Innovation and creativity evaluation of Apple Corporation Essay

Economic growth and development of any country depends upon a well-knit financial system. Financial system comprises, a set of sub-systems of financial institutions financial markets, financial instruments and services which help in the formation of capital. Thus a financial system provides a mechanism by which savings are transformed into investments and it can be said that financial system play an significant role in economic growth of the country by mobilizing surplus funds and utilizing them effectively for productive purpose. The financial system is characterized by the presence of integrated, organized and regulated financial markets, and institutions that meet the short term and long term financial needs of both the household and corporate sector. Both financial markets and financial institutions play an important role in the financial system by rendering various financial services to the community. They operate in close combination with each other. Financial System The word â€Å"system†, in the term â€Å"financial system†, implies a set of complex and closely connected or interlined institutions, agents, practices, markets, transactions, claims, and liabilities in the economy. The financial system is concerned about money, credit and finance-the three terms are intimately related yet are somewhat different from each other. Indian financial system consists of financial market, financial instruments and financial intermediation Role/ Functions of Financial System: A financial system performs the following functions: * It serves as a link between savers and investors. It helps in utilizing the mobilized savings of scattered savers in more efficient and effective manner. It channelises flow of saving into productive investment. * It assists in the selection of the projects to be financed and also reviews the performance of such projects periodically. * It provides payment mechanism for exchange of goods and services. * It provides a mechanism for the transfer of resources across geographic boundaries. It provides a   mechanism for managing and controlling the risk involved in mobilizing savings and allocating credit. * It promotes the process of capital formation by bringing together the supply of saving and the demand for investible funds. * It helps in lowering the cost of transaction and increase returns. Reduce cost motives people to save more. * It provides you detailed information to the operators/ players in the market such as individuals, business houses, Governments etc. Components/ Constituents of Indian Financial system: The following are the four main components of Indian Financial system 1. Financial institutions 2. Financial Markets 3. Financial Instruments/Assets/Securities 4. Financial Services. Financial institutions: Financial institutions are the intermediaries who facilitates smooth functioning of the financial system by making investors and borrowers meet. They mobilize savings of the surplus units and allocate them in productive activities promising a better rate of return. Financial institutions also provide services to entities seeking advises on various issues ranging from restructuring to diversification plans. They provide whole range of services to the entities who want to raise funds from the markets elsewhere. Financial institutions act as financial intermediaries because they act as middlemen between savers and borrowers. Were these financial institutions may be of Banking or Non-Banking institutions. Financial Markets: Finance is a prerequisite for modern business and financial institutions play a vital role in economic system. It’s through financial markets the financial system of an economy works. The main functions of financial markets are. To facilitate creation and allocation of credit and liquidity; 2. to serve as intermediaries for mobilization of savings; 3. to assist process of balanced economic growth; 4. to provide financial convenience Financial Instruments Another important constituent of financial system is financial instruments. They represent a claim against the future income and wealth of others. It will be a claim against a person or an institutions, for the payment of the some of the money at a specified future date. Financial Services: Efficiency of emerging financial system largely depends upon the quality and variety of financial services provided by financial intermediaries. The term financial services can be defined as â€Å"activites, benefits and satisfaction connected with sale of money, that offers to users and customers, financial related value†. Pre-reforms Phase Until the early 1990s, the role of the financial system in India was primarily restricted to the function of channeling resources from the surplus to deficit sectors. Whereas the financial system performed this role reasonably well, its operations came to be marked by some serious deficiencies over the years. The banking sector suffered from lack of competition, low capital base, low Productivity and high intermediation cost. After the nationalization of large banks in 1969 and 1980, the Government-owned banks dominated the banking sector. The role of technology was minimal and the quality of service was not given adequate importance. Banks also did not follow proper risk management systems and the prudential standards were weak. All these resulted in poor asset quality and low profitability. Among non-banking financial intermediaries, development finance institutions (DFIs) operated in an over-protected environment with most of the funding coming from assured sources at concessional terms. In the insurance sector, there was little competition. The mutual fund industry also suffered from lack of competition and was dominated for long by one institution, viz. , the Unit Trust of India. Non-banking financial companies (NBFCs) grew rapidly, but there was no regulation of their asset side. Financial markets were characterized by control over pricing of financial assets, barriers to entry, high transaction costs and restrictions on movement of funds/participants between the market segments. This apart from inhibiting the development of the markets also affected their efficiency. Financial Sector Reforms in India It was in this backdrop that wide-ranging financial sector reforms in India were introduced as an integral part of the economic reforms initiated in the early 1990s with a view to improving the macroeconomic performance of the economy. The reforms in the financial sector focused on creating efficient and stable financial institutions and markets. The approach to financial sector reforms in India was one of gradual and non-disruptive progress through a consultative process. The Reserve Bank has been consistently working towards setting an enabling regulatory framework with prompt and effective supervision, development of technological and institutional infrastructure, as well as changing the interface with the market participants through a consultative process. Persistent efforts have been made towards adoption of international benchmarks as appropriate to Indian conditions. While certain changes in the legal infrastructure are yet to be effected, the developments so far have brought the Indian financial system closer to global standards. The reform of the interest regime constitutes an integral part of the financial sector reform. With the onset of financial sector reforms, the interest rate regime has been largely deregulated with a view towards better price discovery and efficient resource allocation. Initially, steps were taken to develop the domestic money market and freeing of the money market rates. The interest rates offered on Government securities were progressively raised so that the Government borrowing could be carried out at market-related rates. In respect of banks, a major effort was undertaken to simplify the administered structure of interest rates. Banks now have sufficient flexibility to decide their deposit and lending rate structures and manage their assets and liabilities accordingly. At present, apart from savings account and NRE deposit on the deposit side and export credit and small loans on the lending side, all other interest rates are deregulated. Indian banking system operated for a long time with high reserve requirements both in the form of Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR). This was a consequence of the high fiscal deficit and a high degree of monetisation of fiscal deficit. The efforts in the recent period have been to lower both the CRR and SLR. The statutory minimum of 25 per cent for SLR has already been reached, and while the Reserve Bank continues to pursue its medium-term objective of reducing the CRR to the statutory minimum level of 3. 0 per cent, the CRR of SCBs is currently placed at 5. 0 per cent of NDTL. As part of the reforms programme, due attention has been given to diversification of ownership leading to greater market accountability and improved efficiency. Initially, there was infusion of capital by the Government in public sector banks, which was followed by expanding the capital base with equity participation by the private investors. This was followed by a reduction in the Government shareholding in public sector banks to 51 per cent. Consequently, the share of the public sector banks in the aggregate assets of the banking sector has come down from 90 per cent in 1991 to around 75 per cent in2004. With a view to enhancing efficiency and productivity through competition, guidelines were laid down for establishment of new banks in the private sector and the foreign banks have been allowed more liberal entry. Since 1993, twelve new private sector banks have been set up. As a major step towards enhancing competition in the banking sector, foreign direct investment in the private sector banks is now allowed up to 74 per cent, subject to conformity with the guidelines issued from time to time. Conclusion: The Indian financial system has undergone structural transformation over the past decade. The financial sector has acquired strength, efficiency and stability by the combined effect of competition, regulatory measures, and policy environment. While competition, consolidation and convergence have been recognized as the key drivers of the banking sector in the coming years

The Usefulness of Accounting Estimates for Predicting Cash Flows

The Usefulness of Accounting Estimates for Predicting Cash Flows and Earnings Baruch Lev* New York University Siyi Li University of Illinois Theodore Sougiannis University of Illinois and ALBA January, 2009 * Contact information: Baruch Lev ([email  protected] nyu. edu), Stern School of Business, New York University, New York, NY 10012.The authors are indebted to the editor and reviewers of the Review of Accounting Studies for suggestions and guidance, and to Louis Chan, Ilia Dichev, John Hand, James Ohlson, Shiva Rajgopal, and Stephen Ryan for helpful comments, as well as to participants of seminars at Athens University of Economics and Business, London Business School, Penn State University, Purdue University, University of Illinois at Urbana-Champaign, University of Texas at Dallas, Washington University in St.Louis, the joint Columbia–NYU Seminar, the 16th Financial Economics and Accounting Conference, the 2006 AAA FARS Midyear Meeting, and the 2008 AAA Annual Meeting. 1 ABSTRACT Estimates and projections are embedded in most financial statement items. These estimates potentially improve the relevance of financial information by providing managers the means to convey to investors forward-looking, inside information (e. g. , on future collections from customers via the bad debt provision).On the other hand, the quality of financial information is compromised by: (i) the increasing difficulty of making reliable forecasts in a fastchanging, often turbulent economy, and (ii) the frequent managerial misuse of estimates to manipulate financial data. Given the ever-increasing prevalence of estimates in accounting data, whether these opposing forces result in an improvement in the quality of financial information or not is among the most fundamental issues in accounting. We examine in this study he contribution of accounting estimates embedded in accruals to the quality of financial information, as reflected by their usefulness in the prediction of enterpr ise cash flows and earnings. Our extensive out-of-sample tests, reflecting both the statistical and economic significance of estimates, indicate that accounting estimates beyond those in working capital items do not improve the prediction of cash flows. Estimates do, however, improve the prediction of next year’s earnings, though not of subsequent years’ earnings. Our economic significance tests corroborate that accounting estimates do not improve cash flow or earnings prediction.We conclude that the usefulness of accounting estimates to investors is limited, and provide suggestions for improving their usefulness. 2 The Usefulness of Accounting Estimates For Predicting Cash Flows and Earnings 1. Introduction Financial statement information, be it balance sheet items such as net property, plant and equipment, goodwill and other intangibles, accounts receivable and inventories, or key income statement figures, such as revenues, pension expense, in-process R&D, or the rec ently expensed employee stock options, is largely based on managerial estimates and projections.The economic condition of the enterprise and the consequences of its operations as portrayed by quarterly and annual financial reports are therefore an intricate and ever changing web of facts and conjectures, where the dividing line between the two is largely unknown to information users. With the current move of accounting standard-setters in the U. S. and abroad toward increased fair-value measurement of assets and liabilities, the role of estimates and projections in financial reports will further increase.We ask in this study: what is the effect of the multitude of managerial estimates embedded in accounting data on the usefulness of financial information? straightforward. The answer is far from On the one hand, estimates/projections are potentially useful to investors because they are the primary means for managers to convey credibly forward-looking proprietary information to invest ors1. Thus, for example, the bad debt provision, if estimated properly, informs investors on expected future cash flows from customers, restructuring charges predict future employee severance payments and plant closing costs, and the capitalized portion of We say â€Å"credibly† primarily because post Sarbanes-Oxley the firm’s CEO and CFO have to certify that â€Å"†¦information contained in the periodic report fairly represents, in all material respects, the financial condition and results of operations of the issuer†¦Ã¢â‚¬  3 software development costs (SFAS 86) informs investors about development projects that passed successfully technological feasibility tests and are accordingly expected to enhance future revenues and earnings. 2 This potential contribution of managerial estimates to investors’ ssessment of future enterprise cash flows underlies the oft-quoted statement by the Financial Accounting Standard Board (FASB) in its Conceptual Framewor k about the superiority of accruals earnings—mostly based on estimates—over the largely fact-based cash flows in predicting future enterprise cash flows: Information about enterprise earnings based on accruals accounting generally provides a better indication of an enterprise’s present and continuing ability to generate favorable cash flows than information limited to the financial aspects of cash receipts and payments (FASB, 1978, p. IX).On the other hand, the contribution of estimates to the usefulness of financial information is counteracted by two major factors: (i) Objective difficulties. In the current volatile and largely unpredictable business environment, due to fast-changing market conditions (deregulation, privatization, emerging economies) and rapid technological changes, it is increasingly difficult for managers to make reliable projections of business events. Consider, for example, the estimated future return on pension assets—a key componen t of the pension expense: This estimate is essentially a prediction of the long-term performance of capital markets.Are managers better predictors of market performance than investors? 3 Or, reflect on the generally large impairment charges of fixed assets and acquired intangibles (including goodwill) mandated by SFAS 121 and SFAS 142: The determination of these 2 Indeed, Aboody and Lev (1998) document a positive association between capitalized software development costs and future earnings. 3 Consider, for example, the 2001 pension footnotes of three financial institutions, Merrill Lynch, Bank of NewYork, and Charles Schwab, which report the following estimates of the expected returns on pension assets: 6. 60%, 10. 50%, and 9. 00%, respectively (Zion, 2002). The wide range of estimates (6. 6%-10. 5%) of the long term performance of capital markets reflects the inherently large uncertainty (unreliability) of the pension expense estimate. 4 charges requires managers to estimate futur e cash flows from tangible and intangible assets. In today’s highly competitive and contested markets the reliability of asset cash flows forecasted over several years is obviously questionable.Accordingly, the accounting estimates and projections underlying financial information introduce a considerable and unknown degree of noise, and perhaps bias to financial information, clearly detracting from their usefulness. 4 (ii) Manipulation. Add to the above objective difficulties in generating reliable estimates the expected and frequently documented susceptibility of accounting estimates to managerial manipulation, and the consequent adverse impact of estimates on the usefulness of financial information becomes apparent.Given that it is very difficult to â€Å"settle up† with manipulators of estimates—even if an estimate turns out ex post to be far off the mark, it is virtually impossible to prove that ex ante the estimate was intentionally manipulated—there are no effective disincentives for managers to manipulate accounting estimates. Indeed, many of the Securities and Exchange Commission (SEC) enforcement cases alleging financial reporting manipulation concern misuse of estimates underlying accruals (e. g. Dechow et al. , 1996). Thus, the impact of estimates underlying accounting measurement and reporting procedures on the usefulness of financial information is an open question, to be examined in this study. The relevance of this examination cannot be overstated. Accounting estimates and projections underlie much of Generally Accepted Accounting Principles (GAAP) and consume 4 A case in point (Wall Street Journal, August 4, 2004, p. c1): â€Å"Investors in Travelers have needed more than that ed umbrella protection from what has been raining on them since the company was spun out from Citigroup in early 2002. Late last month, St. Paul Travelers Cos. , †¦ announced what Morgan Stanley termed a ‘blockbuster reserve chargeà ¢â‚¬â„¢ of $1. 625 billion. The charge was about twice as large as analysts have been expecting. The insurer contends that the charge stems largely from the need to reconcile differing accounting treatments at the two companies [Travelers and its acquisition—St. Paul Cos. ]. It was just a â€Å"reserve valuation adjustment,† the company said†¦.Sadly there seems to be little reason why Travelers’ executives didn’t anticipate problems with St. Paul’s insurance methodologies†¦ Mr. Benet [Travelers’ CFO] said:†¦we recognized early on that there was a difference in some of the methodologies [to estimate reserves] that would have to be addressed. † (emphasis ours). Thus, different accounting methodologies used to estimate the same reserves, all approved by auditors, yield a difference of $1. 625 billion. 5 most of standard-setters’ time and efforts.Just consider the major issues addressed by the FASB in recent yearsâ₠¬â€financial instruments, employee stock options, fixed assets and goodwill impairment, and the valuation of acquired intangibles, to name a few—all require major estimates and forecasts in the process of accounting measurement and reporting. If these and other accounting estimates do not contribute significantly to the usefulness of financial information, the efforts of accounting regulators, and even more importantly, the resources society devotes to the generation of estimates in the process of financial statement preparation and their auditing, are misdirected.Worse yet, if financial information users are led by the estimates-based accounting information to misallocate resources, an additional dead-weight cost is imposed on society. We define and test the usefulness of estimates embedded in accrual earnings in terms of their ability to predict enterprise performance. 5 This predictive use of financial information is central to security analysis and valuation and is also a fundamental premise of the FASB’s Conceptual Framework as indicated by the quote above. Future enterprise performance is mainly reflected by cash flows and earnings.Future cash flows are at the core of asset and liabilities accounting valuation rules. Thus, for example, asset impairment (SFAS 144) is determined by expected cash flows, and the useful lives of acquired intangibles (SFAS 142) are a function of future cash flows. More fundamentally, asset or enterprise cash flows are postulated by economic theory as the major determinants of their value. Given a certain ambiguity about the specific definition of cash flows used by investors, we perform our tests with two widelyused and frequently prescribed cash flow constructs: cash from operations (CFO) and free cash flows (FCF).Much of prior related research focused on CFO. Free cash flows are central to 5 There are, of course, other uses of financial data, such as in contracting arrangements, which are not aimed at predicti ng future enterprise performance. 6 many practitioners’ valuation models (e. g. Brealey and Myers, 2003), and play an important role in research too (e. g. , FCF is a primary variable in the valuation constructs of Feltham and Ohlson, 1995). Cash flow prediction is thus a predominant element of accounting measurements and practitioners’ valuation processes.Despite the prominence of cash flows in economic asset valuation models, there is no denying that many investors and analysts are using financial data to predict earnings. The underlying heuristics are somewhat obscured; perhaps investors predict earnings first, and derive future cash flow estimates from the predicted earnings. In any case, earnings prediction is prevalent in practice, and we therefore also examine the usefulness of accounting estimates for the prediction of earnings, both operating and net income.The focus of this study is on accounting estimates, but many of the estimates underlying financial infor mation are not disclosed in the financial reports. 6 We, therefore, focus in this study on accruals, most of which are based on estimates. In particular, we distinguish between accruals which are largely unaffected by estimates (changes in working capital items, excluding inventory), and accruals which are primarily based on estimates (most non-working capital accruals). This enables us to draw sharper inferences on the effect of estimates on the usefulness of financial information.We also analyze a smaller sample of firms with data on specific estimates which we split into recurring and non-recurring to separate noise (the non-recurring estimates) from information (the recurring estimates). Our empirical analysis is based on a sample of all non-financial Compustat firms with the required data—ranging from roughly 1,500 to 3,200 companies per year—and spanning the 6 For example, General Electric reports in its revenue recognition footnote that various components of rev enues derived from long-term projects are based on the estimated profitability of these projects.GE, however, does not break down total revenues into estimates and â€Å"facts. † 7 period 1988-2005. Our tests are conducted in three stages: (1) In-sample, industry-by-industry, predictions of future enterprise cash flows and earnings, based on: (a) current cash flows only (the benchmark), (b) earnings, and (c) the set of cash flows, the change in working capital (excluding inventory), and various components of accruals based on estimates. Here we follow the regression procedures of Barth, Cram, and Nelson (2001) and find, on more recent data, results which are generally consistent with Barth et al.This is our departure point. (2) Out-of sample firm specific predictions of future cash flows and earnings using the industry specific parameter estimates of the in-sample regressions. The focus of this analysis is on the improvement in the quality of predictions brought about by the addition of estimates (accruals) to the predictors. We thus predict cash flow from operations, free cash flows, net income before extraordinary items, and operating income over various horizons: one year ahead, second year ahead, aggregate two years ahead, and aggregate three years ahead.Our results show that accounting estimates do not improve the prediction of future cash flows (both operating and free cash flows), compared with predictions based on current CFO and the change in working capital excluding inventory. However, accruals do improve next year’s prediction of net and operating income. Notably, cash flow predictions based on current earnings only are significantly inferior to those generated by current CFO, contrary to Kim and Kross (2005). In our small sample analysis, neither recurring nor nonrecurring estimates improved significantly the predictions of either cash flows or earnings.The bottom line—accounting estimates beyond those in working capital items (except inventory) do not improve the prediction of cash flows. 8 (3) Finally, we examine the economic significance of estimates. These tests complement stage two, which is based on the statistical significance of differences in the quality of alternative predictors. Since it is difficult to gauge economic significance from statistical significance, we perform various portfolio tests, where portfolios are constructed from predicted cash flows and earnings based on various predictors, some of which are based on estimates.The abnormal returns on these portfolios, generated by alternative predictors, are our gauge of economic significance. The focus here is on comparing the returns on portfolios constructed from predictions based on current cash flows only (the benchmark), with returns on portfolios constructed from predictions based on current earnings or current cash flows plus changes in working capital and estimates. The results from these tests generally corroborate the out-of-sa mple prediction tests.In practically all our portfolio tests the model that uses current operating cash flows only to predict firm performance generates higher abnormal returns than models which add estimates to the prediction process used for the portfolio formation, though most of these returns are insignificant. Furthermore, the portfolios constructed from predictions based on current cash flows only yield abnormal returns with generally lower standard deviation than the alternative portfolios which include earnings or estimates among the predictors. We caution against sweeping conclusions.We examine the usefulness of accounting estimates in terms of predictive ability with respect to future firm performance. Accounting information is used for other purposes too (contracting, national accounting), for which estimates may be useful. Furthermore, our prediction tests are based on fairly simple models. Users may be using different, more sophisticated models where estimates could pro ve to be useful. 9 Nevertheless, we believe that our findings draw attention to the significant vulnerability of financial information from the multitude of underlying estimates and projections, and to the urgent need for improving the eliability of estimates, on which we comment in the concluding section. The order of discussion is as follows: Section 2 relates our findings to available research, and Section 3 outlines our research design. Section 4 describes our sample, and Section 5 reports our prediction tests. Section 6 informs on a battery of robustness checks, and Section 7 focuses on a subsample with an extended set of accounting estimates. Section 8 reports our portfolio (economic significance) tests, while Section 9 concludes the study. 2.Relation to Available Research Our study interfaces with several active research areas, and below we comment on the relation between our work and various representative studies. We are not familiar with empirical studies which assess the impact of accounting estimates on the informativeness of financial information, but there is a substantial number of studies that examine the contribution of accruals to the prediction of future cash flows and other variables. These studies can be roughly classified into regression-based (in-sample) analyses, and out-of-sample prediction tests.An example of the former is the comprehensive work by Barth, Cram and Nelson (2001), who regress CFO on lagged values of CFO and components of accruals (primarily the changes in accounts receivable, inventories, and accounts payable, as well as depreciation & amortization and other accruals). The authors report (p. 27) that â€Å"each accrual component reflects different information relating to future cash flows†¦[and] is significant with the predicted sign in predicting future cash flows, incremental to current cash flows. Note that 10 predictive ability is assessed in this and similar studies by the significance of the estimated accrua ls’ coefficients and by the improvement inR 2. 7 An interesting extension of the regression strand is provided by Subramanyam and Venkatachalam (2007) who examine the relative explanatory power of earnings and cash flows with respect to an ex post measure of the intrinsic value of equity which uses Ohlson’s (1995) equity valuation framework, based on realized values of earnings and book values.The authors argue that such measurement of equity values avoids the necessity to assume capital market efficiency, as in Dechow’s (1994) study relating accruals to contemporaneous stock returns. Dechow documents a significant association between accruals and stock returns, but the implications of such association for market efficiency are challenged by Sloan’s (1996) findings of strong return reversals (market inefficiency) following extreme accruals.Subramanyam and Venkatachalam (2007) conclude that operating cash flows are more strongly associated with future cash flows than earnings, and that current earnings are more strongly associated with future earnings than cash flows. Regressing the ex-post equity measure on earnings and cash flows indicates that earnings exhibit a higher explanatory power than cash flows. By and large, the in-sample regression studies suggest that accruals are associated with subsequent cash flows and contemporaneous equity values, a finding we largely update and corroborate in the initial stage of our analysis (Section 5. ). However, as is argued in Section 5. 1, in-sample regressions are not prediction tests, and may even provide misleading inferences concerning prediction power. We move, therefore, to out-of-sample tests. An early and innovative out-of-sample prediction test is Finger (1994), who concludes from a sample of 50 companies with long historical data that cash flow is marginally superior to 7 Bowen et al. (1986) and Greenberg et al. (1986) perform similar regression-based, in-sample predictions. 11 ear nings for short-term predictions and performs similar to earnings in long-term cash flow predictions.However, time-series and cross-sectional out-of-sample short-term prediction tests by Lorek and Willinger (1996) and Kim and Kross (2005), respectively, show that current earnings predict more accurately future cash flows than current cash flows do. Thus, a mixed picture emerges from the out-of-sample tests, calling for further research. Note also that most previous studies, in- and out-of-sample, focus on the prediction of cash from operations, despite the fact that free cash flows (a measure included in our tests) is frequently used by analysts and investors.Barth, Beaver, Hand and Landsman (2005) provide an interesting perspective on the usefulness of accruals. Using the valuation framework of Feltham and Ohlson (1995, 1996), they examine the ability to predict equity value of various disaggregations of earnings: aggregate earnings, cash flows and total accruals, as well as cash f lows and four major components of accruals. The prediction methodology is out-of-sample in a particular sense: cross-sectional valuation models are run for each year (equity values regressed on contemporaneous earnings disaggregations), excluding each time a particular sample firm.The equity value of that firm is then predicted from the estimated coefficients of the models. Barth et al. (2005, p. 5) â€Å"†¦find evidence of some reduction in mean prediction errors from disaggregating earnings into cash flows and total accruals, and some additional reduction from disaggregating total accruals into its four major components†¦median prediction errors generally support disaggregation of earnings only into cash flows and total accruals. Overall, these findings vary considerably by industry, and appear to indicate a more consistent success for the cash flows and total accruals model than for the cash flows and disaggregated accruals model. 8 8 Studies such as Bathke et al. (198 9) and Lorek et al. (1993) also perform out-of-sample prediction tests. 12 The substantial body of research on the accruals anomaly initiated by Sloan (1996) is tangentially related to our study.This research establishes that accruals are often misinterpreted by investors: large (small) accruals firms are contemporaneously overvalued (undervalued) in capital markets, and these misvaluations are largely reversed within a couple of years. Notably, much of the accruals anomaly resides in small, thinly traded firms, which are unattractive to most institutional investors (Lev and Nissim, 2006), a fact that contributes significantly to the persistence of this anomaly. It is important to note that our focus in this study is different from the ccruals anomaly research: we do not examine investors’ perceptions of accruals, and the consequences of such perceptions. Rather, we focus on the contribution of accruals and by implication of the embedded estimates to the primary role of finan cial information—assisting users in predicting future enterprise performance. The short-term market inefficiencies highlighted by the accruals anomaly are, of course, worth noting, but they do not inform much on the presumed role of accruals—to improve the prediction of enterprise performance.Stated differently, while extreme accruals are often mispriced contemporaneously by investors, a misperception corrected fairly shortly thereafter, accounting accruals in general, prevalent in every financial report, may still enhance the multi-year prediction of firm performance. It is this fundamental role of accruals and their underlying estimates that is the main theme of our study. The lack of convergence of the extant accruals’ usefulness research makes it very difficult to draw firm conclusions.Some studies are in-sample, while others are out-of-sample; some researchers relate accruals to contemporaneous returns or equity values whereas others to future values. Some predict cash flows while others predict equity values based on models using forecasted or realized residual earnings. Our main contribution to extant research is the focus on the estimates embedded in accruals and the provision of certain closure to the usefulness of 13 accruals issue. We distinguish between accruals which are largely based on facts and those primarily reflecting estimates, to focus on the usefulness of accounting estimates.Our main tests are out-of-sample predictions, replicating what most investors actually do—predict, with no ex post information (as implicitly assumed by in-sample studies), various versions of future earnings and cash flows. The comprehensiveness of our predicted performance measures (two versions of earnings and two of cash flows), and the number of future periods examined (years t+1, t+2, and aggregate next two years and next three years) enables us, we believe, to draw general conclusions about the contribution of estimates to firm perf ormance rediction. Furthermore, our study is the first, we believe, to examine both the statistical and economic performance of accruals-based prediction models. Inferences from statistical significance are sometimes difficult to draw and generalize. Consider, for example, the Barth, Beaver, Hand and Landsman (2005, p. 5) conclusion: â€Å"we find evidence of some reduction in mean prediction errors from disaggregating earnings†¦Ã¢â‚¬  (emphasis ours). While definitely interesting, this conclusion leaves open the important question of: how material is â€Å"some reduction†?Is it, for example, sufficiently large to support the current move of the FASB and IASB toward increased reliance on estimates in financial reports (fair value, stock option expensing, etc. )? Statistical significance coupled with economic significance, as provided below, allows for a more comprehensive evaluation of the evidence. 9 The focus on accounting estimates, the out-ofsample methodology, and the examination of both statistical and economic significance, all bringing certain closure to the research question, is our main contribution. 3. Research Design Examples of studies including economic significance tests are Ou and Penman (1989), Stober (1992), Abarbanell and Bushee (1998), and Piotroski (2000). 14 Our research design consists of three stages: (a) in-sample association tests of cash flows (earnings) regressed on lagged values of these variables and accruals, (b) out-of-sample forecasts of cash flows (earnings) based on these variables and accruals and (c) calculation of hedge future excess returns on portfolios constructed from the out-of-sample predicted cash flows (earnings) in stage (b).We conduct the first stage as a link to and departure from previous research by estimating cross-sectional in-sample regressions as in the Barth, Cram and Nelson (2001) study (BCN hereafter). We use several prediction constructs, primarily to distinguish between accruals largely based on facts and those based on estimates. At one extreme of the accruals disaggregation we classify all the accruals in the â€Å"operations† section of the cash flow statement into working capital changes excluding inventory (? WC*) and the remaining accruals, termed â€Å"estimates† (EST): EARNINGSCash from Working Capital Operations Change excluding (CFO) inventory (? WC*) Estimates (EST) ACCRUALS Working capital items with the exception of inventory, such as accounts payable and short-term marketable securities, are generally not materially impacted by managerial estimates,10 whereas 10 The accounts receivable change, net of the provision, is an exception, since it is subject to an estimate. But this estimate is included in our second accruals component, EST. 15 most of the remaining accruals are in fact pure estimates (e. g. , depreciation and amortization, bad debt provision, in-process R&D).At the other end of the accruals disaggregation we separate out the c hange in inventory (? INV) from the aggregate estimates (EST), given the evidence (e. g. , Thomas and Zhang, 2002) that much of the accruals anomaly resides in inventory, probably due to intentional and unintentional misestimations of this item. We further break out depreciation and amortization (D&A) and deferred taxes (DT) from other estimates because the identification of these items is possible from Compustat data over the entire sample period. This disaggregation is depicted thus: EARNINGS CFO WC* (minus inventory) ?Inventory (? INV) Dep. & Amortization (D&A) ACCRUALS Def. Taxes (DT) Other estimates (EST*) The various components of accruals along with cash from operations (CFO),11 depicted in the two exhibits above are the independent variables in the estimation models underlying our in-sample predictions. We add to these variables the cash flow statement figure of capital expenditures (CAPEX), since the dependent variables in our models are future cash flows or earnings, which are generally affected by current investment (capital expenditures). We believe 11We measure CFO as in Barth et al. (2001), namely net cash flow from operating activities, adjusted for the accrual portion of extraordinary items and discontinued operations. 16 that the addition of capital expenditures to the regressors improves the specification of the insample prediction models, and sharpens our focus on the relative performance of the accruals components, our focus of study. Indeed, the capital expenditures variable is statistically significant in most of our annual in-sample predictions models. 12 3. 1 Prediction tests Our prediction tests take the following general form.We predict two versions of cash flows (cash from operations and free cash flows) and two constructs of earnings (net income before extraordinary items and operating income) in years t+1 and t+2, as well as in aggregate years t+1 & t+2, and t+1 through t+3. To gain insight into the usefulness of estimates in predi cting firm performance, we use five prediction models with increasing disaggregation of accruals (regressors): Model 1: current CFO only—the benchmark model; Model 2: current net income (NI) only; Model 3: current CFO and the change in working capital items excluding inventory (?WC*)—namely, largely fact-based regressors; Model 4: current CFO, the change in working capital items excluding inventory ? WC*, and total remaining accruals, largely based on estimates (EST); and Model 5: current CFO, the change in working capital items excluding inventory ? WC*, the change in inventories (? INV), depreciation & amortization (D&A), the change in deferred taxes (DT), and all other estimates (EST*)—the most disaggregated model. The purpose is to examine whether the gradual addition of components of accruals 12 For robustness, we reran our predictions (reported in Table 3) without capital expenditures, and conclude that one of our inferences changes in the absence of capit al expenditures. 17 estimates to current cash flows (the benchmark) improves the prediction of future cash flows or earnings. Increasing the disaggregation of accruals should, in general, enhance the quality of prediction (from model 1 to 5), since the individual accrual components are allowed to have different effects (multiples) on the predicted values. We examine model 2 because the predictor, earnings, is a summary accounting variable that has been extensively investigated for its information content and has been used in most prior studies (e. . BCN and Kim and Kross 2005). It is important to note that the cross-sectional estimates of the five in-sample prediction models are obtained for 2-digit SIC industry groups. These industry specific estimates make the implicit assumption of constancy of coefficients across firms reasonably tenable. We implement the second stage of our research design by using the industry specific estimated coefficients from each of the above five predict ion models to calculate firm specific predicted values for cash from operations (CFO), free cash flows (FCF), net income (NI) and operating income (OI).We then calculate firm specific prediction errors as the difference between the actual and predicted values of each variable examined. The following examples of the prediction of free cash flows (FCF) will clarify our prediction procedures. A. Prediction of next year’s free cash flows, FCF (t+1) (a) Benchmark Model using CFO only (example for 1990): 1. Estimate cross-sectionally for each 2-digit industry the following regression: FCF (89) = ? + ? CFO(88) + ? . , 2. Predict for each firm in a given 2-digit industry: EFCF (90) = ? + ? CFO(89) using the previously determined industry specific estimated coefficients. . Determine prediction error for each firm in a given 2-digit industry: EFCF (90) . FCF (90) – 18 Here we predict 1990 free cash flows (EFCF(90) from current cash from operations, CFO (89) (and capital expendit ures). First, for each 2-digit industry we regress cross-sectionally free cash flows of 1989 on CFO in 1988, and obtain the estimated coefficients ? and . ? Those coefficients are then used to predict firm specific free cash flows (EFCF) in 1990, using the firm’s actual CFO of 1989. Then, a firm specific prediction error is determined by comparing the firm’s actual 1990 FCF with the predicted one.The same procedure is repeated for every firm and sample year. (b) Restricted Estimates, Model 4 (example for 1990): Estimate cross-sectionally for each 2-digit industry: FCF (89) = ? + ? 1CFO(88) + ? 2? WC * (88) + ? 3EST (88) + ? . The subsequent prediction and error determinations are done as in (a) above. Here we predict 1990 free cash flows from CFO, ? WC* (change in working capital items excluding inventory), EST (estimates), and capital expenditures (not shown in the equation). First, a cross-sectional regression of 1989 free cash flows is run on the 1988 values of CFO, ? WC*, and EST, yielding coefficients ? ? 1, ? 2, and ? 3. Then, firm specific 1990 free cash flows are predicted, using the four industry specific estimated coefficients and the 1989 actual values of CFO, ? WC*, and EST. Finally, these 1990 FCF predictions are compared with the 1990 actual free cash flows to determine the prediction error. The same procedure is repeated for each firm and sample year. (c) Expanded Estimates, Model 5 (example for 1990): Estimate cross-sectionally for each 2-digit industry: FCF (89) = ? + ? 1 CFO(88) + ? 2 ? WC * (88) + ? 3? INV (88) + ? 4 D & A(88) + ? 5 DT (88) + ? 6 EST * (88) + ? . 19The prediction and error determinations are done as in (a) above. Here we predict 1990 free cash flows from 1989 CFO, capital expenditures, and the disaggregated set of estimates (see second diagram at the beginning of this Section). Once more, we run by industry a cross-sectional regression of 1989 FCF on the 1988 values of the independent variables, estimating the ? and ? 1†¦ ? 6 coefficients (and a ? 7 coefficient for 1988 capital expenditures). The firm-specific 1990 free cash flows are predicted using these industry specific coefficients and the actual values of the independent variables in 1989.Computation of the 1990 FCF prediction error follows. B. Prediction of year 2 free cash flows, FCF (t+2) Benchmark Model (example for 1992): 1. Estimate cross-sectionally by 2-digit industry: FCF (90) = ? + ? 1CFO(88) + ? 2. Predict for each firm in a given 2-digit industry: EFCF (92) = ? + ? 1CFO(90) 3. Prediction Error for each firm in a given 2-digit industry: FCF (92) – EFCF (92) This is the prediction of free cash flows in t+2. It follows the earlier procedure with one difference: Here the cross-sectional estimate (first equation) and the forecast (second equation) involve a two-year lag (e. . , FCF in 1990 regressed on CFO of 1988). Same procedure is performed for each firm and sample year. The expanded prediction models incorpora ting disaggregated accruals follow steps (b) and (c), above. We also predict free cash flows for aggregate years t+1 plus t+2, and t+1 through t+3. These predictions are based on the procedures described above, except that aggregated future free cash flows are substituted for single year free cash flows as left-hand variables in the various models. The procedure demonstrated above for FCF is also used to predict cash from operations 20 CFO) in t+1, t+2, and aggregated future years, and to predict earnings in t+1, t+2 and aggregated future years. Two versions of earnings—net income before extraordinary items (NI) and operating income (OI)—are predicted. The various prediction models for earnings are identical to those of free cash flows described above, except that earnings in t+1 and t+2 are substituted for FCF in those models. To summarize, we perform out-of-sample predictions of two versions of cash flows and two versions of earnings from current values of CFO, curre nt values of NI, and CFO plus changes in working capital and various combinations of accruals.To evaluate the quality of the out-of-sample predictions, we compute summary measures of prediction errors derived from the firm- and year-specific estimated errors: the mean and median signed prediction errors indicating the bias in the forecasts, and the mean and median absolute prediction errors which abstract from the sign of the error and indicate forecast accuracy. The firm-specific prediction error in a given year is computed as the realized value of cash flow or earnings minus the predicted cash flow or earnings, divided by average total assets in year t. . 2 Portfolio analysis The third stage of our research design is motivated by Poon and Granger (2003, p. 491) who note: â€Å"Instead of striving to make some statistical inference, [prediction] model performance could be judged on some measures of economic significance. † We interpret their statement as saying that we shoul d not rely solely on the statistical significance of our prediction errors calculated in stage two but should also examine and perhaps even rely more on measures of economic significance.To gauge the economic significance of the contribution of estimates to the usefulness of financial information we perform a series of portfolio tests focusing on the incremental stock returns generated by the estimates-based prediction models. 21 Essentially, we use the out-of-sample predicted values of cash flows (CFO and FCF) and alternatively of earnings (NI and OI), obtained in the second stage of our analysis, to form portfolios.Specifically, for each sample year we rank all firms (across all industries) on predicted firm-specific cash flows or earnings (four rankings, two for cash flows and two for earnings), scaled by average total assets in the end of year t. We then form ten portfolios from each annual ranking and compute risk-adjusted (size & book-to-market adjusted) returns from holding t hese portfolios over several future periods. In assessing the performance of the various predictors (CFO, NI, ? WC*, accruals of estimates), we primarily focus on a zero-investment (hedge) strategy: going long (investing) in the top ortfolio (the 10% of firms with the largest (scaled) predicted cash flows or earnings), and shorting (selling) the bottom portfolio (10% of firms with the lowest predicted cash flows or earnings). The abnormal returns on these zeroinvestment portfolios indicate the economic contribution to investors of using accounting estimates as predictors. Thus, if estimates are useful to investors then portfolios constructed from predictions based on current cash flows and estimates-based accruals should consistently outperform portfolios formed from predictions based on current cash flows only.It should be noted that if markets are efficient concerning the information in accruals—a big if, in light of Sloan (1996)—and if investors select securities us ing procedures similar to our industry-based prediction models specified above, then our subsequent portfolio abnormal returns should be roughly zero. Our purpose in these portfolio tests, however, is not to examine market efficiency, rather to compare the performance of portfolio selection procedures with the estimates-based accruals against similar procedures without accruals (based on past cash flows only).We are thus focusing on the with- and without-accruals comparisons, being agnostic about market efficiency. Stated differently, the comparative abnormal hedge returns across the 22 five prediction models, rather than the statistical significance of those returns, is our focus of analysis. 4. Sample Selection and Descriptive Statistic We obtain accounting data from the 2006 Compustat annual industrial, full coverage, and research files, and use data from the statement of cash flows because Collins and Hribar (2002) suggest that such data are preferable to accruals derived from t he balance sheet.Since reporting a statement of cash flows was mandated by SFAS 95 in 1987, our accounting data span the period 1988 to 2005. 13 In the in-sample regression analysis, each year from 1988 to 2004 is a predictor year (generating the independent variables) while each year from 1989 to 2005 is a predicted year (providing the dependent variables). Thus, 17 in-sample annual regressions are estimated for each industry. Our sample selection procedure is as follows. We start with 75,571 observations with values for NI, CFO, ? WC*, INV, D&A, DT, EST, EST* and CAPEX for the current year, year t, and for NI over a three-year horizon, t-1 to t+1. Firms with all fiscal year ends are included. We control for outliers by following the procedures in Barth et al. (2001). Thus, after eliminating the top and bottom one percentile of current NI and CFO we are left with 73,324 firm-year observations. By excluding observations with market value of equity or sales of less than $10 million, or with share prices below $1, to eliminate economically marginal firms, the number of observations decreases to 51,301.By deleting observations with studentized residuals greater than 3 or less than -3, we are left with 50,288 observations. Since we conduct industry-byindustry in-sample regression analysis we require each industry to have a minimum of 600 observations over the period 1988 to 2004. This criterion reduces the sample to its final size of 13 Valid statement of cash flows data for the year 1987 are available for a relatively small number of firms not enough to do a meaningful industry-by-industry analysis. Thus, we do not use 1987 data. 23 41,124 observations.We obtain stock returns data for the portfolio analysis from the 2006 CRSP files. 14 Table 1 provides summary statistics (variables are scaled by average total assets) and a correlation matrix for out test variables. Panel A shows that depreciation and amortization (D&A) constitutes the bulk of the estimates underl ying accruals (EST): The mean (median) of D&A is 0. 054 (0. 047), close to the mean (median) of EST, 0. 059 (0. 052). The mean of net estimates (EST*), excluding D&A and deferred taxes, is quite large, 0. 019, and is driven mainly by large positive values, as the median value of 0. 04, Q1 of 0. 000 and Q3 of 0. 019 imply. CFO has the lowest while NI has the highest variability (standard deviations of 0. 129 versus 0. 149) among the various earnings and cash flow variables. In panel B all correlations are significant at the 5% level or better. We note the high negative correlations of our estimates variables, EST and EST*, with the income variables, NI and OI. However, the correlations of EST and EST* with both the cash flow variables, CFO and FCF, are much lower; positive for EST and negative for EST*. 4 We repeated all of our analyses with a sample without any outlier removal, namely where we only require non- missing values for the key variables, and at least 600 observations in e ach 2-digit SIC over the sample period 19882004. This sample consists of 65,178 observations and is substantially larger than the sample of 41,124 observations used in the analysis reported below. We find that for many industries the R-squares in the in-sample regressions are higher for the un-truncated data than for the truncated data.The forecast error results are essentially identical to the results from the truncated sample in terms of inferences but the errors are larger. The portfolio abnormal returns results exhibit similar patterns to the results from truncated data. Overall, the un-truncated data yield very similar results to those of the truncated data reported below. 24 5. Empirical Findings: Prediction Tests 5. 1 Stage one: In-sample Regressions Table 2 reports cross-sectional annual regressions, by industry, of CFO (cash from operations) on lagged values of CFO and earnings components (Model 5 in Section 3).The reported coefficient estimates for each industry are the me ans of the yearly coefficients over the 17 year period, 1988 to 2004. The significance of these mean coefficients is based on (nonreported) t-statistics calculated using the mean and standard errors of the 17 yearly coefficients, as in Fama and MacBeth (1973). We report the results for the CFO regressions so that they can be compared to the CFO results reported by BCN. The, in-sample regression results for FCF, NI and OI are very similar to those reported in Table 2. It is evident that in each of the twenty-three ndustries in Table 2 the lagged CFO and ? WC* (change in working capital minus inventory) are highly significant. In the majority of the industries, ? INV (inventory change) is also significant, as is D&A. However, DT (deferred taxes) and EST* (other accruals estimates) are significant for about half of the industries only. These results are quite consistent with BCN’s results reported in their Table 6, Panel B (note that the sum of our DT and EST* variables is the O TH variable in BCN). The fairly large R2s, ranging across industries from 0. 29 to 0. 71, are also consistent with the R2s reported by BCN.Thus, the BCN regression results over the period 1987 to 1996 hold well over our longer period, 1988-2004. Overall, the estimates indicate a strong association between CFO and lagged earnings components, raising expectations about strong out-of-sample performance as well. However, it is important to note that a regression analysis of a given variable on lagged values of that variable along with other data, as frequently conducted in accounting and finance research, is not a conclusive test of predictive ability. As noted in Poon and Granger’s (2003, p. 25 92) survey: â€Å"In all forecast evaluations, it is important to distinguish in-sample and out-ofsample forecasts. In-sample forecast, which is based on parameters estimated using all data in the sample, implicitly assumes parameter estimates are stable through time. In practice, time v ariation of parameter estimates is a critical issue in forecasting. A good forecasting model should be one that can withstand the robustness of an out-of-sample test, a test design that is closer to reality. In our analyses of empirical findings†¦ we focus our attention on studies that implement out-of-sample forecasts. A dramatic example of misplaced inferences drawn on the basis of regression analysis has been recently provided by Goyal and Welch (2007). Their focus is on the prediction of stock market returns based on a variety of variables suggested by prior studies (e. g. dividend yield, earnings-price ratio, book-to-market ratio), using in-sample regression models. After a comprehensive analysis, Goyal and Welch conclude that â€Å"these models have predicted poorly both in-sample and out-of-sample for thirty years now; these models seem unstable, as diagnosed by their out-of-sample predictions nd other statistics; and these models would not have helped an investor with access only to available information to profitably time the market† (Abstract). This important insight motivates our primary analysis which focuses on out-of-sample prediction tests. In the case of predicting stock returns, Goyal and Welch’s concern, in-sample regression results are generally weak and it is therefore not surprising that the out-of-sample predictions of Goyal and Welch perform poorly too.In contrast, in our case of predicting cash flows and earnings, the in-sample regressions (Table 2) perform well, so, whether the more realistic out-of-sample predictions of cash flows and earnings perform equally well is an important empirical issue which we examine next. 26 5. 2 Stage two: Out-of-sample Prediction Tests Table 3 summarizes our main out-of-sample prediction findings. Recall that we predict four key performance indicators: cash from operations (CFO); free cash flows, defined as CFO minus capital expenditures (FCF); net income before extraordinary items (N I); and operating income (OI).There are four prediction horizons: next year, second year ahead, aggregate next two years, and aggregate next three years. Five prediction models are examined (they were discussed and demonstrated in Section 3), where the predictive (independent) variables are: (1) CFO only—the benchmark model, (2) NI only, (3) CFO and the annual change in working capital items excluding inventory (? WC*), (4) CFO plus the change in working capital items excluding inventory (? WC*), as well as the total remaining accruals (EST) which are largely estimates based, including the change in inventory, and (5) our most disaggregated model: CFO, ?WC*, the change in inventories, depreciation and amortization, deferred taxes, and all remaining estimates. Current capital expenditure is included as an additional variable in each of the five models. We report in Table 3 four summary statistics for the prediction errors of our five models: the pooled firm-specific mean absol ute error (MAER) of each of the five models, the pooled mean signed error, or bias (MER), the mean R2s from annual regressions of firm-specific actual values of future cash flows or earnings on the corresponding predicted values, and the average over the years of Theil’s U-statistics. 5 We indicate with an ampersand (&), asterisk (*) or a hash (#) the pooled mean absolute prediction errors (MAER) which are significantly different 15 The reported Theil's U-statistic is the average of the yearly U-statistics. Theil’s U is defined as the square root of ?(actual-forecast)2/? (actual)2. The U statistic can range from zero to one, with zero implying a perfect forecast. Thus, models generating better predictions should have lower U statistics. 27 between Models 1 and 2, Models 1 and 3, and Models 3 and 4, and Models 3 and 5, respectively. 6 We have also computed the sample median signed errors, median absolute errors, and root mean square errors. Results from these indicators are very similar to those reported in Table 3 (we comment in the text on the occasional differences). Below are the main inferences we draw from Table 3, and additional analyses: 1. Prediction of cash flows. Considering the prediction of cash from operations (CFO) and free cash flows (FCF)—left two quadruples of columns in Table 3—we note that the predictions derived from net income only (Model 2) are always significantly inferior to the predictions based on cash from operations only (Model 1).This is true across the four forecast horizons and the four error summary statistics. For example, in predicting one-year-ahead cash from operations (top left panel), the MAER, MER and Theil’s U are lower for Model 1 than for Model 2 (0. 056 vs. 0. 062, 0. 001 vs. 0. 003, and 0. 58 vs. 0. 64, respectively), while the R2 of Model 1 is higher than that of Model 2 (0. 46 vs. 0. 37). The difference in the MAERs is statistically significant, as indicted by the & sign. This pat tern is evident across all eight panels reporting predictions of cash from operations and free cash flows for various horizons.Thus, for one- to three-year forecast horizons, current cash from operations is a better predictor of future cash from operations and free cash flows than current net income. This result is inconsistent with Kim and Kross (2005) findings that in one-year-ahead predictions of cash flows current earnings performs better than current cash flows. 17 16 All the absolute forecast errors (MAER) in Table 3 are statistically significant, with p-values of 0. 01 or better. The majority of the signed errors (MER) are also significant at p-values of 0. 1 or better, and many are statistically significant at least at p-values of 0. 05. The following signed errors are insignificant: Model 1 in forecasting Years 12 CFO, Models 1 and 3 in forecasting Years 1-3 CFO, and Models 2, 4 and 5 in forecasting Years 1-3 OI. 17 It is important to note that Kim and Kross (2005) use bala nce sheet items to calculate cash from operations while we use statement of cash flows data. We were able to replicate the out-of-sample prediction results of Kim and 28Moving on to Model 3, (predictors: CFO and the change in working capital items minus inventory), we note that the CFO and FCF predictions derived from current CFO only (Model 1) under-perform predictions based on current CFO and the change in working capital items excluding inventory, ? WC*. Thus, the mean absolute errors of Model 3 are significantly lower than those of Model 1 in all CFO and FCF panels, except in the FCF panel for the aggregate next three years horizon (bottom FCF panel). 18 The reported R2s and Theil’s U statistics also indicate the under-performance of Model 1 relative to Model 3.For example, in predicting one-yearahead cash from operations (top left panel), the MAER and Theil’s U are lower for Model 3 than for Model 1 (0. 054 vs. 0. 056, and 0. 56 vs. 0. 58, respectively), while the R2 of Model 3 is higher than that of Model 1 (0. 50 vs. 0. 46). Thus, for one- to three-year forecast horizons, the total change in working capital items excluding inventory is incrementally informative over current cash flows. This is relevant for our focus on the usefulness of accounting estimates, because the working capital items, excluding inventory, and with the exception of accounts receivable, are largely free of estimates.We now move to examine the contribution of accounting estimates to cash flow prediction. We do this by comparing the performance of Models 4 and 5 to that of Model 3, where Model 3 becomes now our benchmark given its superior performance up to this point. We note that CFO and FCF predictions derived from Model 4 (based on CFO, the change in working capital items excluding inventory (? WC*), as well as all other accruals including the change in inventory) and Model 5 (based on CFO, ? WC*, the change in inventories, depreciation and amortization, Kross usin g balance sheet items for our sample period.Accordingly, the difference in the results between the two studies is due to the data used. As shown by Collins and Hribar (2002), the cash from operations, and accruals derivation from the statement of cash flows is preferable. 18 Note that despite the very small difference between the MAERs of Models 1 and 3, the mean differences are statistically significant at the 0. 05 level or better (see asterisks). 29 deferred taxes, and all remaining accruals) equally perform or under-perform the predictions from Model 3 (based on CFO and ?WC*). Specifically, the mean absolute errors of Model 3 are significantly lower than or equal to the mean absolute errors of Models 4 and 5 in all the CFO and FCF panels. Furthermore, the reported MERs, R2s and Theil’s U statistics are also consistent with the under-performance of Models 4 and 5 relative to Model 3. For example, in predicting one-year-ahead cash from operations (top left panel), the MAER, MER and Theil’s U for Model 3 are either equal to or lower than for Models 4 and 5 (0. 054 vs. 0. 054 and 0. 055; 0. 001 vs. 0. 02 and 0. 002; and 0. 56 vs. 0. 57 and 0. 57, respectively), while the R2 of Model 3 is equal to or higher than the R2s of Models 4 and 5 (0. 50 vs. 0. 50 and 0. 49). Accordingly, we conclude that for one- to three-year forecast horizons the accounting estimates embedded in accruals, either as a lump sum or disaggregated, do not improve cash flow predictions over current cash from operations and the change in working capital (excluding inventory). 19 Conclusions: Neither total earnings, nor disaggregated estimates-based accruals ystematically improve the prediction of cash flows (CFO or FCF) over the predictions based on current CFO and the change in working capital (excluding inventory). This finding is inconsistent with the FASB’s conceptual stipulation that â€Å"Information about enterprise earnings†¦generally provides a better indi cation of an enterprise’s present and continuing ability to generate favorable cash flows than information limited to the financial aspects of cash receipts and payments† (FASB, 1978, p. IX), though our data start ten years after this statement was issued 2. Prediction of earnings.The two quadruples of columns to the right of Table 3 report prediction performance statistics for net income (NI) and operating income (OI). Here, the 19 These inferences do not change when we examine median signed and absolute prediction errors (available on request). 30 predictions derived from net income (Model 2) significantly outperform those based on cash from operations only (Model 1), for the one-year-ahead forecasts. For example, in predicting next year’s operating income (top right panel), the MAER of Model 2 is significantly lower than that of Model 1 (0. 057 vs. 0. 061).The R2s and Theil’s Us confirm the stronger performance of Model 2, for one-year predictions. Inte restingly, Model 2’s predictions are significantly inferior to Model 1’s in the two-years-ahead and aggregate next three years predictions (second and bottom NI and OI panels). For example, in predicting aggregate three-years-ahead operating income (bottom right panel), the MAER of Model 2 is significantly higher than that of Model 1 (0. 257 vs. 0. 253). Thus, for a one-year-ahead forecast horizon, current net income is a better predictor of future net income and operating income than current cash from operations. 0 Of the five models examined for earnings predictions, the best performer is Model 4— with three variables: CFO, ? WC* (change in working capital excluding inventory), and EST (all other accruals)—for all forecast horizons. Intriguingly, Model 5, where EST is disaggregated to several estimates-based accruals, is somewhat inferior to Model 4. Apparently, predicting from disaggregated accruals results in noisy forecasts. Conclusions: Earnings is a better predictor of near-term earnings than cash flow.Accounting accruals, when disaggregated to working capital items and other accruals, improve further the prediction of operating and net income. No further improvement is achieved from a finer disaggregation of accruals. 6. Robustness Checks 1. How good are our prediction models? 20 Our prediction models are admittedly The median absolute errors are lower for Model 2 than for Model 1 in all NI and OI panels except in the bottom two panels (for the aggregate next two and three years horizons). 31 simple—they obviously abstract from many of the complexities of real life security analysis.Nevertheless, the R2s in Table 3—derived from annual regressions of actual values (future cash flows or earnings) on predicted values—are quite large. Thus, for example, for next year’s predictions (top panels of Table 3), the R2 range is 0. 33-0. 58. As expected, the R2s drop for second year predictions, yet they are still in the reasonable range of 0. 21-0. 37. Thus, despite their simplicity, our prediction models perform reasonably well. 2. Trimming extreme prediction errors. The results of Table 3 are after trimming the top 2% of the absolute forecast errors.We also computed prediction errors after trimming the top and bottom 1% of the forecast errors and without any trimming. The resulting patterns of prediction errors (not reported) are in both cases very similar to those of Table 3. As expected, Table 3 trimmed errors are substantially smaller than the non-trimmed errors, the R2s are larger, and the Theil’s U statistics are lower, yet our conclusions regarding the relative performance of the five models equally apply to the non-trimmed errors. substantially our inferences. 3. Classification by size of accruals.Since the estimates we examine are components of total accruals, we classified the sample firms into three groups, by the size of accruals, to check whether accruals size affe cts our findings. Specifically, for each sample year we ranked the firms by the size of total accruals (scaled by total assets), and then formed three groups: the top 25% of firms (high accruals), the middle 50% (medium accruals), and the bottom 25% (low accruals). We then generated cash flow and earnings predictions for each of the three accruals g

Johann Kilian and the Wends: the Foundation of Lutheranism in Texas

Through this course (LCMS History) and others, I have heard the story of German Lutherans who left Europe and settled near Saint Louis, Missouri, under the leadership of Martin Stephan and (soon thereafter) C. F. W. Walther. This story seems quite familiar to many of my seminary classmates who originate from the Midwest and nearby regions. As a nearly lifelong resident of Texas, I had never before heard much of that story. The Lutherans in my communities generally have a different history – one involving a people group known as the Wends. These histories have merged at some point between their beginnings and the present; both communities are currently at home in the Lutheran Church – Missouri Synod and share in fellowship and confession. Naturally several questions arise for further investigation. Who are the Wendish people? Who led them to America? Why did they come to America? What is their religious history? How did they integrate with the Missouri Synod? Why are they a valuable people group in our church body? Answering each of these essential questions necessitates a fairly broad scope, though certainly a coherent inspection. To address the topics at hand, I will present first a brief overview of the European climate during the time that the Wends left Germany as well as an account of their migration. Second, I will offer a concise biography of Johann Kilian, the early leader of the Texan Wendish community. Third, I will describe historically significant moments of interaction between the Lutheran Wends and the LCMS (and its predecessors and associated church bodies) and illustrate how these events contributed to the Wendish assimilation into the LCMS. Each of these components serves the purpose of presenting the Wendish community as a significant component of American Lutheranism, and one with an enduring impact on the LCMS church body. The necessary information is gathered mostly through printed and published texts on the subject at hand. It is also shaped by personal memory of this topic through experiences with members of the Wendish community as well as its associated institutions. Content in support of my purpose is present in these following paragraphs. European Pressures and the Wendish Migration In the early 19th century, the Wends were culturally and politically suppressed by their dominant political leaders. The land of the Wendish people, Lusatia, was intentionally divided between Saxon and Prussian rule. This virtually eliminated any possibility for national independence; the Wendish language became increasingly distinct between the nationalities (Caldwell1961). Also, they were economically dependent on German landholders and had little opportunity for social success. Those who sought better standards of living left their farmland for cities such as Bautzen and generally assimilated into the German culture in the process. A very small group of the Wends was training for the clergy in Prague and in Leipzig; as these students encountered political theories and topics of higher education they developed into the intelligentsia of the Wendish community. These educated people served as the leadership that the Wends needed to rise out of their lowly confinement (Grider 1982). Religious difficulties also characterized this time period. The Wends experienced great pressure to participate in Prussian Unionism, instituted by the Calvinist-leaning King of Prussia, Frederick William III (Nielsen 1989). Since the time of the Reformation, the majority of the Wendish people had been Protestants. This switch to Lutheranism distinguished the Wends religiously from the mainly Catholic Czechs and Poles with whom they shared many cultural and linguistic similarities (Grider 1982). As a people they were very interested in maintaining a definite and self-defined identity, distinct from surrounding people groups. This mandate of Prussian Unionism was an affront to this endeavor. Many spoke against this offensive consolidation, including Johann Kilian who was at that time a young student of theology at the University of Leipzig. In this context of religious pressure, a group of deeply conservative Wends began worshipping together in a private house-church. By 1845 they had established a small congregation with a building devoted as their worship space. After nine more years enduring religious antagonism, a core group of lay leaders drafted, in 1854, a constitution to govern the migration of the whole congregation to a new land with religious freedom. At this time, the congregation issued a call to Kilian, requesting that he shepherd them on their journey and minister to them in their future situation (Grider 1982). Kilian, eager to employ his missionary education, accepted their call. Additionally â€Å"agricultural disasters† during the mid-1800s spurred the Wends into discussions of leaving Germany/Prussia and seeking a new land for a new opportunity. Some impoverished German farmers, with whom the Wends were amiable, had already immigrated to America and Australia. Their joyous letters to the homeland were published by the German press and encouraged these hopeful Wendish immigrants. Of the Wends immigrating to Texas, the â€Å"first trickle of Wendish adventurers† (Grider 1982) arrived around 1850. A group of 35 set sail for America in 1853 but wrecked off the shore of Cuba. While stranded on the island, many learned how to roll cigars to supplement their income during their stranded time. Eventually compassionate German organizations in Havana, Cuba, and New Orleans funded and arranged for their transport to Galveston. One year after this small group’s arrival in Galveston, the â€Å"highly educated and forceful† (Grider 1982) Pastor Johann Kilian led a boatload of 600 of his congregants, pious and devout Wendish Lutherans, from Germany to Galveston. They made their voyage on the Ben Nevis, still considered within the Texan Wendish community as a counterpart of the English Pilgrims’ Mayflower (Grider 1982). Kilian was the only professional, educated man in the congregation; all the others were farmers and craftsmen. Yet the people possessed between them an adequate variety of skills to guarantee a self-sufficient colony. This group established the town of Serbin, which continues to be a place of cultural influence in central Texas. The Life of Johann Kilian The only son of Wendish farmers in Upper Lusatia, Johann Kilian was born on March 22, 1811. Two years later his mother, Maria Kilian nee Mattig, and his infant sister died. His grandmother helped to care for him for the next three years at which time his father, Peter Kilian, remarried. Soon thereafter his grandmother died. In 1821, while Kilian was ten years old, his father also died. Following the death of his parents, he inherited enough money to fund his education at the gymnasium (high school) in the chief Wendish city of Beutzen (Caldwell 1961). Johann found himself under the care of his uncle who leased the child’s inherited property and used the income to support the boy’s schooling. One can only imagine what sort of psychological impact these deaths must have had on young Kilian. According to Nielsen (2003), â€Å"nothing in his writings indicate any anxiety during these early years. † It is likely that during his youth with his extended family he began to learn about Christian living and developed a deep hope in the resurrection promise. Kilian spent more than four years at the Gymnasium in Beutzen. There he was educated in Hebrew, Greek, Latin, French, and German; Wendish was only used in private and in his earlier years in grade school. Kilian and some of his classmates organized a Wendish club on campus to facilitate informal conversation in their mother tongue (Nielsen 2003). He was quite successful in Beutzen and soon enrolled at the University of Leipzig to study theology, where he once again encountered a Wendish circle. This organization propagated a rising attitude of Wendish nationalism, especially in contrast with German culture. Rather than associating with this divisive group, Kilian joined a German club whose central goal was â€Å"the preservation of pure Lutheran teaching† (Nielsen 2003). This decision seems to have been more of a growing attraction toward orthodox Lutheranism than a rejection of Wendish culture. It also seems that in this association He was taking a stand in contrast to the majority of the faculty of Leipzig who were heavily influenced by rationalism at the time. In 1835, Kilian obtained his license to preach and was assigned to an assisting position at Hochkirch, a large parish which included several surrounding viliages. The following year, he travelled to Switzerland and attended a small mission school in Basel, remembering his childhood vow to become a foreign missionary. Back in eastern Germany, his uncle (different from the one who had helped to raise him as a child) was the pastor of a Lutheran church in Kotitz; he died while Killian was away at school. Then in 1837 Kilian returned to Kotitz and received his full ordination. This enabled him to assume the senior pastorate there (Nielsen 2003). Most of the Wends in his congregation could not understand German, so Kilian undertook several translation projects for the benefit of his flock. He published a book containing twenty eight hymns in Wendish; some were translations of German hymns and a few were his original pieces. These musical arrangments were very well received by both his own congregation and numerous other Lutheran Wendish assemblies. He continued to translate many German songs and eventually produced more than one hundred of his own hymns (Nielsen 2003). These hymns emphasize the centrality of Jesus in Christian living and often contain declarations of profound hope. Several of his songs and poems are contained in a collection edited by David Zersen (2010). Included, here, is one verse from Kilian’s hymn, â€Å"Blessed Land†: Jesus leads his saints on earth: Witnesses are we! Sadness, trials, suffering? Faithful we will be! Christ is our life. There’s a kingdom waiting there; No more sorrow, no more care. Christ is our life. In addition to his musical translation efforts, Kilian translated the Lutheran Confessions into Wendish. He began with Luther’s Small Catechism in the late 1840s and finished the remainder of the confessions in 1854. Other prominent Wendish intellectuals frequently frowned upon his efforts, insisting that importing German religious thinking would contaminate the Wendish culture. They preferred to advance hopeful nationalism for the Wends and showed little priority for proper doctrinal adherence. Kilian disagreed with their attitude and continued â€Å"translating religious works into the mother tongue to enrich the language and simultaneously nourish religious life† (Nielsen 2003). These exercises in translation eventually led to a reasonable popularity for Kilian, especially among likeminded Wendish Lutherans. One such congregation of people at Weigersdorf was becoming increasingly troubled by the pressures of Prussian Unionism. In 1844 they issued a call to Kilian with hopes that he would agree to lead them in their migration away from their oppressive setting. Kilian accepted the call on two conditions. He required that the congregation would pledge faithfulness to pure Lutheran doctrine and also that the congregation acquire an immigration permit from the appropriate Prussian authorities. (Nielsen 2003). Kilian over the next several years served this as well as other parishes (especially one in Klitten) which shared in the Lutheran confession. During that time, he married Maria Groschel, with whom he had four children while they remained in Europe – only one of which survived into maturity (Nielsen 2003). Religious pressures continued to build until in 1854, a group of 600 Wendish Lutherans (under Kilian’s shepherding) began the process of relocating to Texas. While Kilian is often credited with leadership of this venture, such wording is misleading at best. He did not object to the exodus from Europe, but the instigation of the process was from the laypeople. Kilian’s role was to accompany them as their pastor (Nielsen 2003). The journey was characterized by illness, danger, and loss of life. Kilian was heavily relied upon for his pastoral care at several points on the journey. In one instance while at sea, several people were suffering from sea-sickness below the deck. The captain of the Ben Nevis (the ship that carried them across the Atlantic) instructed that the migrants come up for fresh air to improve their health. Some did not cooperate with the captain’s orders. Kilian gently persuaded those who remained below deck to come up. While this shows the authority the Wends saw in Kilian, it also caused resentment from some because he was exceeding his religious responsibilities. The voyagers eventually crossed the Atlantic and arrived at the port of Galveston. They then travelled to central Texas and established the colony of Serbin. For the next three decades, Kilian served the Texan Wends as their pastor and endeavored to connect them with likeminded believers in their new land (Nielsen 2003). Eventually he was able to forge a confessional relationship with the Missouri Lutherans and connect his people to a larger church body. After Kilian’s death on September 12, 1884, many tributes were written about him. These included a handful of lengthy pieces n Der Luteraner, the official periodical of the synod (Martens 2011). The Texan Road to Missouri â€Å"Religious isolation was not part of his tradition† (Nielsen 2003). In Texas, Kilian became a friend of Caspar Braun, a Lutheran who had already been in Texas for about five years. Braun had formed the Evangelical Lutheran Synod in Texas and served as its first president. While Kilian certainly en joyed his friendship with Braun, he was hesitant to join this Texas Synod because he considered that it shared too many similarities with the Prussian Union which he had left. He also lamented the lack of enriching liturgy in its churches (Nielsen 2003). Rather he became drawn to the German Evangelical Lutheran Synod of Missouri, Ohio, and Other States. Geography was certainly a hindrance to fellowship with this church body, he considered it far less of a barrier than theological incompatibility. In his effort to establish fellowship with the Missouri Synod, he wrote a letter introducing himself and the Wends to C. F. W. Walther, who was also born in 1811. Though Kilian and Walther did attend the University of Leipzig simultaneously in 1832, there is no indication in any of their correspondence that they knew each other before they were in America. Kilian had learned of Walther chiefly through his writings. He owned a copy of Walther’s Stimme der Kirche in der Frage von Kirche und Amt. Kilian agreed with Walther’s position on church polity which â€Å"empowered the voters’ assembly as the supreme authority and diminished the power of the ecclesiastical leaders† (Nielsen 2003). His congregation joined the Missouri Synod in 1866 with Kilian as the first Missouri Synod pastor in the state of Texas. Under Kilian’s pastoral leadership, the Wends became fervent supporters of synodical education and eventually began to issue calls to American-trained pastors. By 1877 nearly a dozen pastors were serving Missouri Synod congregations in Texas and the group gained recognition as the Texas Conference of the Western District. Only a couple years later, the Southern District was organized, ranging from El Paso, Texas, to San Augustine, Florida. Then in 1903, the Texas District of the LCMS was formed; it contained 23 congregations, nearly 40 pastors, and 11 school teachers. Concluding Remarks The Texas District of the LCMS owes its genesis to the migration of the Wends and the pastoral leadership of Johann Kilian. It is now one of the largest districts in the LCMS and has produced more synodical presidents (Behnken, Harms, and Kieschnick) than any other district. The Wendish culture and religious experiences have shaped and continue to shape the theological thinking of Texas Lutherans. It is especially for these reasons that the Wends are a valuable people group in the Lutheran Church – Missouri Synod.

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