Analysts Earnings Forecast for Firms Making Earnings Restatement

IS THERE INFORMATION IN FINANCIAL ANALYSTS FORECASTS FOR EARNINGS RESTATEMENT FIRMS?Thesis Proposal by Ge ZhiyangDepartment of Finance & Accounting

National University of SingaporeFebruary 2004CHAPTER ONE INTRODUCTION

1.1 Background of the study

Since the late 1990s, a number of large well-known companies have announced restatements of their financial statements, erasing billions of dollars of earnings from the previously reported numbers. These restatements not only have enormous negative impact on the market; they also call into question the credibility of accounting practices and the quality of corporate financial disclosure and oversight. In his speech at the New York University Center for Law and Business, former Securities and Exchange Committee (SEC) Chairman Arthur Levitt remarks:

I fear that we are witnessing an erosion in the quality of earnings, and therefore, the quality of financial reporting If a company fails to provide meaningful disclosure to investors about where it has been, where it is and where it is going, a damaging pattern ensues. The bond between shareholders and the company is shaken; investors grow anxious; prices fluctuate for no discernible reasons; and the trust that is the bedrock of our capital markets is severely tested

It is therefore not surprising to witness a series of negative consequences triggered by earnings restatement, among which are shareholder class-action suit, SEC sanction, management turnover, resignation and dismissal of the outside auditors, collapse of the companys stock price and decrease in earnings forecasts. Given the significant impact of earning restatement on the capital market, shareholders, and the restatement firms themselves, it merits an in-depth investigation of earnings restatement, including its characteristics, market effect, and social consequences.

The growing number of restatements of financial statements reflects weakness in the current corporate governance and financial reporting system. It is first of all a failure of internal control system within the restatement firms. Moreover, the sharp drop in stock prices upon the restatement announcement also illustrates the failure of financial analysts and credit rating agencies to identify problems before investors and creditors lose huge dollars. For example, the analysts are found to issue buy recommendations to companies that soon restate their earnings and experience dramatic decline in market value. Much research on the financial analysts earnings forecasts (FAF) concludes that compared with statistical and time-series forecast models, FAF reflect comprehensive information, are relatively accurate and are associated with market returns and risk. However, FAF are also documented to exhibit systematic upward bias, that is, they are consistently higher than actual announced earnings. The incidence of earnings restatement announcement provides a special setting to study financial analysts earnings forecast (FAF), specifically, whether there is information in financial analysts forecasts for the earnings restatement firms. 1.2 Objective of the study

Though earnings restatement can be initiated for various reasons (to be discussed in details in the literature review part), this study limits its attention to the earnings restatements due to accounting errors, aggressive accounting practices, accounting irregularities and accounting fraud. Earnings restatements due to these reasons are evident signals that the previous financial statements lack integrity and reliability, and that the management lacks competence or credibility in their oversight. These kinds of earnings restatement most often have negative effects on the firms. This study aims to examine whether the financial analysts (as market intermediaries) have predictive power of such kinds of earnings restatement, and whether the market has aggregate wisdom about such events.Specifically, this study aims to address four issues. The first issue is whether the financial analysts can predict the subsequently corrected earnings of the restatement firms during their misstated period. In other words, whether the financial analysts have prior knowledge of the misstatement and therefore have better prediction of the true earnings information of the restatement firms.The second issue is whether there is any difference in the distribution of ex ante financial analysts earnings forecasts (FAF) for restatement and non-restatement firms. The ex ante FAF examined in this study is the forecast for the annual earnings of the year immediately prior to the year of restatement announcement. Four aspects of the ex ante FAF distribution are examined. Since the earnings restatement firms may exhibit more uncertainty before their restatement announcements than the non-restatement firms, we examine whether the uncertainty is reflected in a difference in the distribution of the ex ante FAF for the restatement vs. non-restatement firms. The third issue is whether the market has aggregate wisdom about the circumstances leading to the restatement announcement. If the market in aggregate has prior knowledge of the earnings restatement, there will be a pre-restatement announcement drift in stock prices for the earnings restatement firms. Furthermore, if the markets aggregate wisdom of the restatement announcement incorporates the information conveyed through the ex ante FAF distribution, then the markets reaction to the restatement announcement is to be mitigated by the information reflected in the ex ante FAF distribution.

The fourth issue is whether the properties of ex ante FAF distribution for the restatement firms capture risk aspects of these firms. To answer this question we examine the relationship between the properties of ex ante FAF distribution and the subsequent risk measures of the restatement firms. A confirmative answer will indicate that the properties in FAF distribution for earnings restatement firms are associated with their potential risk.1.3 Potential contribution of the study

The phenomenon of earnings restatement has drawn researchers attention only in recent years following the accounting scandals of large companies like Enron and WorldCom in the late 1990s. The growing number of earnings restatement due to accounting errors and irregularities in the recent years reflects the deterioration of the problem and the scrutiny of the SEC. The extant research on earnings restatement explores this issue from different aspects, for example, the capital market reaction to the announcement of earnings restatement, the incentives for managers to apply aggressive accounting methods in violation of GAAP that leads to earnings restatement subsequently, the corporate governance characteristics of the restatement firms, etc.

This study adds to the literature of earnings restatement by examining the role of financial analysts with respect to earnings restatement. Financial analysts are important intermediary in the capital market and are considered sophisticated and efficient in information collection and procession. This paper tries to provide evidence on whether the analysts earnings forecast can have predictive power of the misstatement and the subsequent earnings restatement, or in other words, whether there is information in financial analysts forecasts for earnings restatement firms.This study contributes to the empirical studies on earnings restatement by its sample coverage of the latest years. As the manual search for earnings restatement is tedious and time-consuming work, most existing research covers restatements of annual earnings merely and extends their sample collection to year 2000 only. As the number of earnings restatement is shown to grow dramatically over time, the inclusion of earnings restatements made in year 2001 and 2002 may provide more sample cases in support of our analysis. Moreover, our sample period covers the whole period including the beginning and the burst of the economic bubbles in 1990s, making it possible to illustrate, if any, the time-series properties of earnings restatements. This study also contributes to the literature of financial analysts behavior by examining the information content of financial analysts earnings forecasts for earnings restatement firms. Previous studies have documented the capability of financial analysts in capturing comprehensive information about the company. Yet empirical studies also report a lack of efficiency of analysts forecasts such as positive bias. This paper aims to provide empirical evidence of the financial analysts earnings forecast for a special class of firms, namely the earnings restatement firms.1.4 Scope and organization of the study

This study covers US firms listed in NYSE, AMEX and NASDAQ that make earnings restatements due to accounting errors, aggressive accounting principles, accounting irregularities and accounting fraud from 1990 to 2002. The remainder of the paper is organized as follows: Chapter Two gives an overview of earnings restatement and relevant studies on alleged earnings manipulation. Also included in this part is the review of studies on financial analysts earnings forecasts (FAF). Chapter Three identifies the development of hypotheses to be tested in this study and the related models. The data sources, sample selection procedure and research method are outlined in Chapter Four. Chapter Five presents the empirical findings and analyze research results. Finally, Chapter Six concludes the study with implications and suggestions for future research.CHAPTER TWO LITERATURE REVIEW2.1 Overview

This review includes two parts. The first part focuses on studies related to earnings restatement, while the second part reviews studies on financial analysts earnings forecasts (FAF).

2.2. Earnings restatement

2.2.1 Background of earnings restatement

A financial statement restatement occurs when a company, either voluntarily or involuntarily, revises public financial information that was previously reported. Being a rewrite of the companys history, an earnings restatement suggests that the formerly filed financial statement lacks reliability. Though not a new phenomenon, the earnings restatement due to accounting errors and irregularities has been growing in number and in significance during the past decade (relative statistics and evidence will be discussed in detail in the third section).

Restatements can involve SEC-filed annual reports, which are audited by independent auditors, and quarterly reports (in most cases unaudited). They can also involve only the interim quarters of the current fiscal year, including the unfiled quarterly reports that were publicly announced before. The channels of correction of the misstatement used by companies include amended filings (10K/A or 10Q/A), which supersede the original financial statements, the 10K or 10Q in the subsequent period carrying the corrected number, or the form 8-K. 2.2.2 Reasons leading to earnings restatement

The restatement of financial statements can be initiated by a number of reasons. This study limits its scope to the earnings restatements that correct the material misstatement in previous financial results. These types of restatements result from either unintentional accounting error, defined as mathematical mistakes, oversight, or misuse of facts at the time the financial statements were originally prepared, or accounting irregularity, a term for intentional misstatements or omissions of amounts or disclosures in financial statements done to deceive financial statement users, or the pursuit of aggressive accounting in violation of GAAP. Although some firms admittedly acknowledge fraudulent financial reporting in their public announcement, most firms will not do so. It is therefore hard to distinguish between intentional manipulation and unintentional misinterpretation in some cases.

The reasons for material misstatements can be categorized into more elaborate groups. Among them are: improper revenue accounting, including premature recognition of revenues or even recognition of fictitious revenues; improper cost accounting, including improperly recognizing costs or expenses, misstating inventory, other long-term assets, or reserves, improperly capitalizing expenditures and improper treatment of tax-related items; improper accounting in merger and acquisition; improper accounting for in-process research and development at the time of an acquisition; reassessment of investments; and misclassification of accounting items or wrong record entries.

Previous research on earnings restatement finds that of all the reasons mentioned above, improper revenue recognition is found to be the most frequent cause. This category includes instances in which revenue was improperly or prematurely recognized, questionable revenues were recognized, or any other mistakes or improprieties that led to misreported revenue. In The United States General Accounting Office Report (GAO Report 2002 hereafter), restatements due to revenue recognition problems constitute 38 per cent of the 919 restatements arising from material misstatement including errors and fraud from 1997 to June 2002. Wu (2002) find that of the 1,221 earnings restatements from 1977 to 2000 arising from accounting errors and irregularities, 487 cases are caused by problems in revenue recognition, representing the highest percentage (39 per cent) of the whole sample. Besides accounting errors, irregularity and aggressive accounting methods, there are other reasons arising from normal corporate activity or presentation issues that lead to earnings restatement; for example, general changes in accounting principles under GAAP, stock splits, dividend distributions, discontinuation of operations, change of the accounting period, merger and acquisitions, and changes made for presentation purposes. However, restatements caused by these reasons do not reveal previously undisclosed and economically meaningful information to the investors, and do not directly signal a lack of integrity or quality in previous financial statements. Therefore they are excluded for the purpose of this study. 2.2.3 Growing number of restatements due to accounting misconduct

Early studies on earnings restatement find modest number of restatements in the 1970s and 1980s. Kinney and McDaniel (1989) examine firms that correct previously reported quarterly earnings in a footnote to their annual reports because of accounting errors covering the sample period from 1976 to 1985. They identify reports with year-end restatement of previously issued quarterly financial statements sourced from the National Automated Accounting Research System (NAARS) database of annual reports. After excluding eight restatements related to prior fiscal year and two extreme outliers, they observe 73 firms (178 quarters) that correct previous quarterly earnings due to material errors.

DeFond and Jiambalvo (1991) examine firms making corrections of earnings overstatement errors that existed in a prior years annual report from 1977 to 1988. Their sample is obtained from a search of footnote disclosure of prior period adjustments in NAARS and Accounting Trends and Techniques (ATT) database, and 41 firms are identified.

Recent studies on earnings restatement made since the late 1990s identify a dramatically growing number of earnings restatements due to accounting errors and irregularity. Turner and Sennetti (2001) use key-word searches throughout the financial statements in NAARS for restatements made from July 1987 to June 1995, and supplement their search with the restatements in 1981 to 1987 that are examined by DeFond and Jiambalvo (1991). After eliminating restatements that are other than error corrections and not related to material misstatement, their final sample includes 116 error firms, with the highest frequency in 1988 when 21 firms are identified.

Palmrose and Scholz (2000) examine the companies that made first disclosure of possible restatements between January 1, 1995 and June 30, 1999 and subsequently filed amended 10-K or 10-Q with the SEC. They obtain their sample of restatements from Lexis-Nexis News Library by using key-word searches for restatements and include additional companies discussed for restatements in other unnamed sources. Their final sample consists of 416 restatements for material misstatement, with 384 searched from Lexis-Nexis searches and 32 from other sources. The number of restatements rises from 43 in 1995 to 136 in 1999. In the sample of restatements, 34 per cent are identified as having errors in revenue recognition, 28 per cent are for operating expenses adjustments, and 23 per cent are for in-process R&D adjustments, capturing the three categories with highest frequency of earnings restatements.

Turner, Dietrich, Anderson and Bailey (2001) examine the firms making earnings restatement to prior annual earnings in their amended 10-K filings. They search 10-K Wizard by key words for restatement within 10 words of financial statement. After excluding those restatements not due to accounting errors or fraud, they identify a final sample of 362 restatements, with 104 in year 1997, 116 in year 1998 and 142 in year 1999. Of these restatements 15 per cent have problems in revenue recognition and 7 per cent have errors in restructuring charges. Anderson and Yohn (2002) find 329 firms during the period 1997 to 1999 that restate financial statements and file a 10-K/A due to accounting errors, by searching 10-K Wizard for restated financial statements. They include in their empirical tests only 161 restatements that have available earnings and price data. Among them revenue recognition problems account for 17 per cent of the earnings restatements and 11 per cent are due to errors in restructuring charges.

Similarly, Agrawal and Chadha (2002) examine a sample of restatements announced from January 1, 2000 to December 31, 2001 by keyword searches from Lexis-Nexis, Newspaper Source and Proquest Newspapers. They identify 303 cases of restatements of quarterly or annual earnings because of accounting problems during the two-year period.

The GAO Report (2002) use Lexis-Nexis to search for restatement announcements and identify 919 restatements from January1997 to June 2002 that involve accounting errors or fraud resulting in material misstatement of financial results. The number grows from 92 in 1997 to 225 in 2001, and the projected number for 2002 is 250. When grouping these restatements by reasons, the error in revenue recognition is found to be the most common reason, accounting for 38 per cent of the restatements. Cost or expense-related issues are the next most frequently identified reason, accounting for almost 16 per cent of all the restatements in the sample.

The Huron Consulting Group (2003) performs a keyword search for all 10-K/A and 10-Q/A filings in the EDGAR database from 1998 through 2002. They refine their search to restatements due to accounting errors and exclude restatements due to accounting principles changes and non-financial related restatements. Their results show 993 restatements filed from 1998 to 2002, and the number rises from 158 in 1998 to 330 in 2002. Revenue recognition is shown to be the leading cause for earnings restatement, accounting for 20 per cent of the sample restatements.

Wu (2002) obtains the sample of restatement firms by manual search of online news libraries including Lexis-Nexis, Dow-Jones Library and ABI/Info databases. She identifies a total number of 1221 earnings restatements due to accounting misrepresentation, irregularities, fraud or errors from January 1, 1977 to December 31, 2001. In her sample, the number of restatement firms is in single digit from 1977 to 1982, and remains stable at less than 50 from mid-1980s to mid-1990s. The number soars to 96 in 1998 and reaches 153 in 2001, with a peak of 204 in 1999. When classified by reasons, 487 (40 per cent) of the restatements are caused by errors in revenue recognition, representing the largest category. This is followed by improper record of costs or expenses, accounting for 463 (38 per cent) of all the restatements in the sample.

In summary, various studies uncover a growing number of earnings restatement due to accounting errors or fraud from the late 1990s. They document consensually that problems in revenue recognition is the leading reason for earnings restatement.

2.2.4 Market reactions to earnings restatements announcements and other disclosures of accounting errors

When the accounting error or irregularity is disclosed and the earnings restatement is publicly announced, the market often reacts negatively in a sharp way. Kinney and McDaniel (1989) compute cumulative abnormal stock returns (CAR) for their sample firms that make corrections to quarterly earnings reports in their year-end financial statement footnote. The CAR is computed for each firm quarter from two days after the first erroneous quarterly earnings were reported to five trading days after the 10-K filing date. Their results show a significantly negative CAR of -23.2% for the entire sample during this period.Feroz, Park and Pastena (1991) analyze Accounting and Auditing Enforcement Releases (AAER) issued by the SEC between April 1982 and April 1989, which describe alleged violations of accounting provisions of the securities laws by 188 firms. They examine the CAR for 58 firms among the whole sample with available price and disclosure data. They document a significant CAR of 12.9% from one day before to the day of the first financial press disclosures of the disputed accounting. They also document a significant CAR of -6% from one day before to the day of disclosure of SEC investigations, even though the market has already knowledge about the error at the date of disclosure of the SEC investigation.Dechow, Sloan and Sweeney (1996) examine 92 firms subject to AAER by the SEC for violations of GAAP by overstating their reported earnings from 1982 to 1992. Among these firms, 26 disclose the earnings manipulation problems first in their earnings restatement announcement. For these 26 firms, the average market adjusted stock return on the day of the restatement announcement is -5.7% and significant at one percent level.

Palmrose, Richardson and Scholz (2001) examine the market reactions to restatement announcements of 403 companies that announced and filed amended 10-K or 10-Q from 1995 to 1999. They document a significant CAR of -5.3% on the day of the restatement announcement and a significant CAR of -4% on the day after the announcement. They also report negative CAR (with a mean of -17.4%) in the 120 trading days following the restatement announcement. They find that the severity of the reaction is associated with restatements that include negative information about management integrity and competence. Palmrose and Scholz (2001) document negative raw returns of -11% over the three-day window around the earnings restatement announcements for firms that restate annual or quarterly earnings in amended filings from 1995 to 1999. They provide evidence that the negative reactions are associated with shareholder litigation against the firms.

Anderson and Yohn (2002) analyze 161 firms that restated financial statements and filed a 10-K/A due to accounting errors during the period 1997 to 1999. They find a significant CAR of -3.5% on average during the seven-day window surrounding the announcement of earnings restatement. In the official report of GAO (2002), the 689 publicly traded firms identified as having announced financial statement restatements between January 1997 and March 2002 have suffered a 10% fall in stock prices over the three-day window around restatement announcement.

Wu (2002) report that on average the market reacts significantly negative to the announcements of earnings restatements with a CAR of -11% over a three-day window. Moreover, her results show a significant downward pattern starting about half a year prior to the restatement announcements, and a persistent negative post-announcement drift for up to four months. She suggests that the preannouncement pattern hints at other value-reducing events the restating firms have experienced before their announcements, for example, earnings warnings, missing analysts forecasts, analysts downward revisions or SEC investigations or enquiries. She also suggests that the post-announcement drift is due to the release of additional details pertaining to the restatement and investors incessant revision of their beliefs about the firms economic prospects.However, Turner and Wheatley (2003) find that when firms correct the previous misstatement in the subsequent annual statement without interim announcement regarding the restatement, they can have positive benefits and help achieve the objectives of the aggressive managers. They name this effect as stealth earnings management and document that minimal disclosures of recounted earnings are overlooked by securities markets. They argue that in the case of premature recognition of revenue with minimal disclosure, not only can the same recounted income be recognized twice; comparison of the two years is improved by moving the income from one year to the next.

In summary, many empirical studies on earnings restatements report significantly negative abnormal market returns for the restatement firms over the short-window around the announcement of earnings restatement. Some studies report the continuous negative market drift up to four months after the restatement announcement.

2.2.5 Qualitative attributes and economic incentives leading to earnings restatement

There is mixed evidence on the qualitative information of the restatement firms, especially when the studies are conducted during different time periods. Kinney and McDaniel (1989) analyze the economic characteristics of firms that correct previously reported quarterly earnings in the footnote to their year-end financial statements. They find that the earnings restatement firms are smaller, less profitable, have higher debt, are slower growing and face more serious uncertainties relative to their industries. DeFond and Jiambalvo (1991) report that restatement firms tend to have diffuse ownership, lower growth in earnings, relatively fewer income-increasing alternatives within GAAP, and are less likely to have audit committees compared to control companies without restatements. Turner and Sennetti (2002) find that their sample of restatement firms that correct previously issued erroneous statements from 1981 to 1995 have higher debt ratios and lower asset size, revenue, income, and profitability ratios compared with other companies in the same industry.

Recent studies identify a growing trend of large firms making earnings restatement and the restatement firms tend to be high growth firms or in high growth industries (e.g., software industry). GAO Report (2002) identifies an increase in the size of the restatement firms. The average size by market capitalization of a restating company increases from $500 million in 1997 to $2 billion in 2002. Richardson, Tuna and Wu (2002) examine firms with earnings restatement involving only SEC filed annual reports from 1971 to 2000. Their results indicate that restatement firms tend to be high growth firms that are under great pressure to inflate earnings to meet or beat analysts expectations. They also document that restatement firms have higher industry-year-adjusted leverage, have reported consistent increases in quarterly earnings and have consistently reported small positive earnings surprises in the period leading up to the alleged manipulation. Yet they do not find difference between restatement firms and non-restatement firms with respect to profitability or size. The potential problem with looking at reported rather than corrected earnings in their paper, however, is that the reported number has already incorporated the impact of aggressive accrual choices.

Other studies examining firms making earnings manipulation or accounting errors have similar findings. Kreutzfeldt and Wallace (1986) find that firms with profitability problems also have larger and more frequent accounting errors. Dechow, Sloan and Sweeney (1996) find that firms subject to SEC Enforcement Actions have weaknesses in their internal governance structures relative to the control firms. They also find the manipulating firms have higher market to book ratios and are highly leveraged.

Most previous studies suggest that financial distress may motivate management to engage in more aggressive positions in reporting practices, e.g., to raise external financing and to camouflage or avoid violations of debt covenants by increasing net income. Within firms there is great pressure for sales to meet quarterly growth goals. Individuals whose compensation packages are pegged to sales are also expected to chase sales targets. DeFond & Jiambalvo (1991) find that accounting errors are motivated by the same type of economic incentives that influence managers management of accruals, for example, bonus compensation incentive and debt covenant incentive. Richardson et al (2002) conclude that both explicit contractual arrangements such as bonus plans and debt covenants and heightened capital market pressures have created incentives for firms to engage in aggressive accounting principles.

2.3 Role of financial analysts and their earnings forecasts Securities analysts play an important role in providing investors with information that may affect investment decisions. Through the search of the current and prospective financial conditions of certain publicly traded companies, they report earnings forecasts for the companies and make recommendations about investing in those companies securities based on their research. The research explores information about the company and its businesses, the industry, the product or sector; public statements by and interviews with executives of the company and its customers and suppliers. Yet the growing number of earnings restatements and the accompanying problems in financial reporting bring about many criticisms on the financial analysts roles. According to GAO Report 2002, many of the securities analysts recommend investment in now-bankrupt companies and fail to downgrade ratings for those companies before the accounting problems are disclosed, such as in the cases of Enron and WorldCom. The gatekeepers role has been seriously compromised.

This study examines the role of financial analysts with respect to earnings restatement by investigating their earnings forecasts for earnings restatement firms as well as for non-restatement firms. For the purpose of this study the previous literature on financial analysts earnings forecast is reviewed, followed by a review of the association of analysts forecasts with irregular events both within and outside the capital markets.

2.3.1 Properties of financial analysts earnings forecast (FAF)There is extensive research exploring the properties of financial analysts earnings forecasts and their implications. Two major properties frequently covered are the accuracy and the dispersion of analysts earnings forecast.Accuracy of FAFMuch research has been conducted to evaluate the accuracy of FAF collected from different sources at different forecast horizons by employing different time-series benchmark models, error measures and statistical tests. The findings of these studies, though not in unanimous agreement, tend to suggest that analysts produce earnings predictions that are more accurate than those generated by time-series models (Brown and Rozeff, 1979; Fried and Givoly 1982; OBrien 1988; and Alexander 1995).

Previous studies on the superiority of FAF to time-series models suggest that FAF contains comprehensive information including macroeconomic events, industry information and firm-specific non-accounting information, while time-series models rely exclusively on accounting information. Compared with time-series models, FAF appears to have both a contemporaneous advantage and a timing advantage (Brown, Hagerman, Griffin and Zmijewski 1987a). The contemporaneous advantage means that financial analysts can better use information existing on the date that time-series models can be initiated, and the timing advantage means that the financial analysts can use information that occurs after the cut-off date for the time-series data but before the report of the analysts forecast.

Some research has been extended to examine the relationship between the superiority of FAF and the firms information environment. Brown et al (1987) argue that financial analysts superiority is positively related to the dimension of the FAF information set and negatively related to both variance of the interim observations of earnings and the correlation between the information variables. They use firm size, divergence of analysts opinions and number of lines of business as proxies for the above three variables. Their sample consists of 168 quarterly forecasts from Value Line and 702 annual forecasts from I/B/E/S from 1977 to 1982. Their results are consistent with the information interpretation of the FAF superiority. Kross et al (1990) collected FAF from Value Line for 279 firms from 1973 to 1981. Their results show that the advantage of FAF over time-series models is related to the variability of historical earnings and the extent of coverage in The Wall Street Journal, which is consistent with the proposition that analyst advantage increases with increases in information gathering incentives and information dissemination activities. However, they fail to find a positive relation between analyst advantage and firm size, as documented in Brown et al (1987c). Studies also show that the accuracy of FAF is related to firms financial risk and business risk, and the error in earnings forecasts is analytically associated with the uncertainty that a firm faces. Cukierman and Givoly (1982) develop a model of earnings expectations and they show that the cross-sectional error in earnings forecasts is the correct empirical counterpart of uncertainty; that is, of the dispersion of the distribution of expected earnings. Their model also implies (and is confirmed by empirical tests) that the cross-sectional error is positively associated with the dispersion of forecasts across forecasters. Ciccone (2003) tests three measures of forecast error, namely, the absolute value of the difference between the mean annual earnings forecast at fiscal year end and the actual annual earnings, the absolute value scaled by actual earnings, and the absolute value scaled by price as of fiscal year end. He finds that for all the US firms listed on the NYSE, AMEX and NASDAQ from 1978 to 1996, the forecast error has a positive relationship with the standard deviation of annual earnings in the three previous years prior to the year of forecast, no matter which measure of forecast error is used. Moreover, the firms with large forecast errors are more likely to have negative earnings and earnings declines. He concludes that firms that are distressed have systematically higher forecast error. Additionally, firms with high business risk, as measured by earnings standard deviation, tend towards higher forecast error. Information content of FAF

Studies on the information content of analyst forecast have examined the association between FAF and the securities market, and they conclude that FAF is a better surrogate for market expectation. Fried and Givoly (1982) provide evidence that the forecast errors of FAF are more closely associated with security price movements than are the forecast errors from the submartingale model and the index model. They conclude that FAF provides a better surrogate for market expectations than forecasts generated by time-series models. Brown, Hagerman, Griffin and Zmijewski (1987b) investigate the relationship between abnormal returns and five alternative proxies for the markets assessment of unexpected quarterly earnings. Their results show that the unexpected earnings based on FAF explains abnormal returns better than other proxies. Alexander (1995) finds that the forecast errors of the most recent analysts forecasts are more closely correlated with the abnormal returns than the errors of the less recent analysts forecasts and the forecasts derived from time-series models.

Dispersion of FAF

Prior research has examined the relationship between dispersion in analysts earnings forecasts and the uncertainty about firms future economic performance and provided empirical evidence on such relationship. Givoly and Lakonishok (1984) argue that the level of forecast dispersion is perceived by investors as valuable information about the level of uncertainty concerning firms future economic performance. Forecast dispersion is suggested to reflect both uncertainty and lack of consensus among market participants about future events (Barry and Jennings 1992; Barron, Kim, Lim and Stevens 1998). Givoly and Lakonishok (1988) examine the relationship between dispersion of FAF, used as a measure of uncertainty, and the stock properties, particularly risk characteristics, such as the beta (computed over the two years preceding the year for which the correlation is compared), marketability (shares traded during the year as a percentage of shares outstanding), firm size (natural logarithm of the market value of the firms equity at the end of the year), and earnings growth variability (measured as the standard deviation of the rate of growth in EPS over the years 1961-1980). They find a positive and significant association between forecast dispersion and the traditional market-based risk measure (beta) and the accounting-based risk measure (earnings growth variability), and a negative although insignificant correlation between size and forecast dispersion. They also find a positive association between forecast dispersion and marketability. Malkiel (1981) uses a measure of the dispersion among Wall Street security analysts concerning the future earnings and dividend growth of the company as a risk variable, and he compares this risk variable with other risk variables such as beta, inflation risk, interest rate risk, and economic activity risk with respect to expected returns. His results show that dispersion of analysts forecast produces the highest correlations with expected returns with the highest significance. He suggests that companies for which there is a broad consensus with respect to future earnings and dividends seem be less risky than companies for which there is little agreement among security analysts. He concludes that dispersion of FAF is the best single measure of systematic risk available.

Imhoff and Lobo (1992) use dispersion in analyst forecast as a measure of ex ante earnings uncertainty, which may reflect either the fundamental uncertainty of a firms future cash flows or noise in the financial reporting system. They measure dispersion of analysts forecasts reported in the month prior to actual annual earnings announcements from 1979 to 1984 by calculating the standard deviation of the forecasts for each period deflated by the stock price two days prior to the earnings announcement. They then divide firm-years into three strata based on the ranking of the earnings uncertainty. Their results show a negative relationship between Earnings Response Coefficient and forecast dispersion, which is consistent with the argument that dispersion reflects uncertainty. They further conclude that the earnings uncertainty reflected in the forecast dispersion originates largely from noise in the earnings signals and that the greater ex ante earnings uncertainty is a signal of lower quality of the earnings information. Barron and Stuerke (1998) construct a forecast dispersion measure from forecasts that are revised during the first 30 days following announcements of either prior year annual earnings or current year interim earnings, and calculate it as the standard deviation of revised forecasts following the earnings announcements divided by the mean revised forecast. By doing so, they argue, the spurious dispersion caused by stale forecasts can be controlled, and the forecasts reported are conditioned on a public announcement. They compile their forecast observations from I/B/E/S Detail data from 1990 to 1994, and find a positive association between ex ante dispersion and the magnitude of price reactions around subsequent earnings releases, even after controlling for other measures of uncertainty like beta and the variance of stock returns. They postulate that dispersion in FAF serves as a useful indicator of uncertainty about the price relevant component of firms future earnings.

In summary, dispersion among analyst forecasts is indicated to reflect uncertainty of the firms future economic performance, though whether such uncertainty originates from the uncertainty of the underlying future cash flows or the noise in the accounting information is not resolved. 2.3.2 Analysts forecasts and irregular events Some studies have related the research on analysts forecasts to certain events outside the security market to test how the properties of analysts forecasts change with respect to these events. They have drawn inference on the association between analysts forecast for the firm and the specific event. Moses (1990) examines differences in FAF properties between failing and healthy firms and investigates whether measures developed from FAF are useful indicators of impending bankruptcy. He studies firms that declared bankruptcy from 1977 through 1985 and collects FAF data from I/B/E/S Summary data for these firms for four years prior to bankruptcy. He then matches each bankrupt firm with a non-bankrupt firm from the same industry and of approximately the same size resulting in a total sample of 136 firms. His results show that compared with the healthy firms, the failing firms have significantly larger error in forecast EPS up to as early as 4 years prior to failure and significantly greater increase in forecast errors from year 2 to year 1 prior to bankruptcy. They also have larger forecast dispersion from as early as three years prior to failure than the healthy firms do. The bankrupt firms have consistently increasing dispersion in forecasts both within and across years in the three years prior to failure. These results are consistent with the notion that uncertainty increases as failure approaches. He concludes that analysts forecasts do reflect conditions that are associated with failure, and analysts forecasts are of poorer quality for firms approaching failure. Dechow, Sloan and Sweeney (1996) examine the forecast dispersion for firms subject to alleged violations of GAAP according to AAER. They measure forecast dispersion as the standard deviation of analysts forecast of current-year earnings reported in the month of the firms fiscal year-ends. They compare the median dispersion of analysts forecasts in the three years prior to the median dispersion of analysts forecasts in the three years following the year allegation of earnings manipulation is announced. They find a significant increase in the dispersion of analysts forecasts for the alleged firms from pre-announcement period to post-announcement period, but not for the control firms. They interpret this finding as consistent with investors revising downward their beliefs about both the firms future economic prospects and the credibility of the firms financial disclosures once the earnings manipulation is disclosed. Griffin (2002) examines the response of First Call financial analysts to corrective restatements and disclosures that lead to securities fraud litigation and measures the response in terms of forecast coverage and forecast accuracy around a corrective disclosure. His sample is composed of 731 U.S. exchange-traded firms that are alleged of fraud in federal class actions with end of class period dates between June 27, 1994 and March 31, 2001. He uses median EPS forecast reported in each month for the current fiscal year as the forecasted earnings to derive the forecast error. His results show that the number of analysts covering companies with corrective disclosures declines significantly in the months after the disclosure, but not in anticipation. Moreover, the analyst forecast error is essentially unchanged prior to a corrective disclosure month, decreases significantly in the disclosure month and the following month, and changes little thereafter. He suggests that financial analysts are reluctant to follow companies with the bad news of corrective disclosure, and that financial analysts demonstrate little ability to anticipate such bad news. However, few studies have provided comprehensive evidence on the information content of financial analysts forecasts for earnings restatement firms by examining the FAF prior to the restatement announcements. The analysis on the properties of analyst forecasts for the earnings restatement firms is even limited in the literature.CHAPTER 3 RESEARCH QUESTIONS AND HYPOTHESES DEVELOPMENT

3.1 Objectives and research questionsThe late 1990s witness a growing number of firms restating their previous financial statements due to accounting errors, irregularities or fraud. These kinds of earnings restatement indicate that the previous financial statements of restatement firms contain inaccurate information and lack reliability. In response to the restatement announcement, stock price drops sharply, shareholders file lawsuits, and management resigns. In consideration of these negative consequences, we are particularly interested to examine whether the financial analysts (as market intermediaries) have predictive knowledge of the misstatement and the subsequent earnings restatement, and whether the market in aggregate has prior wisdom of the restatement. Furthermore, we compare the distributions of analysts ex ante forecasts for the restatement firms versus for the non-restatement firms. Specifically, we aim to address the following four questions: 1). Can financial analysts predict the subsequently corrected earnings of the restatement firms when the erroneous numbers were reported? 2). Is there any difference in the distribution of ex ante financial analysts earnings forecasts (FAF) for restatement and non-restatement firms? 3). Does the market have aggregate wisdom about the circumstances leading to the restatement announcement, and moreover, does the market incorporate the information conveyed through the distribution of ex ante FAF for earnings restatement firms? 4). Do the properties of ex ante FAF distribution for the restatement firms capture risk aspects of these firms?3.2 Hypotheses development

3.2.1 Financial analysts predictive ability of the restatement firms erroneous earnings information

FAF is documented to be superior to nave time-series estimate model in terms of accuracy. One argued reason is that financial analysts can capture comprehensive information relevant to the company (Fired and Givoly 1982; Brown et al 1987a). However, when the actual earnings information is misstated either erroneously or purposely, the benchmark against which the forecast accuracy is often measured is being called to question. This happens in the case of earnings restatement, when restatement firms disclose the previous mistakes and restate the previously reported earnings numbers. This irregular event provides a special setting to examine the accuracy of analysts forecasts from a unique perspective. Does the accuracy of financial analysts forecasts for the misstated period still hold when the inaccurate earnings information has been restated? Do the analysts earnings forecasts have predictive power for the corrected earnings information as early as in the misstated period? To answer these questions, we examine the analysts forecast error using the originally misstated and subsequently restated earnings numbers respectively. Figure 1 depicts the time chronology of the misstatement and restatement, and the forecast period of the earnings forecasts we propose to examine in this study. Earnings forecast for the misstated period

time Misstated period Announcement of earnings restatement Figure 1 Earnings forecast for the misstated periodThe misstated period could span one quarter, several quarters, one year or several years. We are interested to examine the earnings forecasts for the whole misstated period so as to test the predictive ability of financial analysts to misstated information.We use forecast error to investigate the accuracy of earnings forecasts. Following Moses (1990), we define forecast error (FE) as the absolute difference between actual (misstated and restated respectively) earnings and the forecasted earnings.

The forecast error for period t with misstated earnings number is:

,where is the misstated earnings per share in period t as previously reported, is the most current earnings per share forecast reported before the earnings announcement of period t. We take the most recent earnings forecast for period t because it is documented that the most current forecast dominates the mean and median forecasts in accuracy (OBrien 1988). Correspondingly, the forecast error for period t with restated earnings number is:

where is the restated earnings number in t as corrected by the subsequent restatement.

Previous studies suggest that financial analysts are sophisticated and experienced in collecting and processing comprehensive (accounting and non-accounting) information about the company, the industry and the economy in forming earnings expectations (Fired and Givoly 1982; Brown et al 1987c; Alexander 1995), and they report that analysts forecasts are superior to nave time-series models in terms of accuracy. If this is the case, we expect that the financial analysts have predictive ability of the misstatement so as to detect the true earnings information of the restatement firms during the misstated period. In other words, the FAF for the misstated period is closer to the restated earnings than to the misstated earnings, i.e. .

In reality however, the relationship between forecast error and financial analysts ability to collect and process information may be manipulated by management. Specifically there are two scenarios. The managers may purposely misstate their earnings to meet the financial analysts earnings forecasts (e.g., Degeorge et al 1999). They may also intentionally provide earnings guidance to analysts, misleading the analysts forecasts to the misstated earnings (e.g., Matsumoto 2002). If these two scenarios dominate, it is possible that the FAF for the misstated period is closer to the misstated earnings than to the corrected earnings, i.e., .Another overlaying reason that may cause the forecast errors measured using restated earnings numbers to be larger than the forecast errors using the misstated earnings numbers is the well-documented positive bias in analysts forecasts (OBrien 1988, Easterwood and Nutt 1999, Abarbanell and Lehavy 2003). When the earnings restatement decreases the previously reported earnings numbers, as most cases of earnings restatement do, the positively biased analysts forecasts will have larger forecast errors using the restated earnings numbers than using the misstated earnings.In consideration of the above situations, we hypothesize:

: The forecast error of FAF for the misstated period is larger when the actual earnings are measured as the misstated earnings than as the restated earnings.The rejection of this hypothesis is an indication that the analyst forecasts are closer to the restated earnings numbers and that the financial analysts have predictive ability of the true earnings information despite the manipulation or misleading guidance from the management. On the contrary, the failure to reject this hypothesis may result from the management manipulation or the inefficiency of analyst forecasts as mentioned earlier.

3.2.2 The properties of ex ante FAF distribution for restatement firms

The properties (e.g., accuracy, efficiency and dispersion) of FAF are shown to be associated with firms future performance and capture aspects of risk (Givoly and Lakonishok 1984). As FAF are expected to reflect macroeconomic, industry and firm-specific information, the distribution of FAF may differ systematically across firms depending on the conditions faced by the firms. In a sense FAF distribution may reflect information relevant to predicting future events.

The event of earnings restatement provides a special setting to examine the information conveyed through analysts earnings forecast. The announcement of earnings restatement itself can bring tremendously negative consequences to the restatement firms like stock price decline and shareholder litigation. It is a signal of potential cost to be incurred on the restatement firms, such as litigation cost, SEC penalty, and monitoring cost. Thus the restatement announcement may negatively influence the investors expectations on the firms future cash flows and therefore firm value. It also deteriorates investors perception of the managements credibility and the financial reporting quality.

We are interested in investigating whether the properties of ex ante FAF convey information associated with the conditions that lead to subsequent restatements. To evaluate this question, we examine whether there is difference in the properties of FAF for restatement firms and non-restatement firms. Specifically we examine the properties of FAF from three aspects, namely, the size of the forecast error, the forecast dispersion, and the skewness of FAF distribution.The earnings forecast studied hereof is the current-year forecast for the annual earnings of the fiscal year preceding the fiscal year in which the restatement is announced, and is therefore ex ante to the restatement announcement, as depicted in Figure 2. For example, if the earnings restatement is announced in fiscal year t, then we take the analyst forecasts for annual earnings of fiscal year t-1 as the ex ante earnings forecast.

Ex ante earnings forecast

t-3 t-2 t-1 t time Announcement of earnings restatementFigure 2 Ex ante earnings forecast

The time lag between the misstating of financial results and the announcement of the restatement decision may differ across firms. We categorize the restatement firms into three groups based on the time lag. Suppose the restatement firms announce their restatement decisions in fiscal year t. The first category of firms began the misapplications of accounting methods from year t-2 or earlier, so that the erroneous financial results have been released at the time the financial analysts make current-year earnings forecasts for fiscal year t-1. The second category of restatement firms began their accounting misconducts from fiscal year t-1, so that the erroneous interim financial results are reported during the process the analysts make and revise current-year forecasts for fiscal year t-1. The third category of restatement firms began their accounting misstatement in fiscal year t, which means the beginning of the misstated period is within the same fiscal year of the restatement announcement. This happens when a firm decides to restate its first three quarters financial results at the fiscal year end. In this case the firms have not yet began their accounting misconduct at the time the analysts make forecasts for annual earnings of year t-1 and therefore their forecasts are not influenced by the release of the erroneous financial statements. We include the first two categories of restatement firms in our analysis while excluding the third category of restatement firms. The latter offers too short a time lag between the misstatement and the restatement for any significant impact on the analysts ex ante forecasts. To construct a group of non-restatement firms for future analysis, we match each restatement firm with a firm from the same industry and of approximately the same size without a history of earnings restatement. Matching on industry is desirable to control for industry-specific endogeneity. Forecast uncertainty may be related to industry and forecast revisions may result from industry-wide information events (Bhushan 1989). Matching on size is justified by the association size can have with risk and analyst attention (Fama and French 1992; Bhushan 1989; Imhoff and Lobo 1992). Forecast error

Forecast error has been shown analytically to be an appropriate indicator of uncertainty (Cukierman and Givoly 1982; Lang and Lundholm 1993; Ciccone 2003). The issue of interest here is whether there is difference in forecasts accuracy for restatement firms versus non-restatement firms. If restatement firms are associated with conditions characterized by more uncertainty about their future performance and credibility in financial reporting prior to their restatement announcement than the non-restatement firms, and this uncertainty is reflected in the forecast error for the fiscal year prior to the earnings restatement, then we expect the ex ante forecast error for restatement firms to be larger than that for the non-restatement firms.

: The forecast error of ex ante FAF is larger for the restatement firms than for the non-restatement firms.

Recall that forecast error was previously measured as

EMBED Equation.3 ,

is the mean forecast for year t-1 across forecasters, with year t being the fiscal year in which the earnings restatement is announced. The above hypothesis expects that restatement firms have larger than non-restatement firms.

Forecast dispersion

Forecast dispersion measures the cross-sectional variation of analysts earnings forecasts around the average forecasts. It reflects the divergence of the analysts beliefs about the firms future economic performance and is often interpreted as an earnings uncertainty measure. Studies on forecast dispersion find empirical evidence that forecast dispersion is associated with the firms earnings uncertainty and other commonly employed risk variables (Givoly and Lakonishok 1984, 1988; Daley 1988; Swaminathan 1991; Imhoff and Lobo 1992; Barron and Stuerke 1998). Uncertainty about future earnings stems from two sources: one is the difficulty of predicting future cash flows, the other is the noise created by the accounting system itself (Givoly and Laknoshishok 1988). When the financial analysts formulate earnings forecasts for the restatement firms, they may be influenced by the misstated earnings information which has been released publicly. Compared with the non-restatement firms, there is more earnings uncertainty about the restatement firms. Uncertainty arises because of the firms underlying economic performance as well as the credibility of their financial reporting. Therefore we hypothesize a higher forecast dispersion of ex ante FAF for restatement firms than for non-restatement firms.

: The forecast dispersion of ex ante FAF is larger for restatement firms than for non-restatement firms. Different measures of forecast dispersions have been employed in the empirical studies. Following Moses (1990), we take the standard deviation of forecasts across multiple forecasters for fiscal year t-1 as a measure of forecast dispersion.

.Hypothesis 3 suggests that the restatement firms have a higher than that of the non-restatement firms.

Skewness of ex ante FAF distribution This study extends the study of ex ante FAF distribution to its skewness, though few previous studies have made such effort. We try to make a thorough study of the ex ante FAF distribution by further examining whether there is difference in the skewness of the distribution of ex ante FAF for restatement firms and non-restatement firms.The skewness measures how the distribution of ex ante FAF deviates from symmetry. A left-skewed distribution indicates that there are more extreme values in the lower end of the distribution, while the opposite holds for the right-skewed distribution. If the uncertainty over earnings of restatement firms were to result in more cases of extreme low earnings forecasts, we would expect the distribution of ex ante FAF for restatement firms to be more left-skewed than that for the non-restatement firms.

: The FAF distribution is more left-skewed for the restatement firms than for the non-restatement firms.We develop a measure of skewness of analysts earnings forecast as the follows:

,

where is the mean forecast for the annual earnings of year t-1 across n forecasters, is the standard deviation of the analyst forecasts, and n is the number of forecasters following a specific firm. A negative SKW means that the distribution of FAF has a longer tail in the negative direction and therefore more extreme values in the lower end of the distribution. The above hypothesis suggests that the distribution of ex ante FAF for restatement firms has lower SKW than that for the non-restatement firms.

3.2.3 The markets aggregate wisdom of the restatement announcement and its incorporation of the information about the restatement conveyed through FAF

Markets prior knowledge of the earnings restatement

Since the restatement announcement often triggers a series of negative consequences, especially the negative responses in the capital market, we are interested to examine whether the market in aggregate has prior knowledge of this event. To do so we look into the abnormal returns prior to the earnings restatement announcements. Wu (2002) finds that the market starts to exhibit the decline six to eight months ahead of the announcements and that the market has already had a cumulative abnormal return of -15% before the announcement is made. To make our results comparable with hers, we also investigate the cumulative abnormal returns over the window from 120 days prior to the restatement up to the event date (CAR (-120, 0)). If market has some prior knowledge of the restatement, then we expect CAR (-120, 0)