with 5 representing the lowest level of stability. According to Value Line, STABILITY includes sensitivity to the market (beta) as well as the firms inherent volatility. We expect a negative coefficient on STABILITYt-1 to the extent that systematic and unsystematic risk factors are negatively related to cash flow predictability. In addition to Value Lines proprietary predictability and stability indexes, we include historical measures of CFO and CNOA variability. We measure the standard deviation of each over the five year period ending with the forecast date. We expect positive coefficients on CFO and CNOA because greater variability is associated with lower predictability. We include the decile rank of assets, RANK_SIZEt-1, to control for potential structural differences associated with firm size. Larger firms may have more diverse operating segments, making prediction of future cash flows difficult. Alternatively, larger firms may have richer 23
information environments that assist analysts in accurately forecasting cash flows. Because RANK_SIZE can operate to either increase or decrease forecast accuracy, we do not predict a sign for this variable. Similarly, we include PROFITABILITY, the average return on assets over the five years ending with the forecast period, to control for aspects of the information environment not captured by the other variables. Firms that consistently report high levels of profitability may obscure the underlying data generating process for cash flows through earnings management, leading to greater forecast errors. Alternatively, highly profitable firms may have richer information environments and be followed by more skilled analysts. Because PROFITABILITY may operate to increase or decrease forecast accuracy, we do not predict a sign for this variable. Finally, because Barth et al. (2001) suggests that the time series properties of cash flows are influenced by industry, we include twelve industry control variables consistent with their classification. Results of the Quasi-Experiment Panel A of Table 5 presents descriptive statistics for the sample of Value Line firms used in the archival analysis. Means and quartiles are presented in the first three columns and means by life-cycle stage are presented in the last four columns. The average cash flow forecast error for the sample of Value Line firms is 28.9% and varies by life-cycle stage consistent with Dickinson (2006). Specifically, cash flow forecast errors are lowest for firms in the Mature stage (25.1%) and highest for firms in the Startup stage (50.6%). Firms in the Shake-out and Decline stages experience forecast errors of 40.2% and 41.9%, respectively.14 The proportion of firms with MIXED cash flows and accruals is highest in the Mature life-cycle stage (94%), followed
14
The equally-weighted forecast error across the life-cycle stages of 37.12% is lower than the average experimental forecast error in the last eight trials of the experiment (68.0%). We observe equally-weighted average forecast errors for mixed sign (same sign) observations of 47.3% (29.0%) in the archival data and 79.0% (35.0%) in the last eight trials of the experimental forecasting task.
24
by the Growth stage (87.1%). Overall, the values of variables across the life-cycle state partitions suggest that MIXED is positively correlated with firm attributes that result in more predictable cash flows, and that failure to control for these attributes will bias the coefficient on MIXED toward zero. Across all Value Line observations, the average rank of the absolute value of income and the average rank of the absolute value of accruals is 3.8 and 4.0, respectively, indicating that the magnitudes of income and accruals as a fraction of total assets are less extreme than the population of Compustat firms.15 The mean sample PREDICTABILITY and STABILITY index values of 48.7 and 50.5, respectively, are lower than the midpoints of the scale, which ranges from 5 to 100 for each measure. Compared to firms in other life-cycle stages, firms in the Mature stage have higher PREDICTABILITY and STABILITY and lower variance in cash flows, accruals and net income across the preceding five years. These findings are consistent with the life-cycle theory of the firm discussed in Dickinson (2006). For the overall sample, the rank of SIZE is 6.68, which is higher than the midpoint of the Compustat rank, and consistent with Value Lines propensity to cover larger, better-known firms. The average standard deviation of CFO (CNOA) over the five year period ending with the forecast date is 4.7% (6.0%), and the average standard deviation of income is 5.4%, consistent with the negative correlation between CFO and CNOA observed in other studies (e.g. Dechow, et al 1998). This smoothing pattern for accruals holds only for the Mature firm group. Panel B of Table 5 presents descriptive statistics for the Compustat population over the same period. The majority of observations in the Compustat population comprise Mature firms and Growth firms (42.4% and 27.2%, respectively). These groups are also the most highly
15
Values are ranked from 0-9; therefore, the median rank is between 4 and 5.
25
represented in the Value Line sample (52.1% and 31.1%, respectively). Firms in the Value Line sample are more profitable than those in the Compustat population and the mean variability of income, accruals and cash flows is lower. However, the pattern of variability across life-cycle groups is consistent between the Compustat population and the Value Line sample. For example, INCOME, CFO, and CNOA are each least variable in the Mature firm group, and most variable in the Startup and Decline groups. Barth et al. (2001) suggests that the time series properties of cash flows are influenced by industry. Panel C of Table 5, shows the distribution of sample firms across each of 13 industries defined by Barth et al. (2001) and across life-cycle stages defined by Dickinson (2006). A significantly higher percentage of Startup and Decline firms are in the Computer and Pharmaceutical industries. Because the Startup and Decline groups contain lower proportions of firms with MIXED accruals and cash flows, we include INDUSTRY dummies in our regression to control for potential effects of INDUSTRY on forecast errors. The correlation matrix of regression variables presented in Table 6 shows that forecast error is positively correlated with GROWTH (Pearson Correlation = 0.650), CFO (Pearson Correlation = 0.180), CNOA (Pearson Correlation = 0.104). These relations are as expected variability in historical performance measures appears to make forecasting cash flows more difficult, as does GROWTH in assets. Extreme changes in net operating assets (CNOA) in the period of the forecast are positively associated with cash flow forecast errors (Pearson Correlation = 0.107). This is consistent with Bradshaw et als 2001 conjecture that analysts fail to anticipate lower levels of persistence associated with extreme values of accruals. In contrast, forecast error is negatively associated with Value Lines PREDICTABILITY index (Pearson Correlation = -0. 300) as well as the STABILITY index (Pearson Correlation = -0.233). Most 26
important to our research question, we find that MIXED is positively correlated with forecast errors (Pearson Correlation = 0.190). Coefficients from the estimation of equation (1) are presented in Table 7. The third and fourth columns show the estimates obtained from regressing |ERROR| on MIXEDt-1 and lifecycle STAGE without any of the control variables in the model. The intercept is positive and significant (coefficient of 0.149; t-value of 2.74). The coefficient on MIXEDt-1 is also significant and positive (coefficient of 0.108 ; t-value of 2.06). The coefficients on the life-cycle indicator variables are positive and mostly significant, consistent with theory that Start-up firms, Growth firms, Shake-out firms and Decline firms experience higher forecast errors than Mature firms. Holding the effects of MIXED constant, fitted values can be obtained by adding the intercept to the coefficient for each STAGE. Results suggest that the average error is highest in the Startup group (45.8%), followed by the Decline group (37.3%) and the Shake-out group (31.2%). The average error in the Growth group (18.3%) is not statistically different from the average error for Mature firms (14.9%). These findings provide support for the hypothesis that informational complexity is associated with higher forecast errors across firms at different lifecycle stages. In addition, our results suggest that the effect of MIXED is economically significant. Specifically, MIXED increases forecast error between 23.5% and 72%, depending on life-cycle group. The center two columns present coefficients from estimation of the model with industry controls. In the presence of industry controls, the intercept becomes insignificant. Four industries are significantly associated with higher forecast errors: (1) Textiles, printing and publishing, (2) Extractive industries, (3) Computers, and (4) Transportation. The effects of INDUSTRY are incremental to life-cycle STAGE, suggesting that INDUSTRY and STAGE 27
capture different factors contributing to forecast errors. Inferences about MIXED and life-cycle STAGE are unchanged when INDUSTRY is included in the regression. The fully augmented regression coefficients are presented in the two right-most columns of Table 7. In the presence of other control variables, both the intercept and the coefficients on STAGE and INDUSTRY variables become insignificant. However, in the fully augmented regression, the coefficient on MIXEDt-1 retains its sign and becomes more significant. Moreover, the explanatory power of the model increases significantly (the R2 increases from 0.037 for the INDUSTRY-augmented regression to 0.122 in the fully augmented model). These results suggest that the control variables subsume the explanatory power of STAGEt-1 and INDUSTRY, but provide stronger support for the hypothesis that informational complexity is associated with higher forecast errors. In particular, the deleterious effect of informational complexity on forecast errors is incrementally and economically significant in the presence of many other forecast-error explanatory variables. The coefficients estimated for the control variables have the predicted signs, and are generally significant. The coefficient on ASSET_GROWTH is positive and marginally significant, consistent with analysts having greater difficulty forecasting future cash flows when firms are undergoing expansion, either through internal growth or acquisition. Consistent with the significant positive correlation between forecast error and RANK_|CNOAt-1|, the coefficient on RANK_|CNOAt-1| is marginally significant (coefficient 0.013; t-value 1.72). The coefficient on RANK_|INCOMEt-1| is not significant. Therefore, there is no evidence that extreme values of income mean-revert in an unanticipated and systematic way that incrementally contributes to forecast errors.
28
In contrast, the coefficient on CFOt-1 is negative and significant, consistent with the notion that low cash flow levels are more difficult for analysts to project into the future (coefficient -1.052; t-value -3.22). The coefficient on Value Lines proprietary PREDICTABILITY index is negative and very significant (coefficient = -0.004; t-value -5.26), suggesting that realized forecast errors are higher for firms that, ex-ante, analysts perceive more difficult to forecast. The coefficient on Value Lines STABILITY index is also negative and significant (coefficient = -0.002; t-value = -2.17). Factors adversely affecting price stability also seem to adversely affect analysts ability to forecast future cash flows. Incremental to PREDICTABILITY and STABILITY, the five-year standard deviation of cash flows (CFO) is significant and positively associated with forecast errors (coefficient = 1.147; t-value 2.45). The five-year standard deviation of accruals (CNOA) is positively associated with forecast errors, but only marginally so (coefficient = 0.405; t-value = 1.70). This suggests that variation in operating cash flows and accruals is associated with future forecast errors, and is incompletely incorporated into Value Lines PREDICTABILITY index. We did not predict signs for the coefficients on SIZE and PROFITABILITY; however, we find each is incrementally significant and positively associated with forecast errors (coefficients = 0.031 and 0.647; t-values = 2.48 and 2.03). VI. Summary and Conclusion Plumlee (2003) reports that analysts forecast errors are positively related to the complexity of forecast-relevant information. We extend her work by proposing that two features of the information contained in the operating-activities section of the indirect-approach statement of cash flows can contribute to forecasting complexity. First, we show that the currently required structure of the operating-activities section of the indirect approach statement of cash 29
flowswhen compared to an alternative, forward-order format that conforms more closely to the positively related, inter-temporal behavior of accruals and cash flowscauses lower levels of learning and higher levels of forecast error and dispersion. Second, we show that the offsetting (i.e., mixed sign) pattern of operating cash-flows and operating accruals information reported by most companies also impedes investors ability to learn the time-series properties of operating cash flows and operating accruals. While we find that mixed-sign forecast inputs lead to significantly higher cash-flow forecast errors and forecast dispersion across both forecast conditions, the forecast errors from the mixed-sign inputs are highest in the currently mandated reverse-order indirect-approach statement of cash flows. We also report the results of a quasi-experimental analysis of publicly available cashflow forecasts provided by Value Line analysts. Inferences from this analysis are consistent with inferences drawn from the experiment and support our hypotheses about the complexity of mixed-sign forecast inputs. Specifically, after controlling for firms life-cycle stage and other economic factors commonly associated with the accuracy of analysts forecasts, we find that forecast errors are higher for companies that report mixed-sign operating cash flows and operating accruals information. These results suggest that cognitive limitations that impede information acquisition manifest as systematic errors in a market setting. This study extends prior research on the effects of alternative financial-reporting classification, measurement, and presentation schemes on financial-statement users learning and judgment processes (e.g., Dietrich, Kachelmeier, Kleinmuntz, and Linsmeier 2001). In this study, we provide evidence that is directly relevant to questions regarding the form and content of an important performance statement. Obtaining a better understanding of financial statement users information acquisition processes will help to inform the deliberations of standard setters 30
seeking to weigh the costs and benefits of proposed financial-statement disclosure requirements. Interestingly, the reverse presentation format for the operating section of the indirect approach statement of cash flows is often derided as confusing by students, accounting educators, and users of financial statements (see, e.g., Stice, Stice, and Skousen 2004, 245; Stickney and Weil 2003, 184)16 and is opposite of the predictive, future-oriented perspective taken in most conceptual descriptions of the balance sheet elements and the accruals process (e.g., FASB 1985). Our study also illustrates an unintended consequence of giving primacy to reported net income in the reconciliation between cash flows and accruals. The results of this study should be useful to standard setters as they reconsider the structure of performance reporting and the prominence currently afforded accrual-basis net income across currently required performance reports. In particular, accrual-basis net income is (1) the focus of the statement of earnings, (2) the numerator in the only ratio required by generally accepted accounting principles (i.e., earnings per share), (3) the first item included in the determination of comprehensive income, and (4) the first item presented in the indirect approach statement of cash flows. However, because net income is not defined in the conceptual framework, it is one of an infinite number of potentially reportable, arbitrary partitions of the change in net assets during a given period. Although we believe the comparative advantages of experiments are particularly wellsuited to the research questions in this study (Libby, Bloomfield and Nelson 2002), critics of experiments-based research occasionally suggest that documented empirical findings in experiments are caused by under-motivated participants in idiosyncratic, vacuum-like laboratory
16
In some cases, textbooks will supplement the detailed lists with a discussion of the rationale for indirect-approach statement of cash flows operating-section adjustments that are unrelated to the expected timing of cash receipts and disbursements. For example, Harrison and Horngren (2004) provide this explanation for the reverse adjustment for increases in operating assets: An increase in a current asset other than cash indicates a decrease in cash. Thats because it takes cash to acquire assets.
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settings producing results that cannot sustain in market settings. Our analysis attempts to partially address these concerns by (1) providing our participants with accuracy-based compensation and cumulative forecast-by-forecast feedback and (2) supplementing our experimental results with a quasi-experimental archival analysis of publicly released cash-flow forecasts (Libby, Bloomfield and Nelson 2002). The outcome-feedback mechanism operationalized in our experiment is more transparent than those appearing in real-world financial-forecasting settings and is more comprehensive and informationally complete than the outcome feedback appearing in similar accounting-related experiments (e.g., Luft and Shields 2001; Bloomfield, Libby and Nelson 2003). Indeed, the form of outcome feedback included in the present study has informational properties (i.e., single source, no delay, and linear) that typically yield the highest levels of learning in prior research (Diehl and Sterman 1995).
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FIGURE 1 Format of Financial Information, Judgment Elicitation and Feedback Screens across Conditions FORWARD ORDER REVERSE ORDER
Statement of Cash Flows Screens
YEAR 1 Cash Flows from Operations YEAR 1 Increase (Decrease) in Non-Cash Operating Net Assets YEAR 1 Operating Earnings XXX YEAR 1 Operating Earnings YEAR 1 (Increase) Decrease in Non-Cash Operating Net Assets YEAR 1 Cash Flows from Operations
YYY
(YYY) XXX
Judgment Screens
Forecast of YEAR 2 Cash Flows from Operations
ZZZ
Forecast of YEAR 2 Cash Flows from Operations
ZZZ
Feedback Screens
Cash Flows from Operations YEAR 1 Increase (Decrease) in Non-Cash Operating Net Assets Your forecast of YEAR2 Cash Flows from Operations ACTUAL value of YEAR2 Cash Flows from Operations Percentage Error XXX YYY ZZZ ACT %ERR YEAR 1 (Increase) Decrease in Non-Cash Operating Net Assets Cash Flows from Operations Your forecast of YEAR2 Cash Flows from Operations ACTUAL value of YEAR2 Cash Flows from Operations Percentage Error
(YYY) XXX ZZZ ACT %ERR
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FIGURE 1 (continued) ___________________ Participants were randomly assigned to one of two format conditions. After completing the preexperiment questions, participants provided forecasts of year-two cash flows from operations for 16 different companies. We counterbalanced the order of the companies (eight unique orders: four different rotated orders with reversals of those orders) to ensure that all 16 companies were equally represented across all 16 trials. For any given company, the information provided across the conditions was identical. However, the presentation of the information differed in a manner consistent with the Statement of Cash Flows Screens and Feedback Screens illustrated in the Figure. After each judgment, participants were provided with comprehensive and cumulative feedback information for all of their judgments (e.g., immediately prior to their 16th forecast, participants could view the information on the Feedback Screens for all 15 of their preceding forecasts. Upon completing forecasts for the 16 different companies, the participants provided responses to an identical set of post-experiment questions (e.g., demographic questions). %ERR = (ACT ZZZ)/ACT
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FIGURE 2 Experiment Results Graphs ...