A research and education initiative at the MIT
Sloan School of Management
The Effect of Disclosures by Management,
Analysts, and Financial Press on the Equity Cost of
Capital
Paper 195
SP Kothari
Jim Short
October 2003
For more information,
please visit our website at http://ebusiness.mit.edu
or contact the Center directly at ebusiness@mit.edu
or 617-253-7054
The Effect of Disclosures by Management, Analysts, and
Financial Press On the Equity Cost of Capital
S.P. Kothari
kothari@mit.edu, (617) 253-0994
Sloan School of Management
Massachusetts Institute of Technology
50 Memorial Drive, E52-325
Cambridge, MA 02142
and
J.E. Short
jshort@mit.edu, (617) 452-3228
Sloan School of Management
Massachusetts Institute of Technology
50 Memorial Drive
Cambridge, MA 02142
October 2003
We are grateful to Xu Li, Kalani Oshiro, Stanley Woods, Mitul Mehta and Tanis Fidelholtz for
their research assistance. We have benefited from discussions and comments from Philip
Wright, Joel Kurtzman, Alison Thomas, Gabrielle Wong, Sunil Misser and Mark Lutchen. We
acknowledge financial support from Pricewaterhouse Coopers and the MIT Center for eBusiness.
The Effect of Disclosures by Management, Analysts, and
Financial Press On the Equity Cost of Capital
1.
Introduction and Problem Statement
Demand for financial reporting and disclosure arises from information asymmetry and
agency conflicts between managers, outside investors and intermediaries. Disclosure regulation
and institutions facilitating credible disclosure between managers and investors play important
roles in mitigating these problems (Healy and Palepu, 2001). Corporate disclosures, reports in
the financial press, and analysts’ reports and discussion of corporate performance all enhance the
information reflected in stock prices. That is, they reduce information asymmetry between the
average investor and informed market participants, e.g., company management. The consensus
among financial economists is that a rich information environment and low information
asymmetry have many desirable consequences.
These include the efficient allocation of
resources in an economy, capital market development, liquidity in the market, and decreased cost
of capital.1
Evidence of the effect of disclosure on the cost of capital is sparse (see Healy and Palepu,
2001, for a review of the literature). Evidence is weak in the few studies that examine the issue,
e.g., Botosan (1997). Conclusions from previous research on the effect of disclosures on the cost
of capital are tenuous because disclosures analyzed in previous research are far from
comprehensive and the measurement of disclosure proxies is generally subjective, not objective.
Our study is the first to document systematic evidence of the cost of capital effects of disclosures
culled from a virtually exhaustive set of sources from the print medium. Our analysis of
1
See Diamond and Verrecchia (1991), Healy and Palepu (2001), Bushman and Smith (2001), and Core (2001).
3
disclosure is not only comprehensive, but the construction of disclosure proxies is quantitative
and amenable to replication in future.
In addition to analyzing the impact of all disclosures, we study the impact of disclosures
segregated by source – management, analysts, and news stories in financial press. There are
economic reasons to study how the source of disclosures affects the firm’s cost of capital.
Disclosures by management, analysts, and news reporters differ on at least two salient
dimensions.
First, incentives facing management, analysts, and news reporters might
differentially taint the contents of their disclosures or reports. In particular, management and
investment analysts are alleged to have strong incentives to optimistically skew disclosures,
whereas news reporters’ incentives to be optimistic in their reports are muted. An efficient
capital market weights disclosures according to the credibility of the source of the disclosure.
That is, on average, in pricing stocks we expect the market to filter out the bias and deemphasize
noisy disclosures by the management, analysts, and news reporters.
Second, news stories in the financial press are likely to be timelier than analysts’ reports.
Analysts frequently repackage and re-transmit available information from corporate disclosures
and financial press news stories in presenting in-depth analysis in their own report (see Lang and
Lundholm, 1996). While such reports might be valuable to the market participants in their
(investment) decisions, they may also have limited price impact (i.e., the reports might lack new
information affecting the stock price) or limited implications for the cost of capital.
Summary of results. Our empirical analysis tests for the impact of disclosure on cost of
capital. Our findings are based on a very large, content database of disclosure text constructed
from disclosure content published on four electronically-available information sources, Dow
Jones Industrial, Investex, Factiva and SEC Edgar. We downloaded all disclosures made by
4
companies, analysts, and business articles available from over 400 content sources, for a sample
of 887 companies in four industry sectors (Technology, Telecommunications, Pharmaceutical
and Financial) for the time period 1996-2001. We used large-sample content analysis methods to
analyze 326,357 texts. Results from our information analysis were then used in financial models
to test for the effects of disclosure on cost of capital.
We find that when content analysis indicates favorable disclosures (that might include
references to management credibility and reduced uncertainty about the firm’s products and
markets etc.), we observed a reduction in the firm’s cost of capital and stock return volatility. In
contrast, unfavorable disclosures were accompanied by increases in the cost of capital and stock
return volatility.
We next analyzed disclosures by source – corporations, analysts, and the financial press.
When we examined the impact of positive and negative news disclosure by companies, we found
that the market discounts the impact of the strength of the positive statements. Positive and
strongly positive news statements do not materially affect cost of capital. However, when
management makes negative and strongly negative news disclosure, the market responds and the
cost of capital goes up. Negative news, therefore, is strongly weighted.
Our second set of results considers the impact of positive and negative news disclosure by
analysts. We found that both positive and negative news disclosures from analysts appear to be
heavily discounted. Analysts on the whole were slightly less positive in their disclosures on
firms, and similarly less negative (more favorable) in negative disclosure. The strength of impact
of their disclosure on cost of capital is insignificant compared to that for companies. Intuitively
the results suggest that analysts have source credibility problems. They are seen as responding to
market changes after they have taken place, discounting their impact.
5
Our third set of results considers the impact of positive and negative news disclosure by
the business press. Here the evidence is more consistent. We find that when the business press
makes both positive and negative news disclosures there are impacts on cost of capital. Positive
news disclosure decreases cost of capital, negative news disclosure increases it. Intuitively the
strength of this finding suggests that the credibility of news disclosure by the press is higher than
that for companies or for analysts, and in the case of analysts, press disclosure is seen as more
timely. The press is less affected by incentive concerns (agency), and this is reflected in the
consistency of evidence.
Outline of the paper. We have organized our paper in five sections. Section 2 presents
the background for our main hypothesis that the credibility and timeliness of the source of
disclosures affects the firm’s cost of equity capital. We present the key arguments and
background literature. Section 3 discusses our data and content analysis methodology. We detail
our building of the disclosure text dataset, our content analysis methodology, and our financial
risk measures and firm characteristics. In Section 4 we present our results. Section 4.1 provides
background descriptive statistics, and Section 4.2 covers regression results. Our concluding
section discusses the management implications of our findings and opportunities for future
research. Further detail on our content analysis methods and dataset are included in five
Appendices.
2.
Background and Hypothesis Development
Here we provide the background for our main hypothesis that the credibility and
timeliness of the source of disclosures affect their impact on firm’s cost of equity capital.
Economic rationale leading up to the hypotheses with respect to the cost of capital effects of
disclosures by corporations, analysts, and financial press is developed in this section.
6
Credibility and timeliness of management disclosures.
We analyze SEC-mandated
corporate disclosures, i.e., 10-K and 10-Q filings. These disclosures contain a combination of
the following two types of disclosures: disclosures through the Management’s Discussion and
Analysis (MD&A) of the firm’s performance that are designed to bring the market’s expectations
in line with the management’s superior information; and mandated other disclosures such as
financial footnotes that are largely an affirmation of the information already available to the
market participants, but could occasionally include qualitative and forward-looking information,
e.g., discussion of loss contingencies and litigation risk.
In utilizing corporate disclosures, market participants are likely to recognize that
management has an incentive to portray a self-serving assessment of past corporate performance
and future outlook. Therefore, management’s favorable disclosures may not be credible to
market participants. Of course, in making any favorable, optimistic disclosures, the management
runs the risk of being accused of making misleading and fraudulent disclosures, i.e., face
litigation risk. Litigation risk thus serves to discipline managers from making overly optimistic
disclosures. However, the disciplining forces might be less operative when disclosures are
qualitative and long-term in nature (e.g., discussion in the MD&A section on the 10-K filings)
rather than quantitative (e.g., point estimate for an EPS forecast) and short-term (e.g., next
quarter’s sales). In contrast to good news disclosures, management has an aversion to disclose
bad news. So, when it volunteers such information, it is believable. That is, unfavorable
voluntary disclosures by the management are more credible regardless of whether they are
qualitative or quantitative in nature.
In addition to being credible, management’s unfavorable disclosures are likely to be more
timely because of litigation risk facing the firm under the Securities and Exchange Commission’s
7
Rule 10b-5 (Skinner, 1994). The Rule together with other securities laws state that management
has a duty to disclose material information as and when it becomes known to the management.
The rationale is that new information might render the management’s previous disclosures
misleading. Alternatively, in light of the new information, the stock would be mis-valued in the
absence of disclosures to correct the mispricing of the stock. However, the securities laws are
likely to have an asymmetric impact on corporate disclosure practices with respect to good and
bad news disclosures. Litigation against a firm is more successful if the management is accused
of withholding bad news such that the stock price remained above its fundamental value.
Consistent with this rationale, Kellogg (1984) finds that buyers’ claims of losses are 13 times as
likely as sellers’ claims of foregone profits, i.e., an opportunity loss. Therefore, management has
an incentive to volunteer bad news on a more timely basis than good news.
Credibility and timeliness of analyst reports.
Analysts are important information
intermediaries in capital markets. They expend real resources on gathering information and
producing research reports. These activities are typically assumed to enhance the ability of
prices to reflect available information. However, several forces influence the credibility and
timeliness of analyst research, which shape its price impact and effect on the firm’s cost of
capital.
Many argue that the strong economic incentives stemming from brokerage commissions
and investment-banking revenues taint the quality of analyst research. United States Senator
Joseph Lieberman claims that the millions of dollars analysts earn from investment-banking
services “compromise analysts’ objectivity” and therefore “the average investor should take their
bottom line recommendations with at least a grain of salt…” (The Wall Street Journal, February
28, 2002, p. A3). Consistent with the senator’s claim, a large body of academic research
8
suggests analysts compromise their objectivity and issue optimistic forecasts and stock
recommendations as a result of their economic incentives arising from brokerage revenues and
investment-banking relationships (e.g., Lin and McNichols, 1998, Michaely and Womack, 1999,
Dechow, Hutton, and Sloan, 2000, Bradshaw, Richardson, and Sloan, 2003, and Lin, McNichols,
and O’Brien, 2003). The lack of objectivity and bias in research together weaken the credibility
of analyst research, which, in turn, would diminish its influence on the pricing or on the cost of
capital of stocks
With respect to the timeliness of information in analyst reports, previous research offers
compelling evidence that analyst reports do convey new information that affects security prices.
However, the magnitude of price impact is typically small. This might be because considerable
analyst research might be simply a retrospective analysis of past events that repackages and retransmits publicly available information, i.e., corporate disclosures and news stories (see Lang
and Lundholm, 1996, Beaver, 1998, p. 10, and Frankel, Kothari, and Weber, 2003). If this
activity dominates the contents of analyst research, it might not be particularly timely in
providing much new information to the market. Worse yet, evidence in Lys and Sohn (1990)
and Guay, Kothari, and Shu (2003) suggests that analyst forecasts are sluggish in that they fail to
fully incorporate the information available and reflected in stock prices at the time of their
forecasts.
In summary, lack of credibility and the absence of timely forecasts and research all make
it less likely that analysts research reports would contain much information that would affect
stock prices and/or the cost of capital.
Financial press news stories. We expect news stories in financial press to be quite
timely with an emphasis on factual reporting. News reporters and the financial press typically do
9
not have strong economic ties and relationships with individual firms. Moreover, each reporter
reports on a large number of corporations as new developments take place with individual
corporations. Therefore, we expect news stories to have both credibility and timeliness and
hence the contents of news stories are likely to be informative about the cost of capital.
3.
Data and content analysis methodology
In this section we present summary information about the data used in this study. First,
we discuss disclosure data obtained from electronic information sources, which we used in
performing content analysis. We then describe the data on financial risk measures and firm
characteristics.
3.1 Disclosure text data
The most widely available source of corporate disclosure is financial statements. These
are SEC mandated disclosures for all U.S. publicly-traded corporations. These statements are
prepared using accrual rather than cash accounting to allow managers to reflect their better
knowledge of the firm’s financials through accrual estimates. In addition to financial statements,
managers also provide other information to investors. Some, such as footnotes and management
discussion and analysis, are disclosed within the financial report. Other disclosures are provided
voluntarily through other information channels, including analyst presentations and conference
calls, press releases and Internet sites. Through these disclosures, managers provide information
that facilitates external users of financial reports to better understand the true economic picture of
the business.
Disclosures are accompanied by third party certification to help assure credibility. That
is, in the absence of third party certification, there would be questions in investors’ minds about
the credibility of the information contained in the disclosures.
For disclosure in financial
10
statements, credibility is typically enhanced in two ways: first, statements are prepared using
generally accepted accounting standards and conventions. Standards can potentially reduce
processing costs for outsiders and curb opportunistic reporting by managers.
Second,
independent audit firms certify whether managers’ financial reporting decisions are consistent
with accounting standards and conventions.
Importantly, management disclosures made outside financial statements are not directly
certified by independent parties. Rather, their source credibility is enhanced through their use
and evaluation by outside parties, including financial analysts and business journalists. These
“information intermediaries” provide a measure of implicit certification of management’s
financial statements and non-financial disclosures. However, this certification is not in any way
formal nor can it be argued to be necessary complete. Our analysis is designed to empirically
assess whether the credibility issues with respect to disclosures from various sources, as
discussed above, are manifested in the intensity of their impact on the firms’ cost of capital.
We obtained data for our study from four electronic data information sources, Dow Jones
Interactive (Dow Jones, Inc.), Investex Plus (Infotrac), Factiva (Dow Jones & Reuters), and
Edgar (Securities and Exchange Commission, US Government). Text retrieval software was
developed and implemented to efficiently download disclosure texts in their entirety for a
company research sample frame of 887 firms over the time period 1996-2001 (Riloff and
Hollaar, 1996).
We downloaded all disclosure texts available, by company by year, for three sources: (1)
corporate reports (Edgar), (2) analyst disclosure and briefings (Investex and Factiva), and (3)
disclosures made by the general business press (over 400 content sources available in Dow Jones
Interactive and Factiva). All disclosure texts were coded by company ID, date and source of
11
publication, and when available, by author. The resulting content dataset is very large. For the
887 companies over the six-year period, 326,357 texts were downloaded, representing over a
million pages of source material. The 887-company sample was drawn from four industry
sectors, Financial Services (225 companies), Technology (197 companies), Pharmaceutical (72
companies), and Telecommunications (395 companies), and the data coded to enable industry
sector analysis. Finally source material was organized into business quarters, corresponding to
the quarterly financial reporting cycle. Each company in the dataset therefore had 72
observations: disclosure content aggregated into 24 time periods (6 years by 4 quarters per year),
by three sources of disclosure (corporate, analyst, business press). The list of companies in the
dataset is presented in Appendix I.
3.2 Classification of text into content categories
Our first step in preparing the data for content analysis was to filter each disclosure text
through a classification scheme that we developed. Words associated with six-categories of
business meaning were defined and used to test a categorization scheme using Riloff (1993) as a
guide. The six categories identified words and word phrases associated with:
i.
market risk, industry structure and competitive forces, the external market and/or
external environment of the firm;
ii.
the development and execution of firm strategy and strategic intent;
iii.
the building of organizational capital and human resource management;
iv.
the image, brand and reputation of the company;
v.
the investment and financial performance of the firm;
vi.
the announcement and impacts of governmental regulation on the company.
Appendix II presents the classification scheme and the words and word phrases making up each
category.
12
All disclosure text was passed through the classification scheme, producing six subsample datasets containing words and word phrases matching the words and word phrases
defined. Our subsequent analysis focused on content analyzing within and across the sub-sample
datasets.
3.3 Content analysis
The underlying principle in content analysis is that the many words of a text can be
classified into many fewer content categories, where each category consists of one or many
similar words or word phrases, and that each word or phrase occurrence can be counted and the
counts compared analytically. Word and phrase similarities are based on the precise meanings of
the words themselves (for example, grouping synonyms together), or may be based on word
groupings sharing similar connotations (for example, grouping together several words associated
with a concept such as market share, revenue growth, or forecasted earnings). Content analysis
therefore, can be useful in analyzing different types or "levels" of communication, as defined by
the meaning of the words themselves.
A flowchart of the typical process of content analysis research is presented in Figure 1.
The study is first specified theoretically and then conceptually in terms of an information model,
content (data samples) and the hypotheses to be addressed. The study is then operationalized
with respect to the variables (content categories) and coding schema to be used for classification
of words and word groups for analysis. Third, the content texts are analyzed using content
analysis procedures and the results interpreted.
3.4 Unit of analysis and measurement in content analysis
Our unit of analysis was the disclosure text. That is, the content of each disclosure text
was analyzed using content analysis procedures. The analysis software used, the General
13
Inquirer, has in its analysis approach a range of categories where word and word phrases are
mapped for each input text submitted, and word frequencies counted on dictionary-supplied
categories. In this research we were especially concerned with the degree and strength of positive
and negative statements in disclosure texts. We hypothesize that the pattern and strength of
favorable or unfavorable statements represent beliefs and judgments about the relative strength
of the firm, and assessments of risks the firm faces in its marketplace, strategy and operations.
We expected that highly favorable (positive) statements, or conversely, highly negative
statements would be made based on tangible factors viewed as important in the minds of
disclosure author(s). Accurately gauging the strength of positives and negatives in an extensive
sample of disclosure text would therefore tell us much about the consistency of views about the
firm and its promise in the market.
To illustrate how our content analysis procedures work in practice, we analyze two
example disclosure texts published in the Wall Street Journal on Amgen Corporation. The two
texts (news stories), published in February and April of 1996, report positive and negative news
on Amgen. On February 1, 1996, the Journal published a story reporting that clinical research
undertaken on the effects of Leptin, the company’s experimental obesity drug, showed that the
drug did not have the hoped-for benefits in a controlled patient study. The second story,
published on April 18, 1996, reported the company’s 32% increase in net quarterly income and
the filing of an application for testing a new drug designed to combat Hepatitis C. The text of
both stories is reproduced in Appendix III.
We submitted the two texts to the General Inquirer (GI) content analysis package.
Developed and written by Professor Philip Stone and colleagues at Harvard University, and later
extended by Vanja Buvac and colleagues, the General Inquirer (GI) is essentially a content
14
mapping tool.2 The GI software maps each text file with word counts on dictionary supplied
categories. That is, the software algorithm identifies and counts word frequencies in the
submitted text files, matching them against words and/or word meanings present in one or more
dictionary of categories. The currently distributed version of GI combines the “Harvard IV-4”
dictionary content-analysis categories, the dictionary content analysis categories defined by
Lasswell, and five categories based on the social cognition work of Semin and Fiedler.3 Each
category contains a list of words and word senses. For example, the category “negative” is the
largest, with 2,291 entries of words or word phrases denoting “negative.” To provide the reader
background on this procedure, example entries for the positive and negative categories are
presented in Appendix IV. When text is submitted for processing, the General Inquirer matches
words and word phrases with those in its dictionaries, and separates inflected word forms. For
example, the root word “sell” would match “selling” if selling was not a separate category entry,
and the word sense “sell cycle”, for example, would be correctly identified by software routines
designed to disambiguate word senses. That is, “cycle” would be correctly identified as
specifying the time periodicity of selling, and not a word short for bicycle or motorcycle. These
English stemming procedures, integrated with English dictionaries and routines for separating
English word senses, comprise the software’s extensive dictionaries and disambiguation routines.
The complexity of the dictionary and disambiguation routines limits the current GI software to
English text applications.
The results of our simple, two-disclosure text submission to the software appear in
Appendix V. Interpreting the output, note that for each text, there are two rows of data, the first
2
3
P.J. Stone in C. Roberts (1997). The General Inquirer’s Home page is at www.wjh.harvard.edu/~inquirer/
See Semin and Fiedler
www.wjh.harvard.edu/~inquirer/
(1992,
1996).
Categories
are
described
on
the
GI
home
page,
15
corresponding to the raw frequency count of matched words (“r”), and the second giving the
percentage of matched words over the total number of words in the text. The February 2, 1996
Wall Street Journal Article, for example, contained 635 words, of which 20 are positive and 21
negative. Positives comprised 3.14 percent of the total number of words in the text, and negatives
slightly higher, 3.30 percent. 116 words were not matched against any of the words supplied in
the GI dictionary categories, or 18.26%. In the case of the April 18th Wall Street Journal article,
the corresponding numbers are the text contained 423 words, 19 positives and 8 negatives, or
4.4% of the words in the disclosure were positive and 1.8% were negative. Seventy-seven words,
or 18.2%, were leftovers (unmatched with dictionary categories).
Interpreting results in content analysis is always a question of comparing word counts
and frequencies across texts and categories. In this example, we can say that the first article
much more negative than the second article, and, as well, less positive. Conversely, the second
article was much more strongly positive and significantly less negative in its frequency and word
sense.
In analyzing the large number of disclosure texts in the database, our procedure was to
calculate the number of positives and negatives in each disclosure text, for each of the six
business categories defined (market, firm strategy, etc., see Appendix II). The resulting matrix of
counts and percentages calculated for each disclosure text was then subjected to one column and
one row operation. To organize the results matrix for input into financial models, row data was
summed and averaged by business quarter. That is, Positive and Negative frequencies calculated
for each disclosure text were summed across all texts and the average taken for each quarter. In
similar fashion, Positive and Negative frequencies calculated for each of the six business
categories (columns) defined were summed and the average taken. The resulting matrix of
16
positive and negative word frequencies represented an average score for all disclosure texts in
the quarter, by company by disclosure source, averaged across the six business categories
(columns). The resulting matrix was then used as input into our econometric analysis.
One final note concerns the computer processing time required to content analyze the
very large disclosure database. Our procedure was to calculate positive and negative frequencies
by company sequentially. A typical firm with the large number of disclosures over the sample
time frame required several hours of processing time.
3.5 Financial risk measures and firm characteristics
We used three measures of risk to assess the impact of disclosures on firm risk: cost of equity
capital, standard deviation of stock returns, and standard deviation of the analyst forecast errors.
We describe each measure below.
Cost of capital.
We estimate the equity cost of capital using the Fama and French (1993)
three-factor model, where the size factor is defined as small minus large firm returns (SML), the
book-to-market factor is defined as high minus low book-to-market firm returns (HML), and the
market factor is defined as the excess return on the CRSP value weight portfolio (Rm - Rf). We
obtain monthly time-series returns on the three factors, SML, HML, Rm - Rf, from Kenneth
French’s website. The loadings on the factors, b, s, and h, are slope coefficients estimated from
the following regression model for firm i:
Ri - Rf = ai + bi [Rm - Rf] + si SML + hi HML+ ei.
(1)
We re-estimate the three-factor model each quarter for each firm using a rolling window
of five years of monthly returns ending in the quarter under examination. Firm i’s estimated
loadings, i.e., estimated b, s, and h coefficients, multiplied by the average returns for the three
17
factors from 1963-2000 gives the cost of capital for firm i (see Fama and French, 1997). We
then annualize the number, which is our cost-of-capital measure.
Standard deviation of stock returns. Standard deviation of stock returns is a commonly
used risk measure. The greater the uncertainty of cash flows generated by a firm, i.e., the risk of
equity, the greater is the standard deviation of returns. This measure is also an indication of the
infrequency of information reaching the market and the degree of information asymmetry among
market participants. If some individuals are informed and others are less informed, then the
trading in the stock is less frequent and stock prices do not reflect a rich body of information.
Under these circumstances, disclosures by a corporation or analysts or disclosures in financial
press can all influence the degree of uncertainty. Favorable disclosures can inform the market of
higher levels of cash flows than previously expected and/or reduce the uncertainty in the market.
We measure the standard deviation of returns using daily return data for each firm every quarter.
Standard deviation of analysts’ forecast errors. Standard deviation of analyst forecast
errors indicates the extent to which the market is surprised by earnings announcements.
Favorable disclosures are expected to reduce uncertainty and enable analysts to better forecast
earnings, which would in turn reduce the magnitude of their forecast errors, i.e., the standard
deviation of forecast errors.
Firm characteristics. While our goal in the paper is to test for the effect of disclosures
on the firm’s cost of capital, it’s important to control for the effect of other determinants of the
cost of capital to avoid drawing erroneous inferences. We consider three firm characteristics that
previous research shows as significant determinants of the cost of capital, standard deviation of
returns, and analyst forecast errors: firm size, book-to-market ratio, and leverage. Small firms
are more risky than large firms in part because small firms typically are a single-project firm, i.e.,
18
a firm with undiversified portfolio of assets and projects. We measure firm size as the market
capitalization of a firm’s equity capital, i.e., number of shares outstanding times the share price,
at the beginning of each quarter.
Book-to-market ratio increases in risk in part because the market valuation of equity is in
the denominator of the ratio. Successful firms with expectations of a steady stream of high
levels of future cash flows are highly valued in the market, which drives their book-to-market
ratio down. These are typically considered to be low risk firms. In contrast, if the market has
little confidence in a firm and thus perceives the cash flow stream to be uncertain and not too
high, then the market capitalization of such a firm would be low. This drives up the book-tomarket ratio, so high book-to-market ratio proxies for high risk firms. Book-to-market ratio is
calculated as a ratio of the book equity of a firm divided by its firm size, i.e., the market
capitalization of equity.
Finally, risk increases in the firm’s financial leverage, i.e., the amount of debt relative to
the equity capital invested in the firm. We measure leverage as the ratio of long-term dent to the
total assets of the firm.
4.
Results
This section presents the results of empirical analysis of the effects of disclosure on the
cost of capital. We begin with descriptive statistics and correlations among the variables used in
the analysis. The main set of results are based on regressions of various risk measures on
content-analysis-based measures of disclosure by source and firm characteristics as control
determinants of the different risk measures.
4.1 Descriptive statistics
19
Tables 1 and 2 provide univariate descriptive statistics and simple bi-variate correlations
for our dependent and independent variables, by industry sector and by all sectors. The number
of observations reported in each Panel represent the number of firms times the number of years /
quarters that had sufficient data to compute the variables. Some firms were excluded from
analysis based on insufficient data.
In Table 2, note the two columns corresponding to index variables “Favorable” and
“Unfavorable”, constructed as the mean of the content-analysis based Positive and Negative
measure for disclosures across all six business categories and disclosure source. With the
exception of the lack of a significant correlation between Cost of Capital and “Unfavorable”, all
correlations are significant at the .01 or .05 level for all variables – that is, Favorable and
Unfavorable assessments are strongly correlated with all financial measures studied, cost of
capital, standard deviation of daily stock return, the firm’s market capitalization of equity at the
beginning of the quarter (natural logarithm), book equity divided by market equity at the
beginning of the quarter, and leverage (long-term debt divided by the total asset). That all crosscorrelations (minus one) are significant between content measures and financial measures
suggests a strong and persistent effect between the two is indicated.
4.2 Regression results
Tables 3 and 4 report the results from multivariate regression models on composite news
measures and source of news. The panels report regression results separately on three dependent
variables: cost of capital, the standard deviation of daily stock return over the contemporaneous
quarter, and the standard deviation of all available quarterly analyst forecast errors from 19962001. We use five independent variables: Favorable, Unfavorable, Size (natural logarithm of a
firm’s market capitalization of equity at the beginning of the quarter), B/M (book equity divided
20
by market equity at the beginning of the quarter), and Leverage (long-term debt divided by total
asset). Across all of the panels reported the adjusted R2, that is, the adjusted measure of fit of the
individual regression models analyzed, is significant for all of the models at the one-percent
level. The pattern of results intuitively suggests that the regression models are analyzing a
systematic effect.
Our first set of results considers the impact of positive and negative news disclosure by
company and by industry sector (Table 3, Panels A-E). In interpreting the coefficients for
Favorable and Unfavorable for all and individual industries, we find that when management
makes positive and strongly positive statements in company disclosure, the market anticipates
this and discounts the impact of the strength of these statements. Positive and strongly positive
news statements do not materially affect cost of capital (coefficients are weakly positive or not
significant). However, when management makes negative and strongly negative news
disclosures, the market responds and the cost of capital goes up. Intuitively, the market may be
stating that when negative news is disclosed, these are made in the context of a positive
disclosure bias by the company. Negative news, therefore, is strongly weighted.
Our second set of results considers the impact of positive and negative news disclosure by
analysts (Table 4, Panel B). We found that for both positive and negative news disclosure,
information from analysts appears to be discounted. Analysts on the whole are slightly less
positive in their disclosures on firms, and similarly less negative (more favorable) in negative
disclosure. The strength of impact of their disclosure on cost of capital is less significant than for
companies – the coefficients reported are not significant for cost of capital, but the coefficients
are significant for stock returns and analyst forecast errors. Intuitively the results suggest that
21
analysts have source credibility problems. Our interpretation is that they are seen as responding
to market changes after they have taken place, this discounting their impact.
Our third set of results considers the impact of positive and negative news disclosure by
the business press. Here is the evidence is quite consistent (Table 4, Panel C). We found that
when the business press makes both positive and negative news disclosures there are impacts on
cost of capital. Positive news disclosure decreases cost of capital, negative news disclosure
increases it. Intuitively the strength of this finding suggests that the credibility of news disclosure
by the press is higher than that for companies or for analysts, and in the case of analysts, press
disclosure is seen as more timely. The press is seen as less affected by incentive concerns
(agency), and this is reflected in the consistency of evidence.
5.
Implications for Management and Future Research
Regulation of disclosure and the materiality of information that a reasonable investor
would want to consider in making an investment decision are subjects of intense scrutiny in the
post-Enron and Worldcom business climate. The materiality of information relates to the
importance and impact that corporate information and development have on the earnings of the
company, and how disclosure should reach the market and investors (Brown, 1998). As the
disclosure system has grown in size with the importance of evaluation of disclosure by other
information intermediaries, analysts, business press, and the investors themselves, information
asymmetries and agency conflicts between participants creates greater demand for reporting and
assurance of disclosure.
The research reported here is the first to document systematic evidence of the cost of
capital effects of disclosure. Our analysis of disclosure was based on a virtually exhaustive set of
sources from the print medium, segregated by disclosure source - corporate, analysts, and news
22
stories published in the financial press. We found support that the market weighs disclosures
according to the credibility of source, and that on average the market filters out and
deemphasizes noisy disclosures by all participants. Our results found that negative news
disclosure is strongly weighted by the market, and positive news is discounted as firms and
investment analysts have incentives to skew disclosure. We found in the data evidence that
companies and analysts have credibility problems affecting cost of capital. Specifically,
disclosure from investment analysts is heavily discounted, suggesting they are seen as
responding to market changes after they have taken place, discounting their impact.
Problems of agency and the incentives for the business press to skew disclosure are more
diffuse for journalists than for companies or analysts, and our results reflect this more
independent intermediary role - positive news disclosure decreases cost of capital, and negative
news disclosure increases it. Intuitively this finding suggests the credibility of news disclosure by
the press is higher than that for companies or analysts.
The strength and pattern of our results suggests that there are important economic reasons
for companies to understand more precisely how disclosure affects the firm’s cost of capital.
Pursuant to securities regulation and business norms, companies have a duty to disclose
corporate information and developments through timely and accurate disclosure. That we have
found evidence showing that the market discounts disclosure by source credibility suggests that
companies should give high priority to developing appropriate and complete communications
policies. Skewing disclosure of critical corporate information or development has economic
risks. Asking whether consistent, accurate and complete disclosure is best accomplished through
centralizing the corporate communication process, or decentralizing it with strong behavioral
norms and safeguards, is a key senior management question. An implication of our results is that
23
companies cannot depend on analysts to enhance source credibility or timeliness of disclosure.
Analysts have their own credibility and timeliness problems, and are subjects of increased
scrutiny in disclosure reform.
We conclude by noting that financial reporting and disclosure will continue to be an
important field of empirical inquiry. There are numerous factors with the potential to alter
disclosure practices and financial reporting – technological innovation, changes in the business
economics of audit firms and analysts, globalization of capital markets, and changes in
disclosure channels and the number and type of information intermediaries – which continue to
reshape disclosure and financial reporting practices. They create new and exciting opportunities
for research.
24
References
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Upper Saddle River, NJ 07458.
Botosan, C., 1997, Disclosure level and the cost of capital, The Accounting Review 72, 323-350.
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relation between corporate financing activities and sell-side analyst research, working
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Brown, J. Robert, Jr., 1998, The Regulation of Corporate Disclosure, 3rd Edition. Aspen
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research, working paper, MIT Sloan School of Management.
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forecasts, working paper, MIT Sloan School of Management.
Healy, P., Palepu, K., 2001, Information asymmetry, corporate disclosure, and the capital
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405-441.
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Kelly, Edward Francis, 1975, Computer recognition of English word senses /Edward F. Kelly,
Philip J. Stone. Amsterdam : North Holland Pub. Co. ; New York : American Elsevier
Pub. Co., 1975.
Lang, M., Lundholm, R., 1996, Corporate disclosure policy and analyst behavior, The
Accounting Review 71, 467-492.
Lin, H., McNichols, M., 1998, Underwriting relationships, analysts’ earnings forecasts and
investment recommendations, Journal of Accounting & Economics 25, 101-127.
25
Lin, H., McNichols, M., O’Brien, P., 2003, Analyst impartiality and investment banking
relationships, working paper, Stanford University.
Lys, T., Sohn, S., 1990, The association between revisions of financial analysts’ earnings
forecasts and security price changes, Journal of Accounting & Economics 13, 341-363.
Michaely, R., Womack, K., 1999, Conflict of interest and the credibility of underwriter analyst
recommendations, Review of Financial Studies 12, 573-608.
Riloff, E. 1993. Automatically Constructing a Dictionary for Information Extraction Tasks. In
Proceedings of the Eleventh National Conference on Artificial Intelligence. AAAI
Press/The MIT Press. 811-816.
Riloff, E., Hollaar, L., 1996, Text Databases and Information Retrieval, ACM Computing
Surveys, Volume 28 Number 1, pp. 133-135.
Semin, G.R., Fiedler, K. (eds.), 1992, Language, Interaction and Social Cognition, Sage
Publications, 1992.
Semin, G.R., Fiedler, K. (eds.), 1996, Applied Social Psychology, Sage Publications, 1996.
Skinner, D., 1994, Why firms voluntarily disclose bad news, Journal of Accounting Research 32,
38-60.
26
FIGURE 1
Flow of Content Analysis
27
Table 1
Descriptive Statistics for Variables: Quarterly data for 887 firms from 1996 to 2001
This table provides descriptive statistics for variables used in subsequent tests. To be
included in the sample, each firm has sufficient data to compute the variables below. The
time period is from 1996 to 2001.
Panel A: All industries, 5,350 observations
Cost of Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
Cost of Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
Cost of Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
Std.
Dev.
Mean
Min.
Q1
Median
Q3
0.148
0.076
0.001
0.097
0.138
0.183
0.027
0.016
0.001
0.016
0.023
0.033
0.948
1.938
0.000
0.065
0.206
0.780
4.676
1.748
0.000
3.434
4.970
5.991
1.674
0.712
0.000
1.188
1.629
2.146
6.762
2.524
0.189
4.835
6.489
8.533
0.742
0.958
0.001
0.256
0.510
0.850
0.163
0.176
0.000
0.017
0.101
0.256
Panel B: Pharmaceutical firms, 962 observations
Max.
0.499
0.218
12.943
10.159
5.139
13.230
9.893
0.837
Std.
Dev.
Mean
Min.
Q1
Median
0.146
0.110
0.002
0.064
0.116
0.036
0.021
0.009
0.021
0.030
0.842
1.369
0.029
0.049
0.169
4.634
1.515
0.000
3.816
4.763
1.907
0.746
0.000
1.421
1.953
7.100
2.678
2.046
4.800
7.055
0.371
0.778
0.001
0.098
0.196
0.107
0.142
0.000
0.000
0.049
Panel C: Telecom firms, 987 observations
Q3
0.204
0.045
1.212
5.823
2.456
8.949
0.345
0.172
Max.
0.499
0.218
6.130
8.444
4.322
12.578
9.416
0.729
Q3
0.169
0.038
4.107
4.763
2.039
9.875
1.422
0.300
Max.
0.472
0.086
12.943
7.409
3.633
13.134
9.893
0.837
Mean
0.140
0.031
2.364
3.643
1.544
8.211
1.203
0.216
Std.
Dev.
0.073
0.014
3.299
1.541
0.720
2.146
1.702
0.183
Min.
0.012
0.005
0.000
0.000
0.000
2.263
0.007
0.000
Q1
0.091
0.020
0.206
2.270
0.933
6.923
0.238
0.071
Median
0.129
0.028
0.911
3.744
1.540
8.224
0.505
0.200
28
Panel D: Financial firms, 3,007 observations
Cost of Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
Cost of Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
Std.
Dev.
Mean
Min.
Q1
Median
0.152
0.063
0.001
0.109
0.144
0.022
0.012
0.001
0.014
0.020
0.371
0.804
0.000
0.048
0.119
4.983
1.800
0.000
3.696
5.356
1.589
0.658
0.000
1.154
1.553
6.135
2.300
1.683
4.429
5.906
0.716
0.567
0.045
0.411
0.608
0.169
0.182
0.000
0.022
0.103
Panel E: Tech firms, 394 observations
Mean
0.141
0.032
1.560
5.021
2.077
7.090
0.689
0.121
Std.
Dev.
0.077
0.018
1.819
1.303
0.727
2.905
0.642
0.136
Min.
0.003
0.004
0.110
0.000
0.000
0.189
0.030
0.000
Q1
0.085
0.019
0.164
4.139
1.530
5.065
0.174
0.002
Median
0.130
0.027
0.970
5.381
2.077
7.099
0.492
0.084
Q3
0.183
0.027
0.340
6.320
1.961
7.281
0.873
0.265
Max.
0.426
0.175
5.589
10.159
3.881
12.467
8.673
0.776
Q3
0.187
0.042
1.654
6.035
2.583
9.555
1.008
0.203
Max.
0.370
0.099
6.233
7.915
5.139
13.230
3.980
0.723
Variable definitions:
Cost of Cap.: Expected annual cost of capital calculated based on Fama-French three-factor model over the
past five-year’s return data
σ(R): Standard deviation of daily stock return over the contemporaneous quarter
σ(FE): Standard deviation of all available quarterly analyst forecast errors from 1996 to 2001
Favorable: Favorable assessment index, constructed as mean of the content-analysis-based scaled favorable
content measures for six categories of disclosures across three sources, i.e., corporations, analysts, and
financial press.
Unfavorable: Unfavorable assessment index, constructed as mean of the content-analysis-based scaled
unfavorable content measures for six categories of disclosures across three sources, i.e., corporations,
analysts, and financial press.
Size: natural logarithm of a firm’s market capitalization of equity at the beginning of the quarter
B/M: book equity divided by market equity at the beginning of the quarter
Leverage: long-term debt divided by the total asset
29
Table 2
Cross Correlation Matrix
This table provides correlations among the variables used in subsequent tests. Spearman (Pearson) correlations are above (below) the
diagonal. To be included in the sample, each firm has sufficient data to compute the variables below. The time period is from 1996 to
2001.
Cost of
Cap.
σ(R)
σ(FE)
Favorable
Unfavorable
Size
B/M
Leverage
1.000
0.161***
0.178***
0.031**
0.006
-0.122***
0.138***
0.050***
σ(R)
0.178***
1.000
0.284***
-0.058***
0.077***
-0.069***
-0.152***
-0.184***
σ(FE)
0.122***
0.228***
1.000
-0.079***
0.094**
0.021
-0.056***
-0.089***
Favorable
-0.012
-0.044***
-0.103***
1.000
0.710***
0.061***
-0.076***
0.021
Unfavorable
-0.012
0.068***
0.042**
0.720***
1.000
0.226***
-0.167***
-0.005
Size
-0.149***
-0.127***
0.099***
0.047***
0.203***
1.000
-0.499***
0.137***
B/M
0.038***
-0.019
-0.050***
-0.180***
-0.144***
-0.242***
1.000
0.174***
0.022
-0.167***
-0.091***
0.032**
-0.028**
0.035**
0.087***
1.000
Cost of Cap.
Leverage
Variable definitions:
Cost of Cap.: Expected annual cost of capital calculated based on Fama-French three-factor model over the past five-year’s return data
σ(R): Standard deviation of daily stock return over the contemporaneous quarter
30
σ(FE): Standard deviation of all available quarterly analyst forecast errors from 1996 to 2001
Favorable: Favorable assessment index, constructed as mean of the content-analysis-based scaled favorable content measures for six categories of disclosures across
three sources, i.e., corporations, analysts, and financial press.
Unfavorable: Unfavorable assessment index, constructed as mean of the content-analysis-based scaled unfavorable content measures for six categories of disclosures
across three sources, i.e., corporations, analysts, and financial press.
Size: natural logarithm of a firm’s market capitalization of equity at the beginning of the quarter
B/M: book equity divided by market equity at the beginning of the quarter
Leverage: long-term debt divided by the total asset
Table 3
Fama-MacBeth regressions on the composite news measures
This table provides summary statistics for the coefficients from quarterly cross-sectional FamaMacBeth regressions using the composite Favorable and Unfavorable content measures across the
six categories and three sources of disclosures, i.e., corporations, analysts, and financial press.
The time period is from 1996 to 2001.
Panel A: All industries, 5,350 observations
Independent
Variables
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
Dependent Variable
Cost of Cap.
Coefficient
T-stat
0.1768***
43.35
-0.0012*
-1.82
0.0031
1.33
-0.0047***
-10.81
0.0007
0.73
0.0181***
2.76
0.0159***
4.21
σ(R)
Coefficient
0.0410***
-0.0027***
0.0061***
-0.0015***
-0.0030***
0.1161***
T-stat
27.18
-12.4
8.5
-9.15
-4.92
9.75
σ(FE)
Coefficient
T-stat
1.4121***
13.72
-0.3319***
-11.19
0.6514***
8.89
0.0164**
1.8
-0.1658***
-7.66
0.0224***
3.91
Panel B: Pharmaceutical firms, 962 observations
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
0.3102***
-0.0064**
0.0114*
-0.0221***
-0.0390***
0.0724***
0.2220***
34.71
-2.64
1.65
-30.25
-2.74
3.64
13.48
0.0724***
-0.0005
0.0022*
-0.0051***
-0.0106***
19.28
-0.98
1.92
-15.22
-3.55
3.6188***
-0.0145
0.3875**
-0.3809***
-0.6167***
8.02
-0.14
1.98
-11.51
-2.81
0.4668***
18.13
0.3461***
11.09
Panel C: Telecom firms, 987 observations
Independent
Variables
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
Dependent Variable
0.2010***
0.0035
-0.0221***
-0.0059***
0.0061***
0.0147
0.1094***
7.75
0.86
-3.85
-3.23
2.90
0.72
4.62
0.0383***
0.0008
0.0001
-0.0013***
-0.0021***
14.81
1.31
0.08
-4.24
-4.46
2.0324***
-0.1732
-0.1599
0.2095***
-0.5358***
3.46
-1.07
-0.54
4.98
-6.6
0.1316***
5.48
-0.0885***
-5.35
32
Panel D: Financial firms, 3,007 observations
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
Cost of Cap.
Coefficient
T-stat
0.1011***
14.75
-0.0024**
-2.18
0.0093***
2.66
0.0064***
12.17
0.0158***
2.75
-0.0083
-1.16
0.0489***
6.25
σ(R)
Coefficient
0.0259***
-0.0005***
0.0004
-0.0006***
0.0029***
0.0618***
T-stat
20.29
-3.05
0.79
-3.37
3.55
4.43
σ(FE)
Coefficient
T-stat
0.2671***
6.56
-0.0685***
-6.58
0.2944***
8.22
-0.0113***
-2.65
0.0674
1.52
-0.0290***
-3.92
Panel E: Tech firms, 394 observations
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
0.1203***
0.0006
0.0324***
-0.0091***
0.0186
0.0753***
0.1386**
3.33
0.13
4.00
-4.15
1.26
2.89
2.13
0.0469***
-0.0004
0.0073***
-0.0027***
-0.0150***
6.97
-0.31
5.09
-6.14
-6.76
6.5686***
-0.2533
1.4355***
-0.6057***
-3.5801***
4.94
-1.12
4.53
-8.41
-7.24
0.1097***
2.74
0.2031***
3.37
Variable definitions:
Cost of Cap.: Expected annual cost of capital calculated based on Fama-French three-factor model over the past
five-year’s return data
σ(R): Standard deviation of daily stock return over the contemporaneous quarter
σ(FE): Standard deviation of all available quarterly analyst forecast errors from 1996 to 2001
Favorable: Favorable assessment index, constructed as mean of the content-analysis-based scaled favorable content
measures for six categories of disclosures across three sources, i.e., corporations, analysts, and financial press.
Unfavorable: Unfavorable assessment index, constructed as mean of the content-analysis-based scaled unfavorable
content measures for six categories of disclosures across three sources, i.e., corporations, analysts, and financial
press.
Size: natural logarithm of a firm’s market capitalization of equity at the beginning of the quarter
B/M: book equity divided by market equity at the beginning of the quarter
Leverage: long-term debt divided by the total asset
33
Table 4
Fama-MacBeth regressions by the source of news
This table provides summary statistics for the coefficients from quarterly cross-sectional FamaMacBeth regressions using the Favorable and Unfavorable content measures for each source of
disclosure, i.e., corporations, analysts, and financial press. The content measures are summed
across the six categories of disclosures. The time period is from 1996 to 2001.
Panel A: Disclosure source is Corporate Disclosures
Independent
Variables
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
Dependent Variable
Cost of Cap.
Coefficient
T-stat
0.1825***
31.94
0.0003
0.50
-0.0036***
-2.68
-0.0047***
-10.78
0.0017
0.70
0.0113**
2.03
0.0161***
3.78
σ(R)
Coefficient
0.0422***
-0.0015***
0.0025***
-0.0014***
-0.0033***
0.1008***
T-stat
28.70
-10.09
5.16
-7.69
-5.06
8.38
σ(FE)
Coefficient
T-stat
1.6587***
10.7
-0.2123***
-9.28
0.3429***
6.69
0.0010
0.07
-0.2583***
-4.77
0.0391***
3.12
Panel B: Disclosure source is Analyst Reports from Investext
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
0.1771***
0.0000
0.0022
-0.0051***
0.0003
0.0159***
0.0234***
46.75
0.00
0.32
-12.96
0.30
2.93
5.15
0.0398***
-0.0017***
0.0051***
-0.0015***
-0.0029***
27.18
-5.74
4.97
-7.53
-5.01
0.9812***
-0.0758***
0.0175
0.0548***
-0.1128***
9.28
-3.01
0.20
6.42
-4.61
0.0965***
7.83
-0.0029
-0.95
Panel C: Disclosure source is Financial Press
Intercept
Favorable
Unfavorable
Size
B/M
Leverage
Adjusted R2
0.1757***
-0.0013***
0.0026**
-0.0045***
0.0011
0.0167**
0.0166***
54.98
-2.80
2.10
-8.79
1.02
2.50
4.66
0.0367***
-0.0004***
0.0017***
-0.0014***
-0.0026***
28.3
-2.89
6.84
-7.62
-4.77
0.5192***
-0.0823***
0.2635***
0.0448***
-0.0766***
10.16
-4.18
7.71
4.20
-3.99
0.0779***
6.72
-0.0018
-0.59
Variable definitions:
Cost of Cap.: Expected annual cost of capital calculated based on Fama-French three-factor model over the past
five-year’s return data
34
σ(R): Standard deviation of daily stock return over the contemporaneous quarter
σ(FE): Standard deviation of all available quarterly analyst forecast errors from 1996 to 2001
Favorable: Favorable assessment index, constructed as mean of the content-analysis-based scaled favorable content
measures for six categories of disclosures across three sources, i.e., corporations, analysts, and financial press.
Unfavorable: Unfavorable assessment index, constructed as mean of the content-analysis-based scaled unfavorable
content measures for six categories of disclosures across three sources, i.e., corporations, analysts, and financial
press.
Size: natural logarithm of a firm’s market capitalization of equity at the beginning of the quarter
B/M: book equity divided by market equity at the beginning of the quarter
Leverage: long-term debt divided by the total asset
35
Appendix I
List of Companies
FINANCIAL (225 FIRMS)
3COWP
3DLTA
3FACOQ
3FHRI
3HOOB
3NRGD
3NSLB
3RATE
3STFA
AACE
ABBK
ABN
AEG
AIG
AJG
ALL
ALLE
AMFH
AML
AMTD
ARE
ARI
AROW
ASFC
ASFI
AUTH
AXM
AXP
BAC
BBV
BCS
BED
BFSB
BK
BKCT
BLM
BPOP
BRK
BXG
BXS
C
CAA
CBC
CBL
CBNY
CBSS
CCFH
CCRT
CFBC
CFCP
CFSB
CIT
CNFL
CANAL CAPITAL CORP
DELTONA CORP
FIRST ALLIANCE CORP/DE
FULL HOUSE RESORTS INC
HOLOBEAM INC
NRG INC
NS & L BANCORP INC
BANKRATE INC
STRATFORD AMERICAN CORP
ACE CASH EXPRESS INC
ABINGTON BANCORP INC
ABN AMRO HLDG N V -SPON ADR
AEGON NV
AMERICAN INTERNATIONAL GROUP
GALLAGHER (ARTHUR J.) & CO
ALLSTATE CORP
ALLEGIANT BANCORP INC
AMERICAN FINL HLDGS INC
AMLI RESIDENTIAL PPTYS TR
AMERITRADE HLDG CORP -CL A
ALEXANDRIA R E EQUITIES INC
ARDEN REALTY INC
ARROW FINL CORP
ASTORIA FINL CORP
ASTA FUNDING INC
AUTHORISZOR INC
APEX MORTGAGE CAPITAL INC
AMERICAN EXPRESS
BANK OF AMERICA CORP
BANCO BILBAO VIZCAYA -ADR
BARCLAYS PLC/ENGLAND -ADR
BEDFORD PPTY INVS INC
BEDFORD BANCSHARES INC
BANK OF NEW YORK CO INC
BANCORP CONN INC
BLIMPIE INTL INC
POPULAR INC
BERKSHIRE HATHAWAY -CL A
BLUEGREEN CORP
BANCORPSOUTH INC
CITIGROUP INC
CAPITAL ALLIANCE INCM TRUST
CENTURA BANKS INC
CBL & ASSOCIATES PPTYS INC
COMMERCIAL BANK/NY
COMPASS BANCSHARES INC
CCF HOLDING CO
COMPUCREDIT CORP
COMMUNITY FIRST BKG CO/GA
COASTAL FINANCIAL CORP/DE
CITIZENS FIRST FINL CORP
CIT GROUP INC
CITIZENS FINL CORP KY
COHB
COLB
CORS
CPT
CRLTS
CRRB
CTGI
CYL
DOM
DORL
DSL
DWYR
ECMN
EQR
FBF
FCH
FED
FFA
FFDB
FFFD
FFHH
FFIN
FFP
FFWC
FHCC
FIF
FII
FKFS
FKKY
FLAG
FLBC
FLPB
FNCM
FNF
FNIN
FNM
FR
FRE
FRGB
FRME
GBE
GBP
GFR
GGP
GL
GPT
GS
HARB
HBC
HGT
HRBT
HRP
HRZB
COHOES BANCORP INC
COLUMBIA BKG SYS INC
CORUS BANKSHARES INC
CAMDEN PROPERTY TRUST
CENTURY REALTY TRUST
CARROLLTON BANCORP/MD
CAPITAL TITLE GROUP INC
COMMUNITY CAP CORP/SC
DOMINION RES BLACK WARRIOR
DORAL FINANCIAL CORP
DOWNEY FINANCIAL CORP
DWYER GROUP INC
ECHAPMAN.COM INC
EQUITY RESIDENTIAL PPTYS TR
FLEETBOSTON FINANCIAL CORP
FELCOR LODGING TR INC
FIRSTFED FINANCIAL CORP/CA
FRANCHISE FINL CORP AMERICA
FIRSTFED BANCORP INC
NORTH CENTRAL BANCSHARES INC
FSF FINANCIAL CORP
FIRST FINL BANCSHARES INC
FFP PARTNERS -LP-CL A
FFW CORP
FIRST HEALTH GROUP CORP
FINANCIAL FEDERAL CORP
FEDERATED INVESTORS INC
FIRST KEYSTONE FINL INC
FRANKFORT FIRST BANCORP INC
FLAG FINL CORP
FINGER LAKES BANCORP INC
FIRST LEESPORT BANCORP INC
FINET.COM INC
FIDELITY NATIONAL FINL INC
FINANCIAL INDS CORP
FANNIE MAE
FIRST INDL REALTY TRUST INC
FEDERAL HOME LOAN MORTG CORP
FIRST REGIONAL BANCORP
FIRST MERCHANTS CORP
GRUBB & ELLIS CO
GABLES RESIDENTIAL TRUST
GREAT AMERN FINL RESOURCES
GENERAL GROWTH PPTYS INC
GREAT LAKES REIT INC
GREENPOINT FINANCIAL CORP
GOLDMAN SACHS GROUP INC
HARBOR FLORIDA BANCSHARES
HSBC HLDGS PLC -SPON ADR
HUGOTON ROYALTY TRUST
HUDSON RIVER BANCORP INC
HRPT PPTYS TRUST
HORIZON FINANCIAL CORP/WA
36
HT
HTL
HUMP
ICBC
ICII
IDH
IFCJ
IGC
INBI
IRWN
ISTN
IUBC
JDN
JPR
KILN
KIM
KRB
LM
LNC
LTC
LTFD
LXBK
MADB
MAFB
MBHI
MBI
MBWM
MER
MET
METB
MGP
MHC
MHX
MI
MLAN
MM
MMC
MTR
MWD
NEIB
NFB
NFS
NHI
NHL
NLY
NSDB
NTRS
NYM
OAKF
ONE
OSBC
OSKY
OWWI
PEBK
PEDE
PEI
PFCO
PHC
PMI
PRSP
PUK
PVFC
PVSA
HERSHA HOSPITALITY TRUST
HEARTLAND PARTNERS -LP-CL A
HUMPHREY HOSPITALITY TR INC
INDEPENDENCE CMNTY BK CORP
IMPERIAL CREDIT INDS INC
IPI INC
INTERCHANGE FINL SVCS CP/NJ
INTERSTATE GENERAL CO -LP
INDUSTRIAL BANCORP INC
IRWIN FINL CORP
INTERSTATE NATL DEALER SVCS
INDIANA UTD BANCORP
JDN REALTY CORP
JP REALTY INC
KIRLIN HOLDING CORP
KIMCO REALTY CORP
MBNA CORP
LEGG MASON INC
LINCOLN NATIONAL CORP
LTC PROPERTIES INC
LITTLEFIELD CORP
LSB BANCSHARES INC/NC
MADISON BANCSHARES GROUP LTD
MAF BANCORP INC
MIDWEST BANC HLDGS INC
MBIA INC
MERCANTILE BANK CORP
MERRILL LYNCH & CO
METLIFE INC
METROBANCORP
MERCHANTS GROUP INC
MANUFACTURED HOME CMNTYS INC
MERISTAR HOSPITALITY CORP
MARSHALL & ILSLEY CORP
MIDLAND CO
MUTUAL RISK MANAGEMENT LTD
MARSH & MCLENNAN COS
MESA ROYALTY TRUST
MORGAN STANLEY DEAN WITTER
NORTHEAST INDIANA BANCORP
NORTH FORK BANCORPORATION
NATIONWIDE FINL SVCS -CL A
NATIONAL HEALTH INVS INC
NEWHALL LAND &FARM -LP
ANNALY MORTAGE MGMT INC
NSD BANCORP INC
NORTHERN TRUST CORP
NYMAGIC INC
OAK HILL FINANCIAL INC
BANK ONE CORP
OLD SECOND BANCORP INC/IL
MAHASKA INVESTMENT CO
OMEGA WORLDWIDE INC
PEOPLES BK N C
GREAT PEE DEE BANCORP INC
PENNSYLVANIA RE INVS TRUST
PAULA FINANCIAL/DE
PEOPLES HLDG CO
PMI GROUP INC
PROSPERITY BANCSHARES INC
PRUDENTIAL PLC -ADR
PVF CAPITAL CORP
PARKVALE FINL CORP
PXT
QUIZ
RA
RBNC
RFS
RIGS
RPP
RVSB
SAL
SASR
SBAN
SBCO
SCH
SFEF
SIGI
SIVB
SJT
SMBC
SMT
SNH
SPN
SSLI
STBA
STD
STRS
STSA
STT
SUBK
SUSQ
SV
SWS
TRMK
UBH
UBSC
UFHI
UHCO
UHT
UIRT
UMBF
UNBJ
UNBO
UPFC
UPK
VLY
VSTN
WABC
WAYN
WES
WFC
WFI
WLP
WOFC
WSB
WSFS
WTU
XL
PXRE GROUP LTD
QUIZNOS CORP
RECKSON ASSOCS RLTY CORP
REPUBLIC BANCORP INC
RFS HOTEL INVESTORS INC
RIGGS NATL CORP WASH D C
STONEHAVEN REALTY TRUST
RIVERVIEW BANCORP INC
SALISBURY BANCORP INC
SANDY SPRING BANCORP INC
SOUTHBANC SHARES INC
SOUTHSIDE BANCSHARES CORP
SCHWAB (CHARLES) CORP
SANTA FE FINANCIAL CORP
SELECTIVE INS GROUP INC
SILICON VY BANCSHARES
SAN JUAN BASIN ROYALTY TR
SOUTHERN MISSOURI BANCP INC
SUMMIT PROPERTIES INC
SENIOR HOUSING PPTYS TRUST
SECURITY PA FINL CORP
SOUTHERN SEC LIFE INS
S & T BANCORP INC
BANCO SANTANDER CENT -ADR
STRATUS PROPERTIES INC
STERLING FINL CORP/WA
STATE STREET CORP
SUFFOLK BANCORP
SUSQUEHANNA BANCSHARES INC
STILWELL FINL INC
SOUTHWEST SECURITIES GROUP
TRUSTMARK CORP
U S B HOLDING INC
UNION BANKSHARES LTD/CO
UNITED FINANCIAL HOLDINGS
UNIVERSAL AMERICAN FINL CP
UNIVERSAL HEALTH RLTY INCOME
UTD INVESTORS REALTY TRUST
UMB FINANCIAL CORP
UNITED NATIONAL BANCORP/NJ
UNB CORP/OH
UNITED PANAM FINANCIAL CORP
UNITED PARK CITY MINES
VALLEY NATIONAL BANCORP
VESTIN GROUP INC
WESTAMERICA BANCORPORATION
WAYNE SAVINGS BANCSHARES INC
WESTCORP
WELLS FARGO & CO
WINTON FINANCIAL CORP
WELLPOINT HLTH NETWRK -CL A
WESTERN OHIO FINL CORP
WASHINGTON SVGS BANK F S B
WSFS FINL CORP
WILLIAMS COAL SEAM RYL TRUST
XL CAPITAL LTD
37
PHARMACEUTICAL (72 FIRMS)
ABT
AGN
AHP
AKC
ALO
ALT
AMGN
ANX
AVM
AVN
AZN
BGEN
BMY
BNT
BOL
BRL
BTX
BVF
CBM
CHIR
CLL
COB
COR
CVM
DMI
DNA
DOR
DP
ELI
ELN
EZM.A
FRX
GNT
GSK
HEB
ICN
IG
ABBOTT LABORATORIES
ALLERGAN INC
AMERICAN HOME PRODUCTS CORP
ACCESS PHARMACEUTICALS INC
ALPHARMA INC -CL A
ALTEON INC
AMGEN INC
ANTEX BIOLOGICS INC
ADVANCED MAGNETICS INC
AVANIR PHARMACEUTCLS -CL A
ASTRAZENECA PLC -SPON ADR
BIOGEN INC
BRISTOL MYERS SQUIBB
BENTLEY PHARMACEUTICALS
BAUSCH & LOMB INC
BARR LABORATORIES INC
BIOTIME INC
BIOVAIL CORP
CAMBREX CORP
CHIRON CORP
CELLTECH GROUP PLC -SP ADR
COLUMBIA LABORATORIES INC
CORTEX PHARMACEUTICALS INC
CEL-SCI CORP
DEPOMED INC
GENENTECH INC
ENDOREX CORP
DIAGNOSTIC PRODUCTS CORP
ELITE PHARMACEUTICALS INC
ELAN CORP PLC -ADR
E-Z-EM-INC -CL A
FOREST LABORATORIES -CL A
GENSTAR THERAPEUTICS CORP
GLAXOSMITHKLINE PLC -SP ADR
HEMISPHERX BIOPHARMA INC
ICN PHARMACEUTICALS INC
IGI INC
IMA
ISV
IVX
JNJ
KG
KV.A
LBC
LLY
MEDI
MEW
MRK
MRX
MYL
NVAX
NVO
NVS
NYE
ORG
PFE
PHA
PRX
PTN
QSC
RDY
SGP
SHM
SHR
SIAL
SRA
TGX
TTP
UG
VRA
WNI
WPI
INVERNESS MEDICAL TECHNOLGY
INSITE VISION INC
IVAX CORP
JOHNSON & JOHNSON
KING PHARMACEUTICALS INC
K V PHARMACEUTICAL -CL A
LABORATORIO CHILE -SPON ADR
LILLY (ELI) & CO
MEDIMMUNE INC
VASOGEN INC
MERCK & CO
MEDICIS PHARMACEUT CP -CL A
MYLAN LABORATORIES
NOVAVAX INC
NOVO-NORDISK A/S -ADR
NOVARTIS AG -SPON ADR
AMERSHAM PLC -ADR
ORGANOGENESIS INC
PFIZER INC
PHARMACIA CORP
PHARMACEUTICAL RES INC
PALATIN TECHNOLOGIES INC
QUESTCOR PHARMACEUTICALS INC
DR REDDYS LABS LTD -ADR
SCHERING-PLOUGH
SHEFFIELD PHARMACEUTICALS
SCHERING AG -ADR
SIGMA-ALDRICH
SERONO S A -ADR
THERAGENICS CORP
TITAN PHARMACEUTICALS INC
UNITED GUARDIAN INC
VIRAGEN INC
WEIDER NUTRITION INTL -CL A
WATSON PHARMACEUTICALS INC
TECHNOLOGY (197 FIRMS)
3128B
3ALLN
3BMLS
3CNDR
3CVTL
3DGLH
3FRTL
3INFO
3ISOL
3MEDY
3MGXI
3MSOF
3SHPI
3SYCM
3TISVE
3TSSW
3USCM
3WCIIQ
ACRU
NOVADIGM INC
ALLIN CORP
BURKE MILLS INC
CONDOR TECH SOLUTIONS INC
CONVERSION TECHNLGS INTL INC
DIGITAL LIGHTHOUSE CORP
FORTEL INC/CA
INFONAUTICS CORP -CL A
IMAGE SOFTWARE INC
MEDICAL DYNAMICS INC
MICROGRAFX INC
MULTI SOFT INC
SPECIALIZED HEALTH PDS INTL
SYSCOMM INTERNATIONAL CORP
TENET INFORMATION SVCS INC
TOUCHSTONE SOFTWARE CORP
USCI INC
WINSTAR COMMUNICATIONS
ACCRUE SOFTWARE INC
ADP
ALPH
AMEN
AMZN
AOLA
ARBA
ASIA
ASKJ
AVRT
BARZ
BEAS
BFLY
BFRE
BRCM
BTGI
CAMZ
CBR
CENI
CERO
AUTOMATIC DATA PROCESSING
ALPHANET SOLUTIONS INC
CROSSWALK.COM INC
AMAZON.COM INC
AMERICA ONLNE LTN AMR -CL A
ARIBA INC
ASIAINFO HLDGS INC
ASK JEEVES INC
AVERT INC
BARRA INC
BEA SYSTEMS INC
BLUEFLY INC
BE FREE INC
BROADCOM CORP -CL A
BTG INC
CAMINUS CORP
CIBER INC
CONESTOGA ENTERPRISES
CONCERO INC
38
CFA
CGZ
CKC
CLRN
CLRS
COE
CONE
CPTH
CSCC
CSGS
CSPI
CTB
CTRA
DIGX
DOCX
DRCO
DSET
DTAB10
DTLK
EDS
EGOV
EGPT
ELNK
ELOTC
EPAY
EPRS
ESAFE
ESTM
EXAP
EXDS
FCGI
FCOM
FDC
FILE
FIRE
FIT
FIVE
FLH
FMXI
FSST
FUTR
GBIX
GENU
GFD
GIB.A
GLC
GNL
GNSM
GRIC
GSPT
HAXS
HNCS
HTEI
IACT
IATV
IBM
IFGM
ILND
INAI
INGR
ININ
INSW
INTD
CHEMFAB CORPORATION
COTELLIGENT INC
COLLINS & AIKMAN CORP
CLARENT CORP
CLARUS CORP
CONE MILLS CORP
CARRIER1 INTL S A -ADR
CRITICAL PATH INC
CENTERSPAN COMMUN CORP
CSG SYSTEMS INTL INC
CSP INC
COOPER TIRE & RUBBER
CENTRA SOFTWARE INC
DIGEX INC
DOCUMENT SCIENCES CORP
DYNAMICS RESEARCH CORP
DSET CORP
DATATAB INC
DATALINK CORP
ELECTRONIC DATA SYSTEMS CORP
NATIONAL INFO CONSORTIUM INC
EAGLE POINT SOFTWARE CORP
EARTHLINK INC
ELOT INC
BOTTOMLINE TECHNOLOGIES INC
EPRISE CORP
SAFELINK CORPORATION
E-STAMP CORP
EXCHANGE APPLICATIONS
EXODUS COMMUNICATIONS INC
FIRST CONSULTING GROUP INC
FOCAL COMMUNICATIONS CORP
FIRST DATA CORP
FILENET CORP
FIREPOND INC
FAB INDUSTRIES INC
5B TECHNOLOGIES CORP
FILA HLDGS S P A -SPON ADR
FOAMEX INTERNATIONAL INC
FASTNET CORP
IFX CORP
GLOBIX CORP
GENUITY INC
GUILFORD MILLS INC
GROUPE CGI INC -CL A
GALILEO INTERNATIONAL INC
GALEY & LORD INC
GENSYM CORP
GRIC COMMUNICATIONS INC
GLOBAL SPORTS INC
HEALTHAXIS INC
HNC SOFTWARE INC
H T E INC
INTERACT COMMERCE CORP
ACTV INC
INTL BUSINESS MACHINES CORP
INFOGRAMES INC
INTERLAND INC
INTELLICORP INC
INTERGRAPH CORP
INTERACTIVE INTELLIGENCE INC
INSWEB CORP
INTELIDATA TECHNOLOGIES CORP
IPLY
ISMT
ITIG
ITWO
IWOV
IXX
LGTO
LIOX
LNTY
LNUX
LOAX
LOOK
LQID
LUMT
LVEL
MAPS
MMTM
MRGE
MSFT
MYE
NATI
NEOM
NETP
NLI
NOVL
NQLI
NTIQ
NTRT
NTWK
NUAN
NWSS
OAOT
OGNC
OPTK
OSII
PCD
PECS
PEGS
PLR.A
PMIX
PNS
PPOD
PPRO
PRSF
PTEC
PWEI
QMDC
QRSI
RAZF
RHAT
RMI
RNWK
ROSS
RWAV
SEBL
SFTY
SIDE
SMI
SNHK
SNOW
SSNC
STRM
SUNQ
INTERPLAY ENTERTAINMENT CORP
CHINAB2BSOURCING.COM INC
INTELLIGROUP INC
I2 TECHNOLOGIES INC
INTERWOVEN INC
IVEX PACKAGING CORP
LEGATO SYSTEMS INC
LIONBRIDGE TECHNOLOGIES INC
L90 INC
VA LINUX SYSTEMS INC
LOG ON AMERICA INC
LOOKSMART LTD
LIQUID AUDIO INC
LUMINANT WORLDWIDE CORP
LEVEL 8 SYS INC
MAPINFO CORP
MOMENTUM BUSINESS APPS INC
MERGE TECHNOLOGIES INC
MICROSOFT CORP
MYERS INDUSTRIES INC
NATIONAL INSTRUMENTS CORP
NEOMEDIA TECHNOLOGIES INC
NET PERCEPTIONS INC
NTL INC
NOVELL INC
NQL INC
NETIQ CORP
NETRATINGS INC
NETSOL INTERNATIONAL INC
NUANCE COMMUNICATIONS INC
NETWORK SIX INC
OAO TECHNOLOGY SOLUTIONS INC
ORGANIC INC
OPTIKA INC
OBJECTIVE SYS INTEGRATRS INC
PLANETCAD INC
PEC SOLUTIONS INC
PEGASUS SOLUTIONS INC
PLYMOUTH RUBBER -CL A
PRIMIX SOLUTIONS INC
PINNACLE DATA SYSTEMS INC
PEAPOD INC
PURCHASEPRO.COM
PORTAL SOFTWARE INC
PHOENIX TECHNOLOGIES LTD
PW EAGLE INC
QUADRAMED CORP
QRS CORP
RAZORFISH INC
RED HAT INC
ROTONICS MANUFACTURING INC
REALNETWORKS INC
ROSS SYSTEMS INC
ROGUE WAVE SOFTWARE INC
SIEBEL SYSTEMS INC
ESAFETYWORLD INC
ASSOCIATED MATERIALS INC
SPRINGS INDUSTRIES -CL A
SUNHAWK.COM CORP
SNOWBALL.COM INC
SS&C TECHNOLOGIES INC
STARMEDIA NETWORK INC
SUNQUEST INFORMATION SYS INC
39
SWTX
SYMC
TACX
THQI
THV
TIR
TSCN
TTWO
TWNE
TWSTY
TZIX
UBIX
UGS
UIS
UPRO
USI
USIX
VAST
VCLK
VIZY
VLCT
VRSO
VRTL
VRTS
VSTR
VUL
WCG
WEBT
WITS
WKGP
XOXO
SOUTHWALL TECHNOLOGY
SYMANTEC CORP
A CONSULTING TEAM INC
THQ INC
THERMOVIEW INDS INC
CHINA TIRE ECOMMERCE.COM LTD
TELESCAN INC
TAKE-TWO INTERACTIVE SFTWR
TOWNE SERVICES INC
TELEWEST COMMUNICATION -ADR
TRIZETTO GROUP INC
UBICS INC
UNIGRAPHICS SOLUTIONS INC
UNISYS CORP
UPROAR INC
U S INDUSTRIES INC
USINTERNETWORKING INC
VASTERA INC
VALUECLICK INC
VIZACOM INC
VALICERT INC
VERSO TECHNOLOGIES INC
VERTEL CORP
VERITAS SOFTWARE CO
VOICESTREAM WIRELESS CORP
VULCAN INTL CORP
WILLIAMS COMMUNICATIONS GRP
WEBTRENDS CORP
WITNESS SYSTEMS INC
WORKGROUP TECHNOLOGY CORP
XO COMMUNICATIONS INC -CL A
TELECOM (395 FIRMS)
AATK
ABIZQ
ACTG
ACTT
ADCT
ADPT
ADTN
AETH
AFCI
AINN
AIRN
ALA
ALGXQ
ALMO
ALSK
ALVR
AMT
AMX
ANCC
ANDW
ANK
APSG
ARDI
ARDTQ
ARRS
ASAT
American Access Technologies
Inc
Adelphia Business Solutions
Acacia Technologies
Act Teleconferencing Inc
ADC Telecommunications Inc
Adaptec Inc
Adtran Inc
Aether Sys Inc
Advanced Fibre Communications
Applied Innovation Inc
Airspan Networks
Alcatel
Allegiance Telecom Inc
Alamosa Hldgs Inc
Alaska Communications Sys Grp
Alvarion Ltd
American Tower Corp
America Movil S A De C V
Airnet Communications
Andrew Corp
Atlantic Tele Network Inc
Applied Signal Technology Inc
@Road Inc.
Ardent Communications Inc
Arris Group Inc
Esat Inc
ASPT
AT
ATEL
ATGN
ATNG
ATS
ATSC
ATTL
AVCI
AWE
BBOX
BCE
BCGI
BCICF
BFH
BIZZ
BKET
BLB
BLS
BMKS
BOGN
BRCD
BRKT
BRP
BSY
BTM
BTY
CACS
CAMP
CAPA
CATS
CCI
CCMS
CEL
CFW
CHA
CHL
CHNL
CHTR
CHU
CIC
CLEC
CMCSK
CMNT
CMTL
CMTN
CNEZF
CNOW
COLT
COMM
COMS
CORV
COVD
COX
CRDS
CRNT
CSA
CSCO
Aspect Communications Corp
Alltel Corporation
Accesstel Inc
AltiGen Communications Inc
Atng Inc
APT Satellite
ATSI Communications Inc.
AT&T Latin America Corp.
Avici Systems Inc
AT&T Wireless Services Inc.
Black Box Corp
BCE Inc.
Boston Communications Group
Bell Cda Intl Inc
Cabco Tr For Bellsouth Debs
Biznessonline Com Inc
Bankengine Technologies Inc
Bellsouth Cap Fdg Corp
BellSouth Corporation
Brandmakers Inc
Bogen Communications
International Inc
Brocade Communcations Systems
Inc
Brooktrout Inc
Brasil Telecom Participacoes
British Sky Broadcasting Group
ADS
Brasil Telecom Sa
Bt Group Plc
Carrier Access Corp
California Amplifier Inc
Captaris Inc
Catalyst Semi
Crown Castle International
Corp
Champion Communications Svcs
Grupo Iusacell S A De C V New
CyberGuard Corp
China Telecom Corp Ltd
China Mobile Hong Kong Ltd
Chanell Commercial Corp
Charter Communications
China Unicom Ltd
A T & T Cap Corp
US Lec Corp
Comcast
Computer network Technology
Comtech Telecommunications
Corp
Copper Mountain Networks Inc
Call-Net Enterprises Inc
Call Now Inc
Colt Telecom Group Plc
Atx Communications Inc
3Com Corp
Corvis Corp
Covad Communications Group Inc
Cox Communications
Crossroads Systems Inc
Ceragon Network
Chilesat Corp S A
Cisco Systems Inc
40
CSOL
CSTL
CTC
CTCI
CTCO
CTEL
CTL
CTLM
CTV
CVC
CVST
CWLC
CWON
CWP
CYAD
CYCL
CYTP
CZN
CZNPR
DAVL
DCEL
DCM
DDDC
DECC
DGII
DIGL
DISH
DITC
DSPG
DT
DTIX
DTVN
DVID
ECTX
ELEC
ELMG
EMT
ENCW
ENG
ENWV
EPHO
ERICY
ERS
ETLK
ETS
FCSE
FGWC
FIBR
FNSR
FON
FRNS
FTE
FULO
GHGI
GIGM
GLDN
GLW
GMH
GNCMA
GNSY
GTCC
GTTIF
Call Solutions Inc
Castelle Inc
Compañia de Telecomunicaciones
de Chile S.A.
Ct Communications Inc
Commonwealth Tel Enterprises
City Telecom H K Ltd
Centurytel Inc
Centillium Communications Inc
Commscope Inc
Cablevision Systems
Covista Communications Inc
China Wireless Communications
Choice One Communication Inc
Cable and Wireless Plc
Cyberads Inc
Centennial Communctns Corp New
Cybertel Communications Corp
Citizens Communications Co
Citizens Utils Tr
Davel Communications Inc
Dobson Communications Corp
Ntt Docomo Inc
Deltathree Inc
D&E Communications Inc
Digi International Inc
Digital Lightwave Inc
EchoStar Communications
Ditech Communcations Corp
DSP Group Inc
Deutsche Telekom Ag
Dial Thru International Corp
Dtvn Hldgs Inc
Digital Video Systems Inc
ECtel Ltd
Elec Communications Corp
EMS Technologies Inc
Empratel Participacoes
Encore Wireless Inc
ENGlobal Corp
Endwave Corp
Ephone Telecom Inc
Ericsson LM Telephones
Empire Resources Inc
Elephant Talk Comm Inc
Enterasys Networks
Focus Enhancements Inc
5G Wireless Communications Inc
Sorrento Networks Corporation
Finisar Corp
Sprint Corporation
First Ave Networks Inc
France Telecom
Fullnet Communications Inc
Greenhold Group Inc
Gigamedia Ltd
Golden Telecom Inc
Corning Inc
Hughes Electronics Corp
General Communications Inc
Genesys S A
Gtc Telecom Corp
Grandetel Technologies Inc
GTTLQ
HANA
HCT
HLIT
HRCT
HRS
HSVI
HTC
HTCO
IBAS
ICGC
IDCC
IDSY
IDTC
IGTT
IIIM
IIT
ILNK
INPH
INTA
INTI
ITCD
ITSN
ITXC
JCOM
JNIC
JNPR
KPN
KQIPQ
KTB
KTC
KVHI
KVZ
LCCI
LCST
LIC
LLL
LOR
LQ
LTBG
LU
LWINQ
LXNT
MANW
MBT
MBYTF
MC
MCDT
MCKC
MCLD
MCWEQ
MDLK
MFNXQ
MNCP
MNPL
MOORZ
MOT
MPOW
MRCY
MTA
Gt Group Telecom Inc
Hanaro Telecom Inc
Hector Communications Corp
Harmonic Inc
Hartcourt Cos Inc
Harris Corp
Home Svcs Intl Inc
Hungarian Telephone and Cable
Corp.
Hickory Tech Corporation
Ibasis Inc
Icg Communications Inc
Interdigital Communications
I.D. Systems Inc
IDT Corporation
Indiginet Inc
I3 Mobile Inc
Perusahaan Perseroan (Persero)
P.T. Ind. Sat. Corp.
I-Link Corp
Interphase Corp
Intasys Corp
Inet Technologies Inc
Itc Deltacom Inc
Its Networks Inc
Itxc Corp
J2 Global Communications Inc
JNI Corp
Juniper Networks
Royal PTT Nederland NV
Kpnqwest N V
Corts Tr Bellsouth
Kt Corp
KVH Industries Inc
Verizon New Eng Inc
LCC International
Lecstar Corp
Lynch Interactive Corp
L-3 Communications Holdings,
Inc
Loral Space & Communications
Quinenco SA
Lightbridge Inc
Lucent Technologies
Leap Wireless Intl Inc
Lexent Inc (USA)
Mobile Area Networks Inc
Mobile Telesystems Ojsc
Moving Bytes Inc
Matsushita Electronics
McDATA Corp
MCK Communications Inc
Mcleodusa Inc
Worldcom Inc Worldcom Group
Medialink Worldwide Inc
Metromedia Fiber Network Inc
Motient Corp
Minorplanet Sys Usa Inc
Chadmoore Wireless Group Inc
Motorola Inc
Mpower Holding Corp
Mercury Computer Systems, Inc
Magyar Tavkozlesi Rt
41
MTE
MTOH
MTON
MTPPRA
MTRM
MTSL
MWVT
NASC
NETX
NIHD
NIPNY
NMDV
NMRX
NOK
NPLSQ
NPSI
NSK
NT
NTIAQ
NTKKQ
NTL
NTLOQ
NTOP
NTRO
NTT
NUEP
NULM
NVNW
NVTL
NWK
NWRE
NXTL
NXTP
NZT
OBAS
OCCF
OECI
OOM
OPTC
OTE
PACW
PCSA
PCTI
PCW
PDYN
PHGW
PHI
PNTA
PR
PRMC
PROX
PRTL
PSEM
PT
PTEK
PTNR
Mahanagar Tel Nigam Ltd
Metrocall Hldgs Inc
Metro One Telecommunications
Inc
Montana Power Cap I
Metromedia International
Group, Inc
MER Telemanagement Solutions
Microwave Transmission Sys Inc
Network Access Solutions Cor
Netlojix Communications Inc
Nii Hldgs Inc
NEC Corporation
New Millennium Dev Group
Numerex Corp
Nokia Corp
Network Plus Corp
North Pittsburgh Systems Inc
New Skies Satellites N.V.
Nortel Networks
Netia Hldgs S A
Net2000 Communications Inc
Nortel Inversora S A
Ntelos Inc
Net2Phone
Netro Corp
Nippon Telegraph and Telephone
Corporation
Net 1 Ueps Technologies Inc
New Ulm Telecom Inc
Novo Networks Inc
Novatel Wireless
Network Equipment Technologies
Inc
Neoware Systems Inc
Nextel Communications Inc Cl A
Nextel Partners Inc
Telecom Corporation of New
Zealand Limited
Optibase Ltd
Optical Cable Corp
Orbit E-Commerce Inc
Mmo2 Plc
Optelecom Inc
Hellenic Telecom Organizatn Sa
Pac-West Telecom Inc
Airgate Pcs Inc
PC-Tel Inc
Pccw Ltd
Paradyne Networks Inc
Phone 1Globalwide Inc
Philippine Long Distance
Telephone Company
Pentastar Communications Inc
Price Communications
Corporation
Prime Cos Inc
Proxim Corp
Primus Telecommunications Grp
Pericom Semiconductor Corp
Portugal Telecom, S.A.
Ptek Hldgs Inc
Partner Communications Co Ltd
PVSS
PWAV
Q
QCOM
RADN
RCCC
RCN
RCNC
REMC
RFMI
RG
RIMM
RNDC
ROS
RSTN
RSTRC
SAT
SBAC
SBC
SBEI
SCKT
SCM
SCMR
SDAY
SFA
SHEN
SI
SILCF
SKM
SLNK
SMSC
SONT
SPAL
SPM
SPOT
SRTI
SSPI
STGC
STHLY
STXN
SURW
SVVS
SWIR
SYMM
T
TALK
TAR
TBE
TBH
TCN
TCOM
TCP
TDA
TDP
TDR
TDS
TDSPRB
Pivotal Self Service Tech Inc
Powerwave Technologies Inc
Qwest Communications Intl Inc
Qualcomm Inc
Radyne Comstream Inc
Rural Cellular Corporation
Rogers Wireless Communications
RCN Corp
Remec Inc
RF Monolithics Inc
Rogers Communications Inc
Research In Motnion Ltd
Raindance Communications Inc
Rostelecom Open Jt Stock Lng
Dst
Riverstone Networks Inc
Rstar Corp
Asia Satellite
Telecommunications Holdings
Ltd
SBA Communications Corp
SBC Communications Inc.
SBE Inc
Socket Communications Inc
Swisscom Ag
Sycamore Networks Inc
Sunday Communication Ltd
Scientific-Atlanta Inc
Shenandoah Telecommunications
Siemens AG
Silicom Ltd
Korea Mobile
Telecommunications Corp.
Spectralink Corp
Standard Microsystems Corp
Sontra Medical Corp
Spantel Communications Inc
Spirent PLC
Panamsat Corp
Sunrise Telecom Inc
Spectrum Signal Processing Inc
Startec Global Communication
Stet Hellas Communications S A
Stratex Networks Inc
Surewest Communications
Savvis Communications Corp
Sierra Wireless Inc
Symmetricom Inc
AT&T Corp.
Talk America Hldgs Inc
Telefonica De Argentina S A
Tele Leste Celular
Participacoes S.A.
Telebras HOLDRS
Tele Norte Celular Part S A
Telecom Communications Inc
Telesp Celular Part S A
Telephone & Data Sys Inc
Telefonica Del Peru S A
Tricom Sa
Telephone And Data Systems,
Inc.
Tds Cap Ii
42
TEF
TELN
TEM
TEO
TFONY
TI
TKA
TKC
TLAB
TLD
TLL
TLMD
TLS
TLTOB
TMB
TMX
TND
TNE
TNTX
TPC
TRO
TROY
TSD
TSP
TSTN
TSU
TSYS
TU
TUTS
TWTC
UAX
UCSY
ULCM
UMGPRC
UNWR
UPCS
USM
USPO
USRT
UTSI
UVCL
VEDO
VICM
VIP
VNT
VNWI
VNWK
VOD
VRLK
VSAT
VSL
VYYO
VZ
VZC
WEBX
WFII
WGNR
WJCI
WONE
Telefonica de España, S.A.
Telenor Asa
Telefonica Moviles S A
TELECOM ARGENTINA STET France Telecom S.A.
Telefonos De Mexico S A
Telecom Italia Spa
Telekom Austria Ag
Turkcell Iletisim Hizmetleri
Tellabs Inc
Tdc A/S
Teletouch Communications Inc
Telemonde Inc
Telstra Corp Ltd
Tele2 Ab
Telemig Celular Part S A
Telefonos de Mexico, S.A. de
C.V.
Tele Nordeste Celular Part S A
Tele Norte Leste Part S A
T-NETIX Inc
Triton Pcs Hldgs Inc
Tele Centro Oeste Celular S A
Troy Group Inc
Tele Sudeste Celular Part S A
Telecomunicacoes De Sao Paulo
Turnstone Systems
Tele Celular Sul Part S A
TeleCommunication Systems
Telus Corp
Tut Systems Inc
Time Warner Telecom Inc
Usurf America Inc
Universal Comm Sys Inc Nev
Ulticom Inc
Mediaone Fin Tr Iii
US Unwired Inc
Ubiquitel Inc
United States Cellular
Corporation
Usip Com Inc
U S Realtel Inc
UTStarcom Inc
Univercell Hldgs Inc
Villageedocs Inc
Vicom Inc
Open Jt Stock Co-Vimpel
Communic
Compania Anonima Nacionl Tel
Via Net Wrks Inc
Visual Networks Inc
Vodafone Group Plc New
Verilink Corp
ViaSat Inc
Videsh Sanchar Nigam Ltd
Vyyo Inc
Verizon Communications
Verizon South Inc
Webex Communications Inc
Wireless Facilities Inc
Wegener Corp
WJ Communications Inc
Wire One Technologies
WQNI
WRDP
WRLS
WSTL
WTEQ
WVCM
WWCA
WWCO
WWVY
WWWNQ
XETA
XING
ZENX
ZICA
ZNTH
ZOOM
ZTEL
WorldQuest Networks Inc
Worldport Communications Inc
Telular Corp
Westell Technologies Inc
Worldteq Group Intl Inc
Wavecom S.A. ads
Western Wireless Corp Cl A
Wireless Webconnect Inc
Warwick Valley Tel Co
Worldwide Wireless Networks
Xeta Technologies Inc
Qiao Xing Universal Telephone
Inc
Zenex Intl Inc
ZI Corporation
Zenith Technology Inc
Zoom Technologies Inc
Z Tel Technologies Inc
Appendix II
Business Category Dictionary
We defined six categories of business words and word meanings to classify disclosure
texts. Below are the categories and words comprising each category.
1. Statements of Market Risk, Industry Structure and Competitive Forces
Market
search terms: market, marketplace, environment, segment, sector
Competition
search terms: competitor, competition, dominant
Industry structure
search terms: industry, entrant, supplier, buyer, substitute, scale, product, brand, switching,
capital, access, cost, rivalry, capacity, concentration, exit, barrier, price, profit, quality, input,
volume, purchase, integration, power
Competitive analysis
search terms: customer, channel, value, first-mover, technology, alliance, partnership,
venture, regulation, litigation
2. Statements of Firm-Level Strategy Intent, Product Market Performance,
Performance of Business Strategy Model in Use
Strategic intent:
search terms: strategy, strategic, value, sales, revenue, share, profit, profitability, product,
service, lead, leader, quality, customer, buyer, growth, opportunity, risk, resource
Innovation and research and development
search terms: research and development, r&d, patent, discovery, license, licensing,
regulation, regulatory, trial, monitor, innovate, innovation, competence
Mode of entry
search terms: entry, cost, business, complementary, green-field, venture, investment,
capital, solution, price
Business model
44
search terms: model, best, lowest, low, highest, high, supplier, distribution
Partnerships
search terms: partner, alliance, merger, acquisition, joint, venture, relationship, equity, asset
3. Statements of Human and Organizational Capital, Quality of Management
Performance, Corporate Governance and Leadership
Leadership
search terms: leader, leadership, record, value, culture, responsibility, goal, objective
Management quality
search terms: management, quality, best, proven, experience, teamwork
Governance
search terms: governance, corporate, board, incentive, owner, ownership, compensation,
recruitment, development
Disclosure
search terms: disclosure, transparent, transparency, information, audit, auditing, oversight,
assurance, regulation, mandate, mandated
Measures
search terms: up, down, better, worse, recover, advance, advancing, progress, progressing,
expand, expanding, improve, improving, reduce, reducing, reduction, decline, declining, retain,
retention, profit, profitability, feedback, scorecard, growth, growing, performance, projected,
projections.
4. Statements of market recognition, power and consistency of branded image,
measures of consumer confidence and trust in branded image
Customer
search terms: customer, satisfaction, feedback, trust
Brand
search terms: brand, image, name, trademark, recognition, stretch, quality, awareness
45
Media
search terms: media, radio, television, newspaper, internet, promotion, media spend, media
budget, announcements, release, media spend, media budget
Advertising
search terms: advertising, ad, direct, channel, advertising spend, ad spend, advertising
allocation, advertising budget, ad budget
Corporate image
search terms: corporate image, reputation, integrity, community, trust, trusted name,
confidence, durability, strength, character
5. Statements of corporate and business unit financial performance
Financial performance
search terms: gross, net, return on investment, return on sales, return on assets, return on
equity, ROI, ROA, ROE, profit, earnings, margin, capital, debt, sales, EBITDA, ratings,
leverage, valuation, cost of capital
Forecasting
search terms: forecast, forecasting, cash flow, prospectus, quarterly
Insider stock transactions
search terms: insider buy, insider sell
6. Statements and/or References to Federal Government Regulation Enacted or
Pending Influential to firm Competitiveness, Product Market Performance, and/or
Disclosure Practices
Regulation
search terms: regulation, federal, state, securities and exchange commission, commerce,
legislation, congress, law, legal, hearings, enacted, pending, sec, medicare, medicaid, FDA
Special interest groups
search terms: lobby, lobbyists, special, interest, expert, testimony, industry, watchdog,
consumer rights, patient rights
Appendix III
Examples of Disclosure Texts, Amgen Corporation
Technology & Health
Study on Amgen's Obesity Drug, Leptin, Raises Questions About Its Effectiveness
By Robert Langreth and Rhonda L. Rundle
Staff Reporters of The Wall Street Journal
02/01/1996 The Wall Street Journal B5
New research raises questions over whether Amgen Inc.'s experimental obesity drug will work
on most overweight patients.
Amgen has gambled millions of dollars on the new drug, leptin, which will be tested in humans
later this year. Leptin, a hormone produced by fat cells, is believed to regulate weight by
signaling the brain when enough fat has been stored and ordering it to slash food intake and
speed metabolism.
But the new study, published this week in the New England Journal of Medicine, found that
obese people have no problem producing leptin, as some researchers had suspected; in fact, they
have an average of four times more leptin in their bloodstream than lean people.
"The idea that leptin will be a magic bullet for everyone with obesity, this study puts that idea
aside," said lead researcher Jose Caro from the Thomas Jefferson University in Philadelphia.
Amgen shares fell 1.9% in Nasdaq Stock Market trading yesterday, closing at $60.125, down
$1.1875, when word of the study began to leak out. After the market closed, Amgen reported
fourth-quarter net income of $145.6 million, or 52 cents a share, on a 16% increase in revenue, in
line with analysts' forecasts.
Amgen said its profit in the quarter jumped 20%, excluding a large charge in the year-earlier
quarter. The latest research may hint that, instead of a deficiency of leptin in an overweight
person, the problem could lie in an inability of the brain and its "receptors" to receive leptin's
signals.
That would put Amgen at odds with a small rival. Last year Amgen paid Rockefeller University
a record $20 million for rights to the newly discovered gene that produces leptin. But a rival,
Millennium Pharmaceuticals in Cambridge, Mass., is betting the receptor, a protein in the brain,
is the key. Backed by Roche Holding Ltd.'s Hoffman-LaRoche Inc., Millennium said last month
it had discovered the gene that makes the receptor. It is attempting to design a drug that would
boost the leptin receptor's sensitivity to signals from the hormone.
Amgen disputed the study's significance. "This doesn't really answer the question whether giving
leptin to people will cause them to lose weight or not. That can only be answered in a human
clinical trial, which we will conduct in a few months," said Gordon Binder, chief executive of
Amgen, Thousand Oaks, Calif. Millennium countered that the new finding bolsters its own
47
approach. "This is very important research and confirms our own internal studies," said Louis
Tartaglia, a Millennium researcher who helped discover the receptor gene. Last year, scientists at
Amgen and Rockefeller University showed that grossly obese mice lost 22% to 40% of their
weight after getting shots of the leptin hormone. The results raised hopes that the same hormone
might work in humans.
But in the New England Journal study, Dr. Caro and his colleagues measured leptin levels in 139
obese people and 136 lean people. On average, the overweight subjects had four times as much
leptin as lean ones. Some obese subjects had levels of the hormone elevated 20 or 30 times
normal. About 5% of the overweight subjects, however, had lower leptin levels, and might be
more likely to react to leptin therapy, Dr. Caro noted. Eli Lilly & Co. researchers also
participated in the New England Journal study.
Amgen's fourth-quarter earnings included a charge of $10 million, or two cents a share, in
license fees to NPS Pharmaceuticals Inc., a Salt Lake City drug-development company. In the
year-earlier quarter, Amgen had earnings of $4.8 million, or two cents a share, after a $116
million charge related to the acquisition of Synergen. Revenue in the latest quarter rose to $513.5
million from $442.9 million.
48
Technology & Health
Amgen Says Profit Increased by 32%, Plans New Product
By Rhonda L. Rundle
Staff Reporter of The Wall Street Journal
04/18/1996 The Wall Street Journal B6
Biotechnology leader Amgen Inc. reported a 32% increase in first-quarter net income and
disclosed for the first time a new product candidate and the filing of an application for an
experimental Hepatitis C drug. Net income climbed to $143.6 million, or 51 cents a share, from
$108.6 million, or 39 cents a share, a year earlier, a penny ahead of analysts' expectations. Last
year's first-quarter results were reduced by a $20 million technology payment.
Revenue rose 16% to $507.9 million from $439.4 million, reflecting continued strong growth of
Amgen's two drugs. Epogen sales rose 23% to $244 million, while Neupogen sales rose 10% to
$233 million.
"It was a good, solid quarter," said Joyce Lonergan, an analyst at Cowen & Co. First-quarter
domestic Neupogen sales were weak because of a lag effect after the holidays when there aren't
as many cancer chemotherapy treatments, she said. Neupogen bolsters white blood cells that are
destroyed by anticancer drugs. The results were announced after the stock market had closed.
Amgen shares fell $1.375 to $53.125 in Nasdaq trading.
Amgen, Thousand Oaks, Calif., said it is developing a new protein that stimulates the growth of
red blood cells, and "we believe we may have some superior properties to Epogen," said Gordon
Binder, chairman and chief executive officer. Human tests are expected to start next year.
Epogen, widely used by kidney dialysis patients, was Amgen's first product and remains one of
biotech's best-selling drugs.
Amgen said it filed with the Food and Drug Administration April 10 for a new drug application
for Infergen, a consensus interferon developed as a treatment against Hepatitis C. Amgen said it
will probably sell Infergen in the U.S. and either license or co-promote it in other countries. Mr.
Binder said it's unlikely the drug will be approved before next year.
Analysts have been skeptical of the potential for Infergen, whose human test results haven't
demonstrated clear superiority over other interferons. Infergen's approval could trigger a patent
battle with Schering-Plough Corp., which markets alpha interferon, analysts say. A ScheringPlough spokesman declined to comment on that speculation, but said the company is monitoring
Amgen's activities.
Ms. Lonergan, like many analysts, isn't counting on significant Infergen profits in her earnings
models. She estimates the potential U.S. market at $100 million. She said the new red blood cell
protein under development, if successful, could extend Amgen's Epogen "patent position" and
"support their franchise" for a long time.
Appendix IV
General Inquirer Word Category Examples
Examples of Words and Word Meanings Included in GI Categories
Positive and Negative (entries are shown with assigned tags and sense meanings)
POSITIVE
Category includes 1,915 words of positive outlook. For example:
ABILITY
H4Lvd Positiv Strong Virtue EVAL Abs@ ABS MeansLw Noun
ABLE
H4Lvd Positiv Pstv Strong Virtue EVAL MeansLw Modif adjective: Having
necessary power, skill, resources, etc.
ACCENTUATE
H4 Positiv Active Ovrst IAV SUPV
ACCEPT
H4Lvd Positiv Pstv Submit Passive SocRel IAV PosAff SUPV verb: To take,
receive or accede to something
ACCEPTABLE
H4Lvd Positiv Pstv Virtue EVAL PosAff Modif
ACCEPTANCE
H4Lvd Positiv Pstv Affil Passive SocRel PosAff Noun
ACCOMPLISH
H4Lvd Positiv Pstv Strong Power Active Complet IAV EndsLw SUPV verb: To
bring to its goal or conclusion
ACCOMPLISHMENT
H4Lvd Positiv Pstv Strong Power Active Goal EndsLw Noun
-------------------NEGATIVE
Category includes 2,291 words of negative outlook
ADVERSARY
H4Lvd Negativ Hostile Polit@ Role SocRel HU PowCon PowTot Noun
ADVERSE
H4Lvd Negativ Ngtv Vice NegAff Modif
50
ADVERSITY
H4 Negativ Vice Noun
ALLEGATION
H4Lvd Negativ Ngtv Hostile Legal ComForm COM PowCon PowTot FormLw
Noun
ALLEGE
H4 Negativ Weak Undrst ComForm IAV SUPV
AMBIGUITY
H4Lvd Negativ Ngtv Undrst Perceiv If Noun
AMBIGUOUS
H4Lvd Negativ Ngtv Undrst Perceiv If Modif
AMISS
H4Lvd Negativ Ngtv Vice NegAff LY
ANOMALY
H4Lvd Negativ Undrst Rel Noun
Appendix V
Example General Inquirer Output Matrix
AMGEN Corporation
Disclosure Text Input File
AMGN 02-02-1996.txt
AMGN 02-02-1996.txt
AMGN 04-18-1996.txt
AMGN 04-18-1996.txt
Format Wordcount Leftovers Positive Negative
r
635
116
20
21
s
635
18.26772 3.149606 3.307087
r
423
77
19
8
s
423
18.20331 4.491726 1.891253