Financial Reporting Quality: Red Flags and Accounting Warning Signs

fiscal Reporting Quality and Investment Efficiency Rodrigo S. Verdi The Wharton School University of public address system 1303 Steinberg H al unmatched-Dietrich H e precise last(predicate) Philadelphia, PA 19104 Email emailprotected upenn. edu Ph iodin (215) 898-7783 Abstract This newsprint studies the resemblance among monetary inform choice and coronation talent on a render of 49,543 buckram- division observations amidst 1980 and 2003. monetary report feature has been posited to meliorate adorniture might, but in that location has been little existential evidence fiscal support this claim to date.Consistent with this claim, I witness that proxies for pecuniary inform musical none be discon warmingly associated with two buckram under enclotheiture funds and over enthronization funds funds. Further, financial inform woodland is to a greater extent strongly associated with under investing funds for securelys facing funding constraints and with over enthronement for fast(a)s with large bills balances, which apprizes that financial inform feeling mitigates learning asymmetries arising from adverse survival problems and action conflicts.Fin e truly last(predicate)y, the carnal knowledge betwixt financial reportage tone of voice and enthronisation efficacy is stronger for tautens with subaltern step randomness environments. Over each(prenominal), this authorship has implications for interrogation examining the determinants of coronation cleverness and the frugal consequences of enhanced financial insurance coverage. Current Version February 14, 2006 _____________________________________________ I thank members of my oration committee John Core, Gary Gorton, Christian Leuz, Scott Richardson, and Catherine Schrand (Chair) for their guidance on this paper.I rate comments from Patrick Beatty, Jennifer Blouin, Brian Bushee, Gavin Cassar, Francesca Franco, Wayne Guay, Luzi Hail, Bob Holtha utilizatio nn, Rick Lambert, Frank Moers, Jeffrey Ng, Tjomme Rusticus, Irem Tuna, Ro Verrecchia, Missaka Warusawitharana, Sarah Zechman, Zili Zhuang, and seminar participants at the Wharton School. I also grate fully acknowledge the financial support from the Wharton School and from the Deloitte Foundation. Any errors atomic bod 18 my own. pecuniary Reporting Quality and Investment Efficiency . Introduction This paper studies the social intercourse among financial insurance coverage calibre and enthronisation funds aptitude. Recent text file (e. g. , Healy and Palepu, 2001 Bushman and Smith, 2001 Lambert, Leuz, and Verrecchia, 2005) suggest that enhanced financial describe feces stimulate classical economic implications such(prenominal) as change magnitude enthronisation funds competency. However, despite satisfying theoretical support for such a sex act, there is little empirical evidence sustenance these claims.I hypothesize that financial reportage feel can improve coronation skill by reducing culture imbalance in cardinal ways (1) it tames the culture asymmetry in the midst of the squ ar and investors and then miserableers the sloppeds bell of ski tow change and (2) it falls data asymmetry amongst investors and the manager and thus lowers the sh argonholders cost of supervise managers and improves vomit cream. The two key constructs in the perfumemary ar investment faculty and financial inform grapheme.I conceptually define a hearty as investing high-octanely if it undertakes all and exactly projects with autocratic net grant foster (NPV) under the scenario of no food trade frictions such as adverse selection or potency be. Thus in nitty-grittyive investment includes passing up investment opportunities that would have official NPV in the absence of adverse selection (underinvestment). Likewise, inefficient investment includes undertaking projects with disallow NPV (overinvestment).I monetary var.(a) investment efficiency as deviations from judge investment exploitation a parsimonious investment computer simulation which foreshadows expected investment as a function of evolution opportunities (Tobin, 1982). Thus, both underinvestment ( detrimental deviations from expected investment) and 1 overinvestment ( compulsory(p) deviations from expected investment) be carryed inefficient investment. I conceptually define financial reportage part as the precision with which financial reporting conveys information ab unwrap the smasheds operations, in particular its expected specie hunts, in commit of magnitude to inform equity investors.As described in the FASB Statement of Financial accountancy Concepts No. 1, financial reporting should cater information that is useful to present and probable investors in qualification acute investment decisions (par. 34) and go forth information to help present and potential investors in assessing the measuring rods, timing, and u ncertainty of prospective change receipts (par. 37). Further, expected coin in time rate of accrues is a key foreplay to firm uppercase letter budgeting, which is particularly strategic in the context of this paper which studies financial reporting implications for corporate investment.I legate for financial reporting tone of voice utilize accounts of accruals timber establish on the idea that accruals improve the informatoryness of wages by smoothing out transitory fluctuations in cash fluxs (Dechow and Dichev, 2002 McNichols, 2002). The use of accruals bore relies upon the fact that accruals be deems of future cash arises and payment bequeath be more representative of future cash escapes when there is lower idea error embedded in the accruals process.I study the apprisal between financial reporting feel and investment efficiency on a ensample of 49,543 firm- grade observations during the sample utmost of 1980 to 2003. The epitome yields triad key f indings. First, the proxies for financial reporting choice atomic number 18 ostracizely associated with both firm underinvestment and overinvestment. This result extends research in Wang (2003) who predicts and finds a dogmatic social intercourse between 2 smashing allocation efficiency and cardinal earnings attributes ( raft-relevance, persistence, and precision) without making the distinction between under- and overinvestment.Second, cross-section(a) exams indicate that the involve of financial reporting tone of voice on investment efficiency is delinquent to the alleviation of adverse selection and agency costs. For instance, financial reporting quality is more strongly negatively associated with underinvestment for firms facing finance constraints. This result suggests that, for this suit of firm, financial reporting quality improves investment efficiency by moody its cost of altitude funds. Likewise, financial reporting quality is more strongly negatively associ ated with overinvestment for firms with large cash balances.This result suggests that financial reporting quality improves investment efficiency for these firms by lowering sh arholders cost of observeing managers and meliorate project selection. Finally, I predict that the sex act between financial reporting quality and investment efficiency is stronger for firms with poor information environments. Financial reports are just one source of information to investors, and investors are more apt(predicate) to rely on financial report information to infer the economic conditions of the firms operations for companies with early(a)wise unclouded information environments.I placeholder for the information environment using the number of analysts following a firm as an ex-ante footfall for the amount of publicly forthcoming information astir(predicate) the firm, and gaming-ask bed covers as an ex-post euphony of the information asymmetry between the firm and investors (e. g. , A mihud and Mendelson, 1986 Roulstone, 2003). Consistent with the prediction, the recounting between financial reporting quality and investment efficiency is stronger for firms with low analyst following and for firms with high bid-ask circularizes. These results suggest that financial reporting quality can affect investment efficiency right away in add-on to the link through price informativeness documented in Durnev, Morck, and Yeung (2004). In addition, the findings using analyst following are coherent with Botosan (1997) who finds that greater disclosure is associated with lower cost of jacket crown for firms with low analyst following. Although my results suggest that firms with higher financial reporting quality are associated with more efficient investment, one can non conclude from this paper that change magnitude financial reporting quality would necessarily translate into higher investor welfare.Enhanced financial reporting whitethorn improve investment efficiency by r educing information asymmetry. However, firms must weigh this benefit once against the costs (e. g. , proprietary costs) and against choice ways to reduce information asymmetry such as courting more analysts. Further, it may even be impossible for some firms to increase financial reporting quality condition the limitations imposed by GAAP. Nonetheless, this paper contributes to literary works on the economic consequences of enhanced financial reporting by showing that financial reporting quality can be associated with more fficient investment. The lieder of the paper proceeds as follows. part 2 develops the hypotheses and Section 3 describes the step of investment efficiency and financial reporting quality. Section 4 presents the results. Section 5 offers some aesthesia outline and Section 6 concludes. 2. as tote upption development In this section I rootage re behold the determinants of investment efficiency. Then I talk about how financial reporting quality can affect investment efficiency. Finally, I develop predictions on the apprisal between financial reporting quality and investment efficiency, and the channels through which this congeneric is expected to take place. conception 1 describes these links. 2. 1. Determinants of investment efficiency on that point exist at least two determinants of investment efficiency. First, a firm needs to advert capital in hallow to pay its investment opportunities. In a perfect mart, all projects with positive net present treasures should be funded however, a large literature in pay has shown that firms face financing constraints that limit managers cogency to pay potential projects (Hubbard, 1998). mavin proof of this literature is that a firm facing financing constraints go out pass up positive NPV projects due to large costs of raising capital, resulting in underinvestment (Arrow 1 in Figure 1). Second, even if the firm decides to raising capital, there is no guarantee that the jell inves tments are implemented. For instance, managers could pick to invest inefficiently by making problematical project selections, consuming perquisites, or even by expropriating existing resources. or so of the literature in this line of business predicts that poor project selection leads the firm to overinvest (Stein, 2003), but there are also a few papers which predict the firm could underinvest (e. g. , Bertrand and Mullainathan, 2003). These links are presented respectively by Arrows 2A and 2B in Figure 1. Information asymmetry can affect the cost of raising funds and project selection. For instance, information asymmetry between the firm and investors (comm simply referred as an adverse selection problem) is an classical driver of a firms cost of raising the capital required to finance its investment opportunities Arrow 3 in Figure 1). Myers and Majluf (1984) develop a position in which information asymmetry between the firm and investors gives rise to firm underinvestment. Th ey show that when managers act in prefer 5 of existing shareholders and the firm needs to raise funds to finance an existing positive NPV project, managers may refuse to raise funds at a discounted price even if that supposes passing up intelligent investment opportunities. Also, information asymmetry can prevent efficient investment because of the distinctial degree of information between managers and shareholders (commonly referred as a principal-agentive role conflict).Since managers maximise their personal welfare, they can choose investment opportunities that are not in the best interest of shareholders (Berle and Means, 1932 Jensen and Meckling, 1976). The exact reason why managers inefficiently invest shareholders capital varies crosswise different puzzles, but it includes perquisite consumption (Jensen, 1986, 1993), vocation concerns (Holmstrom, 1999), and preference for a quiet life (Bertrand and Mullainathan, 2003), among separates.More gravely, the predicted rela tion is that agency problems can affect investment efficiency due to poor project selection (Arrow 4A in Figure 1) and can increase the cost of raising funds if investors anticipate that managers could expropriate funded resources (Arrow 4B in Figure 1) (Lambert, Leuz, and Verrecchia, 2005). In sum, the news above suggests that information asymmetries between the firm and investors and between the principal and the agent can prevent efficient investment. In the next section, I discourse how financial reporting quality can enhance investment efficiency by mitigating these information asymmetries. . 2. Role of financial reporting Financial reporting quality can be associated with investment efficiency in at least two ways. First, it is commonly argued that financial reporting mitigates adverse selection costs (Arrow 5 in Figure 1) by reducing the information asymmetry between the 6 firm and investors, and among investors (Verrecchia, 2001). For instance, Leuz and Verrecchia (2000) f ind that a committal to more disclosure reduces such information asymmetries and increases firm liquidity.On the former(a) hand, the existence of information asymmetry between the firm and investors could lead suppliers of capital to discount the line of descent price and to increase the cost of raising capital because investors would infer that firms raising money is of a bad type (Myers and Majluf, 1984). Thus, if financial reporting quality reduces adverse selection costs, it can improve investment efficiency by reducing the costs of external financing and, as dealed in more detail below, the potential for financial reporting quality to improve investment efficiency is greatest in firms facing financing constraints.Second, a large literature in accounting suggests that financial reporting plays a critical role in mitigating agency problems. For instance, financial accounting information is commonly employ as a direct input into compensation contracts (Lambert, 2001) and is an important source of information utilize by shareholders to monitor managers (Bushman and Smith, 2001). Further, financial accounting information contributes to the monitoring role of pedigree markets as an important source of firmspecific information (e. g. Holmstrom and Tirole, 1993 Bushman and Indjejikian, 1993 Kanodia and Lee, 1998). Thus, if financial reporting quality reduces agency problems (Arrow 6 in Figure 1), it can then improve investment efficiency by increasing shareholder ability to monitor managers and thus improve project selection and reduce financing costs. 1 2. 3. Predictions For example, Bens and Monahan (2004) find a positive association between AIMR disclosure ratings and the additional value of diversification as defined by Berger and Ofek (1995).They conclude that disclosure plays a monitoring role in mitigating managements investment decisions. 1 7 Based on the discussion above that financial reporting affects both adverse selection and agency conflicts, I predict an fare negative relation between financial reporting quality and both underinvestment and overinvestment. These links complement research in Bushman, Piotroski, and Smith (2005), which studies the relation between country measures of timely loss recognition and the country propensity to squander bad projects (i. e. , itigate overinvestment), and in Wang (2003) which explores the relation between capital allocation efficiency and accounting information quality for a sample of US firms, without making a distinction between under- and overinvestment. 2 H1 Financial reporting quality is negatively associated with underinvestment. H2 Financial reporting quality is negatively associated with overinvestment. In addition to analyze the median(a) relation between financial reporting quality and investment efficiency, I also check the mechanisms through which financial reporting quality can affect investment efficiency using cross-sectional analysis.First, I predict that the relation between financial reporting quality and firm underinvestment is stronger for firms facing financing constraints. By translation, forced firms are those for which the ability to raise funds is the most seeming impediment to efficient investment, and for these firms, financial reporting quality is especially important in mitigating adverse selection costs. H3 The relation between financial reporting quality and underinvestment is stronger for financing constrain firms. 2 nonpareil concern with Hypotheses 1 and 2 is that antecedent goes the other way. For instance, poorly performing managers could be investing inefficiently and thus choose to report low quality financial information in order to hide their bad performance (e. g. , Leuz, Nanda, and Wysocki, 2003). I discuss the empirical tests use to address this alternative guesswork in Section 4. 8 Second, I predict that the relation between financial reporting quality and firm overinvestment is stronger for firms with large cash balances and degage cash conflates.Managers of firms with large cash balances and uninvolved cash periods have more opportunity to engage in value destroying investment activities (e. g. , Jensen, 1986 Blanchard, Lopezde-Silanes, and Shleifer, 1994 Harford, 1999 Opler et al. , 1999 Richardson, 2006). Consequently, financial reporting quality can play a more important monitoring role in mitigating agency problems for these firms. H4 The relation between financial reporting quality and overinvestment is stronger for firms holding large cash balances and excuse cash flows.Third, I study the complementary and step in relation between financial reporting quality and a firms information environment, and how it affects investment efficiency. Financial reporting quality is just one source of information about the firms operations utilize by investors. For instance, investors in firms followed by a large number of analysts or firms with informative stock prices may be less bloodsucking on financial reports when other elements of the firms information environment are of high quality.Thus I hypothesize that financial reporting quality is more important in ameliorate investment efficiency when the amount of information publicly available about the firm is low. 3 H5 The relation between financial reporting quality and investment efficiency is stronger for firms with relatively poor information environments. 3. Empirical work 3. 1. Proxies for investment efficiency One concern with Hypothesis 5 is that financial reporting quality and the firms information environment are liable(predicate) to be tally.Indeed, Verdi (2005) shows that the firm information environment can be aggregated in accounting-based and market-based cor link up constructs. Hypothesis 5 implicitly assumes away this correlation by investigating the import of financial reporting quality on investment efficiency holding the market-based information environment constant. 3 9 In order to construct measures of investment efficiency, I premier(prenominal) count a model that predicts firm investment levels and then use residuals from this model as a procurator for inefficient investment.The data are from the Compustat Annual file during the old age 1980 to 2003. Total new Investment in a attached firm- division is the sum of capital expenditures (item 128), R&D expenditures (item 46), and acquisitions (item 129) minus sales of PPE (item 107) and depreciation and amortization (item 125) figure by 100 and scaled by average fare assets (item 6), following Richardson (2006). This measure uses an accounting-based framework to estimate new investment as the difference between aggregate investment and investment required for tutelage of assets in place.In the sensitiveness section I also discuss the robustness of the results to the use of only capital expenditures as an alternative proxy for investment that is oftentimes used in the literature (e. g. , Hubbard, 19 98). I estimate a parsimonious model for investment demand as a function of growth opportunities measured by Tobins Q (Tobin, 1982). This model is based on the argument that growth opportunities should explain corporate investment when markets are perfect (Hubbard, 1998). Investmenti,t = ? 0 j,t + ? 1 j,t * Qi,t-1 + ? i,t (1) I estimate the model cross-sectionally for all industries with at least 20 observations in a given year based on the Fama and French (1997) 48-industriousness classification. Q is calculated as the ratio of the market value of arrive assets (defined as 4 A large finance literature uses investment cash flow sensitivities as a proxy for inefficient investment (or market frictions).I do not use this approach for two reasons First, traditional papers measure cash flow without making the distinction between cash flows and accruals, and Bushman, Smith, and Zhang (2005) illustrate the sensitivity of the results to the assign bill of operating cash flows. Second, po sitive investment cash flow sensitivities could mean both financing constraints and/or agency problems which makes it impossible to test the cross-sectional hypotheses of the paper (Hypotheses 3 to 5). 10 otal assets (item 6) plus the product of stock price (item 199) and the number of common shares outstanding (item 199) minus the book value of equity (item 60)) to book value of tot assets (item 6) at the start of the fiscal year. The sample consists of 98,675 firm-year observations with available data to estimate Investment and Q during the sample period of 1980 to 2003. Consistent with previous literature, financial firms (i. e. , SIC codes in the 6000 and 6999 set out) are excluded because of the different nature of investment for these firms.In order to mitigate the influence of outliers I winsorize all variants at the 1% and 99% levels by year. 5 tabulate 1 presents the results from the investment model in comparison 1. embellish A offers descriptive statistics for Inves tment and Q. The mean (median) firm in the sample invests 7. 26% (3. 84%) of fare assets per year and has an average (median) Q mates to 1. 90 (1. 32), accordant with associate literature (e. g. , Richardson, 2006 Almeida, Campello, and Weisbach, 2004). plank B presents mean and median set of the estimated perseverance coefficients on Q, the average R-square, and the number of significant positive coefficients for distributively year. In all years the mean and median coefficients are positive and relatively stable during the sample period. The mean R-square ranges from 6% in 1997 to 14% in 1991. 6 Finally, in to each one year, more than half of the industry coefficients on Q are positive and statistically different from zero at a five percent significance level. 7The model in Equation 1 includes an pink which imposes that for each industry-year the mean firm will have a zero residual. In untabulated analysis, I re-estimate the model adding the intercept back to the residual so that it allows industry-years to have a non-zero mean (for example, industries that overinvest or periods with large economic growth). The results are robust (in general even stronger) to this test. 6 Note that the report R-squares measure only the within industry-year discrepancy because the model is estimated separately for each industry-year.An equivalent approach in which the model is estimated across all industry-years with separate intercepts and coefficients for each industry-year leads to an R-square of 23. 5%, suggesting that the overall informative designer of the model is larger than that reported in control board 1. 7 A authorized ongoing debate in the finance literature is the implications for measurement error in the musical theme of Q (Erickson and Whited, 2000 Gomes, 2001 Alti, 2003). Since the subsequent analysis hinges on the investment model in Equation 1, I perform two sensitivity tests First, I include past returns in 5 1 I measure investment efficienc y using the residuals from the model in Equation 1. Overinvestment is the positive residuals of the investment model and Underinvestment is the negative residuals of the investment model multiplied by negative one, such that both measures are decreasing in investment efficiency. In untabulated analysis, I take on all tests by and by excluding firms with the smallest 10% and 20% investment residuals because these firms are more likely to be bear upon by measurement error in the investment model (i. e. , mis class advertisement as overinvesting or underinvesting firms).The results for these analyses are interchangeable to those reported below. flurry 1 Panel C presents descriptive statistics for Investment rest, Overinvestment and Underinvestment. By construction, Investment Residual has a mean value of zero ranging from -64. 46% to 80. 43%. There are 39,107 (59,568) firms classified as overinvesting (underinvesting) firms. The mean (median) value is 9. 73% (5. 63%) for Overin vestment and 6. 39% (4. 71%) for Underinvestment. These results show that the residuals from the investment model are more frequently negative, although in smaller magnitude.Panel D presents Pearson correlations between the measures of investment efficiency and firm characteristics. Investment Residual is uncor link up with firm size (measured as the log of total assets (item 6) at the start of the fiscal year) and slightly negatively correlated with return capriciousness (measured as the stock(a) deviation of cursory returns during the prior fiscal year). However, when the residuals are separated into Overinvestment and Underinvestment, I find that these versatiles are negatively correlated with size and positively correlated with return unpredictability and Q (the magnitude of the he investment model to beguile growth opportunities not reflected in Q (Lamont, 2000 Richardson, 2006) and second, I exclude all industry-year observations in which the estimated coefficient on Q i s not positive and significant. The subsequent results are not raw to these tests. 12 correlations range from 0. 18 to 0. 32). These results suggest either that (1) small firms, with more growth opportunities and vaporizable operations, have more inefficient investment or (2) the investment model is a poor fit for these firms.In any case, it highlights the importance to tell for these firm characteristics in the subsequent analysis. In order to better see the properties of the residuals from the investment model I perform analyses testing the persistence of investment efficiency over time. First, I find that 40% (48%) of the firms in the overhaul (bottom) Investment Residual quintile in a given year re master(prenominal) in the top (bottom) quintile in the following year, and 27% (36%) remain one-third years later (Panel E).In addition, one lag of Investment Residual in an autoregressive model explains 16% of current Investment Residual (untabulated). The inclusion body of hig her orders of past residuals has a small contribution in explanatory power (R-square of only 18% if five lags are included in the model). These analyses suggest that residuals of the investment model are not random, which seems to support the view that they capture a firm investment characteristic. However, I cannot rule out the explanation that the persistence in the residuals is a function of an omitted correlated inconstant in the investment model. . 2. Proxies for financial reporting quality The conceptual definition of financial reporting quality used in this paper is the truth with which financial reporting conveys information about the firms operations, in particular its expected cash flows, in order to inform investors in terms of equity investment decisions. This definition is accordant with the FASB SFAC No. 1 which states that one objective of financial reporting is to inform present and potential investors 13 in making rational investment decisions and in assessing t he expected firm cash flows.I proxy for financial reporting quality using measures of accruals quality derived in prior work (Dechow and Dichev, 2002 McNichols, 2002) based on the idea that accruals are estimates of future cash flows, and earnings will be more representative of future cash flows when there is lower inclination error embedded in the accruals process (McNichols, 2002). 8 I estimate discretionary accruals using the Dechow and Dichev (2002) model augmented by the fundamental variables in the Jones (1991) model as suggested by McNichols (2002). The model is a regression of running(a) capital ccruals on lagged, current, and future cash flows plus the change in receipts and PPE. All variables are scaled by average total assets. Accrualsi,t = ? + ? 1*Cash lighti,t-1 + ? 2*CashFlowi,t + ? 3*CashFlowi,t+1 + ? 4*? Revenuei,t + ? 5*PPEi,t + ? i,t. (2) where Accruals = (? CA ? Cash) (? CL ? STD) Dep, ? CA = replace in current assets (item 4), ? Cash = Change in cash/cash equivalents (item 1), ? CL = Change in current liabilities (item 5), ? STD = Change in short-term debt (item 34), Dep = Depreciation and amortization expense (item 14), CashFlow = light up income before extraordinary items (item 18) minus Accruals ?Revenue = Change in revenue (item 12), and PPE = Gross property, plant, and equipment (item 7). All variables are deflate by average total assets (item 6). Following Francis et al. (2005), I estimate the model in Equation 2 crosssectionally for each industry with at least 20 observations in a given year based on the Fama and French (1997) 48-industry classification. AccrualsQuality at year t is the 8 I discuss the sensitivity of the results to the use of alternative measures of accruals quality and other attributes of earnings in Section 5. 4 cadence deviation of the firm-level residuals from Equation 2 during the years t-5 to t-1, assuring that all explanatory variables are measured before period t for the computation of AccrualsQual ity in that year. I multiply AccrualsQuality by negative one so that this variable becomes increasing in financial reporting quality. As discussed in Dechow and Dichev (2002) and McNichols (2002), the estimation of AccrualsQuality captures the absolute summercater in the residuals of Equation 2 rather than the variation relative to a benchmark.One concern with this approach is that AccrualsQuality may be capturing some underlying degree of volatility in the business, and the results in set back 1 show that investment efficiency is negatively correlated with firm uncertainty. Thus, I follow the suggestion in McNichols (2002) and create a relative measure of accruals quality. In particular, I measure AccrualsQualityRel as the ratio of the standard deviation of the residuals from Equation 2 during the years t-5 to t-1 to the standard deviation of total accruals during the years t-5 to t-1 multiplied by negative one.This measure captures the relative division of the estimation errors in accruals compared to the total variance. I show below that this measure is only slightly correlated with firm size and cash flow volatility, mitigating the concern that the proxies for financial reporting quality are associated with investment efficiency because of the spurious put together of firm uncertainty. 4. Results To investigate hypotheses 1 and 2, I first present preliminary analysis on the univariate relation between the measures of investment efficiency and financial reporting quality.Table 2 Panel A presents descriptive statistics for a smaller sample than reported in Table 1 due to data availability for AccrualsQuality and AccrualsQualityRel. 15 The sample consists of 49,543 firm-year observations and all variables are winsorized at the 1% and 99% levels by year. In this sample, there are 19,473 (30,070) firms classified as overinvesting (underinvesting) firms. The mean (median) value for Overinvestment is 7. 81% (4. 45%) and for Underinvestment is 5. 37% (4. 09%) .The magnitudes are smaller than reported in Table 1 because the data required to estimate AccrualsQuality and AccrualsQualityRel bias the sample toward larger firms. Among the financial reporting quality proxies, the mean (median) firm in the sample has an AccrualsQuality of -0. 04 (0. 03) and an AccrualsQualityRel of -0. 74 (-0. 64). Finally, I include descriptive statistics on firm size, cash flow volatility, and Tobin Q because these firm characteristics are shown to be associated with investment efficiency in Table 1. The dissemination of Q is slightly changed (as compared to Table 1) to a mean (median) Q of 1. 63 (1. 23) again reflecting the sample bias toward larger firms. Panel B presents Pearson (Spearman) correlations above (below) the main diagonal for the variables in Panel A. By construction, Overinvestment and Underinvestment cannot be correlated because each firm-year observation can only be in one group. Most importantly, Overinvestment is negatively correlated with AccrualsQuality (Pearson correlation equals -0. 19) and with AccrualsQualityRel (Pearson correlation equals -0. 8) the like is true for Underinvestment (Pearson correlations equal -0. 22 and -0. 10 respectively). These results present preliminary evidence for the relation between financial reporting quality and investment efficiency in hypotheses 1 and 2. Finally, as in Dechow and Dichev (2002), AccrualsQuality is highly correlated In Table 1, I use return volatility instead of cash flow volatility to avoid imposing the five-year data requirement for the estimation of cash flow volatility. However, this data is required to estimate AccrualsQuality and does not impose any sample bias at this stage of the analysis.I use cash flow volatility in the remainder of the paper because AccrualsQuality is highly correlated with cash flow volatility as discussed by Dechow and Dichev (2002). However, the results are not sensitive to this choice. 9 16 with Size (Pearson correlation equals 0. 42 ) and with CashFlowVol (Pearson correlation equals -0. 66). However, note that AccrualsQualityRel is ofttimes less correlated with these variables (correlations of -0. 08 and 0. 04 with size and cash flow volatility respectively), supporting the argument that this variable is uncorrelated with firm uncertainty. 0 Table 3 presents the multiple regressions. The estimated model is a regression of investment efficiency on financial reporting quality, firm characteristics, and industry (based on the Fama and French (1997) 48-industry classification) and year fixed effects. The dependent variable is Underinvestment in the first two columns and Overinvestment in the remaining columns. All standard errors are clustered by firm using the HuberWhite procedure. 11 As predicted in hypothesis 1, Underinvestment is negatively related to AccrualsQuality and AccrualsQualityRel (both coefficients are significant at 1% level).The estimated coefficients are also negative and significant for Overinves tment, supporting the prediction in hypothesis 2. The estimated coefficients suggest that increasing AccrualsQuality (AccrualsQualityRel) by one standard deviation is associated with a reduction on Underinvestment of 0. 21% (0. 11%) and on Overinvestment of 0. 31% (0. 22%). Given that the mean values for Underinvestment and Overinvestment in Table 2 are 5. 73% and 7. 81%, these changes average between 1% and 5%, suggesting that the economic significance of the effect is moderate.One alternative explanation for the results in Table 3 is that causality goes the other way. For instance, suppose that poorly performing managers are more likely to The signs of the correlations between AccrualsQuality and size and cash flow volatility are the opposition of the ones presented in Dechow and Dichev (2002) because I multiply AccrualsQuality by negative one so that this variable is increasing in reporting quality. 11 Petersen (2005) suggests two methods to correct for both cross-sectional and time-series dependence in the data the Huber-White procedure and correct Fama-MacBeth.Since, neither method is perfect, I repeat all subsequent analysis using Fama-MacBeth (1973) estimators adjusting for time-series dependence. The results lead to the alike inferences as reported in the text. 10 17 invest inefficiently and also choose to report low quality financial information in order to hide their bad performance (e. g. , Leuz, Nanda, and Wysocki, 2003). Then one could spuriously find a positive association between financial reporting quality and investment efficiency. In order to address this concern, I perform two tests.First, I repeat the analysis using the financial reporting quality proxies lagged by two periods (the variables in the model are already lagged by one period). Second, I explicitly control for past investment efficiency in the model. The lore behind this test is that if past investment efficiency drives financial reporting quality then there should be no rela tion between financial reporting quality and future investment efficiency after controlling for past investment efficiency. Table 4 Panel A presents the results of the two sensitivity analyses when Underinvestment is used as the dependent variable.When AccrualsQuality and AccrualsQualityRel (Columns I and II) are lagged by two periods, the inferences are unchanged. The estimated coefficients are statistically negative at conventional levels. In Columns III and IV, I include past Underinvestment in the model. In this case, the estimated coefficient on AccrualsQuality is still negative and significant, while the coefficient on AccrualsQualityRel is negative but only marginally significant (two-sided p-value of 0. 14). Table 4 Panel B repeats the analysis for Overinvestment.Again, all the inferences are unchanged since the estimated coefficients on AccrualsQuality and AccrualsQualityRel are statistically negative in all models. Overall, the results in Tables 3 and 4 support hypothese s 1 and 2 that financial reporting quality is negatively associated with both underinvestment and overinvestment, 18 consistent with the argument that financial reporting mitigates both adverse selection and agency costs. 4. 1. Cross-sectional air divisions In this section, I discuss the empirical approach used to test hypotheses 3, 4, and 5.These hypotheses involve cross-sectional predictions about the relation between financial reporting quality and investment efficiency across sub-groups of the sample. Thus, I estimate separate coefficients for these sub-groups as described in the model below (Investment Inefficiency) i,t = ? 0 + ? 1* Partition i,t-1 + ? 2* ReportingQuality i,t-1 + ? 3* ReportingQuality* Partition i,t-1 + ? 4* Controls i,t-1 ? ? t * Year t + ? ? j * pains j + ? it. where Investment Inefficiency is either Underinvestment or (3) Overinvestment, ReportingQuality is either AccrualsQuality or AccrualsQualityRel.Partition is coded as an indicator variable based on me asures of financing constraints, excess cash, or information environment described below (results are similar if the Partition is used as a continuous or graded (deciles) variable). The partitioning variables are defined such that a negative coefficient on the fundamental interaction term (? 3) implies that the relation between financial reporting quality and inefficient investment is stronger for firms in the subgroup of interest (e. g. , financially bound firms). As additional analysis, I test the null hypothesis that the sum of the coefficients ? and ? 3 is equal to zero in order to test whether the relation between financial reporting quality and investment efficiency is at least present in the sub-group of interest. 12 12 Hypotheses 3 to 5 are also important in mitigating the concern that an omitted correlated variable could be driving the positive association between financial reporting quality and investment efficiency. For instance, if managers choose better (worse) inves tment projects and report more (less) informative financial accounting information when they know more (less) about growth opportunities and expected cash flows, 9 4. 1. 1. Financing Constraints In this section, I investigate hypothesis 3 which predicts that the relation between financial reporting quality and Underinvestment is stronger for financing constrained firms because these firms are, by definition, limited in their ability to raise funds. I follow the approach in Hubbard (1998) to tell apart firms into financially constrained and free categories. In particular, I use five different criteria because of the neglect of consensus about which approach provides the best classification (Almeida, Campello, and Weisbach, 2004).First, I classify firms into Payout forced if the firm is in the bottom deuce-ace quartiles in terms of total payout in a given year and unconstrained otherwise. I measure total payout as the sum of dividends and share repurchases deflated by closing mar ket capitalization using the method described in Boudoukh et al. (2005). Second, I classify firms into Age Constrained if the firm is in the bottom three quartiles of firm age in a given year (and unconstrained otherwise) based on the argument that young firms are more likely to face financing constraints.Age is measured as the difference in years since the first year the firm appears in the CRSP database. Third, I classify firms into Size Constrained if the firm is in the bottom three quartiles of total assets in a given year and unconstrained otherwise. Fourth, I measure Rating Constrained if the firm has long-term debt outstanding (item 9) but does not have public debt rated by S&P (item 280) and unconstrained otherwise. Finally, I construct the KZ indication following the approach in Kaplan and Zingales (1997) and classify a firm as KZ world power Constrained hen a positive relation between financial reporting quality and investment efficiency could just be a reflection of the quality of the managers information set and might not be related to financial reporting quality. However, this alternative hypothesis would not predict the relation between financial reporting quality and investment efficiency to be dependent on financing constraints, cash balances, or the existing information environment. Thus, if such interactions exist, then it would strengthen the result that financial reporting quality per se is associated with investment efficiency. 0 if the firm is in the top three quartiles of the KZ forefinger in a given year and unconstrained otherwise. 13 Untabulated analysis show that the first four classifications are positively correlated (Pearson correlations ranging from 0. 11 to 0. 45) but the KZ Index classification is not correlated with the remaining criteria (Pearson correlations ranging from -0. 01 to 0. 11), consistent with previous research (e. g. , Almeida, Campello, and Weisbach, 2004). 14 Further, all financing constraint proxies are pos itively correlated with Underinvestment (Pearson correlations range from 0. 1 to 0. 14). Table 5 presents the results related to hypothesis 3. All models include the control variables size, cash flow volatility, Q, and industry and year fixed effects as before but the coefficient estimates on these variables are not tabulated for brevity. The estimated coefficients on the control variables are similar to those reported in Table 3. The results are separated for AccrualsQuality and for AccrualsQualityRel. For AccrualsQuality, the estimated coefficients on the main effect (third column labeled Reporting Quality) are all egative with only one statistically significant coefficient. These results indicate that, for a sample of unconstrained firms, the relation between AccrualsQuality and Underinvestment is basically not significant. The estimated coefficients on the interaction terms, however, are negative in four out of five cases and significant in two. Further, the F-test rejects the h ypothesis of no relation between AccrualsQuality and Underinvestment in almost all cases for the sample of financially constrained firms. The only exception is 3 The KZ Index is calculated using the following formula KZ Index = -1. 002 * CashFlow + 0. 283 * Q + 3. 139 * Leverage 39. 368 * Dividends 1. 315 * Cash. For more details see Almeida, Campello, and Weisbach (2004, p. 1790). 14 Principal component analysis on the five financing constraints proxies yields two factors. The first factor explains 40% of the variation and loads on all proxies but the KZ Index. The second factor explains another(prenominal) 20% of the variation in the data and loads on the Payout and the KZ Index measures. 1 when the KZ Index is used as the criteria for financing constraint classification. 15 When AccrualsQualityRel is used as the financial reporting quality proxy, the results are more often than not the alike. In terms of economic significance, increasing AccrualsQuality (AccrualsQualityRel) b y one standard deviation is associated with a reduction in Underinvestment of 0. 26% (0. 16%) for firms classified as Rating Constrained and 0. 08% (0. 06%) for unconstrained firms (compared to 0. 21% (0. 11%) for the full sample as discussed above).Overall, the results present marginal support for hypothesis 3 that the relation between financial reporting quality and Underinvestment is stronger for financing constrained firms. 4. 1. 2. Cash Balances In this section, I investigate hypothesis 4 which predicts that the relation between financial reporting quality and Overinvestment is stronger for firms with large cash balances and free cash flows because these firms are more likely to overspend existing resources (Jensen, 1986). I use two criteria to classify firms based on cash holdings and one proxy for free cash flow.First, I create an indicator variable, richly Cash, coded as 1 if the firm is above the median in the distribution of cash balances deflated by total assets in a giv en year and 0 otherwise. Second, I follow the approach in Opler et al. (1999) who predict cash balances as a function of firms characteristics, and use residuals from this model as a proxy for excess cash. Opler et al. show that firms hold more cash in the presence of growth opportunities and firm uncertainty, and less cash when they are forced to payout interest obligations and have more access to financing (proxied by leverage and size).Thus, I estimate annual regressions of cash balances (item 1) deflated by total 15 The inconsistent result using the KZ Index is consistent with prior work in the finance literature (e. g. , Almeida, Campello, and Weisbach, 2004 Almeida and Campello, 2005) which finds opposite results when this variable is used as a proxy for financing constraints. 22 assets (item 6) on firm size, leverage, Q, and cash flow volatility. Leverage is measured as the sum of the book value of short term (item 34) and long term debt (item 9) deflated by the book value of equity (item 60) and the remaining variables are the same as described above.The explanatory power of the models ranges from 16% in 1986 to 42% in 2003. I create an indicator variable, Excess Cash, coded as 1 if the firm has a positive residual from the model predicting cash balances, and 0 otherwise. Finally, following Richardson (2006), Free Cash Flow is equal to cash flow from operations plus R&D expenses minus depreciation and the predicted investment for the firm as estimated in Table 1. Free Cash Flow is recoded as an indicator variable coded as 1 if the computation of free cash flow is positive and 0 otherwise.Table 6 presents the results related to hypothesis 4. As before, all models include the control variables size, cash flow volatility, Q, and industry and year fixed effects (estimates not tabulated). The first set of results presents estimated coefficients for AccrualsQuality and the second reports coefficients for AccrualsQualityRel. The results show that the estimate d coefficients on the main effect of financial reporting quality are negative but not significant in all six models (three models for AccrualsQuality and three for AccrualsQualityRel).The estimated coefficients on the interaction term, on the other hand, are negative in all cases and significant in three out of six cases, and the F-test rejects the hypothesis of no relation in all cases. In terms of economic significance, increasing AccrualsQuality (AccrualsQualityRel) by one standard deviation is associated with a reduction on Overinvestment of 0. 41% (0. 35%) for firms classified as High Cash and 0. 06% (0. 06%) for firms with low cash (compared to 0. 31% (0. 22%) for the full sample as discussed above).Overall, the results support hypothesis 4 by showing that the 23 relation between financial reporting quality and Overinvestment is stronger for firms with large and excessive cash balances but the results are not statistically significant for firms generating free cash flows. This support the hypothesis that financial reporting quality reduces firm overinvestment by lowering shareholders cost of monitoring managers and thus limiting managers ability to undertake inefficient investment projects. 4. 1. 3.Information Environment In this section, I investigate hypothesis 5 which predicts that the relation between financial reporting quality and investment efficiency is stronger for firms with poor information environments because investors of these firms are more likely to rely on financial accounting information to infer the economic conditions of the firms operations. I use two proxies for the firm information environment the number of analysts following the firm and the bid-ask spread. I use the number of analysts following a firm as a proxy for the amount of publicly available information about the firm. analysts are an important source of information for investors they hack forecasts, reports about individual companies, and stock recommendations. Roulstone (2003) examines the role of analysts in improving market liquidity and finds that analysts provide public information that reduces information asymmetries between firms and market participants. I collect data on analyst following from IBES and measure the number of analysts following the firm as the maximum number of analysts prognostic annual earnings for a firm during the fiscal year t.If the firm is not followed by IBES I assume that the number of analysts following the firm is zero. I consider a firm as Low Analyst if the firm is in the bottom three quartiles in a given year (coded as 1 and 0 otherwise). 24 The second proxy for a firms information environment is the bid-ask spread. See Amihud and Mendelson (1986) and Roulstone (2003) among others for discussions of spreads as a proxy for the information asymmetry between the firm and investors.I collect intraday trade data to compute bid-ask spread from the Trades and Quotes database (TAQ) and from the Institute for the get a line of Security Markets database (ISSM). The TAQ database includes trades and quotes starting in 1993, and the ISSM database contains intraday data for NYSE/AMEX firms from 1983 to 1992 and for NASDAQ firms from 1987 to 1992. I measure quoted bid-ask spread as the ask price minus the bid price divided by the average of the bid and ask prices. The bid-ask spread is averaged across all transactions during the day for each firm, then day by day mean bid-ask spreads are averaged during the month t.Finally I compute bid-ask spread as the average of the monthly bid-ask spreads during the fiscal year t. I consider a firm as High Spread if the firm is in the top three quartiles in a given year (coded as 1 and 0 otherwise). Table 7 presents the results related to hypothesis 5. As before, all models include the control variables (estimates are untabulated). The table is divided into Underinvestment and Overinvestment results. The first set of results presents estimated coefficients for Acc rualsQuality and the second reports coefficients forAccrualsQualityRel. When bid-ask spread is used as the partitioning variable, I find that none of the coefficients on the main effect of financial reporting quality are significant, and three out of four coefficients on the interaction term are significant. The only exception is the coefficient on the interaction between High Spread and AccrualsQualityRel for Underinvestment. Further, in three out of four cases the F-test rejects the hypothesis of no effect of financial reporting quality on investment efficiency 25 for the sample of firms with High Spread.As for Low Analyst, the results on the estimated coefficients on the interaction terms are weaker only one coefficient is statistically negative. Still, in three out of four models the F-test rejects the hypothesis of no relation for the sample of firms with Low Analyst. Overall, the results provide weak support for the hypothesis that the effect of financial reporting on investme nt efficiency is more important when the firm information environment is of low quality. 16 5. Sensitivity analytic thinking In this section I discuss some robustness tests to the analysis presented in the paper.First, I study the sensitivity of the results to inclusion of omitted control variables using firm fixed-effect estimation. The advantage of this approach is that it controls for all time-invariant unobservable firm characteristics. However, since the estimation of AccrualsQuality and AccrualsQualityRel is done using five years of data, the within-firm variation is small, which makes the fixed-effect estimation very conservative. The analysis is done for all firms with at least five, ten, or cardinal years of data in order to increase the within firm variation (sample sizes of 43,739, 33,454, and 24,420 firm-year observations respectively).Untabulated analyses show that the results in Hypotheses 1 and 4 are more often than not robust to the firm fixed-effect estimation. R esults of Hypotheses 2 and 3 are weaker (coefficients are of the same sign but in most cases not significant at conventional levels) and, in the case of Hypothesis 5, the results are similar (weaker) when Underinvestment (Overinvestment) is used as the dependent variable. I also performed tests using a 22 classification based on the firms financial reporting quality and information environment (sorted independently as a low/high).Either high financial reporting quality or high information environment is sufficient to mitigate Underinvestment but only financial reporting quality is sufficient to mitigate Overinvestment, suggesting a substitute relation between financial reporting quality and the firm information environment in improving investment efficiency. 16 26 Second, I investigate the sensitivity of the results to the use of alternative measures of accruals quality such as the non-linear discretionary accruals model in Ball and Shivakumar (2005) and the accrual quality measures true by Wysocki (2006).The key innovation in Wysockis (2006) measures is to remove the suavity effect of accruals in the Dechow and Dichev (2002) model. Results using the Ball and Shivakumar (2005) model are very similar to those reported on the paper. The use of Wysockis measure, on the other hand, leads to similar results for hypotheses 1, 2, and 5 but unimportant results for hypotheses 3 and 4. As discussed in more detail below, these results are not surprising given that Wysockis (2006) measure excludes the smoothness component of accruals, and smoothness is positively associated with investment efficiency.In addition, I investigate the sensitivity of the results to the use of alternative attributes of earnings as proxies for financial reporting quality. Accruals quality represents one dimension of financial reporting quality but other dimensions of earnings have also been used as a proxy for financial reporting quality (Francis et al. , 2004). These attributes of earnings wo uld not necessarily affect investment efficiency in the same way.For instance, one could argue Timeliness and Conservatism are more important in conveying information about bad firms economic states, thus improving Overinvestment but may not be associated with Underinvestment. Nevertheless, it is useful to see how these measures are related and the respective association with investment efficiency (Verdi, 2005). Francis et al. (2004) identify six earnings attributes (other than AccrualsQuality) previously used in accounting research to characterize desirable features of earnings. The six attributes are Persistence, Predictability, Smoothness, 27 ValueRelevance, Timeliness, and Conservatism.I also include a measure of price informativeness as used by Durnev, Morck, and Yeung (2004). When Underinvestment is used as the dependent variable (Hypotheses 1 and 3), I find consistent results using Persistence, Predictability, and Smoothness but insignificant results for the remaining variabl es (with the exception of Informativeness in which the relation is positive and significant, against the prediction). The analysis using Overinvestment (Hypotheses 2 and 4) yield weaker results since only the estimated coefficients on Smoothness and Informativeness are negative and significant in the expected direction.The remaining coefficients are either insignificantly negative or positive in the case of Persistence. Overall the results provide marginal support for the relation between other dimensions of earnings and Underinvestment, and weak support for Overinvestment. The finding that Smoothness is negatively associated with both Underinvestment and Overinvestment explains the weaker results using Wysockis measure of accruals quality given that this measure excludes the smoothness component in the accruals quality measure developed by Dechow and Dichev (2002).In the third sensitivity test, I repeat the analysis using capital expenditures (deflated by average total assets) as a measure of investment in order to make the results more comparable with the extant finance literature. In addition, the investment measure used in the paper includes only cash acquisitions and ignores stock acquisitions which constitute the majority of M&A transactions. Untabulated analyses using CAPEX show that the results in Hypothesis 1, 3, and 5 are similar to those reported. Results in Hypothesis 2 are consistent but weaker when AccrualsQuality is used as the proxy for 28 inancial reporting quality. Finally, results are inconsistent with Hypothesis 4 (estimated coefficients on the interaction terms are mostly insignificant). Finally, I include seemliness (item 204) in the discretionary accruals model. As discussed in Jones (1991), PPE is included in the model to capture the sane level of depreciation, and using the same logic, goodwill would capture the normal level of amortization in accruals. This inclusion is justified because the measure of investment includes acquisitio ns. Goodwill is only available from Compustat starting in 1988 which is why it is excluded in the main tests.In untabulated analysis I find little tint on the discretionary accruals model (the Pearson correlation between discretionary accruals including and excluding goodwill is 0. 99), and the results presented in the paper are unchanged if I restrict the sample to post 1988 and include goodwill in the discretionary accruals model. 6. Summary and conclusion Despite recent claims that financial reporting quality can have economic implications for investment efficiency, there is little evidence on this relation empirically. This paper studies the relation between financial reporting quality and investment efficiency.The analysis is done on a sample of 49,543 firm-year observations during the sample period of 1980 to 2003. I find that proxies for financial reporting quality, namely measures of accruals quality, are negatively associated with both firm underinvestment and overinvestme nt. The relation between financial reporting quality and underinvestment is stronger for firms facing financing constraints, consistent with the argument that financial accounting information can reduce the information asymmetry between the firm and investors, and 29 thus lower the firms cost of raising funds.Likewise, the relation between financial reporting quality and overinvestment is stronger for firms with large cash balances, which suggests that financial reporting quality can reduce the information asymmetry between the principal and the agent and thus lower shareholders cost of monitoring managers and improving project selection. Finally, I find that the relation between financial reporting quality and investment efficiency is stronger for firms with low quality information environments. 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