盈余质量分析的评价指标体系外文翻译

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盈余质量分析的评价指标体系外文翻译

原文：Analysis of earnings quality evaluation index system Earnings quality is an important aspect of evaluating an entity's financial health, yet investors, creditors, and other financial statement users often overlook it. Earnings quality refers to the ability of reported earnings to reflect the company's true earnings, as well as the usefulness of reported earnings to predict future earnings. Earnings quality also refers to the stability, persistence, and lack of variability in reported earnings. The evaluation of earnings is often difficult, because companies highlight a variety of earnings figures: revenues, operating earnings, net income, and pro forma earnings. In addition, companies often calculate these figures differently. The income statement alone is not useful in predicting future earnings.The SEC and the investing public are demanding greater assurance about the quality of earnings. Analysts need a more suitable basis for earnings estimates. Credit rating agencies are under increased scrutiny of their ratings by the SEC. Such comfort level and information is not provided in the audit report or the financial statements. Only 27% of finance executives recently surveyed by CFO “feel 'very confident' about the quality and completeness of information available about public companies” [“It's Better (and Worse) Than You Think,” by D. Dupree May 3, 2004].There are a variety of definitions and models for assessing earnings quality. The authors have proposed a uniform, independent definition of quality of earnings that allows for the development of an Earnings Quality Assessment (EQA) model. The proposed EQA model evaluates the degree to which a company's income statement reports its true earnings and the extent to which it can predict and anticipate future earnings.Earnings Quality DefinedA variety of earnings-quality definitions exist. Teats [“Quality of Earnings: An Introduction to the Issues in Accounting Education,” Issues in Accounting Education, 17 (4), 2002] states that “some consider quality of earnings to encompass the underlying economic performance of a firm, as well as the accounting standards that report on that underlying phenomenon; others consider quality of earnings to refer only to how well accounting earnings convey information about the underlying phenomenon.” Pratt defines earnings quality as “the extent to which net income reported on the income statement differs from true earnings” [in F. Hodge, “Investors'Perceptions of Earnings Quality, Auditor Independence, and the Usefulness of Audited Financial Information,” Accounting Horizons 17 (Supplement), 2003]. Penman [“The Quality of Financial Statements: Perspectives from the Recent Stock Market Bubble,” Accounting Horizons 17 (Supplement), 2003] indicates that quality of earnings is based on the quality of forward earnings as well as current reported earnings. Shipper and Vincent [“Earnings Quality,” Accounting Horizons 17 (Supplement), 2003] define earnings quality as “the extent to which reported earnings faithfully represent Hicks Ian income,” which includes “the change in net economic assets other than from transactions with owners.”Using various definitions of earnings quality, researchers and analysts have developed several models. The Sidebar summarizes eight models for measuring earnings quality. The models are used for very narrow, specific purposes. While the criteria used in these definitions and models overlap, none provide a comprehensive view of earnings quality. For example, the primary purpose of the Center for Financial Research and Analysis (CRFA)'s model is to uncover methods of earnings manipulation. Of the eight models discussed, only the Lev-Thiagarajan and Empirical Research Partners models have been empirically tested for evidence of usefulness related to quality of earnings. Lev and Thiagarajan's findings confirm that their fundamental (earnings) quality score correlates to earnings persistence and growth, and that subsequent growth is higher in high quality–scoring groups. Empirical Research Partners' model is based in part on methodology developed and tested by Potosi, whose findings indicate a positive relationship between scores based on the model and future profitability.The following summarizes the criteria considered in each of the eight models for measuring earnings quality.Center for Financial Research and Analysis: Four criteria to uncover methods used to manipulate earnings. Report includes financial summary, accounting policy analysis, discussion of areas of concernEmpirical Research Partners: Three components: net working-capital growth rate, net concurrent assets, deferred taxes; incremental earnings and free cash flow production relative to each new dollar of revenue or book value; and nine financial indicators, put together for a single gauge of fundamentals. Items viewed favorably:positive return on assets and operating cash flow; increases in return on assets, current ratio, gross margin, asset turnover; operating cash flow that exceeds net income.Items viewed unfavorably: increases in long-term debt-to-assets; presence of equity offerings. Each indicator given a 1 if favorable, an O if not; scores aggregated on an O to 9 scales.Ford Equity Research: Earnings variability is minimum standard error of earnings for past eight years, fitted to an exponential curve. Growth persistence considers earnings growth consistency over 10 years; projected earnings growth rate is applied to normal earnings to derive long-term value. Operating earnings calculated by excluding unusual items, such as restructuring charges and asset write-downs; earnings trend analysis done on this adjusted figure. Repurchases of an entity's own shares are analyzed to determine if results are favorable.Lev-Thiagarajan: Each fundamental is assigned a value of 1 for positive signal, O for negative signal. Each of 12 factors is equally weighted to develop aggregate fundamental score. Negative signals include: decrease in gross margins disproportionate to sales; disproportionate (versus industry) decreases in capital expenditures and R&D; increases in S&A expenses disproportionate to sales; and unusual decreases in effective tax rate. Inventory and accounts receivable signals measure percent change in each (individually) minus percent change in sales; inventory increases exceeding cost of sales increases and disproportionate increases in receivables to sales are considered negative. Unusual changes in percent change of provision for doubtful receivables, relative to percent change in gross receivables, are also viewed negatively. Percent change in sales minus percent change in order backlog is considered an indication of future performance.Merrill Lynch (David Hawkins) (see earnings quality: the establishment of a real 360 View, 2002). Higher total capital ratio (pre-tax operating return on total capital) returns a higher quality of earnings equivalent. Liquidity ratio above 1.0 (net income figure how close it is to achieve positive cash) that the higher quality of earnings. Re-investment in productive assets ratio above 1.0 (committed to maintaining the fixed asset investment) that a higher quality of earnings. The ratio ofeffective tax rate for all companies meet or exceed the average level (dependence on low-tax report) that the higher quality of earnings. Model also believes that long-term credit rating and Standard & Poor's S & P earnings and dividend growth and stability of rank-based, over the past 10 years.Raymond James & Partners (Michael Krensavage) (also see the earnings quality monitoring, 2003.) 1 (worst) to 10 (best) rating as a benchmark of 10 exclusive distributions; weighted average rating in the combined to determine the earnings quality scores. Low earnings quality indicators: increase in receivables; earnings growth due to reduced tax rates, interest capitalization, high frequency / time scale of the project. In a recent major acquisition made during the punishment. Practice of conservative pension fund management and improve the R & D budget faster than revenue in return. Cash flow growth and the related net income and gross margin of profit a positive impact on quality improvement.Standard & Poor's core earnings (see also the technical core earnings Bulletin, October 2002). Attempts to give a more accurate performance of the existing business of the real. Core benefits include: Employee stock option expenses; restructuring charges from ongoing operations; offset the depreciation of business assets or deferred pension costs; purchased research and development costs; merger / acquisition costs; and unrealized gains and losses on hedging. Excluded items: Goodwill impairment charges, pension income; litigation or insurance settlements, from the sale of assets (loss) and the provisions of the previous year's expenses and the reversal.UBS (David Bianca) (also see S & P 500 accounting information quality control, 2003.) Comparison of GAAP operating income; the difference between the net one time standard. Employee stock option expenses charged to operating profit. Assuming the market value of return on pension assets to adjust interest rates or the discount rate times. Health care costs adjusted for inflation, if the report is 300 basis points more than the S & P 500 companies are expected weighted average. Joint Oil Data Initiative is AUTHOR_AFFILIATION Bell ovary, CPA, is a Marquette University, Milwaukee, Wisconsin, Don E. Incoming, CPA, graduate students, professors, and Donald E &Beverly Flynn is Chairman of the holder at Marquette University. Michael D • Akers, CPA, CMA, CFE, CIA, CBM, is a Professor and Head of the Department.Of the 51 criteria/measurements used in the eight models, only eight (acquisitions; cash flow from operations/net income; employee stock options; operating earnings; pension fund expenses; R&D spending; share buyback/issuance; and tax-rate percentage) are common to two models, and only two (gross margin and one-time items) overlap in three models.The first step, then, is to develop a standard definition of earnings quality. One of the objectives of Fast’s Conceptual Framework is to assist investors in making investment decisions, which includes predicting future earnings. The Conceptual Framework refers not only to the reliability (or truthfulness) of financial statements, but also to the relevance and predictive ability of information presented in financial statements. The authors' definition of quality of earnings draws from Pratt's and Penman's definitions. The authors define earnings quality as the ability of reported earnings to reflect the company's true earnings and to help predict future earnings. They consider earnings stability, persistence, and lack of variability to be key. As Beaver indicates: “current earnings are useful for predicting future earnings … [and] future earnings are an indicator of future dividend-paying ability” (in M. Bauman, “A Review of Fundamental Analysis Research in Accounting,” Journal of Accounting Literature 15, 1996).Earnings Quality Assessment (EQA)The authors propose an Earnings Quality Assessment (EQA) that provides an independent measure of the quality of a company's reported earnings. The EQA consists of a model that uses 20 criteria that impact earnings quality (see Exhibit 2 ), applied as a “rolling evaluation” of all p eriods presented in the financial statements. The EQA is more comprehensive than the eight models presented, considering revenue and expense items, as well as one-time items, accounting changes, acquisitions, and discontinued operations. The model also assesses the stability, or lack thereof, of a company, which leads to a more complete understanding of its future earnings potential.The criteria were drawn from the eight models discussed, including the 10 criteria overlapping two or more models. The EQA evaluator assigns a point value ranging from 1 to 5 for each of the 20 criteria, with a possible total of 100 points. Ascore of 1 indicates a negative effect on earnings quality, and a score of 5 indicates a very positive effect on earnings quality. EQA scores, then, can range from 20 to 100. Similar to the grading methods for bond ratings, grades are assigned based on the following scale: 85–100 points = A, 69–84 points = AB, 53–68 points = B, 35–51 points = BC and 20–34 points = C. While the EQA evaluator needs to use professional judgment in assigning scores to each of the criteria, the guidelines in Exhibit 2 are recommended.The Application of EQATo illustrate the process of applying the EQA, the authors chose two large pharmaceutical companies, Merck and with. Each of the authors independently applied the EQA to Merck's and Width’s 2003 financial statements, and then met to discuss their results. Based upon each individual assessment and the subsequent discussion, they reached an agreed-upon score, presented in Exhibit 3 .This process is similar to what an engagement team would go through. Each member would complete the EQA independently, and then the group would meet as a whole to discuss the assessment and reach a conclusion. This process allows for varying levels of experience, and takes into account each team member's perspective based on exposure to various areas of the company. The audit team's discussion is also helpful when one member finds an item that another might not have, which may explain variances in the scores assigned by each individual.For the illustration, the EQA was based solely on data provided in the financial statements. The authors found a high level of agreement on the quality of earnings measures, and there was little variation in the scores for both companies. One would expect even less variation when a group more intimately exposed to an organization, such as the audit engagement team, completes the EQA. The consistency provided by use of the EQA model would enhance the comfort level of users of the financial statements and the EQA.Need for Further DevelopmentThere is significant need for the development of a uniform definition and a consistent model to measure earnings quality. This article provides such a definition, positing that the quality of earnings includes the ability of reported earnings to reflect the company's true earnings, as well as the usefulness of reported earnings to predict future earnings. The authors propose an Earnings Quality Assessment (EQA) model that is consistent with this definition. The EQA recognizes many of the fragilities ofGAAP, and takes into account factors that are expected to affect future earnings but that are not explicitly disclosed in the financial statements.The authors propose that auditors conduct the EQA and issue a public report. Auditors' EQA reports will provide higher-quality information to financial statement users and meet the SEC's demand for greater assurance about the reliability of earnings figures.Source: MichaelD.Akers, 2005. “Analysis of earnings quality evaluation index system” the CPA journal. November.pp.199.译文：盈余质量分析的评价指标体系盈余质量是债权人评价一个实体的财务状况的重要方面，但投资者和其他财务报表使用者往往忽略它。

清算企业的盈余管理【外文翻译】

外文文献翻译原文：Earnings Management Using Discontinued Operations When making firm-valuation decisions, investors place a higher value on items of income expected to be persistent in the future. Presumably to aid users’ valuation decisions, GAAP generally requires that material nonrecurring items be separately disclosed in the financial statements. However, the separation of net income into recurring and nonrecurring components also gives managers the opportunity to mislead investors, by misclassifying income and expense items. For example, a manager wanting to increase firm valuation can misclassify recurring expenses as nonrecurring, misleading investors as to the persistence of the income increase. In the accounting literature, this type of earnings management is called classification shifting.McVay _2006_ discusses two reasons why classification shifting can be a relatively low-cost method for managing earnings. First, unlike accrual management or real activity manipulation, there is no “settling-up” in the f uture for past earnings management. If a manager decides to increase earnings using income-increasing accruals, then, at some point, these accruals must reverse. The reversal of these accruals reduces future reported earnings. If a manager decides to increase earnings by managing real activities, such as reducing research and development expenditures, then this likely leads to fewer income-producing projects and reduced earnings in the future. In contrast, classification shifting involves simply reporting recurring expenses in a nonrecurring classification on the income statement, having no implications for future earnings. Second,because classification shifting does not change net income, it is potentially subject to less scrutiny by auditors and regulators than forms of earnings management that change net income.Discontinued operations represent the income and cash flows of a portion of a company that has been _or will be_ discontinued from the continuing operations of the company. Under SFAS No. 144, the gain or loss for discontinued operations is comprised of three amounts. The first amount is the operating income or loss from theoperations of the component being discontinued for the entire year in which the decision to discontinue is made. The second amount is the gain or loss from disposal, which is the net amount realized over the carrying value of net assets of the component. The third amount is an impairment loss on the “assets held for sale” if the component is not disposed in the same year as the decision to discontinue.5 The results of discontinued operations are reported on the income statement as a separate item _net of tax effects_ after income from continuing operations. The “below the line” reporting of disposals as discontinued operations is likely perceived to be desirable for investors, because it communicates to them the results of a firm’s operations on a “with and without” basis. However, it could be detrimental to investors if managers use their reporting discretion to manage earnings when reporting discontinued operations. For example, managers could allocate normal operating expenses to discontinued operations in order to report increased income from continuing operations. Managers have discretion over the allocation of joint costs between continuing and discontinued operations, and this information is not normally available to external investors. It is doubtful that auditors would detect this manipulation during their analytical review because the company’s financial ratios would be sim ilar to what they were previously.The ability of asset disposals to be classified as discontinued operations has varied throughout time. Under APB Opinion No. 30 _APB 1973_, only dispositions of business “segments” were able to qualify for reporting as discontinued operations. In general, business segments were defined as a major line of business or a customer class. The FASB believed that investors would benefit from expanded application of the accounting and disclosure rules of discontinued operations and issued SFAS No. 144, Accounting for the Impairment or Disposal of Long-Lived Assets _FASB 2001a_. SFAS No. 144 reduced the threshold for recognition of discontinued operations treatment by introducing the “component of an entity” concept. A component of an entity is distinguishable from the rest of the entity because it has its own clearly defined operations and cash flows. A component of an entity can be a reportable segment, an operating segment _as defined by SFAS No. 131, FASB 1997_, a reporting unit _as defined by SFAS No. 142, FASB 2001b_, a subsidiary, or an asset group. Thisreduction in the threshold for discontinued operations has allowed for more asset disposals to be classified as discontinued operations. Consequently, the FASB may have inadvertently given managers more discretion to manage earnings, potentially reducing the quality of reported earnings.In addition to the “component of an entity” requirement, SFAS No. 144 mandated two other requirements for an asset disposal to qualify for treatment as a discontinued operation. The first requirement is that the operations and cash flows of the component being disposed are completely separable from the seller’s continuing operations. The second requirement is that the seller has no significant involvement with the component after the sale. In theory, the application of these requirements appears straightforward; however, SFAS No. 144 did not clearly define the meaning of significant involvement. In practice, many asset disposals involve contractual terms: seller financing, retained equity by the seller, royalties paid to the seller by the buyer, service agreements, etc. The inclusion of these terms in the asset-disposal contract could be interpreted as significant involvement by the seller. In order to provide better guidance on the meaning of the term “significant involvement,” the FASB issued EITF 03-13 _FASB 2004_ as a guide for determining whether an entity retains significant involvement or receives cash flows. In short, if a firm receives material direct cash flows from the discontinued operations or maintains significant continuing involvement, then the disposal does not qualify as a discontinued operation. EITF 03-13 took effect in 2005 but could be adopted as early as 2004. It should be noted that, while the recognition criteria for discontinued operations are well defined in GAAP, the costs to be allocated to the discontinued operations are not. Accounting treatment for discontinued operations is also a global accounting issue. Similar to APB No. 30, IFRS 5, Non-Current Assets Held-for-Sale and Discontinued Operations _IASB 2004_ defines a discontinued operation as a separate major line of business or geographical area of operations. IFRS 5 also requires detailed disclosure of revenue, expenses, pre-tax profit or loss, and the related income tax expense, either in the notes or on the face of the income statement. Currently, both the FASB and the IASB are working toward a converged accounting definition and treatment for discontinued operations, with bothboards issuing proposals amending SFAS No. 144 and IFRS 5, respectively.In closing, material asset disposals are reported on the income statement as discontinued operations if they meet the requirements of SFAS No. 144 _or APB No.30 for disposals prior to 2002_ and likely reported as special items if they do not. Either income statement classification gives managers the opportunity to engage in classification shifting. Prior research provides evidence that special items are used for classification shifting. Since the recognition criteria for discontinued operations are better defined in the accounting standards than for special items, this may give managers less flexibility to manage earnings using classification shifting. For example, it may not be possible for managers to time the recognition of discontinued operations to coincide with a period during which earnings management is desired. Consequently, whether managers use discontinued operations for earnings management purposes is an empirical.Earnings management is one of the most studied areas in financial accounting research.Previous studies document that earnings management can be carried out through accrual management_e.g., Dechow et al.1995; Payne and Robb 2000_, real activity management _e.g., Dechow and Sloan 1991; Bushee 1998; Roychowdhury 2006_,8 or classification shifting _Ronen and Sadan 1975; Barnea et al. 1976; McVay 2006; Fan et al. 2010_. However, increasing current earnings using the former two methods has the potential of reducing future earnings. Since classification shifting simply moves certain revenues, expenses, gains, and losses to different line items on the income statement, it does not actually change net income. As a result, classification shifting is likely to be less costly and less scrutinized by auditors and regulators _Nelson et al. 2002_.9 Previous studies have largely focused on earnings management using accruals and real activity management. Relatively few studies have examined earnings management through classification shifting.Ronen and Sadan _1975_ contend that the dimensions and objects of income smoothing are closely associated. They also reason that, if the smoothing object is net income, then classification shifting is irrelevant. However, if the smoothing object is any income subtotal other than “bottomline”net income, then managers have anincentive to engage in classification shifting. This begs the question of which income subtotals interest investors. Lipe _1986_ documents that investors understand the future earnings implications between the different earnings components reported on the income statement. Bradshaw and Sloan _2002_ provide evidence of “street earnings” _i.e., modified-GAAP earnings with noncash and nonrecurring items excluded_ replacing GAAP earnings as one of the primary determinants of stock price. Together, these studies provide evidence of investors’ interest in recurring income subtotals rather than “bottom line” net income that includes nonrecurring items. If investors value recurring earnings higher than nonrecurring earnings, then managers have an incentive to misclassify operating expenses as nonrecurring expenses, to increase recurring income subtotals. Although one cannot observe which income statement subtotals investors use, we follow McVay _2006_ in assuming that core earnings is the managed subtotal. However, using discontinued operations to increase core earnings also increases income from continuing operations and other “above-the-line” earnings subtotals.One of the earliest studies in the area of classification shifting is Ronen and Sadan _1975_. They find evidence consistent with managers using extraordinary items to smooth earnings before extraordinary items. Barnea et al. _1976_ extend this line of research by providing evidence that managers also use extraordinary items to smooth operating income. Taken together, these studies provide evidence that managers are interested in managing multiple income statement subtotals, presumably because investors value various income statement subtotals differently. While the above studies provide evidence of earnings management using “below the line” income statement items, they do not examine the motivations behind classification shifting.Kinney and Trezevant _1997_ investigate whether managers use special items opportunistically to manage both earnings and investors’ perceptions. They find that income-decreasing special items are more likely to be reported as line items on the income statement, presumably to call attention to the transitory nature of the earnings decrease caused by these items. They also find that income-increasing special items are more likely to be reported in the footnotes to the financial statements, presumably toshift attention away from the transitory nature of the earnings increase caused by these items. Their findings suggest that managers behave opportunistically when disclosing special items. However, Riedl and Srinivasan _2010_ further examine managers’ reporting behavior regarding special items. They find that special items separately disclosed on the income statement have a lower persistence than special items disclosed in the financial statement footnotes. Their results are consistent with managers using financial statement presentation to assist investors in evaluating the persistence of special items. This evidence offers an alternative explanation for the managerial opportunism detected by Kinney and Trezevant _1997_.McVay _2006_ investigates whether managers engage in classification shifting, by reporting core expenses in income-decreasing special items. She uses an expectation model to separate core earnings, defined as operating income before depreciation and amortization, into expected and unexpected components. She finds special items are positively associated with contemporaneous unexpected core earnings and negatively associated with the unexpected change in future core earnings. These results are consistent with her hypothesis that managers shift operating expenses to special items. Sh e also provides evidence suggesting that managers’ motivation for classification shifting is to meet or beat analysts’ forecasts. In addition, she finds a negative stock price reaction to the reversal of unexpected core earnings, indicating that investors do not understand _or are not able to fully undo_ the earnings management. One drawback to the core earnings expectation model used by McVay _2006_ is the use of contemporaneous accruals _including accruals related to special items_ as a control for firm performance. The inclusion of special item accruals in the expectation model creates a potential bias in favor of her hypotheses. She acknowledges the reliance on an imperfect model is a limitation of her study.Fan et al. _2010_ extends McVay’s _2006_ co re earnings expectation models by controlling for performance using returns and lagged returns rather than contemporaneous accruals, thus eliminating any potential bias. They confirm McVay’s _2006_ findings that managers shift core expenses to income-decreasing special items. Using quarterly data, they find that classification shifting using income-decreasingspecial items is more prevalent in the fourth quarter than in other quarters. They also find that classification shifting is more prevalent when managers are constrained from using income-increasing accruals.Source:Michael Power. Fair value accounting, financial economics and the transformation of reliability[J]. Accounting and Business Research 2010.11(4) :197-210译文清算企业的盈余管理企业往往喜欢高估账面价值，吸引投资者对其投资。

spss常用统计词汇中英对照表

统计词汇英汉对照Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceptable hypothesis, 可接受假设Accumulation, 累积ccuracy, 准确度Actual frequency, 实际频数Addition, 相加Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析ANOVA （analysis of variance）, 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差Arithmetic mean, 算术平均数rrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Asymmetric distribution, 非对称分布Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight M-estimator, 双权M估计量BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Counting, 计数Counts, 计数/频数Covariance, 协方差Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cumulative distribution function, 分布函数D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Degree of freedom, 自由度Degree of reliability, 可靠性程度Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Downward rank, 降秩Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值 F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Family of distributions, 分布族Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Grand mean, 总均值Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Paired design, 配对设计Paired sample, 配对样本Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Pearson curves, 皮尔逊曲线Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Point estimation, 点估计Poisson distribution, 泊松分布Population, 总体Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate sub-class numbers, 成比例次级组含量Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Reciprocal, 倒数Reducing dimensions, 降维Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Rotation, 旋转Row, 行Row factor, 行因素Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Sigmoid curve, S形曲线Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Smirnov test, 斯米尔诺夫检验Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spherical distribution, 球型正态分布SPSS(Statistical package for the social science), SPSS统计软件包Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层（复数）Stratified sampling, 分层抽样Stratified sampling, 分层抽样Studentized residual, 学生化残差/t化残差Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Upper limit, 上限Upward rank, 升秩Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。

真实盈余管理trem指标

真实盈余管理trem指标全文共四篇示例，供读者参考第一篇示例：真实盈余管理（TREM）指标是财务分析领域中的一种重要工具，用于评估公司管理层是否通过一些非法或不道德的手段来进行盈余管理，以使公司在财务报表上呈现更好的财务表现。

这种盈余管理实践可能会误导投资者和其他利益相关者，对公司的长期发展带来负面影响。

TREM指标的应用对于评估公司的财务稳健性和管理透明度至关重要。

TREM指标是根据公司的财务数据和盈余报告来计算的，通常包括一系列可以反映公司盈余管理程度的指标和比率。

这些指标和比率可以帮助投资者和分析师判断公司是否存在盈余操纵的迹象，从而更准确地评估其真实盈余水平。

根据TREM指标的计算结果，投资者可以更好地决定是否继续投资于该公司，或者进行更深入的尽调工作。

在实际应用中，TREM指标的计算涉及到多个方面的财务数据。

其中最常用的指标包括盈余质量指标、盈余管理程度指标、盈余稳定性指标等。

通过这些指标的分析，可以对公司的盈余管理行为进行全面的评估，从而为投资决策提供更多的参考依据。

盈余质量指标主要用于评估公司盈余数据的质量和可靠性。

高质量的盈余数据应该是真实、可靠且无误导性的。

如果公司通过一些不当手段来操纵盈余数据，就会导致盈余质量下降，从而对投资者和其他利益相关者带来潜在风险。

盈余质量指标通常包括净盈余现金流比率、长期盈余变异系数等。

盈余管理程度指标则主要用于评估公司管理层是否存在盈余操纵的迹象。

盈余管理是指公司为了达到某种目的，通过调整会计政策或进行其他操作来控制盈余表现的行为。

盈余管理程度指标可以反映公司盈余数字的稳定性和一致性，从而揭示其盈余管理行为的真实水平。

常用的盈余管理程度指标包括盈余管理模型得分、盈余管理测度系数等。

盈余稳定性指标是评估公司盈余数据的波动性和稳定性。

盈余稳定性是指公司在不同期间的盈余表现是否保持一致和可预测。

如果公司的盈余数据波动较大，就可能意味着其盈余管理行为存在一定程度的问题。

企业利润分析中英文对照外文翻译文献

中英文对照外文翻译文献(文档含英文原文和中文翻译)原文：Profit PatternsThe most important objective of companies is to create, develop and maintain one or more competitive advantages in order to generate dividends for the shareholders. For a long time, it was simply a question of dominating the market, either by costs or by a policy of differentiation. As Michael Porter advised, it was essential to avoid being “stuck in the middle”. This way of thinking set up competitive rivalry in a closed world, and tended towards stability. This model is less and less relevant today for whole sectors of the economy. We see a multitude of strategic movements which defy the logic of the old system. “Profit Patterns” lists numerous strategies which have joined the small number that we knew before. These patterns often combine to give rise to strategic models which are better adapted to the new and changing needs of the consumer.Increasing the value of a company depends on its capacity to predict Valuemigration from one economic sector to another or from one company to another has unimaginable proportions, in particular because of the new phenomena that mass investment and venture capital represent. The public is looking for companies that will succeed in the future and bet on the winner.Major of managers have a talent for recognizing development market trends There are some changing and development trends in all business sectors. They can be erected into models, thereby making it possible to acquire a technique for predicting them. This consists of recognizing them in the actual economic context. This book proposes thirty strategic prediction models divided into seven families. Predicting is not enough: one still has to act in time! Managers analyze development trends in the environment in order to identify opportunities. They then have to determine a strategic plan for their company, and set up a system aligning the internal and external organizational structure as a function of their objectives.For most of the 20th century, mastering strategic evolution models was not a determining factor, and formulas for success were fixed and relatively simple. In industry, the basic model stated that profit was a function of relative market share. Today, this rule is confronted with more and more contradictions: among car manufacturers for example, where small companies like Toyota are more profitable than General Motors and Ford. The highest rises in value have become the exclusive right of the companies with the most efficient business designs. These upstart companies have placed themselves in the profit zone of their sectors thanks, in part, to their size, but also to their new way of doing business – exploiting new rules which are sources of value creation. Among the new rules which define a good strategic plan are:1. Strong orientation towards the customer2. Internal decisions which are coherent with the overall activity, concerning the products and services as well as the involvement in the different activities of the value chain3. An efficient mechanism for value–capture.4. A powerful source of differentiation and of strategic control, inspiring investorconfidence in future cash-flow.5. An internal organization carefully designed to support and reinforce the company’s strategic plan.Why does value migrate? The explanation lies largely in the explosion of risk-capital activities in the USA. Since the 40’s, of the many companies that have been created, about a thousand have allowed talented employees, the “brains”, to work without the heavy structures of very big companies. The risk–capital factor is now entering a new phase in the USA, in that the recipes for innovation and value creation are spreading from just the risk-capital companies to all big companies. A growing number of the 500 richest companies have an internal structure for getting into the game of investing in companies with high levels of value-creation. Where does this leave Eur ope? According to recent research, innovation in strategic thinking is under way in Europe, albeit with a slight time-lag. Globalization is making the acceptation of these value-creation rules a condition of global competitively .There is a second phenomenon that has an even more radical influence on value-creation –polarization: The combination of a convincing and innovative strategic plan, strategic control and a dominant market share creates a terrific increase in investor confidence. The investors believe that the company has established its position of strength not only for the current, but also for the next strategic cycle. The result is an exponential growth in value, and especially a spectacular out-distancing of the direct rivals. The polarization process typically has two stages. In phase 1, the competitors seem to be level. In fact, one of them has unde rstood, has “got it”, before the others and is investing in a new strategic action plan to take into account the pattern which is starting to redefine the sector. Phase 2 begins when the conditions are right for the pattern to take over: at this moment, th e competitor who “got it”, attracts the attention of customers, investors and potential recruits (the brains). The intense public attention snowballs, the market value explodes to leave the nearest competitor way behind. Examples are numerous in various sectors: Microsoft against Apple and Lotus, Coca-Cola against Pepsi, Nike against Reebok and so on. Polarization of value raises the stakes and adds a sense of urgency: The first company to anticipate market changeand to take appropriate investment decisions can gain a considerable lead thanks to recognition by the market.In a growing number of sectors today, competition is concentrated on the race towards mindshare. The company which leads this race attracts customers who attract others in an upwards spiral. At the transition from phase 1 to phase 2, the managing team’s top priority is to win the mindshare battle. There are three stages in this strategy: mind sharing with customers gives an immediate competitive advantage in terms of sales; mind sharing with investors provides the resources to maintain this advantage, and mind sharing with potential recruits increases the chances of maintaining the lead in the short and the long term. This triple capture sets off a chain reaction releasing an enormous amount of economic energy. Markets today are characterized by a staggering degree of transparency. Successes and failures are instantaneously visible to the whole world. The extraordinary success of some investors encourages professional and amateurs to look for the next hen to lay a golden egg. This investment mentality has spread to the employment market, where compensations (such as stock-options) are increasingly linked to results. From these three components - customers, investors and new talent – is created the accelerating phenomenon, polarization: thousands of investors look towards the leader at the beginning of the race. The share value goes up at the same time as the rise in customer numbers and the public perception that the current leader will be the winner. The rise in share-price gets more attention from the media, and so on. How to get the knowledge before the others, in order to launch the company into leadership? There are several attitudes, forms of behavior and knowledge that can be used: being paranoiac, thinking from day to day that the current market conditions are going to change; talking to people with different points of view; being in the field, looking for signs of change. And above all, building a research network to find the patterns of strategic change, not only in one’s particular sector, but in the whole economy, so as always to understand the patterns a bit better and a bit sooner than the competitors.Experienced managers can detect similarities between movements of value in different circumstances. 30 of these patterns can be divided into 7 categories.Some managers understand migrations of value before other managers, allowing them to continually improvise their business plan in order to find and exploit value. Experience is an obvious advantage: situations can repeat themselves or be similar to others, so that experienced managers recognize and assimilate them quickly. There about 30 patterns .which can be put into 7 groups according to their key factors. It is important to understand that the patterns have three general characteristics: multiplicity,variants and cycles. The principle of multiplicity indicates that while a sector or a company may be affected by just one simple strategic pattern, most situations are more complicated and involve several simultaneously evolving patterns. The variants to the known models are developed in different circumstances and according to the creativity of the users of the models. Studying the variants gives more finesse in model-analysis. Finally, each model depends on economic cycles which are more or less long. The time a pattern takes to develop depends on its nature and also on the nature of the customers and sector in question.1) The first family of strategic evolution patterns consists of the six “Mega patterns”: these models do not address any particular dimension of the activity (customer, channels of distribution and value chain), but have an overall and transversal influence. They owe their name “Mega” to their range and their impact (as much from the point of view of the different economic sectors as from the duration). The six Mega models are: No profit, Back to profit, Convergence, Collapse in the middle, De facto standard and Technology shifts the board. • The No profit pattern is characterized by a zero or negative result over several years in a company or economic sector. The first factor which favors this pattern is the existence of a single strategic a plan in several competitors: they all apply differentiation by price to capture market-share. The second factor is the loss of the “crutch” of the sector, that is the end of a system of the help, such as artificially maintained interest levels, or state subsidies. Among the best examples of this in the USA are in agriculture and the railway industry in the 50’s and 60’s,and in the aeronautical industry in the 80’s and 90’s.• The Back to profit pattern is characterized by the emergence of innovative strategic plans or the projects which permit the return of profits. In the 80’s, the watch industry was stagnating in a noprofits zone. The vision of Nicolas Hayek allowed Swatch and other brands to get back into a profit-making situation thanks to a products pyramid built around the new brand.The authors rightly attribute this phenomenon to investors’ recognition of the superiority of these new business designs. However this interpretation merits refinement: the superiority resides less in the companies’ current capacity to identify the first an indications of strategic discontinuity than in their future capacity to develop a portfolio of strategic options and to choose the right one at the right time. The value of a such companies as Amazon and AOL, which benefit from financial polarization, can only be explained in this way. To be competitive in the long-term, a company must not only excel in its “real” market, but also in its financial market. Competition in both is very fierce, and one can not neglect either of these fields of battle without suffering the consequences. This share-market will assume its own importance alongside the commercial market, and in the future, its successful exploitation will be a key to the strategic superiority of publicly-quoted companies.Increasing the value of a company depends on its capacity to predictValue migration from one economic sector to another or from one company to another has unimaginable proportions, in particular because of the new phenomena that mass investment and venture capital represent. The public is looking for companies that will succeed in the future and bet on the winner.Major managers have a talent for recognizing development market trendsThere are some changing and development trends in all business sectors. They can be erected into models, thereby making it possible to acquire a technique for predicting them. This consists of recognizing them in the actual economic context.Predicting is not enough: one still has to act in timeManagers analyze development trends in the environment in order to identify opportunities. They then have to determine a strategic plan for their company, and set up a system aligning the internal and external organizational structure as a function of their objectivesSource: David .J. Morrison, 2001. “Profit Patterns”. Times Business.pp.17-27.译文：利润模式一个公司价值的增长依赖于公司自身的能力的预期，价值的迁移也只是从一个经济部门转移到另外一个经济部门或者是一个公司到另外一个意想不到的公司。

盈余质量研究综述

需要，量才用人。首先可以在企业内部实现人
四、结束语
才的合理流动。不同部门需要的各种人才可以
对于矿业企业来说，拥有丰富的自然资源
通过双向选择在单位内不同部门之间进行流和充足的资金固然重要，但若不重视人力资源
动，实现个人和岗位最优化匹配。企业内部无在企业中的作用，没有合理的人力资源管理模
法满足人才需求状况时，可以采取社会公开招式，也会造成重大的资源浪费和经济损失。矿
质量，该比值越高表示盈余质量越低。总现金流量表所列示的各项财务数据为基
储一昀与王安武从盈余与经营现金
的来说，盈余中应计利润部分越少，特别本依据，通过一系列现金流量指标的计流量联系的角度出发，考察了我国上市公
是可操控性应计利润越少，经营现金流量算，对公司盈利水平进一步修复与检验，司的盈余质量问题。他们认为，盈余质量
Ｙｏｈｎ等则认为，会计盈余中的现金比应量。”另外，秦志敏认为， “盈利质量是公司则值得怀疑。主营业务利润占利润总额比
计利润具有更高的信息含量，并以应计利盈利水平的内在揭示，是在盈利能力评价重只是盈余持续性分析中的一个指标，以
润与经营现金净流量的比值来判断盈余的基准上，以收付实现制为计算基础，以此来估计盈余持续性，显然比较粗糙。
培训看作是一项看不见摸不着的消费性事情；
４、健全培训体系。培训是提高员工素质的
（２）技术理论知识与实践相脱节。不少技工虽重要途径。在各种投资中，对人员的培训投资
然有丰富的实际操作经验，但很难在理论上得是最有价值和最富远见的。矿业企业应针对不
到提升；（３）职工培训与实际工作相脱节。有些同类型的员工给予不同内容和不同方式的培
企业喜欢用一年办了多少培训班、培训了多少训。（１）对企业管理人员的培训。企业管理人员

企业经营业绩评价外文文献及翻译

1企业经营业绩评价系统的理论基础
(1)资本保全理论
在市场经济条件下，企业是出资者的企业，是一个资本集合体，所有者是惟一的剩余风险承担者和剩余权益享受者，出资者利益是企业最高利益。企业追求利益最大化直接表现为资本增值最大化，要求用尽可能小的垫支资本去获取尽可能大的资本利益。企业经营业绩评价的一个基本前提是企业资本增值最大化。因此，资本保全观念的选择就构成了业绩评价的重要理论基础。资本保值增值的实质是实现资本收益最大化，是在资本经营状态下所得与所费关系的具体体现，即用尽可能小的垫支资本去获取尽可能大的资本利益。因此，所得与所费之间的对比成为企业经营业绩评价的基本表达形式。
(可操作性原则
可操作性原则，要求评价过程中所使用的数据均可从现有的会计核算、统计核算和业务核算数据中获得，以这些可验证的资料为基础，才能使评价结论不偏不倚。鉴于我国评价结果使用者的素质普遍较低，评价过程和结果的表述形式应符合中国人的思维习惯、文化特征与价值观念。可操作性应是设计业绩评价系统必须考虑的一项重要因素，离开了可操作性，再科学、合理、系统、全面的评价系统也是枉然。因此，评价系统设计要本着定义明确、简明扼要、结构合理的原则，在现有条件下，便于评价人员理解和填报，正确使用评价结论。
(5)评价标准
评价标准是指判断评价对象业绩优劣的基准。评价标准是在一定前提下产生的，随着社会的不断进步，经济的不断发展以及外部条件的变化，评价的目的、范围和出发点也会发生变化，作为评价判断尺度的评价标准也会发生变化。从这种意义上说，评价标准是发展变化的。然而，在特定的时间和范围内，评价标准必须是一定的，应具有相对的稳定性。标准应作为评价与计划的基准，并且应以客观业绩而不是主观判断为基础。目前常见的业绩评价标准有：年度预算标准、资本预算标准、历史水平标准、竞争对手标准。在具体选用标准时，应与评价客体密切联系，一般来讲，评价客体为企业时，采用历史水平标准和竞争对手标准，而评价客体为企业管理者时，通常采用年度预算标准较为恰当。

外文翻译--盈余质量的定量分析

外文翻译--盈余质量的定量分析外文文献翻译译文一外文原文原文Quantitative analysis of earning qualityEarnings quality analysis has long been known as the qualitative investment managers best defense against low quality financial reporting While not widely known recent academic and proprietary research shows that earnings quality metrics are also useful in generating alpha in the context of quantitative investment and trading strategies The Gradient Analytics Earnings Quality Model EQM is the first quantitative factor that objectively measures earnings quality across a broad spectrum of companies for both longer-term 3-12 month and shorter-term 1-3 month holding periods The model was developed using rigorous statistical methods to ensure a robust factor that generates excess returns on a standalone basis while also capturing a unique dimension of returns not captured by other quantitative factors The end product is a highly unique factor with exceptional returns and low correlation in relation to other commonly used factors such as those derived from estimate revisions earnings momentum price momentum cashflow corporate insider growth and valueThe US accounting profession and the Securities Exchange Commission SEC have worked diligently through the years to develop the most rigorous system of accounting procedures in the world Nevertheless there is still a significant gap appropriately called the expectations gap between what investors and creditors expect and what the accounting profession can deliverThe expectations gap exists in part because publicly traded companies have a great deal of discretion in choosing accounting principles and in making estimates that impact their reported financial results Under Generally Accepted Accounting Principles GAAP the amount of discretion that a company has in preparing financial statements is controlled by two fundamental principles of accounting conservatism and objectivity However in reality these two guiding principles are often stretched to the limit or ignoredWhen Conservatism or Objectivity is Impaired Earnings Quality is CompromisedWhile in theory a firms accounting staff should employ procedures that are objective and conservative in practice management has many competing motivations that drive their choice of accounting policies and influence their periodic estimates Because of these competing motivations many companies manipulate accounting numbers in order to facilitate thefinancial reporting goals established by management In this regard virtually all firms working within the bounds of GAAP use minor accounting gimmicks to present financial results in a particular light ie overstate or understate their true profitability or financial condition Micro Strategy Inc for example 1999-2000 was using an aggressive revenue recognition policy that while not in violation of GAAP tended to overstate the companys true profitability However it wasnt until the SEC mandated a change in the way that technology companies account for contract revenue that the market ascertained the extent of the overstatement although access to earnings quality analysis – qualitative or quantitative –would have revealed the deception prior to the change in accounting rules After the SEC mandated change Micro Strategy was twice forced to restate its earnings and its shares fell over 98 in the ensuing 12-month period While Micro Strategys treatment of contract revenues was very aggressive arguably it was operating in a gray area of GAAP In more extreme cases however some companies go so far as to commit fraudulent acts that materially misstate their financial statements in ways that do not conform to GAAP For example Enron used an extremely aggressive scheme of off-balance-sheet financing in order to hide mounting losses The end result was arguably one of the most spectacular financial reporting disasters in history Those unlucky enough to hold Enron shares during this period lost close to 100 of the value of their investmentFinally while intentional manipulation of accounting numbers is common earnings quality problems are not always the result of intentional acts by management For example the quality of inventories at Lucent Technologies as reported in their first quarter 10Q filed May 2000 suggested an apparent backlog of inventory that indicated a possible slowdown on the horizon In all likelihood this was at least initially a case of earnings quality problems resulting from unintentional acts slow sales Nevertheless Lucents earnings continued to disappoint and the stock was down more than 85 in the ensuing twelve months Subsequent evidence suggests that there may also have been some intentional misstatements on the part of lucent management in order to hide the magnitude of the sales slowdownHow do companies manipulate earningsDespite the efforts of the accounting profession to ensure objectivity and conservatism it is still relatively easy to manipulate accounting numbers through either unethical but not necessarily illegal andor fraudulent means The list presented below provides a high level overview of how Management can manipulate accounting numbers1 Recording fictitious transactions or amounts2 Recording transactions incorrectly3 Recording transactions early4 Recording transactions late5 Misstating percentages or amounts involved in a transaction6 Misstating the amounts of assets or liabilities7 Changing accounting methods or estimates for no substantive reason8 Using related party transactions to alter reported profitsAcademic Research on Earnings Quality and Future ReturnsIn addition to the anecdotal evidence provided by qualitative earnings quality services ie those that use subjective evaluations of financial statements to render an earnings quality grade academic research also supports the notion that quantitative models of earnings quality can be used to earn excess returns The following brief review of the academic literature highlights some of the most important factors that form the basis for Gradients approach to quantitatively modeling earnings quality and forecasting related excess returnsThe very first studies to investigate issues related to earnings quality were conducted by G Peter Wilson of Harvard University 1986 1987 using an event study methodology Wilsons key conclusions are that operating cash flows and total accruals ie changes in current accruals plus non current accruals are differentially valued and that both are value relevant That is the market appears to react to the disclosure of detailed cash flow and accrual data value relevance and that cash flows are more highly valued than accruals differential valuation Wilsons basic findings are also supported by a number of studies that use anassociation methodology7 including Rayburn 1986 Bowen Burgstahler and Daley 1987 Chariton and Katz 1990 Levant and Zeroing 1990 Vickers 1993 Ali 1994 Pfeiffer et al 1998 and Vickers Vickers and Betties 2000The fact that the market values a dollar of cash flow more than a dollar of current or non current accruals implies that higher levels of accruals are indicative of lower quality of earnings In other words the degree to which a company must rely on accruals to boost net income results in lower quality earnings Nevertheless it is possible that the market sees through the deception and appropriately values companies based on some notion of baseline or sustainable earnings However the first studies to investigate this issue Sloan 1996 and Swanson and Vickers 1997 find that contrary to the efficient markets hypothesis disaggregating earnings into cash flow and accrual components is useful in identifying securities that are likely to outperform or under- perform in the future Thus the results of these studies imply that security prices do not fully reflect the information contained in the cash flow and accrual components of earningsFollowing in the path of Sloan 1996 and Swanson and Vickers 1997 academic researchers are currently focusing on the development of simple empirical models that objectively assess earnings quality in order to predict future return performance See for example Sloan Solomon and Tuna2001 Chan Chan Legatees and Lakonishok 2001 and Penman and Zang 2001 Table 1 below summarizes the results of recent academic working papers that focus on the predictive ability of simple earnings quality models As shown in the table these studies find that companies with relatively high low levels of accruals tend to under-perform outperform for periods of 12-36 months after the disclosure of detailed financial data Specifically the return spread between stocks with the highest level of accruals lowest earnings quality and the lowest level of accruals highest earnings quality ranges from 88 to 217 depending on the approach used by the authors in forming portfolios The implication is that measures of earnings quality can be used in forming profitable investing and trading strategiesGradient Analytics Earnings Quality ModelThe latest academic research demonstrates that the market does not fully impound information about earnings quality at the time that detailed financial statement data are released That is a statistically-based approach to analyzing earnings quality can yield profitable investment and trading strategies Thus the next step was to develop a robust model that is designed to optimize the excess returns that can be realized from an earnings quality strategy The Gradient Analytics Earnings Quality Model EQM has been developed to achieve this goalThe Gradient EQM is the first quantitative factor that measuresearnings quality across a broad spectrum of companies The EQM provides two weekly scores ranging from 1 strong sell to 8 strong buy for each of the top 5000 companies ranked by market capitalization The Long-Term Score provides a 1-8 ranking based on a stocks expected future performance over a 3-12 month holding period The Short-Term Score ranks each stock based on its expected future performance over a 1-3 month holding period In contrast to competing commercially-available earnings quality services the output from the EQM is derived objectively –not subjectively – through statistical analysis of accrual and cash flow components of earnings The model was developed using rigorous statistical methods to ensure a robust factor that generates excess returns on a standalone basis while also capturing a unique dimension of returns not captured by other quantitative factors More specifically the model was constructed using a multiple regression approach including repressors from academic research and our own theoretically sound proprietary earnings quality constructs estimated in pooled time series cross section for 13 sector categories8 Each separate sector model incorporates the most important dimensions of earnings quality for that segment of the market9 When considered together these dimensions or sub-factors provide a means of reliably ranking firms monotonically according to both their expected mean and median excess returns The end product is a highly unique factor with exceptional returns and low correlation in relation to othercommonly used factorsAll Gradient models are developed using a disciplined scientific approach Our approach can be characterized as followsVariable Specification –We begin by carefully specifying each variable to ensure proper measurement and scaling When more than one specification is defensible we choose the simplest specification on the theory that simplicity will yield more generalizable results Modeling Techniques –Each model is estimated using relatively simple linear and nonlinear regression techniques Again we believe that simplicity is the key to generalizabilitySensitivity Analyses –All models are subjected to sensitivity analyses to determine whether or not our results are impacted by outliers changes in regimes alternative variable specifications and modeling techniques and so onProper Use of In-Sample and Out-of-Sample Periods – Each model is estimated using data from a strict in-sample period The model is then tested for generalizabity stability and so on in an out-of-sample periodControl for Potential Threats to Internal and External Validity –Our research efforts are designs to control for common threats to internal and external validity in financial engineering studies such as survivorship bias hindsight bias selection bias and so onConclusionEarnings quality analysis is often regarded as the qualitative investment managers best defense against low quality financial reporting The latest academic research also demonstrates that the market does not fully impound information about earnings quality at the time that detailed financial statement data are released That is a statistically-based approach to analyzing earnings quality can yield profitable investment and trading strategies The Gradient Earnings Quality Model EQM has been developed to achieve this goalThe EQM is the first quantitative factor that objectively measures earnings quality for the purpose of forecasting future returns The model was developed using rigorous statistical methods to ensure a robust factor that generates excess returns on a standalone basis while also capturing a unique dimension of returns not captured by other quantitative factors The end product is a highly unique factor with exceptional returns and low correlation with other commonly used factorsThe Earnings Quality Model has been extensively back-tested across a variety of stock universes and time periods in order to ensure optimal generalizable results The results presented in this white paper provide extremely strong evidence on the usefulness of the EQM As shown in the results section of this document the model produces highly significant excess returns performs extremely well both in- and out-of-sample and hasa low correlation with other commonly used quantitative factors And in contrast to competing commercially-available earnings quality services the output from the EQM is derived objectively – not subjectively –through statistical analysis of accrual and cash flow components of earningsSource gradient analytics 2005 Quantitative analysis of earning quality〔EBOL〕httpcom – website March pp23-25二翻译文章译文盈余质量的定量分析盈余质量分析长时间被认为是定性投资经理针对低质量财务报告的最佳防卫虽然不广为人知但近期的学术和专有的研究表明盈余质量指标也对量化投资和贸易战略中用到的阿尔法的产生有帮助盈余质量的梯度分析模型是第一个量化的因素客观地计量在广泛的公司为期3-12月盈利的质量和短期13月期间举行该模型是开发利用严谨的统计方法以确保一个强大的因素在一个独立的基础上产生的超额利益同时也捕获不受其他因素的捕获量的回报的独特维度最终产品是一种特殊的回报是相对于其他常用的估计如修改盈利势头价格动能现金流企业内部人增长和价值产生这些因素的相关性较低非常独特的因素美国的会计界和证券委员会已通过多年的工作努力发展了世界上最严格的会计程序系统尽管如此仍然有很大的差距适当地称为期望差距即投资者和债权人的期望与什么是会计专业可以提供的之间的差距期望差距的存在是因为上市公司在选择夸己原则和作出估计是存在很大的自由裁量权影响其财务报告的财务结论按照公认会计原则的自由裁量权的金额公司已在编制财务报表中规定了两个基本的夸己原则稳健性和客观性然而在现实中这两项指导原则往往是到了极限或忽略当保守主义或客观性受损盈余质量受到损害虽然在理论上一个公司聘用会计人员的程序应当是客观的保守的做法然而在管理上有许多相互竞争的动机驱使使他们所选择的夸己政策定期估算由于这些相互竞争的动机很多公司操纵会计数字以促进财务报告管理的既定目标在这方面几乎所有的企业在会计准则的范围内使用核算小噱头并从特定的角度呈报财务结果即高估或低估其真实盈利能力或财务状况例如Micro Strategy公司在1999年2000年间采用了激进的收入确认政策虽然不违反会计准则但却夸大了公司的真实盈利能力然后直到美国证券交易委员会规定的方式中技术公司的合同收入帐市场确定的多报虽然获得收益质量分析的程度变化定性或定量会前透露的欺骗在会计规则的改变后美国证券交易委员会规定的变化后Micro Strategy 两次被迫重申其盈利在随后的12个月期股票下跌了98以上而Micro Strategy的合同收入的治疗是非常积极的可以说这是一个公认会计准则经营的灰色地带在极端情况下一些公司竟然从事欺诈行为重大虚报的方式不符合美国通用会计准则的财务报表例如安然使用一个极端激进的表外融资方案以隐藏不断增加的损失最终的结果是有据可查的最壮观的财务报告灾害的历史那些倒霉的其投资足以容纳安然公司股票在此期间失去了将近百分之百的价值最后会计数字的故意操纵尽管很普遍盈余质量问题并不总是管理层有意为之的结果例如朗讯科技存货的质量报告于2000年5月第一季度的10Q文件中显示一个明显表示在地平线上的可能放缓的库存积压十有八九这是至少在最初阶段是盈余质量问题造成的意外行为慢销售案件然而朗讯的收入继续令人失望股票在随后十二个月内下跌85以上后续的证据表明可能也有一些故意虚报朗讯管理的为了隐藏的销售放缓程度公司是如何操纵收益的尽管会计界努力以确保客观性和保守主义它仍然是比较容易通过操纵会计数字或不道德的但不一定是非法的或欺诈的手段下面的列表提供了如何高水平综述管理可以操纵会计数字1记录虚构交易或数额2不正确记录交易3记录交易早4记录交易晚5百分比或在一个事务中涉及的金额6资产或负债的数额7更改会计处理方法或估计没有实质性的原因8使用关联方交易改变报告的利润盈余质量与未来收益的学术研究除传闻证据的定性收益提供了优质的服务即那些使用财务报表的主观评价以使有关收益质量等级学术研究也支持这样的收益质量的定量模型可以用来赚取超额收益的概念以下学术文献的简要回顾强调的最重要因素形成了梯度的定量建模方法的基础盈余质量与预测相关的超额收益部分是哈佛大学G彼得威尔逊1986年1987年威尔逊的主要结论经营性现金流和总应计项目也就是说市场似乎威尔逊的基本结论也支持了研究利用关联包括雷伯恩1986年鲍文和戴利1987年和1990年和1990维克瑞1993号阿里1994年菲弗等1998年和维克瑞维克瑞和贝蒂斯2000年事实上市场价值的现金流超过了目前非流动应计费用美元或更多的美元意味着更高水平的预提费用低收益质量的指标换句话说在何种程度上依赖于一个公司必须在权责发生制提高质量较低收入纯收入的结果不过它可能是市场看穿的欺骗和适当的基准值对一些概念或可持续盈利的公司然而第一次研究以探讨这一问题斯隆1996年和维克瑞1997年发现有效市场假说成现金流量和斯隆1996年和斯万森和维克瑞1997学术研究人员目前正在对简单的经验模型客观地评价收益质量以预测未来的回报表现结了最近的学术工作文件的结果重点放在预测能力简单的盈利质量模型这些研究发现相对高低预提费用水平的公司往往会副执行期限为12-36个月后详细的财务资料披露优于具体来说与应计项目的最高水平最低收益质量和预提费用最高盈利质量范围的最低水平从88％到217％这取决于作者们使用的方法形成的投资组合股票收益之间的传播其含义是收益质量的措施可以形成盈利的投资和贸易战略的方法]梯度分析公司《定量分析盈余质量》梯度分析官网2005323-25。

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