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Review of Accounting Studies
Erschienen in: Review of Accounting Studies 2/2015

01.06.2015

Does concrete language in disclosures increase willingness to invest?

verfasst von: W. Brooke Elliott, Kristina M. Rennekamp, Brian J. White

Erschienen in: Review of Accounting Studies | Ausgabe 2/2015

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Abstract

As part of its push for more plain English in disclosures, the SEC argues that firms should use more concrete language to make abstract concepts clearer to investors. We use two experiments to show that, when concrete language is highlighted in a prospectus, investors are significantly more willing to invest in a firm than when abstract language is highlighted. Furthermore, we show the effect of concrete language is particularly important when investors feel more psychologically distant from a firm. Drawing on psychology theory, we predict and find that concrete language increases investors’ feelings of comfort in their ability to evaluate an investment. Our study contributes to the literature on how language choices in disclosures affect investors’ judgments by demonstrating that a simple, yet potentially powerful reporting tool of emphasizing concrete language may attract investors who may otherwise be reluctant to invest.
Vorheriger Artikel Anticipation of management forecasts and analysts’ private information search
Nächster Artikel The optimal focus of transfer prices: pre-tax profitability versus tax minimization

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Fußnoten
1
The distinction between abstract and concrete language differs from the distinction made in previous accounting studies between “soft talk” and verifiable information (e.g., Hutton et al. 2003) in that abstract language is not necessarily less verifiable than concrete language. For example, the statement “revenue increased” is less concrete, but no less verifiable, than the statement “revenue changed from $100 million to $110 million.”
 
2
Concrete language could reduce willingness to invest if a firm discusses negative expectations for the future and concrete language makes it easier to visualize that information. However, even when performance is poor, most firms are likely to frame future expectations in a relatively positive light. Thus we argue that, in most cases, concrete language in a disclosure should increase willingness to invest. In Sect. 5, we provide additional discussion on how investors’ reactions may differ if purely negative information is presented more concretely.
 
3
Using survey data, Graham et al. (2009) measure investors’ comfort in their ability to evaluate firms, but describe their measure as perceived investing competence. We use the same measure as Graham et al. (2009) to capture participants’ comfort in their ability to evaluate a firm. However, for ease of exposition, we consistently refer to this as “comfort in their ability to evaluate a firm” throughout our paper, so that our discussion maps directly to the measure that we collected.
 
4
This manipulation heightens both geographic distance and social distance by including features on the map (i.e., landmarks) that should feel more or less recognizable. We further increased the salience of the firm’s location in the follow-up study by engaging a graphic designer to develop corporate materials that highlighted the firm’s location.
 
5
As demonstrated in the SEC’s examples, concrete language in financial disclosures will often naturally include numbers as well as words. Thus in addition to words, quantification can be considered an important part of increasing the concreteness of firms’ disclosures.
 
6
Conceptually, our directional predictions for the effects of concrete language should not differ depending on whether concrete language is highlighted (not highlighted) or is present (absent). We choose manipulate whether concrete language is highlighted to help ensure that our observed effects are not driven by real differences in information across experimental conditions.
 
7
The structure and content of prospectus excerpts were based on a composite of several firms that listed on the NASDAQ in recent years.
 
8
We also asked participants to imagine that they had $10,000 to invest in the industry and to indicate how much they would allocate to NaviFast. For this measure, we do not find a significant main effect of highlighting concrete language (p = 0.974) or our predicted interaction (p = 0.170), suggesting that this may be a noisier way of capturing willingness to invest.
 
9
Because participants are randomly assigned, ex ante price expectations should not differ across conditions. Thus, given the average price expectation of all participants (whatever that may be), investors who indicate greater willingness to invest would be more willing to buy at that price.
 
10
We did not collect specific measures of information acquisition. Given the length and detail of the disclosures in the experimental materials, it was not clear ex ante that a recall question of a specific figure or piece of information would effectively measure differences in the acquisition of decision-relevant information. Specifically, a difficult recall question likely would have resulted in a low correct response rate across all conditions, and a simple recall question would have likely resulted in a high correct response rate across all conditions. However, our measure of risk can also serve as a check for differences in information acquisition across the highlighted language conditions, as discussed in Sect. 4.4.
 
11
The shortest driving distance between Austin and Chicago is approximately 1,119 miles.
 
12
This could be either because highlighting concrete language is less important when a firm already feels psychologically close or because participants in Austin possessed a reasonable knowledge of the actual distance between their location and the city of Houston, leading to less variation in their responses.
 
13
In deciding whether a description was abstract or concrete, the coders were instructed to consider the following:
“Compared to abstract language, concrete language facilitates visualization and is less open to interpretation because it is more specific and contextualized. Further, while the concreteness of language may be determined by word choice alone, examples provided by the SEC suggest that quantification, or including numbers with words, can help to increase the concreteness of a disclosure. For example, ‘one share of IBM stock’ is more concrete than ‘an asset,’ although both might describe the same thing.
 
14
Results are inferentially identical if we use any of the three raters’ codings alone. Specifically, all three raters coded descriptions from participants in concrete conditions as more concrete than those in abstract conditions (all p values <0.10).
 
15
In the case of our data, structural equations modeling differs from regression analysis principally in that the direct path from highlighted concrete language to willingness to invest (i.e., Path c in Fig. 2) and the indirect path via the mediator (i.e., Paths a and b in Fig. 2) are estimated simultaneously. This allows us to estimate each of these effects while “partialling out, or statistically controlling for, the other” (Iacobucci et al. 2007, p. 151). As a robustness test, we also confirm that our results are inferentially identical using the three-regression approach suggested by Baron and Kenny (1986).
 
16
The model also includes links (not depicted) for main and interactive effects of firm location and highlighted language type on feelings of comfort and willingness to invest. Fit statistics indicate that the model is a good fit for the data: \(\chi_{(1\,df)}^{2}\) = 0.112 (p = 0.738), RMSEA < 0.001, and CFI = 1.000 (Iacobucci 2009, 2010; Kline 2011).
 
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Metadaten
Titel
Does concrete language in disclosures increase willingness to invest?
verfasst von
W. Brooke Elliott
Kristina M. Rennekamp
Brian J. White
Publikationsdatum
01.06.2015
Verlag
Springer US
Erschienen in
Review of Accounting Studies / Ausgabe 2/2015
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-014-9315-6

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