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Review of Accounting Studies
Erschienen in: Review of Accounting Studies 2/2017

08.04.2017

The internet as an information intermediary

verfasst von: Michael S. Drake, Jacob R. Thornock, Brady J. Twedt

Erschienen in: Review of Accounting Studies | Ausgabe 2/2017

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Abstract

The internet is an enormous and growing source of information for investors about the opinions of others. Virtually any individual with internet access can express opinions about firms and editorialize about company news. However, to date we know very little about the impact these nontraditional internet intermediaries have on markets. We develop a framework wherein internet information intermediaries fall along a spectrum of professionalism and document a nuanced relationship between coverage by these intermediaries and capital market effects. Using a novel dataset that tracks coverage of companies by individuals posting on thousands of websites, we find that coverage by professional and semi-professional intermediaries is associated with positive capital market effects but coverage by nonprofessional internet intermediaries has the opposite effect—hindering price formation. The detrimental effects of nonprofessional coverage are observed most strongly when the intermediaries have larger audiences.
Vorheriger Artikel Accrual quality, skill, and the cross-section of mutual fund returns
Nächster Artikel An examination of firms’ responses to tax forgiveness

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Fußnoten
1
These 1000 websites produce the vast majority of articles in our sample (96%).
 
2
Because of this potential overlap, in interpreting our results, we focus primarily on differences between the professional and nonprofessional groups, where less overlap is likely to occur. We use the term “semi-professional” for the middle group to reflect the idea that it likely contains a mix of both professionals and nonprofessionals.
 
3
See, for example Wysocki (1999), Tumarkin and Whitelaw (2001), Antweiler and Frank (2004), Das and Chen (2007), Lerman (2011), and Chen et al. (2014).
 
4
By price responsiveness, we mean the market’s reaction to the release of earnings; by price timeliness, we mean the speed with which the earnings news is incorporated into the firm’s stock price. We recognize that significant overlap exists between these two constructs, which limits our ability to disentangle their individual effects. However, they both capture important elements of price formation, which is our overarching construct of interest.
 
5
The IPT metric is an area-under-the-curve measure designed to capture the speed with which information is impounded into price over a specified period. Higher values of IPT indicate that earnings information is reflected into prices more quickly over the measurement period.
 
6
In a related study, Blankespoor et al. (2014) focus on firms’ ability to use the internet to disseminate disclosures to their investors. They find that firms that provide links to their press releases via Twitter experience a reduction in information asymmetry.
 
7
Using actual trading data by retail investors, other research provides clear evidence that retail trading is directionally correlated in ways that are unsupported by fundamentals or market factors (e.g., Kumar and Lee 2006; Barber et al. 2009), which in turn impacts stock returns.
 
8
The RavenPack web data begins in 2007, the press release data begins in 2004, and the Dow Jones data begins in 2000. The RavenPack press release dataset contains data on every press release issued by our sample firms through 22 press release distribution services, including the two largest services in the U.S. market (i.e., Business Wire and PR Newswire; Solomon (2012)).
 
9
“Coverage” in the RavenPack dataset means that the firm name is mentioned in the article. Although an included article may therefore be primarily about another company, this is less likely in our setting given that we focus mainly on the earnings announcement window.
 
10
Our results are unchanged when we include websites outside of the top 1000 in our analyses (as part of the nonprofessional group). This is unsurprising, given the small number of articles they produce.
 
11
Consider the following example. Centurylink and Cubesmart announced earnings on Nov. 2 and 3, 2011, respectively. Both earnings announcements were covered by 16 newswire articles. However, while Centurylink’s announcement was covered by eight nonprofessional websites and 25 semi-professional websites, Cubesmart’s announcement was covered by only one semi-professional website and zero nonprofessional websites. The goal of this study is to identify the price formation implications of such variation in coverage by semi-professional and nonprofessional internet information intermediaries.
 
12
We perform this standardization across the full population of observations within each of our tests (i.e., firm-quarters or firm-days). In Section 6, we demonstrate that our results are robust to the use of unstandardized measures of web coverage.
 
13
We require a firm to have at least 250 observations to be included in the analysis.
 
14
Keep in mind that the figures plot daily percentage changes. Thus the negative values on day +1 in all three panels of Figure 2 indicate that coverage has declined slightly from the prior day (the earnings announcement date). However, the level of coverage for all three types of internet intermediaries is higher on day +1 than on any of the five days before the earnings announcement.
 
15
The number of retail investors is determined by taking the difference between the total number of shareholders of record (obtained from Compustat) and INST. COUNT.
 
16
One potential explanation for this result is that professionals and semi-professionals may be more surprised when realized earnings deviates to a greater degree from market (i.e., analysts’) expectations than are nonprofessionals, who may not be aware of the market’s expectations to begin with.
 
17
We follow the characteristic-based approach developed by Daniel et al. (1997) to form the 125 benchmark portfolios each year. We assign firms to size, book-to-market, and momentum portfolios in June of each year. When a firm cannot be assigned to a particular portfolio in a year, we use size-adjusted returns instead.
 
18
Specifically, we exclude any press releases relating to earnings, revenues, dividends, or conference calls from our measure of non-earnings-related press releases.
 
19
As in previous analyses, the abnormal return is computed as the firm’s raw return less the benchmark portfolio return based on the firm’s size, book-to-market, and momentum over the same period.
 
20
Two key assumptions underlying the IPT measure are, first, that the earnings news is fully incorporated into price by the end of the window and, second, that the earnings news is the primary driver of returns during the window. To the extent that these assumptions are violated, this should introduce noise, rather than bias, into our analyses. Note, too, that the choice of the trading day window is somewhat arbitrary but is intended to provide sufficient time for prices to reach their equilibrium level. In untabulated robustness tests, we find qualitatively similar results using a five- or 30-day windows to calculate IPT.
 
21
In Section 6, we discuss results using alternative methods of dealing with outliers.
 
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Metadaten
Titel
The internet as an information intermediary
verfasst von
Michael S. Drake
Jacob R. Thornock
Brady J. Twedt
Publikationsdatum
08.04.2017
Verlag
Springer US
Erschienen in
Review of Accounting Studies / Ausgabe 2/2017
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-017-9395-1

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