Earnings management, financial report readability, and ...fyc.fen.uchile.cl/05092019.pdf ·...

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Earnings management, financial report readability, and

valuation uncertainty

Matias Braun

ESE Business School,

Universidad de los Andes

[email protected]

Tiago Ferreira

Escuela de Negocios, Universidad Adolfo Ibáñez

[email protected]

Marcelo Ortiz

Department of Economics and

Business

Universitat Pompeu Fabra

[email protected]

August 2019

Abstract

Based on full 10K reports we analyze the relationship between the likelihood of having managed

earnings, readability, and market reaction. We find that post-filing valuation uncertainty and

market mispricing are lower for firms that barely meet or just beat last years’ earnings, and for those

that write more readable reports. These two effects are complementary: readability has a larger

impact on reducing uncertainty and mispricing when firms are more likely to have managed

earnings. This result is consistent with a view where the management, that has superior

information, attempts to reduce the volatility and mispricing induced by overreacting investors by

both avoiding earnings surprises and providing better qualitative guidance for future performance.

1. Introduction

The market is long known to overreact to earnings news (Ball and Brown, 1968; Bernard and

Thomas 1989, 1990;) assuming, at times, transitory earnings news to be more persistent than they

really are. The incorporation of information into prices is not instantaneous, either, as evidenced by

the pervasiveness of post-earnings announcement drift. These behaviors lead to increased volatility

and mispricing around earnings announcements. Companies might be interested in lowering their

stock market volatility, as this allows increasing investment thanks to a lower cost of capital (Tao

and Zhao, 2019).

One way to achieve this is by communicating information more effectively. Lee(2012) show that the

post-announcement drift is greater when there is greater information uncertainty. When the

quantitative data reported by the company is very different from the one expected by investors, a

good explanation of it can help avoiding overreaction and incorporating news into prices more

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quickly. The management, that has superior information about the firm´s current and future

performance, may be able to guide the expectation formation process by conveying what the

earnings surprise actually means for future fundamentals. Gary et al.(2012) find that firms with

higher financial report readability enjoy higher investment efficiency, suggesting that easier to

understand annual reports works reducing the information asymmetry between firms and external

suppliers of capital. Presenting the information in more understandable terms can increase the speed

with which it is incorporated into prices, as suggested by Hong and Stein (1999)´s model. Firms

seem to be aware of this, as reflected for instance by the fact that foreign companies cross-listed in

the U.S. tend to produce more readable reports than their peers (Lundholm et.al 2014). Indeed, more

readable 10K reports -one of the key mandated disclosure documents- are related to lower valuation

uncertainty (Loughran and Mcdonald, 2014), while more convoluted ones have been shown to delay

the convergence of stock prices to the firm’s fundamental value (Lee, 2012).

Earnings management has been associated to opportunistic behavior of management in trying to

deceive investors on their view of the firm and in influencing transactions linked to accounting

figures. Management is not necessarily interested in lowering volatility; in fact, it might profit from

it if a relevant part of her compensation takes the form of stock options. High volatility might also

lead the board to choose a lower performance threshold as a trigger for replacing management

(Hallman, Hartzell, and Parsons 2004).

But there is another view. A CEO aiming to protect investors’ wealth from their own overreactions

and interested in the stock price reflecting most accurately the true fundamentals could engage in

earnings management. She would smooth “bad news” (reporting lower earnings than previous year)

and “good news” (reporting higher earnings than previous year) to “no news” (meeting or just

beating previous years’ earnings by a small increment). This would reduce uncertainty and

mispricing.

Earnings management only has a temporary effect, though, because if earnings are overstated today,

they need to be understated tomorrow and vice-versa. Nevertheless, earnings management buys the

CEO time that can be used to guide expectations by better explaining the results in the meantime.

If this is so, one would expect the communication and earnings management mechanisms to be

complementary. The more the administration manages results the higher the need for providing a

clear explanation for what is happening and what to expect in the future. An alternative view is that

the management is not necessarily interested in lowering volatility, in this case earnings

management and writing a confusing report might simply be two substitute ways of deceiving

investors. Then, the degree to which earnings management and the readability of reports are

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complements or substitutes in reducing uncertainty is a critical implication that can help

distinguishing the two views in the data.

The purpose of this paper is to shed light on the role played by earnings management and 10K

report readability in its relation to valuation uncertainty and mispricing. There is a literature linking

readability with valuation uncertainty and volatility (Loughran and Mcdonald, 2014), another

linking earnings management and volatility (Louis and Sun, 2011), and another line of research

looking at readability and earnings management (Lo, Ramos and Rogo, 2017). To the best of our

knowledge, this is the first paper to explore all three concepts together.

According to our view, firms that write more readable reports will enjoy lower post-filing valuation

uncertainty and mispricing (H1), and firms that engage in earnings management will also exhibit

lower uncertainty (H2). Furthermore, the relationship between earnings management and annual

report readability is complementary: the effect of readability is stronger when the firm is more likely

to be managing earnings (H3), and this complementarity is higher when there is more uncertainty

and information asymmetry (H4).

Following Lo, Ramos and Rogo( 2017), (Burgstahler and Dichev(1997) we define earnings

managers firms as those that have likely managed earnings to beat or just beat past year’s earnings

threshold by a small number, further analysis also incorporates discretionary accruals and real

earnings management to increase test power. Our main 10K readability variable is the natural

logarithm of 10K file size, using the dataset made available by Loughram and Mcdonald. This

measure has the advantage of being well correlated to other readability measures and to also capture

possible information overload behavior of the firm, as the file size also includes HTML, XML, pdf

and jpeg file attachments. Post-filing valuation uncertainty is the short-term volatility measured as

the root mean square error (RMSE) from a market model regression for the days [6,28] with respect

to the 10-K filing date, as used in (Loughran and Mcdonald, 2014). We also used a mispricing score

variable, which we built from the monthly mispricing score dataset provided by (Stambaugh and

Yuan, 2017).

We find that post-announcement volatility and stock mispricing are both lower when firms are more

likely to be managing earnings and when firms write more readable reports. Furthermore, the effect

of better-written report on reducing volatility is larger for firms that are probably managing results.

These results are consistent with an asymmetric information setting that we described above where

management buys time by administering earnings to explain better and form more accurate

expectations with the ultimately of reducing volatility.

Our results hold controlling for time, industry and firm fixed effects and using different measures

of readability, such as bog index, made available by Iv, Leone and Miller(2015) and fog index. Our

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results are also robust when we use different measures of earnings management, including

discretionary accruals, real earnings management, downward or upward earnings management.

Further results show that the complementary impact of writing more readable 10K reports when

earnings has been likely managed is stronger for firms that are naturally more difficult to value,

such as younger and more complex ones. We investigated whether the complementarity were

stronger for firms that already had higher levels of SEC pre-filing valuation uncertainty, measured

as the RMSE from a market model regression for the days [-252,-6] in respect to the 10-K filing

date. Consistently, our results indicate that when the firm faces a higher level of pre-filing

uncertainty and earnings has likely been managed to hit the target, a more readable 10K report

would reduce even more the post-filing valuation uncertainty and mispricing.

An important previous paper, Lo, Ramos and Rogo (2017), found a different, negative relationship

between earnings management and readability when examining only the management discussion

and analysis (MD&A) section of the annual 10K report. The MD&A is where managers present an

explanation of the financial statements, changes in financial condition and results of past operations.

Considering this definition, is plausible that it would be much harder for companies to be

transparent and sincere in this section and not be detected when earnings has been managed. Their

result is not inconsistent with ours since we are looking at the full 10k report. Indeed, even in our

context it is perfectly reasonable for the firm to have a need to obscure the MD&A to avoid being

caught managing earnings. In fact, we replicate their result and show that it is independent of our

mechanism.

Our findings contribute to the literature that analyzes the consequences and determinants of

earnings management and textual report readability, as well as determinants of post news

announcement volatility and mispricing. For SEC regulators our results are important in the sense

that they show that firms most likely to have managed earnings will have incentives to deliver more

readable 10K reports and are the most keen in following their set of plain english disclosures rules

The rest of the paper is organized as follows: section 3 describes the methodology, how we measure

readability, earnings management and mispricing. Section 3 presents and discusses the results, and

section 4 concludes.

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2. Methodology, Definitions and Data

2.1. Measuring post-filing valuation uncertainty

We follow Loughran and Mcdonald(2014) and measure post-filing valuation uncertainty as the root

mean square error (RMSE) from a market model regression for the days [6,28] in respect to the

10-K filing date. According to these authors this short-term volatility measure starting 6 days after

the file date has the advantage that it captures more properly the information uncertainty stemming

from difficulties in properly valuing the firm, as the information signal effect would be stronger

immediately or surrounding the 10K file date. They observe that as the firm’s stock is expected to

immediately incorporate information conditional on its comprehensibility, any ambiguity in the

information is likely to persist and be reflected in subsequent stock volatility. As pointed out by

them, the use of this market based measure has the advantage over analyst forecasts because it

allows a larger and more inclusive sample, including all types of investor (not limiting to

sophisticated market intermediaries such analysts) where a more readable 10K is expected to more

effectively convey value relevant information to outsiders and result in lower post-filing stock

return volatility.

Following Loughran and McDonald (2014) we include the following control variables that explain

stock return volatility:

1) pre_uncert: is the pre-filing RMSE from a market model estimated using trading days [-

252, -6] relative to the 10-K file date, being required a minimum of 60 observations of daily

returns to be included in the sample.

2) prefil_alpha: is the pre-filing alpha given by the intercept from a market model estimated

using trading days [-252, -6] relative to the 10-K file date, being required a minimum of 60

observations of daily returns to be included in the sample.

3) Abret: is the absolute value of the filing date excess return measured by the buy-and-hold

return from day zero to day plus one, relative to the 10K filing date, minus the buy-and-

hold return of the CRSP value-weighted index over the same two-days window.

4) Logsize: is the natural logarithm of the CRSP stock price times shares outstanding on the

day prior to the 10-K filing date (in $ millions)

5) book2m: is the natural log of book-to-market using COMPUSTAT book value from most

recent year prior to filing date and market value of equity from CRSP. We removed firms

with negative or zero book value.

6) Nasdaq: is a dummy variable equal to one if the firm is listed on NASDAQ at the time of the

10-K filing, zero otherwise.

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2.2. Readability measures

Our main readability variable is the natural logarithm of 10K complete submitted file size, unless

otherwise expressed, using a dataset made available by Loughran and Mcdonald. According to

Loughran and Mcdonald(2014) this readability measure outperforms the Fog index, a commonly

used readability measure in the finance and accounting textual analysis literature. As one of the Fog

index components is based on the percentage of complex words, (i.e. more than 3 syllables words)

they point out that in the business context it is mis specified since there are many words with more

than 3 syllables that can be easily understood by analysts and investors. As an example they list

words like corporation, company, agreement, accounting and operations arguing that these are very

common complex words occurring in 10-Ks which should not be hard for investors to understand.

They highlight as advantages of using the 10K file size the readiness to determine it, less

measurement errors, as it skips parsing procedures, the facility for replication and the highly

correlation with alternative measures of readability. Their main reasoning behind the use of file

size as a readability measure is that when firms are trying to obscure mandated earnings relevant

information, they are more likely to decrease the readability of their 10K report, burying the results

in longer documents, with higher amount of uninformative text and data, where larger documents

(with higher file size) is found to be positively related to volatility and analyst dispersion (Loughran

and Mcdonald, 2014). In order to make interpretation easier, we multiply the natural logarithm of

file size by negative one to make readability increasing on its magnitude.

Despite of the criticism regarding the Fog index, we still use it in our robustness tests due to its

traditional use on the previous literature. The Fog index is computed as follows:

Fog index = 0.4 * (average number of words per sentence + percent of complex words).

The average number of words per sentence is the ratio of the total number of words divided by the

number of sentences in the entire 10K document. Percent of complex words is the number of words

having three or more syllables (classified as complex words) divided by the total number of words.

A higher percentage of complex words and longer sentences increases the Fog index which means

a lower readability level. We multiply the Fog index by negative one to make readability increasing

on its magnitude facilitating interpretation.

Iv, Leone and Miller (2017) propose a different measure of 10K report readability, the Bog index,

based on the plain English principles outlined by the SEC. According to these authors their

readability measure incorporates the negative plain English attributes, including style problems,

the use of passive verbs, long sentences and other positive attributes as well that make the reading

more interesting. Their measure was built using the StyleWriter’s software and was made available

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to researchers in the authors website. They argue that the 10-K file size variation over time is

strongly driven by the inclusion of content unrelated to text as it includes HTML, XML, pdf and

jpeg file attachments not capturing well then, the textual financial reporting readability attributes

highlighted by the SEC. We use their measure as well in our robustness test multiplying it by

negative one to make readability increasing on its magnitude.

2.3. Accounting quality measurement

Following Burgstahler and Dichev(1997) and Lo, Ramos and Rogo( 2017) we define earnings

management firms as those meeting or just beating past year’s earnings by a small number. These

are likely to have managed earnings to avoid losses and small decreases, to generate small earnings

increasing or to smooth earnings jump. Following Lo, Ramos and Rogo(2017) we define earnings

as earnings before extraordinary items deflated by total assets, and the small number as a percentage

variation in the range between zero and less than either 0.4%,0.5% or 0.6% of total assets. We use a

dummy variable, MBE[4,5,6] to identify these these firms that are more likely to be managing

earnings. If a company has met or just beat past years’ earnings benchmark by less than

[0.4%|0.5%|0.6%] of total assets, MBE= one, otherwise MBE=0.

Since this measure could misclassify a firm that has not managed earnings but just happened to fall

in the benchmark, we also considered as alternative measures whether the firm used discretionary

accruals or real earnings management, in order to reduce this false positive possibilities.

Accordingly, we define upda[4,5,6] and neda[4,5,6] (uprem[4,5,6] and nerem[4,5,6]) as dummy

variables that identifies the lower accounting quality firms as those most likely to have managed

earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets

and used upward or downward discretionary accruals (real earnings management), respectively

upda/neda= one (uprem/nerem= one), otherwise = 0.

Discretionary accruals were measured following Jones(1991) model:

𝑇𝐴𝐶𝐶𝑡

𝐴𝑡−1= 𝛼1

1

𝐴𝑡−1+ 𝛼2

(∆𝑅𝐸𝑉𝑡)

𝐴𝑡−1 + 𝛼3

𝑃𝑃𝐸𝑡

𝐴𝑡−1+ 𝜀𝑡 (Eq. 1)

where 𝑇𝐴𝐶𝐶𝑡 are total operating accruals, ∆𝑅𝐸𝑉𝑡 is the yearly change in revenues, 𝑃𝑃𝐸𝑡 is gross

property, plant, and equipment, and 𝐴𝑡−1 are previous year total assets. The model was estimaded

in cross-section by two digit sic code industry and year, requiring at least 15 firm observations per

firm. Discretionary accruals are given by the residuals from this estimation.

Real earnings management were measured following Roychowdhury(2006) and Lo, Ramos and

Rogo(2017) based on discretionary expenses, are they research and development (R&D) and

advertising expenses. Real earnings management is then defined as the negative sum of (ΔR&D

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expenses +ΔAdvertising expenses) scaled by total assets, where reductions in R&D or advertising

expenses leads to higher values of real earnings management.

2.4. Mispricing score measure

Stambaugh and You(2016) construct a stock mispricing measure (MISP) based on 11 well

documented market anomalies: net stock issues, composite equity issues, accruals, net operating

assets, asset growth, investment-to-assets, distress, O-score, momentum, gross profitability, and

return on assets. As they detail in their supplementary documentation, a rank is assigned to each

stock reflecting the sorting on each of these anomalies at the end of each month, where the lowest

average abnormal return is assigned to the highest rank. They define MISP as the arithmetic

average of its ranking percentile for each of these 11 anomalies ranging between 0 and 100. As they

point out, the highest values of MISP reflects the most “overpriced” stocks and the lowest values

reflects the most “underpriced” ones.

We use their dataset to build our mispricing variables used to test hypothesis 3 and 4. Misprice

score in this paper is defined as the monthly absolute difference between MISP and the number fifty

(MISP median). Our measure captures the degree of mispricing, regardless of whether it is

overpricing or underpricing.

In our tests, fiscal year misprice is the arithmetic average of the 12 monthly misprice score of the

given fiscal year. One, three and six month(s) post-filing misprice is the arithmetic average of the

monthly misprice score for the window period between one, one and three and one and six months

after the 10K filing month. The idea behind using post-filing misprice level is to also verify the

persistence of mispricing reflecting previously disclosed information.

2.5. Sample creation

We start with Loughram and McDonalds file size dataset, using the data from the following reports

10-K, 10-K405, 10-KSB, 10KSB40, which gave us 191,910 observations for the period ranging from

1994 until 2016. We then dropped 1,174 observations that did not have usable dates and 3,572

year/CIK duplicates. We also required 180 days between current and previous filing (losing1,188

observations) and a minimum of 2,000 words for each file (losing 6050 observations). After this first

parsing procedure, we merged with CRSP and Compustat, losing 82,222 and 8,202 observations,

respectively. We kept only the stocks that were ordinary common (2,818 dropped) with a price of

at least $3 the day before the filing. Table 1 details the remaining screening procedure. Our final

sample used to conduct the initial tests has 76,238 observations, while the analysis that considers

the earnings management variables counts 65,686 observations.

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3. Results

Table 2 presents the means of the main variables for the entire period and for two subperiods: 1993-

2004 and 2003-2016. As in Loughran and Mcdonald(2014), we observe an increasing 10K file size

in megabytes in recent years, a large reduction of post-filing uncertainty, and a higher level of

market capitalization. Table 3 reports summary statistics with results similar to those reported by

(Loughran and Mcdonald, 2014). 17.4% of the sample is consists of firm years in which past years

earnings has been beaten or just met by less than 0,6% of total assets. Table 4 shows that these firms

typically exhibit lower post filing uncertainty (MBE6=17.8%). This a first glance on the relation

between these two variables, which will be further more appropriately tested using regressions.

Table 5 contain our benchmark results. We seek to explain the degree of uncertainty with the

readability of the 10k report and indicator for a high probability of the reported earnings having

been managed as independent variables. The dependent variable is measured as the post-filing

volatility of returns. Readability is measured with the natural log of the text document file size in

megabytes multiplied by negative one so that the higher the magnitude, the better readability. For

the probability of earnings management, we consider three measures of closeness to last year’s

earnings. The specification includes several controls, as well as year and industry dummies. Tables

6 to 8 explore different definitions of the same concepts.

Column 1 shows that more readable reports are associated with lower post-filing valuation

uncertainty levels. This is in line with our first hypothesis. The association is very significant in

statistical terms. The regression results indicate that a-one-standard-deviation increment on

readability is related with a decrease of 6,4% of a post-filing uncertainty standard deviation. These

results replicate Loughran and Mcdonald (2014)’s ones. Despite the fact that there are some

differences in the sample used, the magnitude is in line with their findings of 4%.

In columns 2-4 show the relation between valuation uncertainty and earnings management. The

results are consistent with H2, since firms that report earnings that are very close to last years’

exhibit lower post-filing stock returns volatility. The volatility of returns of firms that are likely

managing earnings is 4.48% lower those that are probably not doing it.

By considering both the readability and earnings management variables together in the

specification, columns 5-7 document that the two are not picking the same concept but rather

represent distinct mechanisms.

We test our third hypothesis, namely that there is complementarity of readability and earnings

management in their impact on valuation uncertainty, in columns 8-10. The results are, again,

consistent with our view. Indeed, the coefficient for the interaction between the two key independent

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variables is negative and statistically significant. This means that the two mechanisms are

complementary: as readability increases, valuation uncertainty decreases, but more so for firms that

are more likely to be managing earnings. Said differently, earnings management is associated with

lower volatility, especially so when the text is more readable. That is, if management and readability

do not go hand in hand, the impact on uncertainty is limited. The economic magnitude is quite

relevant: the impact of readability is around twice as high for managing firms.

We can conclude from here that, if the goal of management is to reduce valuation uncertainty, it

should both engage in earnings management and write more clear reports. The management may

be able to reduce the negative effects of earnings management -that is, the impact on volatility when

over or underreporting has to be reversed- by providing a clearer view of the firm’s present and

likely future performance.

In Table 6 and 7 we repeat the same tests from above using the fog index and bog index as

readability measures instead of (the inverse of) file size. The results are robust to the particular way

in which we measure readability. There is still a negative and significant association between

valuation uncertainty and both readability and the likelihood of being managing earnings, with the

former effect being larger for managing firms. This is consistent with H1, H2, and H3.

It is generally assumed that an increase in volatility following the report of results signals more

uncertainty regarding the true value of the stock. In tables 8.1 to 8.3 we explore directly the impact

of the two mechanism on mispricing. Our first conclusion is that readability alone does not seem to

have impact on the mispricing level. Although the coefficient is still negative, it is not statistically

significant. Nevertheless, the impact of earnings management and its interaction with readability

are still significantly negative. This means that writing more clear reports only has a distinguishable

effect for firms that are probably managing earnings. If the firm is aiming at improving the pricing

of the stock, it is not enough to write better 10K reports, this has to be accompanied with the

management of earnings.

On tables 9 and 10 we explore the robustness of the results to the measure of the likelihood that

firms are engaging on earnings management. We consider more restrictive indicators to avoid

classifying firms as managing earnings when they are not. We take a firm as managing earnings if

they both meet or just beat past year's earnings by less than [0.4%|0.5%|0.6%] of total assets and

also use upward, or downward, discretionary accruals(table 9) or real earnings management (table

10). Again, and consistent with our first three hypotheses, firms more likely to be managing earnings

exhibit significantly lower post-filing uncertainty. More readable reports are also associated with lower

uncertainty, with this effect being stronger when firms are also managing earnings. The results are

present regardless of whether the firms are underreporting or overreporting earnings. This is

interesting because it suggests that the management is not just trying to deceive investors by

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overstating the performance. Rather, the deduction in uncertainty seems to be related to the

smoothing of earnings. This is something one would expect in our view.

In our setting, the reason why the management writes more readable reports is that it needs to convey

their superior information so that investors are better positioned to assess the true value of the firm.

If this is so, when the asymmetry of information is higher and the ability to value correctly the stock

is lower, the larger the effects should be. This is our hypothesis number 4 and is what we explore in

tables 11 to 15 by comparing the magnitude of the impact of readability and earnings management

on valuation uncertainty and its complementarity across several dimensions though to capture the

difficulty in valuing the firm. Although not always statistically significant, the results indicate that

the effect of earnings management and its complementarity with readability are higher for younger

firms, and firms that present a larger number of items on Compustat, and firms that operate in more

geographies (tables 11, 14 and 13, respectively). When measuring complexity with the diversity of

businesses, the results are inconclusive (table 12).

We believe that the market revealed pre-filing uncertainty level would be a good tool to verify

whether firms that already face a higher level of market valuation uncertainty would increase even

more the post-filing uncertainty if they report on target earnings results and deliver higher

readability 10K reports. Table 15 shows that this is indeed the case.

Overall, the results are mostly consistent with our hypothesis.

As noted before, firms are likely managing earnings would cast lower valuation uncertainty if they

deliver more readable 10Ks. Thus, if their goal is to reduce uncertainty, and the mechanisms are

complementary, one would expect the two ways of achieving this to be positively correlated. And

this is indeed the case, as reported in table 16. We used all the control variables as used by Li(2008)

and (Lo, Ramos and Rogo, 2017), listed in the appendix1. Our results hold, also at one percent of

significance, when using the bog index as readability measure on columns 7-9. When we consider

the Fog index as readability measure, only the cohort in which firms have met or just beat past

years’ earnings by less than 0.05% of total assets showed significant results, also indicating a

negative relationship at 10% of significance level.

Our results, using readability of the whole 10K, shows a positive relationship between earnings

management and readability, which is different than the negative relationship documented by Lo,

Ramos and Rogo (2017) using readability measure of only the MD&A section. As the MD&A is th

1 The dataset for these tests, including the control variables, was provided by Felipe Ramos one of

the authors of the paper Lo, Ramos and Rogo(2017).

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section where managers present an explanation of the financial statements, changes in financial

condition and results of past operations, their reasoning is that if managers decide to be more

transparent in this section, they would increase the likelihood of earnings management detection.

Our result goes beyond the MD&A section, focusing on the entire 10K report in order to examine

an overall readability management behavior when the firm has the goal to help out investors

reducing their volatility and mispricing behavior. Our result is a complement to theirs in the sense

that it extends the understanding of the impact of earnings management on the readability of the

entire 10K, where earnings management and readability go on the same direction. Another way to

view it, is that companies will try to make up to a lees readable MD&A section, writing even more

clear on the rest of the 10K when earnings has been managed aiming to reduce the likelihood of

detection.

4. Conclusions

This paper showed that earnings management and writing more readable reports are two

complementary mechanisms that are related to a reduction in valuation uncertainty and stock price

volatility. This result is consistent with a view where the management, that has superior

information, attempts to reduce the volatility and mispricing induced by overreacting investors by

both avoiding earnings surprises and providing better qualitative guidance for future performance.

Since earnings management only provides temporary relief, to have full effect it must be

accompanied by a more explanatory text to align investor perceptions to the management’s best

assessment of future performance. Our results also send a warning to SEC regulators, as we show

that firms most likely to have managed earnings will have the incentive to deliver more readable

reports and are the ones most interested in following their set of plain english rules.

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Tao, R. and Zhao, H. (2019) ‘“Passing the Baton”: The effects of CEO succession planning on firm performance and volatility’, Corporate Governance: An International Review, 27(1), pp. 61–78. doi: 10.1111/corg.12251.

Appendix

Appendix A: Variables definitions

14

Uncert is the post-filing valuation uncertainty, measured as the root mean square error (RMSE)

from a market model regression for the days [6,28] in respect to the 10-K filing date

pre_uncert is the pre-filing RMSE from a market model estimated using trading days [-252, -6]

relative to the 10-K file date, being required a minimum of 60 observations of daily

returns to be included in the sample.

prefil_alpha is the pre-filing alpha given by the intercept from a market model estimated using

trading days [-252, -6] relative to the 10-K file date, being required a minimum of 60

observations of daily returns to be included in the sample.

Abret is the absolute value of the filing date excess return measured by the buy-and-hold

return from day zero to day plus one, relative to the 10K filing date, minus the buy-and-

hold return of the CRSP value-weighted index over the same two-days window.

Logsize is the natural logarithm of the CRSP stock price times shares outstanding on the day

prior to the 10-K filing date (in $ millions)

book2m is the natural log of book-to-market using COMPUSTAT book value from most recent

year prior to filing date and market value of equity from CRSP. We removed firms with

negative or zero book value.

Nasdaq is a dummy variable equal to one if the firm is listed on NASDAQ at the time of the 10-

K filing, zero otherwise.

readability the natural logarithm of 10-K complete submitted file size, as used in Loughran and

Mcdonald(2014)

Fog index Fog index = 0.4*(average number of words per sentence + percent of complex words).

The average number of words per sentence is the ratio of the total number of words

divided by the number of sentences in the entire 10K document. percent of complex

words is the number of words having three or more syllables (classified as complex

words) divided by the total number of words. A higher percentage of complex words

and longer sentences increases the Fog index which means a lower readability level. In

order to easy interpretation we multiply the Fog index by negative one to make

readability increasing on its magnitude.

Bog index Is a readability measure created by Editor Softwares's plain English software,

StyleWriter and made available by Iv, Leone and Miller(2017). Higher values of the

Bog index imply lower readability. In order to easy interpretation we multiply the Bog

index by negative one to make readability increasing on its magnitude.

15

Appendix A: Variables definitions – continued

MBE[4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most

likely to have managed earnings beating or just meeting past year's earnings by less

than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0

upda[4,5,6] Is a dummy variable that identifies the lower accounting quality firms as those most


than [0.4%|0.5%|0.6%] of total assets and that have also used upward discretionary

accruals (upda[4,5,6]= one), otherwise = 0

neda[4,5,6] Is a dummy variable that identifies the lower accounting quality firms as those most


than [0.4%|0.5%|0.6%] of total assets and that have also used used downward

discretionary accruals (neda[4,5,6]= one), otherwise = 0

uprem[4,5,6] Is a dummy variable that identifies the lower accounting quality firms as those most


than [0.4%|0.5%|0.6%] of total assets and have used upward real earnings

management (uprem[4,5,6]= one), otherwise = 0.

nerem[4,5,6] Is a dummy variable that identifies the lower accounting quality firms as those most


than [0.4%|0.5%|0.6%] of total assets and have used downward real earnings

management (nerem[4,5,6]= one), otherwise = 0.

MISP Is a stock’s mispricing measure made available by Stambaugh and You(2016) ranging

between 0 and 100. They define it as the arithmetic average of its ranking percentile

for each of 11 anomalies where the highest values of MISP are the

most “overpriced,” and those with the lowest values are the most “underpriced.”

Misprice Misprice in this paper is defined as the monthly absolute difference between MISP and

the number fifty(MISP median). This measure only captures the market information

absorption inefficiency level, not capturing the sign (overpricing or underpricing).

16

Appendix B: control variables used on table 16

Following Li(2008) and Lo, Ramos and Rogo,( 2017)

NegEarnChg =1 if delta earnings per share <0, otherwise =0

earnings operating earnings sclaed by total assets at the fiscal year-end.

Loss =1 earnings <0, otherwise =0

size Natural logarithm of market value of equity at fiscal year-end.

mtb (market value of equityþbook value of liabilities)/book value of total assets, measured

age number of years since firm first appearenc in the CRSP monthly stock return file

SpecItems amount of special items divided by total assets.

earn_vol standard deviation of operating earnings during the prior five years.

ret_vol standard deviation of monthly stock returns in the prior year.

nbseg natural log of the number of business segments.

ngseg natural log of the number of geographic segments.

nitems number of items in Compustat with non-missing values.

ma Dummy =1 if a firm-year is an acquirer according to SDC Platinum M&A database

seo Dummy =1 if a firm-year has a seasoned equity offering according to SDC Global

New Issues database; 0 otherwise

dlw Dummy = 1 if the firm is incorporated in Delaware; 0 otherwise.

17

Tables & Figures

TABLE 1: Sample creation and data screening

Sample Creation

This table details the screening process departing from Loughram and McDonalds(2014) file size dataset.

Our biggest loss of observations are related to match between P.ERMNO CIK, CRSP, and GVKEY,

compustat. Our measure of valuation uncertainty is given by the RMSE, which is the root mean square error

from a market model regression for the days [-252,-6] for Pre-filing Uncertainty, and days [6,28] for Post-

filing uncertainty.

Dropped Sample Size

LOUGHRAN and MCDONALD file size data set 1993

-2016

191,910

Data with not usable dates 1,174 190,736

Drop year/CIK duplicates 3,572 187,164

At least 180 days after prior filing 1,188 185,976

At least 2,000 word on each file 6,050 179,926

CRSP CIK PERMNO match using linking table 82,222 101,162

COMPUSTAT GVKEY match 8,202 92,960

Be ordinary common equity 2,818 90,142

Price on day the before filing of at least $3 12,223 77,919

Positive book value and available book-to-market data 913 77,006

Available data for estimation of Post-filing date market

model RMSE (Uncertainty)

1,210 75,796

Available data for estimation of Pre-filing date market

model RMSE (Pre-filing Uncertainty)

381 75,415

Return data available for the day and day after the filing. 46 75,369

Price data availabe on the day before the filling 4 75,365

Repeated and missing COMPUSTAT variables needed

to measure the earnings management proxies

6,679 65,686

18

TABLE 2: Variables means by time period

Variables Means by Time Period, 1993 to 2016

This table shows the variable means by time period. For the regression analysis we used

logarithmic transformation for the variables File Size, Size, and Book to market. Uncertainty is

the root mean square error (RMSE) from a market model regression for the days [6,28] and

Pre-filing Uncertainty is the RMSE from a market model regression for the days [-252,-6] in

respect to the 10-K filing date. (1) (2) (3)

Variable 1993 -

2004 2003-2016 1993-2016

File size in Megabytes 0.69 10.56 5.3

Uncertainty 3.08 2.07 2.61

Pre-filing alpha 0.09 0.04 0.06

Pre-filing Uncertainty 3.24 2.46 2.88

Abs(filing period abnormal return) 0.03 0.03 0.03

Size (market capitalization) in $ millions 2,721 5,512 4,024

Book-to-market 0.71 0.72 0.72

NASDAQ dummy 0.37 0.41 0.39

Number of observations 40,647 35,591 76,238

19

TABLE 3: Summary statistics

Summary statistics

See the Appendix for more detailed variable definitions. Uncertainty is the root mean square

error (RMSE) from a market model regression for the days [6,28] in respect to the 10-K filing

date. Pre-filing Uncertainty for the days [-252,-6]. Readability is the natural log of the text

document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability). MBE[4,5,6] is a dummy variable that identifies the lower accounting

quality firms as those most likely to have managed earnings beating or just meeting past

year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise

MBE=0.

N Mean St.Dev p25 Median p75

Uncert 65685 2.676 2.137 1.328 2.089 3.352

Readability 65685 -14.11 1.62 -15.191 -13.895 -12.733

File size in

megabytes

65685 5.192 10.82 .339 1.083 3.957

prefil alpha 65685 .064 .219 -.043 .043 .142

pre uncert 65685 2.938 1.862 1.674 2.482 3.728

readXpre unct 65685 -40.541 24.553 -50.939 -34.789 -23.983

Abret 65685 .033 .044 .008 .019 .04

Logsize 65685 12.988 1.859 11.628 12.838 14.191

book2m 65685 -.484 .6 -.765 -.304 -.051 Nasdaq 65685 .368 .482 0 0 1

Earnings 65685 147.072 1040.863 .193 10.496 59.457

Delta Earnings 65685 .004 .727 -.014 .002 .023

acqben4 65685 .138 .345 0 0 0

acqben5 65685 .157 .364 0 0 0 acqben6 65685 .174 .379 0 0 0

20

TABLE 4: pairwise correlation

Pairwise correlations

See the Appendix for more detailed variable definitions. Uncertainty is the root mean square error (RMSE) from a market model regression for the

days [6,28] in respect to the 10-K filing date. Pre-filing Uncertainty for the days [-252,-6]. Readability is the natural log of the text document file

size in megabytes multiplied by negative one (the higher the magnitude, the better readability). MBE[4,5,6] is a dummy variable that identifies the

lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0.

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) uncert 1.000 (2) readability 0.310* 1.000 (3) file_size in

megabytes

-0.221* -0.731* 1.000

(4) prefil_alpha 0.146* 0.127* -0.091* 1.000 (5) pre_uncert 0.630* 0.301* -0.239* 0.357* 1.000 (6) readXpre_unct -0.592* -0.149* 0.154* -0.340* -0.982* 1.000

(7) abret 0.323* 0.086* -0.079* 0.045* 0.340* -0.340* 1.000 (8) logsize -0.333* -0.373* 0.299* -0.019* -0.409* 0.372* -0.169* 1.000 (9) book2m -0.191* -0.072* 0.087* -0.247* -0.216* 0.206* -0.087* -0.233* 1.000 (10) Nasdaq -0.278* -0.171* 0.164* -0.113* -0.347* 0.331* -0.131* 0.526* 0.125* 1.000

(11) earnings -0.111* -0.115* 0.139* -0.013* -0.141* 0.139* -0.070* 0.341* -0.021* 0.153* 1.000 (12) DeltaEAR -0.013* -0.003 -0.001 0.035* -0.006 0.006 -0.012* 0.009 -0.046* -0.001 0.003 1.000 (13) MBE4 -0.161* -0.045* 0.072* -0.023* -0.204* 0.206* -0.106* 0.001 0.218* -0.036* 0.049* 0.001 1.000 (14) MBE5 -0.171* -0.049* 0.077* -0.023* -0.216* 0.218* -0.113* 0.009 0.225* -0.024* 0.052* 0.001 -0.926* 1.000

(15) MBE6 -0.178* -0.053* 0.080* -0.023* -0.225* 0.227* -0.118* 0.019* 0.230* -0.011* 0.054* 0.001 -0.870* -0.939* 1.000

* shows significance at the .01 level

21

Table 5 Relation Between Financial Report Readability, Earnings Management and Post-Filing Valuation

Uncertainty The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6, 28] in respect to the 10-K filing date. The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], which is a dummy variable that identifies the firms most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered

by year and industry. See the Appendix for definitions of control variables. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) uncert uncert uncert uncert uncert uncert uncert Uncert uncert uncert

readability -0.085*** -0.090*** -0.090*** -0.090*** -0.074*** -0.070*** -0.069***

(0.019) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016) MBE4 -0.120*** -0.117*** -1.248***

(0.027) (0.026) (0.268) MBE5 -0.124*** -0.121*** -1.331*** (0.027) (0.027) (0.253)

MBE6 -0.120*** -0.117*** -1.313*** (0.026) (0.025) (0.236)

readXMBE4 -0.079*** (0.018)

readXMBE5 -0.085*** (0.017)

readXMBE6 -0.084*** (0.016)

pre_uncert 0.502*** 0.533*** 0.533*** 0.533*** 0.528*** 0.528*** 0.527*** 0.527*** 0.526*** 0.526*** (0.038) (0.032) (0.032) (0.032) (0.032) (0.032) (0.032) (0.032) (0.032) (0.032)

prefil_alpha -0.826*** -0.721*** -0.720*** -0.719*** -0.705*** -0.703*** -0.703*** -0.708*** -0.706*** -0.705*** (0.142) (0.114) (0.114) (0.114) (0.114) (0.114) (0.114) (0.113) (0.113) (0.113)

Abret 5.074*** 4.518*** 4.515*** 4.513*** 4.508*** 4.505*** 4.503*** 4.529*** 4.527*** 4.525*** (0.398) (0.335) (0.335) (0.335) (0.333) (0.333) (0.333) (0.334) (0.334) (0.334)

logsize -0.123*** -0.102*** -0.102*** -0.102*** -0.118*** -0.118*** -0.117*** -0.117*** -0.116*** -0.116*** (0.016) (0.013) (0.013) (0.013) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

book2m -0.336*** -0.286*** -0.285*** -0.284*** -0.304*** -0.303*** -0.302*** -0.302*** -0.301*** -0.300*** (0.055) (0.048) (0.048) (0.048) (0.050) (0.050) (0.050) (0.050) (0.049) (0.049) nasdaq -0.161*** -0.169*** -0.169*** -0.169*** -0.176*** -0.176*** -0.176*** -0.175*** -0.175*** -0.175***

(0.026) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) Obs. 75365 65686 65686 65686 65686 65686 65686 65686 65686 65686

R-squared 0.459 0.477 0.477 0.477 0.477 0.478 0.478 0.478 0.478 0.478 r2_a 0.459 0.476 0.476 0.476 0.477 0.477 0.477 0.477 0.477 0.478

F 157.032 172.165 173.137 172.276 174.203 175.087 174.231 194.548 195.515 194.877

Year dummies Yes yes yes yes yes yes yes yes yes yes

industry dummies Yes yes yes yes yes yes yes yes yes yes

Standard errors are in parenthesis

*** p<0.01, ** p<0.05, * p<0.1

22

Table 6

Robustness: The Relation Between Financial Report Readability (using the fog index), Earnings Management and Post-Filing Valuation Uncertainty

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6,28] in respect to the 10-K filing date. The independent variable Fog_Readability is Fog Index of the full 10K filing which equals to 0.4* (average number of words per sentence +

percent of complex words) multiplied by negative one (the higher the magnitude, the better readability), MBE[4,5,6] is a dummy variable that identifies the firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise acqben=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies. Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Uncert uncert uncert Uncert uncert uncert uncert uncert uncert uncert

Fog readability -0.014** -0.014** -0.013** -0.014** -0.007 -0.006 -0.006

(0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.008) MBE4 -0.120*** -0.123*** -1.175***

(0.027) (0.028) (0.308) MBE5 -0.124*** -0.126*** -1.187***

(0.027) (0.028) (0.269) MBE6 -0.120*** -0.120*** -1.019***

(0.026) (0.027) (0.249) readXMBE4 -0.052***

(0.015) readXMBE5 -0.053*** (0.013)

readXMBE6 -0.045*** (0.012)

pre_uncert 0.538*** 0.533*** 0.533*** 0.533*** 0.535*** 0.535*** 0.535*** 0.535*** 0.535*** 0.535*** (0.035) (0.032) (0.032) (0.032) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035)

prefil_alpha -0.692*** -0.721*** -0.720*** -0.719*** -0.684*** -0.683*** -0.682*** -0.685*** -0.683*** -0.682*** (0.112) (0.114) (0.114) (0.114) (0.112) (0.112) (0.112) (0.112) (0.112) (0.112)

abret 4.678*** 4.518*** 4.515*** 4.513*** 4.669*** 4.666*** 4.664*** 4.674*** 4.671*** 4.668*** (0.369) (0.335) (0.335) (0.335) (0.367) (0.367) (0.367) (0.368) (0.367) (0.367)

logsize -0.105*** -0.102*** -0.102*** -0.102*** -0.103*** -0.103*** -0.103*** -0.103*** -0.103*** -0.103*** (0.014) (0.013) (0.013) (0.013) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

book2m -0.289*** -0.286*** -0.285*** -0.284*** -0.283*** -0.282*** -0.282*** -0.284*** -0.283*** -0.282*** (0.051) (0.048) (0.048) (0.048) (0.051) (0.051) (0.051) (0.051) (0.051) (0.051)

nasdaq -0.172*** -0.169*** -0.169*** -0.169*** -0.174*** -0.174*** -0.174*** -0.175*** -0.175*** -0.174*** (0.027) (0.025) (0.025) (0.025) (0.027) (0.027) (0.027) (0.027) (0.027) (0.027)

Obs. 60320 65686 65686 65686 60320 60320 60320 60320 60320 60320 R-squared 0.474 0.477 0.477 0.477 0.474 0.474 0.474 0.474 0.475 0.474

r2_a 0.473 0.476 0.476 0.476 0.474 0.474 0.474 0.474 0.474 0.474

F 169.943 172.165 173.137 172.276 166.641 167.234 166.808 167.720 167.768 166.978 Year dummies yes yes yes Yes yes yes yes yes yes yes

industry dummies yes yes yes Yes yes yes yes yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

23

Table 7

Robustness: The Relation Between Financial Report Readability (using bog index), Earnings Management and Post-Filing Valuation Uncertainty

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6,28] in respect to the 10-K filing date. The independent variables are Bog_readability, given by the Bog Index made available by Bonsal et al (2017) multiplied by negative one (higher

the magnitude is the higher readability), MBE[4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variable.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) uncert uncert uncert uncert uncert uncert uncert uncert uncert uncert

bog readability -0.011*** -0.011*** -0.011*** -0.011*** -0.009*** -0.009*** -0.009***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) MBE4 -0.120*** -0.113*** -0.939***

(0.027) (0.028) (0.263) MBE5 -0.124*** -0.118*** -0.940***

(0.027) (0.028) (0.254) MBE6 -0.120*** -0.113*** -0.841***

(0.026) (0.027) (0.240) readXMBE4 -0.010*** (0.003)

readXMBE5 -0.010*** (0.003)

readXMBE6 -0.009*** (0.003)

pre_uncert 0.530*** 0.533*** 0.533*** 0.533*** 0.528*** 0.527*** 0.527*** 0.527*** 0.527*** 0.527*** (0.034) (0.032) (0.032) (0.032) (0.034) (0.034) (0.034) (0.034) (0.034) (0.034)

prefil_alpha -0.707*** -0.721*** -0.720*** -0.719*** -0.700*** -0.698*** -0.697*** -0.700*** -0.698*** -0.698*** (0.112) (0.114) (0.114) (0.114) (0.111) (0.111) (0.111) (0.111) (0.111) (0.111)

abret 4.559*** 4.518*** 4.515*** 4.513*** 4.551*** 4.548*** 4.546*** 4.560*** 4.558*** 4.556*** (0.341) (0.335) (0.335) (0.335) (0.340) (0.340) (0.340) (0.340) (0.340) (0.340)

logsize -0.110*** -0.102*** -0.102*** -0.102*** -0.109*** -0.108*** -0.108*** -0.109*** -0.109*** -0.108*** (0.014) (0.013) (0.013) (0.013) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

book2m -0.294*** -0.286*** -0.285*** -0.284*** -0.289*** -0.288*** -0.288*** -0.290*** -0.288*** -0.288*** (0.050) (0.048) (0.048) (0.048) (0.049) (0.049) (0.049) (0.049) (0.049) (0.049)

nasdaq -0.171*** -0.169*** -0.169*** -0.169*** -0.174*** -0.174*** -0.173*** -0.176*** -0.176*** -0.176*** (0.026) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025)

Obs. 62879 65686 65686 65686 62879 62879 62879 62879 62879 62879 R-squared 0.477 0.477 0.477 0.477 0.477 0.478 0.478 0.478 0.478 0.478 r2_a 0.477 0.476 0.476 0.476 0.477 0.477 0.477 0.477 0.477 0.477

F 177.459 172.165 173.137 172.276 173.250 173.857 173.217 179.603 179.015 179.187 Year dummies yes yes yes yes yes yes yes yes yes yes

industry dummies yes yes yes yes yes yes yes yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

24

Table 8

The Relation Between Readability, Earnings Management and Mispricing The dependent variable in each regression is Misprice, which is built using the mispricing factors score made available by Stambaugh and Yuan (2016). The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], which is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Misprice Misprice Misprice Misprice Misprice Misprice Misprice Misprice Misprice Misprice

readability -0.038 -0.033 -0.033 -0.034 0.001 0.006 0.006

(0.058) (0.058) (0.058) (0.058) (0.057) (0.057) (0.057) MBE4 -0.443*** -0.442*** -2.546***

(0.104) (0.104) (0.932) MBE5 -0.381*** -0.380*** -2.602***

(0.105) (0.105) (0.859) MBE6 -0.334*** -0.332*** -2.425***

(0.105) (0.105) (0.818) readXMBE4 -0.147**

(0.064) readXMBE5 -0.155*** (0.059)

readXMBE6 -0.146*** (0.056)

pre_uncert 0.481*** 0.473*** 0.473*** 0.474*** 0.470*** 0.471*** 0.471*** 0.467*** 0.467*** 0.467*** (0.064) (0.064) (0.065) (0.065) (0.064) (0.064) (0.064) (0.064) (0.064) (0.064)

prefil_alpha -3.275*** -3.244*** -3.242*** -3.244*** -3.239*** -3.238*** -3.239*** -3.245*** -3.245*** -3.244*** (0.366) (0.366) (0.367) (0.367) (0.365) (0.366) (0.366) (0.366) (0.366) (0.367)

abret 3.163*** 3.132*** 3.130*** 3.127*** 3.129*** 3.126*** 3.124*** 3.188*** 3.185*** 3.181*** (0.926) (0.926) (0.926) (0.927) (0.925) (0.926) (0.926) (0.927) (0.927) (0.928)

logsize 0.148*** 0.159*** 0.159*** 0.159*** 0.153*** 0.153*** 0.153*** 0.156*** 0.156*** 0.155*** (0.033) (0.034) (0.034) (0.034) (0.033) (0.033) (0.033) (0.033) (0.033) (0.033)

book2m -1.048*** -1.017*** -1.017*** -1.018*** -1.025*** -1.025*** -1.026*** -1.022*** -1.021*** -1.022*** (0.113) (0.113) (0.113) (0.113) (0.113) (0.113) (0.113) (0.113) (0.113) (0.113)

nasdaq -0.251** -0.259** -0.258** -0.257** -0.262** -0.261** -0.260** -0.262** -0.260** -0.259** (0.104) (0.103) (0.103) (0.103) (0.104) (0.104) (0.104) (0.104) (0.104) (0.104)

Obs. 50410 50410 50410 50410 50410 50410 50410 50410 50410 50410 R-squared 0.058 0.058 0.058 0.058 0.058 0.058 0.058 0.059 0.059 0.059

r2_a 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 F 16.863 17.141 17.142 17.174 16.922 16.921 16.956 16.669 16.662 16.663 Year dummies yes yes yes yes yes yes yes yes yes yes



*** p<0.01, ** p<0.05, * p<0.1

25

Table 8.1

The Relation Between Readability, Earnings Management and one month Post-Filing Mispricing Score

The dependent variable in each regression is 1_month_Misprice, which is given by the one month after filing mispricing factors score built using the dataset made available by Stambaugh and Yuan (2016). The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], which is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

1_month_Misp

rice

1_month_Mis

price

1_month_Mis

price

1_month_M

isprice

1_month_Mis

price

1_month_Mis

price

1_month_Mis

price

1_month_Mis

price

1_month_

Misprice

1_month_Mis

price

readability -0.088 -0.083 -0.083 -0.084 -0.057 -0.055 -0.056 (0.063) (0.063) (0.063) (0.063) (0.063) (0.063) (0.063)

MBE4 -0.535*** -0.532*** -2.091** (0.116) (0.116) (0.890)

MBE5 -0.428*** -0.425*** -1.970** (0.127) (0.127) (0.836)

MBE6 -0.381*** -0.378*** -1.825** (0.124) (0.124) (0.813) readXMBE4 -0.109*

(0.062) readXMBE5 -0.108*

(0.058) readXMBE6 -0.101*

(0.056) pre_uncert 0.714*** 0.707*** 0.708*** 0.708*** 0.701*** 0.703*** 0.703*** 0.699*** 0.700*** 0.700***

(0.069) (0.069) (0.070) (0.070) (0.069) (0.070) (0.070) (0.069) (0.069) (0.070) prefil_alpha -5.786*** -5.752*** -5.753*** -5.754*** -5.742*** -5.743*** -5.744*** -5.747*** -5.750*** -5.750***

(0.514) (0.513) (0.514) (0.515) (0.513) (0.514) (0.514) (0.513) (0.514) (0.515) abret 4.941*** 4.893*** 4.896*** 4.893*** 4.887*** 4.890*** 4.887*** 4.932*** 4.931*** 4.926***

(1.215) (1.213) (1.214) (1.215) (1.213) (1.214) (1.215) (1.212) (1.213) (1.215) logsize 0.147*** 0.168*** 0.168*** 0.168*** 0.154*** 0.153*** 0.153*** 0.156*** 0.155*** 0.155***

(0.037) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037) (0.037) book2m -0.865*** -0.818*** -0.819*** -0.820*** -0.837*** -0.839*** -0.839*** -0.834*** -0.836*** -0.836***

(0.108) (0.108) (0.108) (0.108) (0.108) (0.108) (0.108) (0.108) (0.108) (0.108) nasdaq -0.263** -0.271*** -0.268** -0.266** -0.277*** -0.274*** -0.273*** -0.277*** -0.274*** -0.273***

(0.105) (0.104) (0.104) (0.104) (0.105) (0.105) (0.105) (0.105) (0.105) (0.105) Obs. 50164 50164 50164 50164 50164 50164 50164 50164 50164 50164 R-squared 0.062 0.063 0.062 0.062 0.063 0.062 0.062 0.063 0.063 0.063

r2_a 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 F 13.966 13.924 13.865 13.864 13.822 13.759 13.754 13.661 13.492 13.454

Year dummies yes Yes yes yes yes yes yes yes yes yes industry dummies yes Yes yes yes yes yes yes yes yes yes

Standard errors are in parenthesis *** p<0.01, ** p<0.05, * p<0.1

26

Table 8.2

An Analysis of The Relation Between Financial Report Readability, Earnings Management and three months Post-Filing Mispricing Score

The dependent variable in each regression is 3_month_Misprice, which is given by the three months after filing mispricing factors score built using

the dataset made available by Stambaugh and Yuan (2016). The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include

intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

3months_Misprice

readability -0.033 -0.029 -0.030 -0.031 -0.000 0.004 0.000

(0.063) (0.063) (0.063) (0.063) (0.062) (0.062) (0.062) MBE4 -0.345*** -0.344*** -2.139**

(0.126) (0.126) (0.951) MBE5 -0.257* -0.255* -2.143**

(0.137) (0.138) (0.936) MBE6 -0.179 -0.178 -1.794**

(0.127) (0.127) (0.870) readXMBE4 -0.125* (0.067)

readXMBE5 -0.132** (0.066)

readXMBE6 -0.113* (0.060)

pre_uncert 0.598*** 0.591*** 0.593*** 0.594*** 0.589*** 0.591*** 0.592*** 0.587*** 0.588*** 0.590*** (0.062) (0.062) (0.063) (0.063) (0.062) (0.062) (0.063) (0.062) (0.062) (0.062)

prefil_alpha -5.399*** -5.374*** -5.377*** -5.383*** -5.371*** -5.373*** -5.379*** -5.377*** -5.381*** -5.385*** (0.408) (0.407) (0.408) (0.409) (0.407) (0.407) (0.408) (0.407) (0.407) (0.408)

abret 4.777*** 4.747*** 4.750*** 4.755*** 4.744*** 4.747*** 4.752*** 4.795*** 4.797*** 4.796*** (1.068) (1.068) (1.067) (1.067) (1.068) (1.067) (1.067) (1.067) (1.067) (1.067)

logsize 0.253*** 0.262*** 0.262*** 0.261*** 0.257*** 0.257*** 0.256*** 0.259*** 0.259*** 0.258*** (0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036) (0.036)

book2m -0.910*** -0.886*** -0.888*** -0.891*** -0.892*** -0.895*** -0.898*** -0.890*** -0.892*** -0.895*** (0.108) (0.109) (0.109) (0.110) (0.109) (0.109) (0.109) (0.109) (0.109) (0.109)

nasdaq -0.399*** -0.406*** -0.404*** -0.402*** -0.409*** -0.406*** -0.404*** -0.408*** -0.406*** -0.404***

(0.101) (0.100) (0.100) (0.100) (0.101) (0.101) (0.101) (0.101) (0.101) (0.101)

Obs. 50563 50563 50563 50563 50563 50563 50563 50563 50563 50563 R-squared 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 0.065 r2_a 0.063 0.064 0.063 0.063 0.064 0.063 0.063 0.064 0.064 0.063

F 15.450 15.434 15.547 15.572 15.219 15.325 15.349 14.890 14.968 15.004 Year dummies Yes yes yes yes yes yes yes yes yes yes industry dummies Yes yes yes yes yes yes yes yes yes yes


27

Table 8.3

An Analysis of The Relation Between Financial Report Readability, Earnings Management and six months Post-Filing Mispricing Score

The dependent variable in each regression is 6month_Misprice, which is given by the six months after filing mispricing factors score built using the dataset made available by Stambaugh and Yuan (2016). The independent variables are Readability, given by the na---ural log of the text document

file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions

of control variables. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

6months_Misprice

readability -0.021 -0.017 -0.018 -0.019 0.014 0.019 0.014

(0.065) (0.065) (0.065) (0.065) (0.064) (0.064) (0.065) MBE4 -0.367*** -0.366*** -2.299**

(0.136) (0.136) (1.071) MBE5 -0.244* -0.243* -2.320**

(0.146) (0.147) (1.078) MBE6 -0.156 -0.156 -1.882*

(0.136) (0.136) (1.000) readXMBE4 -0.135* (0.076)

readXMBE5 -0.145* (0.076)

readXMBE6 -0.121* (0.070)

pre_uncert 0.548*** 0.540*** 0.542*** 0.544*** 0.539*** 0.541*** 0.543*** 0.536*** 0.538*** 0.540*** (0.062) (0.062) (0.063) (0.063) (0.062) (0.062) (0.063) (0.062) (0.062) (0.062)

prefil_alpha -4.755*** -4.727*** -4.733*** -4.740*** -4.725*** -4.731*** -4.738*** -4.732*** -4.739*** -4.744*** (0.385) (0.384) (0.385) (0.385) (0.384) (0.384) (0.385) (0.383) (0.384) (0.385)

abret 4.397*** 4.363*** 4.370*** 4.377*** 4.362*** 4.369*** 4.375*** 4.416*** 4.422*** 4.421*** (1.024) (1.024) (1.024) (1.023) (1.024) (1.023) (1.023) (1.023) (1.023) (1.023)

logsize 0.289*** 0.296*** 0.295*** 0.294*** 0.293*** 0.292*** 0.291*** 0.295*** 0.295*** 0.293*** (0.037) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038) (0.038)

book2m -0.806*** -0.783*** -0.787*** -0.791*** -0.787*** -0.791*** -0.796*** -0.784*** -0.788*** -0.792*** (0.115) (0.115) (0.115) (0.116) (0.115) (0.115) (0.115) (0.115) (0.115) (0.115)

nasdaq -0.521*** -0.529*** -0.526*** -0.524*** -0.531*** -0.527*** -0.525*** -0.530*** -0.527*** -0.525*** (0.107) (0.106) (0.106) (0.106) (0.107) (0.107) (0.107) (0.107) (0.107) (0.107)

Obs. 50669 50669 50669 50669 50669 50669 50669 50669 50669 50669 R-squared 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 0.061 r2_a 0.059 0.060 0.059 0.059 0.060 0.059 0.059 0.060 0.060 0.059

F 15.993 15.964 16.143 16.231 15.763 15.952 16.034 15.445 15.596 15.694 Year dummies yes yes yes yes yes yes yes yes yes yes



28

Table 9

Robustness: The Relation Between Financial Report Readability, Earnings Management (using discretionary

accruals) and Post-Filing Valuation Uncertainty The dependent variable in each regression is uncert, which is given by the market model RMSE for

trading days [6, 28] in respect to the 10-K filing date. The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one (the higher the magnitude, the better readability), upda[4,5,6] (neda[4,5,6]), is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets and used upward (downward) discretionary accruals (upda/neda= one), otherwise = 0. readXupda[4,5,6] and readXneda[4,5,6] is the interaction between readability and these two accounting quality measures. All regressions include intercept, year and Fama and French (1997) 48-industry dummies. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) uncert uncert uncert uncert uncert uncert

readability -0.088*** -0.086*** -0.085*** -0.091*** -0.091*** -0.091*** (0.022) (0.022) (0.022) (0.022) (0.022) (0.022)

upda4 -1.155*** -0.165*** (0.284) (0.037) upda5 -1.238*** -0.178*** (0.245) (0.033)

upda6 -1.150*** -0.150*** (0.248) (0.032) neda4 -0.713* -0.032 (0.395) (0.052)

neda5 -0.931*** -0.068 (0.344) (0.046) neda6 -0.988*** -0.076* (0.303) (0.042)

readXupda4 -0.071*** (0.019) readXupda5 -0.076*** (0.017)

readXupda6 -0.071*** (0.017) readXneda4 -0.048* (0.026)

readXneda5 -0.061*** (0.023) readXneda6 -0.064*** (0.020)

pre_uncert 0.477*** 0.477*** 0.477*** 0.477*** 0.477*** 0.477*** (0.038) (0.038) (0.038) (0.038) (0.038) (0.038) prefil_alpha -0.599*** -0.599*** -0.598*** -0.598*** -0.598*** -0.597*** (0.113) (0.113) (0.113) (0.113) (0.113) (0.113)

abret 4.138*** 4.133*** 4.133*** 4.136*** 4.132*** 4.131*** (0.355) (0.355) (0.355) (0.355) (0.355) (0.355) logsize -0.150*** -0.150*** -0.150*** -0.150*** -0.150*** -0.150*** (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)

book2m -0.312*** -0.311*** -0.310*** -0.312*** -0.311*** -0.311*** (0.057) (0.057) (0.057) (0.057) (0.057) (0.057) nasdaq -0.235*** -0.235*** -0.235*** -0.235*** -0.235*** -0.235*** (0.033) (0.033) (0.033) (0.033) (0.033) (0.033)

Obs. 42573 42573 42573 42573 42573 42573 R-squared 0.438 0.438 0.438 0.438 0.438 0.438 r2_a 0.437 0.437 0.437 0.437 0.437 0.437 F 171.834 175.716 176.410 166.695 167.430 169.802

Year dummies yes yes yes yes yes yes industry dummies yes yes yes yes yes yes


29

Table 10

Robustness: The Relation Between Financial Report Readability, Earnings Management (using real earnings

management) and Post-Filing Valuation Uncertainty The dependent variable in each regression is uncert, which is given by the market model RMSE for

trading days [6,28] in respect to the 10-K filing date. The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), uprem[4,5,6] (nerem[4,5,6]), is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets and used upward (downward) real earnings management (uprem/nerem= one), otherwise = 0. readXuprem[4,5,6] and readXnerem[4,5,6] is the interaction between readability and these two accounting quality measures. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6)

uncert uncert uncert uncert uncert uncert

Readability -0.074*** -0.072*** -0.073*** -0.080*** -0.080*** -0.080***

(0.025) (0.025) (0.025) (0.025) (0.025) (0.025)

uprem4 -2.645*** -0.068 (0.741) (0.080)

uprem5 -2.937*** -0.109

(0.708) (0.081)

uprem6 -1.995** -0.041

(0.903) (0.087) nerem4 -1.338*** -0.132**

(0.489) (0.055)

nerem5 -1.381*** -0.101**

(0.506) (0.049)

nerem6 -1.377*** -0.081*

(0.439) (0.043) readXuprem4 -0.177***

(0.051)

readXuprem5 -0.195***

(0.047)

readXuprem6 -0.134** (0.059)

readXnerem4 -0.083**

(0.032)

readXnerem5 -0.087***

(0.033)

readXnerem6 -0.088*** (0.029)

Controls yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes

industry dummies yes yes yes yes yes yes


30

Table 11

The Impact of Information Asymmetry on the Relation Between Financial Report Readability, Earnings

Management and Post-Filing Valuation Uncertainty The dependent variable in each regression is uncert, which is given by the market model RMSE for

trading days [6,28] in respect to the 10-K filing date. The regressions for the even columns, uncert younger, takes only the more information asymmetric younger firms that have their age below the sample median. The independent variables are readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE [4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control

variables.

(1) (2) (4) (5) (7) (8)

Uncert uncert

younger

Uncert uncert

younger

Uncert uncert

younger

Readability -0.074*** -0.066*** -0.070*** -0.061** -0.069*** -0.059**

(0.016) (0.025) (0.016) (0.025) (0.016) (0.025)

MBE4 -1.248*** -1.972*** (0.268) (0.458)

readXMBE4 -0.079*** -0.129***

(0.018) (0.033)

MBE5 -1.331*** -2.090***

(0.253) (0.432) readXMBE5 -0.085*** -0.138***

(0.017) (0.031)

MBE6 -1.313*** -2.045***

(0.236) (0.413)

readXMBE6 -0.084*** -0.135***

(0.016) (0.029) Obs. 65686 30302 65686 30302 65686 30302

R-squared 0.478 0.440 0.478 0.440 0.478 0.440

r2_a 0.477 0.438 0.477 0.438 0.478 0.438

F 194.548 113.368 195.515 114.584 194.877 114.817

Controls Yes Yes Yes yes yes yes Year dummies Yes Yes Yes yes yes yes

industry dummies Yes Yes Yes yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

31

Table 12

The Impact of Higher Business Segment Information Complexity on the Relation Between Financial Report

Readability, Earnings Management and Post-Filing Valuation Uncertainty

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6, 28] in respect to the 10-K filing date. The regressions for the even columns, uncert

high bseg, takes only the firms that have more complex information proxied by when the number of reported business segments are above the sample median. The independent variables are readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], which is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE [4,5,6] is the interaction between these two. All regressions include intercept, year

and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (4) (5) (7) (8)

Uncert uncert

high bseg

uncert uncert

high bseg

uncert uncert

high bseg

Readability -0.074*** -0.078*** -0.070*** -0.074*** -0.069*** -0.073***

(0.016) (0.018) (0.016) (0.018) (0.016) (0.018) MBE4 -1.248*** -1.223***

(0.268) (0.252)

readXMBE4 -0.079*** -0.078***

(0.018) (0.017)

MBE5 -1.331*** -1.309***

(0.253) (0.239) readXMBE5 -0.085*** -0.084***

(0.017) (0.016)

MBE6 -1.313*** -1.281***

(0.236) (0.225)

readXMBE6 -0.084*** -0.083*** (0.016) (0.015)

Obs. 65686 55479 65686 55479 65686 55479

R-squared 0.478 0.482 0.478 0.482 0.478 0.482

r2_a 0.477 0.481 0.477 0.481 0.478 0.482

F 194.548 190.222 195.515 191.612 194.877 192.375

Controls Yes yes yes yes yes yes Year dummies Yes yes yes yes yes yes

industry

dummies

Yes yes yes yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

32

Table 13

The Impact of Higher Geographic Segment Information Complexity on the Relation Between Financial Report

Readability, Earnings Management and Post-Filing Valuation Uncertainty

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6, 28] in respect to the 10-K filing date. The regressions for the even columns, uncert

high gseg, takes only the firms that have more complex information proxied by when the number of reported geographic segments are above the sample median. The independent variables are readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year

and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (4) (5) (7) (8)

Uncert uncert

high_gseg

Uncert uncert

high_gseg

uncert uncert

high_gseg

Readability -0.074*** -0.073*** -0.070*** -0.070*** -0.069*** -0.068***

(0.016) (0.020) (0.016) (0.020) (0.016) (0.020) MBE4 -1.248*** -1.268***

(0.268) (0.282)

readXMBE4 -0.079*** -0.081***

(0.018) (0.019)

MBE5 -1.331*** -1.359***

(0.253) (0.268) readXMBE5 -0.085*** -0.087***

(0.017) (0.018)

MBE6 -1.313*** -1.330***

(0.236) (0.250)

readXMBE6 -0.084*** -0.085*** (0.016) (0.017)

Obs. 65686 47212 65686 47212 65686 47212

R-squared 0.478 0.483 0.478 0.483 0.478 0.483

r2_a 0.477 0.482 0.477 0.482 0.478 0.482

F 194.548 162.029 195.515 162.639 194.877 163.925

Controls Yes yes Yes yes yes yes Year dummies Yes yes Yes yes yes yes

industry

dummies

Yes yes Yes yes yes yes


*** p<0.01, ** p<0.05, * p<0.1

Table 14

33

The Impact of Higher Compustat Information Complexity on the Relation Between Financial Report Readability,

Earnings Management and Post-Filing Valuation Uncertainty

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6, 28] in respect to the 10-K filing date. The regressions for the even columns, uncert highitems, takes only the firms that have more complex information proxied by when the number of Compustat items are above the sample median. The independent variables are readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6], is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (4) (5) (7) (8)

uncert uncert

highitems

uncert uncert

highitems

uncert uncert

highitems

Readability -0.074*** -0.054*** -0.070*** -0.051*** -0.069*** -0.049***

(0.016) (0.017) (0.016) (0.017) (0.016) (0.017)

MBE4 -1.248*** -1.579*** (0.268) (0.331)

readXMBE4 -0.079*** -0.100***

(0.018) (0.022)

MBE5 -1.331*** -1.630***

(0.253) (0.328)

readXMBE5 -0.085*** -0.103*** (0.017) (0.021)

MBE6 -1.313*** -1.640***

(0.236) (0.309)

readXMBE6 -0.084*** -0.104***

(0.016) (0.020) Obs. 65686 47050 65686 47050 65686 47050

R-squared 0.478 0.446 0.478 0.447 0.478 0.447

r2_a 0.477 0.445 0.477 0.446 0.478 0.446

F 194.548 173.603 195.515 171.035 194.877 172.965

Controls yes yes yes yes Yes Yes

Year dummies yes yes yes yes Yes Yes industry

dummies

yes yes Yes yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

34

Table 15

The Impact of Higher Pre-Filling Uncertainty on the Relation Between Readability, Earnings Management and

Post-Filing Valuation Uncertainty The dependent variable in each regression is uncert, which is given by the market model RMSE for

trading days [6, 28] in respect to the 10-K filing date. The regressions for the even columns, uncert highpreu, takes only the firms that have above the sample median Pre-Filling uncertainty, which is

the RMSE from a market model estimated using trading days [-252, -6] relative to the 10-K file date. The

independent variables are readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability), MBE[4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (4) (5) (7) (8)

uncert uncert highpreu



Readability -0.074*** -0.106*** -0.070*** -0.104*** -0.069*** -0.104***

(0.016) (0.023) (0.016) (0.023) (0.016) (0.023) MBE4 -1.248*** -1.845***

(0.268) (0.583)

readXMBE4 -0.079*** -0.112***

(0.018) (0.041)

MBE5 -1.331*** -1.862*** (0.253) (0.535)

readXMBE5 -0.085*** -0.113***

(0.017) (0.038)

MBE6 -1.313*** -1.759***

(0.236) (0.503)

readXMBE6 -0.084*** -0.107*** (0.016) (0.036)

Obs. 65686 32843 65686 32843 65686 32843

R-squared 0.478 0.329 0.478 0.329 0.478 0.329

r2_a 0.477 0.327 0.477 0.327 0.478 0.327

F 194.548 72.939 195.515 74.457 194.877 74.071 Controls yes yes Yes yes yes Yes

Year dummies yes yes Yes yes yes Yes

industry

dummies

yes yes Yes yes yes Yes


*** p<0.01, ** p<0.05, * p<0.1

35

Table 16

The Relation Between Earnings Management and Entire 10K Readability The dependent variable in each regression is Readability. For columns 1-3 Readability is given by the natural log of the text document file size in megabytes multiplied by negative one(the higher the magnitude, the better readability). For columns 4-6 Readability is given by the Fog Index of the full 10K filing which equals to 0.4* (average number of words per sentence + percent of complex words) multiplied by negative one (the higher the magnitude, the better readability). For columns 7-9 Readability is given by the Bog Index made available by Bonsal et al (2017) multiplied by negative one (higher the magnitude is the higher readability). The independent variables are MBE[4,5,6], which is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. The control variables are all used by Feng Li (2008) and are listed in the Appendix. All regressions

include intercept, year and Fama and French (1997) 48-industry dummies.Standard errors clustered by year and industry. See the Appendix B for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Readabilility

file size

Readabilility

file size

Readabilility

file size

readabilility

_fog

readabilility

_fog

readabilility

_fog

readabilility

_bog

readabilility

_bog

readabilility

_bog

acqben4 -0.039*** -0.029 -0.383*** (0.012) (0.020) (0.106) acqben5 -0.034*** -0.035* -0.354*** (0.011) (0.019) (0.099) acqben6 -0.029*** -0.021 -0.355*** (0.011) (0.018) (0.094) Obs. 37772 37772 37772 52472 52472 52472 53527 53527 53527 R-squared 0.648 0.648 0.648 0.119 0.119 0.119 0.379 0.379 0.379

r2_a 0.647 0.647 0.647 0.118 0.118 0.118 0.379 0.379 0.379 F 867.128 867.064 867.000 88.826 88.846 88.816 408.519 408.512 408.544 Controls Yes yes Yes Yes yes yes yes yes yes Year dummies Yes yes Yes Yes yes yes yes yes yes industry dummies Yes yes Yes Yes yes yes yes yes yes


36

Table 17

Robustness: The Relation Between Financial Report Readability, Earnings Management and Post-Filing Valuation Uncertainty Considering Firm Fixed Effect

The dependent variable in each regression is uncert, which is given by the market model RMSE for trading days [6, 28] in respect to the 10-K filing date. The independent variables are Readability, given by the natural log of the text document file size in megabytes multiplied by negative one(the

higher the magnitude, the better readability), MBE[4,5,6] is a dummy variable that identifies the lower accounting quality firms as those most likely to have managed earnings beating or just meeting past year's earnings by less than [0.4%|0.5%|0.6%] of total assets (MBE= one), otherwise MBE=0. readXMBE[4,5,6] is the interaction between these two. All regressions include intercept, year and Firm dummies. Standard errors clustered by year and industry. See the Appendix for definitions of control variables.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) uncert uncert uncert uncert uncert uncert uncert uncert uncert uncert

readability -0.065*** -0.064*** -0.064*** -0.064*** -0.053*** -0.050*** -0.050*** (0.012) (0.012) (0.012) (0.012) (0.013) (0.013) (0.013) MBE4 -0.103*** -0.101*** -0.912*** (0.016) (0.016) (0.118) MBE5 -0.104*** -0.102*** -0.998*** (0.016) (0.016) (0.113) MBE6 -0.097*** -0.096*** -0.943*** (0.015) (0.015) (0.112) readXMBE4 -0.056*** (0.008) readXMBE5 -0.062*** (0.007) readXMBE6 -0.059*** (0.007) pre_uncert 0.341*** 0.342*** 0.341*** 0.341*** 0.340*** 0.340*** 0.340*** 0.339*** 0.338*** 0.338*** (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) prefil_alpha -0.430*** -0.432*** -0.431*** -0.431*** -0.425*** -0.424*** -0.424*** -0.425*** -0.424*** -0.424*** (0.052) (0.052) (0.052) (0.052) (0.052) (0.052) (0.052) (0.052) (0.052) (0.052) abret 4.275*** 4.267*** 4.265*** 4.264*** 4.271*** 4.270*** 4.269*** 4.280*** 4.279*** 4.278*** (0.295) (0.294) (0.294) (0.294) (0.295) (0.294) (0.294) (0.295) (0.294) (0.294) logsize -0.218*** -0.211*** -0.210*** -0.210*** -0.215*** -0.215*** -0.215*** -0.215*** -0.215*** -0.215*** (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) (0.022) book2m -0.411*** -0.402*** -0.401*** -0.401*** -0.406*** -0.406*** -0.405*** -0.403*** -0.402*** -0.401*** (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) (0.035) nasdaq -0.010 -0.010 -0.010 -0.010 -0.011 -0.010 -0.010 -0.013 -0.012 -0.013 (0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.046) Obs. 65686 65686 65686 65686 65686 65686 65686 65686 65686 65686

R-squared 0.272 0.272 0.272 0.272 0.272 0.272 0.272 0.272 0.273 0.273 r2_a 0.271 0.271 0.271 0.271 0.272 0.272 0.272 0.272 0.272 0.272 F 397.069 394.580 394.747 394.783 384.475 384.631 384.667 376.138 376.920 375.990 Year dummies yes yes yes yes yes yes yes yes yes yes Firm dummies yes yes yes yes yes yes yes yes yes yes


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