long-run stock returns following briloff's analyses

CFA Institute

Long-Run Stock Returns following Briloff's AnalysesAuthor(s): Hemang Desai and Prem C. JainSource: Financial Analysts Journal, Vol. 60, No. 2 (Mar. - Apr., 2004), pp. 47-56Published by: CFA InstituteStable URL: http://www.jstor.org/stable/4480556 .

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Long-Run Stock Returns Following

Briloff's Analyses

Hemang Desai and Prem C. Jain

Abraham Briloff is well known for more than four decades of insightful analysis and criticism of the accounting practices of various companies. His critiques, in the form of articles published in Barron's, consist of detailedfinancial analyses of the questionable accounting practices of the companies he examines. Previous research has shown that the companies criticized by Briloff in Barron's experience significant negative abnormal returns around the article's publication date. To understand the valuation effect associated with hisfinancial analyses, this article examines long-run abnormal returnsfollowing the publication date. In addition to the initial negative reaction on publication of the articles, the companies in the sample experienced further significant risk-adjusted returns for one and two years of, respectively, -15.51 percent and -22.88 percent. The results show that a decline in future operating performance appears to be an important reasonfor the poor stock market performance of the companies. Thus, Briloff could apparently foresee the coming decline in operating performance better than the market could. These results underscore the importance of understanding a company's accounting and of the role of carefulfinancial statement analysis.

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,4 braham Briloff, a retired professor of accounting and author of several books, has been well known since the 1950s. In several of his articles published in Barron's

(the first article was published in 1968 and the latest one was published in October 2000), he analyzed company-specific financial statements and provided scathing criticisms of the companies' accounting practices. From a reading of his articles and private conversation with him, we have con- cluded that his articles are based almost exclusively on publicly available information. He focuses pri- marily on financial statements and does not usually address a company's products, the economy, interest rates, or other variables that many analysts frequently incorporate. We report here a study of the valuation implications of his analyses.

Analysis of the sample of companies criticized by Briloff provides at least three advantages. First, it is a unique, albeit small, sample that allows

analysis of the outcomes of "pure" financial statement analysis. Second, in a large sample from several analysts (such as the one from Zacks Investment Research), competent analysts are combined with not-so-competent analysts; therefore, the power of statistical tests to discern the impact of either type of analyst would be low. Third, a fair amount of evidence in recent years suggests that analysts' reports in general are biased and that analysts whose employers have investment banking relationships are unlikely to be critical of the companies they cover.1 Briloff is an independent analyst who not only does not work for a firm with conflicting interests but who has never accepted any direct remuneration (other than small honorariums) for his Barron's articles.2 We are not suggesting that Briloff is the only independent analyst, but his body of work provides a useful laboratory.

Prior research by Foster (1979, 1987) showed that companies criticized by Briloff experience significant abnormal returns of -8.6 percent, on average, on the first day after the publication of the critique and no reversal of that trend for 30 days following the initial reaction. We discuss in this article our study of the stock market performance of the companies criticized by Briloff for three years

Hemang Desai is associate professor of accounting at Cox School of Business, Southern Methodist University, Dallas. Prem C. Jain is Elsa C. McDonough Professor of Accounting and Finance at McDonough School of Busi- ness, Georgetown University, Washington, DC.

March/April 2004 47



Financial Analysts Journal

after the publication of the criticism. Our motiva- tion for examining the long-run stock market performance had two aspects. First, following the market's negative reaction to the publication of Briloff's analysis, several companies responded that his criticism was without merit, that the market's initial negative reaction was excessive, and that a reversal would occur in the long run. For example, both Waste Management and Arthur Andersen LLP responded to Briloff's article on Waste Management (6 August 1990) by vigorously defending Waste Management's accounting practices and Arthur Andersen's audit of the company (see "Taking Exception" 1990). James Koening, vice president, treasurer, and chief financial officer of Waste Management, said that Waste Management had properly applied the accounting rules and that "sophisticated investors will have understood the academic and inconsequential nature of the issues raised" by Briloff. Examination of long-run performance allowed us to examine the validity of such claims. If the initial stock price reactions were negative because of Briloff's reputation and not because of substantial information in the articles, research should observe a reversal in stock prices following the initial negative reaction.3

The second reason for our interest in a long-run study is that only the long run allows a full understanding of the magnitude, speed, and permanence of the market's adjustment to information con- tained in Briloff's financial statement analyses. An examination of the long-run stock market performance is also motivated by empirical evidence in recent years documenting a postevent or postan- nouncement drift in stock prices for a few months following news that moves stock prices.4

Upon finding negative stock price movements following Briloff's critiques, we went on to investigate potential sources of the price declines- specifically, operating performance of the compa-

nies before and after the publication of the articles. Essentially, we were interested in finding the underlying variable that Briloff may be forecasting.

Data Our sample came from reading all the 31 articles published by Abraham Briloff from 1968 through 1998. The final sample consisted of 48 companies that were criticized by him in these articles and for which data were available in the Center for Research in Security Prices (CRSP) files. The sample contains no time clustering because the company names came from 17 different years; the largest number in any given year (1977) is nine. No industry clustering occurred because the 48 companies represent 30 different four-digit SIC industries.

Table 1 presents summary statistics on company size, ratio of book value to market value (BV/MV), and beta. Using the breakpoints based on the NYSE/Amex universe of companies in the CRSP files, we also computed the sample companies' size deciles. For a company in the sample in a given year, a decile of 10 implies that the company was among the top 10 percent of all NYSE/ Amex companies (and a decile of 1 implies the bottom 10 percent). These data on average size of the companies in terms of market value of common shares and on size deciles show that the companies in the sample are large. Thus, these companies are likely to be followed by several analysts. One would expect the information asymmetry for these companies, therefore, to be low and the incremental impact of financial analysis by Briloff to be relatively small.

The book values on which Table 1 is based came from the Compustat files. Because not all the companies were available on Compustat, the sample size for constructing the BV/MV quintiles was

Table 1. Summary Statistics for Sample Companies Criticized by Briloff, 1968-98

Number of Standard Variable Observations Mean Median Deviation

Market value of equity ($ millions) 48 3,609.11 277.22 13,528.26

Size decile rank 48 8.15 8.00 1.76

BV/MV 41 0.47 0.31 0.48

BV/MV quintile rank 41 2.25 2.00 1.37

Beta (relative to CRSP value-weighted index) 48 1.72 1.58 0.81

Beta (relative to CRSP equal-weighted index) 48 1.34 1.19 0.56

Notes: Market value of equity and BV/MV were measured in December of the previous year relative to the publication month of Barron's. Companies were sorted as of June each year. Sample companies' size decile rankings and BV/MV quintile rankings were based on values in June prior to the publication of the article in Barron's.

48 ?2004, AIMR?



Long-Run Stock Returns Following Briloff's Analyses

reduced to 41. The average BV/MV of 0.47 and the corresponding quintile rank of 2.25 suggest that in terms of BV/MV, the companies were not extreme. The median BV/MV quintile ranking, however, indicates that the companies had a somewhat smaller BV/MV than average.

We used the one-factor market model (Fama 1976; Brown and Warner 1980) to compute beta from Month -60 to Month -13 relative to the publication month and required the availability of a minimum of 12 monthly returns.5 The average betas of the companies based on the CRSP value-weighted index and equal-weighted index indicate that the sample consists of companies with above-average betas. Therefore, to the extent that we focus on market- adjusted returns, our results are conservative.

Research Methodology We defined Month 0 as the calendar month in which Briloff's article appeared in Barron's. Because we examined long-run abnormal returns, we used buy-and-hold or holding-period returns in all our analyses. The buy-and-hold return for stock i for T months was computed as

T

RiT = ((l+rit)-l?.O (1)

t=1

where rit is the raw return (with dividends) for stock i in month t. Similarly, RmT represents the buy-and- hold return for the benchmark market index. We report market-adjusted returns with the CRSP equal-weighted index representing the market.6

The main purpose of reporting market- adjusted returns was to calibrate the level of under- performance. The p-values for statistical significance are based on two bootstrap procedures that controlled for several company-specific characteristics.

The buy-and-hold abnormal return for stock i was calculated as

ARiT = RiT - RmT- (2)

The portfolio abnormal return is then the average over all the n stocks in the sample:

1l1 AART = 1 ARiT(

We used several methods to compute the statistical significance levels for portfolio returns. Our procedures are a combination or modifications of approaches discussed in Kothari and Warner (1997), Barber and Lyon (1997), Lyon, Barber, and Tsai (1999), Daniel, Grinblatt, Titman, and Wermers (1997), and Desai and Jamn (1997). We report p-values from two computer-intensive bootstrap procedures.

In the first procedure, we matched each company in the sample with a control company that was in the same industry (two-digit SIC code) in the month of the publication of the article in Barron's. We then computed buy-and-hold returns for the portfolio of matched companies in the same manner as for the portfolio of companies in the sample. We repeated this procedure 5,000 times with replacement to generate an empirical distribution of buy- and-hold returns under the null hypothesis. From this distribution, we computed p-values for the returns on the portfolio of sample companies. The p-values represent the fraction of portfolios whose returns were smaller than the corresponding return of the portfolio of sample companies.7

Although this first procedure generally pro- duces unbiased results, we used another bootstrap procedure to test the robustness of results because some researchers have argued that size, BV/MV, and momentum should be controlled for in evalu- ating portfolio abnormal returns. In the second procedure, the selection of control companies was based on size, BV/MV, and return for the prior six months. For brevity, we do not describe the second procedure in detail here. The results were similar to those produced by the first procedure.

We also used the traditional approach to compute the statistical significance of AART by using a parametric t-statistic with n - 1 degrees of freedom given by

AART t = , - (4)

SET! n

where SET is the cross-sectional standard deviation computed from n observations of ARiT.

Stock Price Performance We first present evidence on the stock market performance of the companies in the sample before Briloff wrote the reports. We then examine the returns around the publication of Briloff's articles and the long-run performance of the companies' stocks after publication of their critiques.

Prepublication Performance. An examination of company returns prior to publication of Briloff's articles helps explain the nature of the companies singled out by Briloff. Table 2 shows returns for the two-year period prior to the publication of the articles. Although Briloff did not state directly that the companies were overvalued, his criticism usually suggested that the market might have misinterpreted the company's financial statements, which implies that he considered the stocks to be overvalued. The results in Panel A of Table 2 show that from Month -25 to Month -13 relative to the publication month, the sample companies

March/April 2004 49




Table 2. Long-Run Stock Market Performance of Companies Criticized by Briloff, 1968-98

Raw Return Portfolio Number of Abnormal

Month Period Observations Sample Market Return p-value

A. Industry-matched control group

-25 to -13 45 75.24% 25.03% 50.21% 0.995***

-12 to -2 48 38.88 14.79 24.10 0.993***

-1 48 -0.55 1.68 -2.23 0.103

0 48 -10.10 -0.15 -9.95 0.000***

+1 to 12 48 -4.83 10.68 -15.51 0.018**

+1 to 24 48 4.92 27.80 -22.88 0.093*

+1 to 36 48 26.30 52.35 -26.04 0.230

B. Characteristics-matched control group

-25 to-13 39 68.40% 23.83% 44.57% 0.996***

-12 to-2 41 43.31 15.17 28.13 1.000***

-1 41 -1.74 1.32 -3.07 0.058*

0 41 -11.79 -0.58 -11.21 0.000***

+1 to 12 41 1.41 13.45 -12.04 0.047**

+1 to 24 41 15.16 31.93 -16.77 0.150

+1 to 36 41 38.21 60.90 -22.69 0.335

Notes: The market was proxied by the CRSP equal-weighted index. The p-value in Panel B is the proportion of the control portfolios that had returns lower than that of the portfolio of sample companies.

*Significant at the 10 percent level in a two-tailed test. **Significant at the 5 percent level in a two-tailed test.

***Significant at the 1 percent level in a two-tailed test.

earned mean raw returns of 75.24 percent, compared with a return of 25.03 percent for the market over the same period. The p-value of 0.995 indicates that the sample portfolio outperformed 99.5 percent of the 5,000 control portfolios in a two-tailed test. In the period of Month -12 to Month -2, the sample companies outperformed the market by 24.10 percentage points (pps), with a p-value of 0.993. Thus, the results in the prepublication period suggest that the sample companies were experienc- ing a large run-up in prices.

We also examined various accounting ratios of companies in the sample during this period but did not find any strong patterns. Thus, we did not locate any traditional fundamental reason for large abnormal returns in the prepublication period. We conjecture that the lack of fundamental reasons accompanied by the stock price run-up may have triggered an analyst such as Briloff to take a close look at whether these companies were overvalued.

Announcement Window. We also examined the results around the day of publication and found the abnormal returns to be similar to those reported in Foster (1979, 1987). Table 2 reports the Month 0 results, which are similar to publication-day

results because Month 0 includes the publication day. For a three-day window around the publication day (not shown in Table 2), the average market-adjusted return for the sample, with the market defined as the CRSP equally weighted index, was -8.9 percent. The associated t-statistic of -4.55 is statistically significant at better than a 1 percent level of significance. For a period of Day +2 to Day +30, the average market-adjusted return was -3.55 percent with a t-statistic of -1.58. Thus, the Day +2 to Day +30 abnormal returns are not statistically significant.8

Long-Term Buy-and-Hold Returns. Whereas Foster examined the abnormal returns close to (up to +30 days) the publication of Briloff's articles, we focused on the long-run abnormal returns following the publication month. The last three rows in Panel A of Table 2 present the long-term buy-and- hold results for the sample companies; the p-values were based on the first bootstrap procedure (industry matching).

In the first year following publication of the article (Months +1 to +12), we found that the companies, on average, continued to perform poorly. For Months +1 to +12, the p-value of 0.018 indicates

50 ?2004, AIMR?



Long-Run Stock Returns Following Briloffs Analyses

that only 1.8 percent of the 5,000 portfolios over the same period had raw returns that were smaller than -4.83 percent. Keep in mind that the bootstrap procedure is based on the industry-matching methodology, not on the traditional t-tests based on market-adjusted or risk-adjusted abnormal returns, but the conclusions were similar when we used the traditional approach.9

For a time horizon of two years (Months +1 to +24), Panel A shows the portfolio underperforming the market by 22.88 pps. The corresponding p-value of 0.093 suggests that 9.3 percent of the randomly selected portfolios of companies from the same industry performed as poorly as or worse than the -22.82 percent. For the 36-month holding period, the abnormal returns deteriorated to -26.04 percent, but with a p-value, that is not statistically significant.

Several studies (e.g., Daniel et al.) have documented strong relationships among size, BV/MV, momentum, and future returns. To control for the potential impact of these characteristics on future returns, we replicated the analysis with a set of matching companies that were close to the sample companies in terms of size, BV/MV, and momentum. Momentum sorts followed size and BV/MV sorts; three momentum groups were based on the six-month raw return from December of the previous year to May of the year of publication (i.e., from Month -6 to Month -1 relative to June). Group one corresponded to the companies with the lowest return in the prior six months. Based on the breakpoints of NYSE/Amex companies, each company was then assigned to one of 150 portfolios each June. Similarly, sample companies were assigned to one of 150 portfolios each June. Then, a control company was randomly selected for each sample company such that the control company was of the same size, BV/MV, and momentum ranking as the sample company in June immediately prior to the publication of the article in Barron's. The buy-and- hold return of this control portfolio was then computed. This procedure was then repeated 5,000 times with replacement to generate an empirical distribution of buy-and-hold returns. The results for the sample of Briloff companies measured against the characteristics-matched control group are reported in Panel B of Table 2. The sample size for this analysis was smaller than in the analysis of abnormal return vis-a-vis the industry-matched control group because book value of equity was not available for all the sample companies. The results, however, are similar to those reported in Panel A. The one-year abnormal returns of the sample were -12.04 percent with a p-value of 0.047. The two-year results are not as significant as those in Panel A.

Figure 1 shows the monthly cumulative abnormal returns (with the CRSP equal-weighted index as the proxy for the market) from 24 months before article publication to 36 months after publication. The pattern is consistent with the buy-and-hold returns documented in Table 2. The companies experienced large run-ups in price prior to publication and then sharp declines in performance following publication.10

Overall, the results show that the valuation effect of Briloff's articles is not restricted to the time period immediately surrounding the day of publication. The market continues to adjust to the information at least for one year and possibly for two years after publication of the articles. Because the significant negative abnormal returns continue for two postpublication years, we conclude that the initial negative reaction at the time of the article's publication was an underreaction.

We do not suggest that Briloff was the cause of this downward effect on the stock prices. His articles seem to have triggered a reevaluation by the market, and his analysis apparently helped move prices toward the fundamentals. If Briloff had not written, someone or something else would in time have brought about a revaluation of these stocks. Without Briloff, however, it would have apparently taken longer for the information to be impounded in the prices.

Comparison with Effects of Other Recommendations The "Briloff effect" we have been analyzing can be compared with the effect of other sell recommendations by analysts and by Value Line rankings for the same companies.

Analyst Sell Recommendations. Including the publication-period abnormal returns, the sample companies experienced significant abnormal returns of (see Table 2) -25.56 percent over one year and -32.83 percent over two years. Thus, the magnitude of abnormal returns is large and economically significant. An interesting question is how these abnormal returns compare with the abnormal returns following the recommendations of other analysts.

Because Briloff has written only negative reports, a comparison with the performance of "sell" recommendations of other analysts is the most appropriate. Supporting this view, prior research examining sell recommendations found that the impact on stock prices of sell recommendations is much greater than the impact of (the much more frequent) buy recommendations. Womack

March/April 2004 51




Figure 1. Monthly Cumulative Abnormal Returns Relative to CRSP Equal-Weighted Index

Cumulative Abnormal Return (%)

60

50

40

30

20

10

-10

-20 l l l l l

-24 -18 -12 -6 0 6 12 18 24 30 36

Month Relative to Publication

(1996) shows, for example, that over the 1989-91 period, sell recommendations from 14 major brokerage companies yielded six-month size-adjusted abnormal returns of -9.15 percent (industry- adjusted abnormal returns were -5.65 percent). For the longer 1985-96 period, Barber, Lehavy, McNi- chols, and Trueman (2001) reported that the annu- alized abnormal return for a portfolio of the least favorably recommended stocks was -4.91 percent. Probably the most interesting comparison is with the results described in our 1995 article, because we examined sell recommendations from the most well-known money managers that were invited to the Barron's Annual Roundtable (the sample period was 1968-1991). We found the one-year abnormal returns for sell recommendations of the Round- table participants to be -8.28 percent. Thus, the impact of Briloff's criticism on stock prices is much stronger than that of a typical sell recommendation.

Furthermore, for the most part, the companies criticized by Briloff are large companies. Typi- cally, stock prices of large companies do not yield large abnormal returns (either positive or negative) because they are followed by a large number of analysts and are heavily traded. Thus, the level of information asymmetry is low for large companies and the opportunity to identify mispriced stocks is limited. The fact that Briloff was able to identify such valuation inefficiencies in large companies is impressive.

Value Line Recommendations of Same Companies. The results reported thus far suggest

that the impact of Briloff's criticism is stronger than the impact of analyst sell recommendations in general, but we went a step further to investigate whether Briloff's selection of companies is influ- enced by existing sell recommendations.

Our findings of large positive returns in the prepublication period suggest that analysts were not likely to be bearish on the companies Briloff criticized. To provide some direct evidence on this issue, however, we examined the Value Line reports on the companies before and after the Briloff article publications.11 On average, we found that the mean (median) Value Line rating immediately prior to publication of Briloff's articles was 2.77 (3.00), where 1 is the best and 5 is the worst rating that a company could receive. Thus, the market appears to have been, on average, neutral on these companies. Briloff was not writing about companies on which analysts were bearish.

Did analysts revise their views of the company following Briloff's criticism? The mean (median) Value Line rating based on the first Value Line reports published after Briloff's articles was 3.07 (3.00). So, although Value Line did turn slightly bearish on these companies after Briloff's criticism, the revision was small and not statistically significant.12

Given these results, Briloff seems to be unique in his ability to analyze financial statements. What does he see, and what does the market learn about the companies for it to reduce the valuation it places on them?

52 ?2004, AIMR?




Operating Performance In an effort to understand the source of the abnormal stock price performance of the companies in our sample, we investigated their operating performance around the time of publication of the articles. In effect, we examined the possibility that Briloff could foresee a decline in profitability. We first report results from an examination of operating performance. We then briefly present changes in Altman's Z-scores that corroborate the operating performance findings.

A problem in examining operating performance is the identification of a correct metric. In some instances, such as pooling-of-interests accounting, excessive write-offs related to acquired in-process research and development, and so forth, can allow companies to report higher net income numbers after mergers or acquisitions. Also, the use of varying accounting treatments for similar transactions that result in vastly different reported net income (e.g., purchase versus pooling accounting) limits the usefulness of net income as a performance metric. Therefore, we used the ratio of operating income before depreciation and amorti- zation to total assets as the measure of operating performance of the companies. This ratio provides

a relatively clean measure of operating performance and is less likely than net income to be affected by the accounting issues that Briloff is critical of in his articles.

Table 3 presents an analysis of the operating performance of the companies in the sample for which accounting data were available on the Com- pustat tapes. We defined operating performance as return on assets (ROA), computed as the ratio of operating earnings (Compustat data item #13) to total assets (Compustat data item #6). To abstract from economywide or industrywide effects, we also used industry-adjusted numbers. The industry benchmark was defined as the median operating performance of all the companies in the same two- digit SIC code as the sample company.

Panel A of Table 3 shows raw and industry- adjusted mean and median ROAs for each year up to Year +3 relative to the publication year. (Year -1 is the last year for which data were available prior to publication of the article, and Year +1 is the first year for which data were available after the publication of the article.) The results in Panel A-raw and industry-adjusted means and medians-show that, on average, the companies' operating performance declined from pre- to postpublication years.

Table 3. Operating Performance around Publication of Briloff's Articles

Raw Company ROA Industry-Adjusted ROA

Number of Period Observations Mean Median Mean Median

A. Performance Year relative to criticism

-3 39 13.88% 13.28% 1.09% 0.70%

-2 41 13.02 14.08 0.73 -0.18

-1 44 13.14 11.78 1.23 0.04

+1 44 10.24 11.03 -1.33 0.09

+2 41 9.28 11.01 -1.88 -0.55

+3 40 9.06 8.83 -2.18 -0.97

B. Change in postpublication ROA (pps) Difference in performance

from Year -1

To Year +1 44 -2.90*** -1.51*** -2.56** -1.04**

To Year +2 40 -4.43*** -1.30*** -3.67** -0.95***

To Year +3 38 -4.82*** -2.39*** -3.26 -0.92

Note: The significance of the mean was determined by using a t-test, and the significance of the median was determined by using the Wilcoxon (1945) signed rank-sum test.

**Significant at the 5 percent level in a two-tailed test. ***Significant at the 1 percent level in a two-tailed test.

March/April 2004 53




Panel B of Table 3 reports the change in postpublication ROA relative to Year -1. The results for raw as well as industry-adjusted ROA show both the mean and the median ROAs in the postpublication years to be significantly lower than ROAs in the year prior to publication.13 Overall, the decline in operating performance is large (economically meaningful) and statistically significant.

The results for stock market performance and operating performance show that the sample companies, on average, performed poorly following publication of Briloff's articles. To corroborate these results, we examined changes in risk and distress probabilities for the sample companies. We used changes in Altman's Z-score as the proxy for measuring such changes.14 Altman's Z-score cap- tures the risk of the company to the extent that the accounting data are reliable. If Briloff's analyses were correct and if the accounting numbers did indeed capture the company's performance properly, Altman's Z-scores should deteriorate over the three years after Year -1.

We found strong support for deterioration in the companies' performance, on average. The Z-scores declined sharply following publication of a Briloff article. The median Z-score in the year prior to publication was 4.88.15 The median Z-scores in Years +1, +2, and +3 following the publication of the articles were, respectively, 4.76, 4.32, and 3.54. The median decreases in Z-score relative to Year -1 were -0.92, -1.20, and -1.82 for, respectively, Years +1, +2, and +3, and each is significant at the 5 percent level.16 This result is consistent with and validates the postpublication stock market performance and operating performance of the sample companies, both of which showed sharp declines following Briloff's critique.

Conclusions We examined abnormal returns for three years following the publication of Abraham Briloff's articles between 1968 and 1998. Our results strongly vindi- cate Briloff's conclusions. The companies criticized by him experienced large negative and significant abnormal returns following the publication of his articles. The common stocks of the companies continued to perform negatively for a period of two years. The two-year buy-and-hold abnormal return following the month of publication is a statistically significant -22.88 percent. Including the initial announcement period of Month 0, the overall effect from Briloff's articles is an abnormal return of -32.83 percent for the two years.

We also examined operating performance of the companies. Our results showed a significant decline

in operating performance following the publication of Briloff's articles. Moreover, the sample companies experienced a significant increase in their risk and distress probability as measured by Altman's Z-score. Thus, overall, the operating performance results are consistent with the stock market results.

Our results indicate that Briloff was better able to foresee the coming decline in performance than the market, on average. Does Briloff identify some red flags that the market has overlooked? When we met with Briloff and asked him several questions to understand his thought process as well as to discern any theme to the articles he writes, he told us that he does not follow any checklist. He mentioned that many years of hard work have given him a sixth sense in analyzing financial statements. His articles indeed cover a wide range of topics, such as pooling accounting, lease accounting, and accounting for in-process R&D costs. In some sense, the topics he covers are discussed in most accounting textbooks. The only theme that we can see is an interest in finding out if the company is making an effort to overstate its financial results through aggressive accounting. His skill, it appears, lies in combining several aspects of financial statements into a coherent analysis.

Briloff is not the only one capable of conducting such an analysis, of course; indeed, prior research indicates that sell recommendations are associated with negative abnormal returns and the one-year abnormal returns are around -9 percent. The companies analyzed by Briloff experience much larger negative abnormal returns. Thus, the impact of Briloff's criticism on prices is much stronger than the impact of a typical sell recommendation.

The impact of Briloff's articles may be related to at least three noticeable differences between him and other financial analysts. First, Briloff is staunchly independent. He does not work for an investment banking company and does not accept compensation for research. His research is not published by a partisan firm. Thus, the chance of a leak of information prior to publication is small. Second, he is an excellent analyst who has published several books in the field, has been active in the academic profession, and has taught accounting for decades. He is also well regarded in the profession. Third, most professional analysts analyze a number of companies and report on them regularly. They may not have as much discretion in reporting only when they feel highly confident about their analyses. Briloff, however, does not face such constraints.

Our results shed light on the importance of accounting and financial analysis in valuation. Careful analysis of published financial statements

54 ?2004, AIMR?




can indeed help an analyst or investor identify mispriced stocks-even in large companies.

There is no shortcut to successful valuation. But although careful analysis of financial statements is likely to be time-consuming, it is also likely to be rewarding.

We thank Patti Fairfield, Srini Krishnamurthy, Charles Lee, Srini Sankaraguruswamy, Sundaresh Ramnath, Michael vanBreda, Scott Whisenant, Shuang Wu, and Teri Yohn for comments and Machiko Hollifield and Niranjan Pathakfor research assistance.

Notes 1. Lin and McNichols (1998) and Michaely and Womack

(1999) provided evidence that recommendations by analysts affiliated with full-service investment banking firms show significant evidence of bias. Moreover, in 2003, 10 of the largest U.S. securities firms agreed to pay a record $1.4 billion to settle government charges that their analysts tai- lored their research reports and stock ratings to win investment banking business (see, for example, Smith, Craig, and Solomon 2003).

2. Because Briloff is legally blind, he needs help in reading financial statements. He mentioned to us that the honorariums are so small that they do not even cover the cost of these readers.

3. In addition to studying a longer postpublication period, we also included 10 additional articles by Briloff since Foster (1987). We included all the Briloff articles except the latest one, published on 23 October 2000 and criticizing Cisco Systems' accounting practices (in particular, the treatment of granting stock options to employees). This latest article was too recent to be included in our analysis because at the time of writing, we did not have machine-readable data for 2001-2002. We can report, however, that on the day of publication of the Cisco article, Cisco's stock price fell by 3 percent while the market (S&P 500 Index) was going up 2 percent. For the following year (starting 1 November 2000), Cisco's stock price fell by 68.6 percent whereas the market fell by only 25.8 percent. Note that Briloff's article was well ahead of the recent market decline, which started in about March 2001.

4. See Fama 1998 and Kothari 2001 for reviews of this literature. Specifically, the literature on post-earnings-announcement drift (e.g., among others, see Ball and Brown 1968; Bernard and Thomas 1989, 1990) shows that prices continue to drift for several months in the direction of the initial market response to earnings surprises. Similarly, Sloan (1996) documented a significant drift in prices for one year after the availability of the accruals data.

5. Four companies did not have monthly returns available for 12 months; for them, we used daily data to compute beta (Brown and Warner 1985) and required a minimum of 50 daily returns.

6. The results, however, were not sensitive to using the CRSP value-weighted index. The conclusions were also not affected when we used the capital asset pricing model to control for the beta effect. The abnormal returns in the CAPM framework were given by (RiT - Rf) - fi (RmT - Rf). These results are available from the authors.

7. Our procedure avoided several biases mentioned in Lyon et al.-in particular, the skewness bias arising from the distribution of long-run returns.

8. For comparison, note that the two-day abnormal returns around the publication of a Center for Financial Research and Analysis report on companies identified as having "quality of earnings" problems was -1 percent (Fairfield and Whisenant 2001).

9. The traditional t-statistic for Month +1 to Month +12 abnormal returns was -2.23, and only 27 percent of the companies had positive abnormal returns.

10. Note that the magnitude of the abnormal returns when we used monthly cumulative abnormal returns (CARs) is different from that of the buy-and-hold returns documented in Table 2, although the inference is unaltered. Abnormal returns were much stronger when CAR was used. Simula- tions in Barber and Lyon show that long-horizon abnormal returns are sensitive to the manner in which returns are cumulated (CAR versus buy-and-hold returns). Our results and inferences, however, are robust to the method of cumulation as well as to the use of alternative benchmark portfolios.

11. A large fraction of the articles were from the 1960s and 1970s, so Value Line was the only publicly available source of analysts' views on the sample companies.

12. The second, third, and fourth Value Line reports after Briloff's articles produced similar results.

13. Note that for the columns of medians, the reported difference is the median of the difference from the pre to post years. It is not meaningful to use differences of the medians.

14. The Z-score, developed by Edward I. Altman in 1968, combines five financial ratios based on reported accounting information and equity values to produce an objective measure of a company's financial health. See Altman (1968) or "Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models" at //pages.stern.nyu.edu/ %7Eealtman/Zscores.pdf for the initial findings and Stick- ney and Brown (2003) for an excellent explanation of how to compute Altman's Z-score.

15. Typically, a Z-score of less than 1.81 (1.23 for nonmanufacturing companies) predicts bankruptcy. A score between 1.81 and 2.99 (1.23 and 2.90 for nonmanufacturing companies) indicates a gray area, and a score above 3.00 (2.90) indicates little bankruptcy risk.

16. The results of the changes in mean were similar; we focus on the medians because of the skewness that is typical in accounting data.

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