FAR__Clatworthy__The Timeliness of UK Private Companies# Financial Reporting

The Timeliness of UK Private Companies’ Financial Reporting

Mark A. Clatworthy Cardiff Business School

Cardiff University Aberconway Building

Colum Drive Cardiff

CF10 3EU UK

[email protected]

Michael J. Peel Cardiff Business School

Cardiff University Aberconway Building

Colum Drive Cardiff

CF10 3EU UK

[email protected]

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The Timeliness of UK Private Companies’ Financial Reporting

Abstract

Timeliness is an important qualitative characteristic of accounting and is a fundamental element of the relevance of financial reporting information. In this paper, we use a large sample of UK private companies to investigate whether corporate governance characteristics impact upon the timeliness of financial reporting information. We also study the link between the reliability and timeliness of financial reporting information, since it is often argued that these are inversely related. Although private companies are typically closely held, meaning that communication with outside shareholders is less important, private companies still rely heavily on outsiders such as banks and trade creditors for long and short term financing; moreover, recent research shows that these outsiders rely on financial statements for information about the status of their claims. After controlling for various firm characteristics, we find that the presence of a professionally qualified accountant on the board, the proportion of women on the board, the size of the board and the presence and quality of an auditor all enhance financial reporting timeliness. We also find that firms that file less reliable information are more likely to be less timely in their filing of their accounts.

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The Timeliness of UK Private Companies’ Financial Reporting

1. Introduction

Timeliness is a fundamental characteristic of the financial reporting of information to agents

to enable them to make informed decisions about an entity. Information potentially loses

relevance with age and extended delays in the availability of financial statement information

render the information less useful for economic decision making. Although regulatory

requirements and pressures exert a significant influence over financial reporting timeliness,

there remains considerable discretion over when firms release their financial statement

information within regulatory windows. In this paper, we examine the extent to which timely

financial reporting information of UK private companies is influenced by corporate

governance characteristics, together with a number of additional corporate and accounting

characteristics.

In line with research showing that corporate governance affects the timelines of loss

recognition (Lara et al., 2009), using a large sample of UK private companies reporting under

two regulatory timeliness regimes, we find that various board and corporate governance

characteristics have an important effect on reporting timeliness. In particular, gender diversity

amongst board members, board size, auditor quality and the presence of a board member with

a professional accounting qualification all have a significant impact on the timeliness with

which companies file their annual financial statements. Our results are generally robust to a

number of different samples and variable definitions and to an estimator (count [negative

binomial] regression) that is more appropriate than the standard OLS regression used in prior

research.

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Numerous prior studies have conducted empirical analyses of the timeliness of public

companies’ financial information; however, there have been few studies of what drives timely

disclosures by private companies. This is surprising since the conceptual frameworks of

standard setters often make little or no distinction between public and private firms,

suggesting that the link between relevance and timeliness remains important irrespective of

corporate legal form. Importantly, the focus of past research upon the financial reporting

characteristics of companies with (principally equity) securities traded on public markets is

changing and increasing research attention is being paid to the role of debt providers in

shaping financial reporting characteristics (Ball et al., 2008) and to the significant role that

private companies play in the economy. For instance, Hope et al. (2010) point out that the

aggregate value of total assets of private companies exceeds that of public companies in most

countries.

The paper contributes to the literature on financial reporting timeliness in a number of

important ways. First, our study represents the first empirical examination of the timeliness of

private companies’ financial reporting information. Despite the fact that private companies

are not subject to stock market pressures for timely financial reporting information, they still

often rely extensively on outside debt finance, providers of which require timely information

for their financial decision making (e.g. Collis, 2008; Peek et al., 2010). Second, our model

includes a number of potentially important corporate governance characteristics that

potentially influence reporting timeliness yet have not previously been examined in empirical

research. For example, although recent studies have assessed the impact of board gender

diversity on governance and performance, there is little evidence on their impact upon

financial reporting processes. Moreover, while several studies have tested for the effects of

professional expertise within large companies, little is known about the impact of such

expertise on smaller private firms, where the marginal effect could well be higher than in very

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large, complex entities. Similarly, since our sample includes companies that are not required

to have their accounts audited, we are able to assess the effects of statutory audits on the

relevance of the accounting information; together with the ‘quality’ of the auditor.

Our research represents the first examination of the effects of firms filing their

financial statements under a new regulatory regime. Following an EU initiative to improve the

speed with which companies make information in their financial statements available to users,

a new statutory reporting regime (the Companies Act 2006) for UK firms was introduced in

April 2008. Under this regime, the maximum statutory period permitted for private companies

to file their accounts before incurring penalties was reduced from 10 to 9 months. Our sample

covers filings under both the old and new reporting regimes. We are therefore able to provide

a novel examination of the impact of a regulatory change, aimed at improving the utility of

financial reporting, on corporate reporting behaviour in practice.

A further noteworthy feature of the study relative to previous research is the large

number of degrees of freedom available in estimating the statistical models. Our initial

sampling frame is the population of available private UK companies. This is important, since

the regulatory reporting framework of private firms is less uniform than for their public

counterparts. Small companies meeting the statutory definition need only file abridged

accounts (in the form of a summary balance sheet), and their accounts are not required to be

audited; whereas those classified as medium sized are permitted to omit the sales figure from

the profit and loss statement. However, in both cases full statements may be voluntarily filed;

and in any event a profit and loss account and balance sheet must be prepared for the

company members. Initially, as well as controlling for firm size, we include variables to

capture any variations in the reporting lag due to these factors; but also estimate models on

sub-samples of firms reporting profit data.

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Finally, we contribute to the literature in this area by employing a more appropriate

regression estimator. Previous studies have estimated the relationship between the reporting

lag of annual accounts and corporate size in log terms using ordinary least squares (OLS)

regression. However, because of the nature of the dependent variable (i.e., the number of days

between companies’ year end and release of their accounts to the public via filing at

Companies House), there are alternative techniques that are more suitable for this setting. For

comparison with previous studies, we report results using OLS; but in addition, we provide

novel empirical analyses using a (negative binomial) count regression technique. Such

estimators are specifically formulated to account for the nature and distribution of cardinal

discreet dependent variables limited on the lower side, but with no upper boundary) such as

the one employed in the current study.

The remainder of the paper is set out as follows: the next section outlines the legal and

regulatory reporting regime faced by private and public (listed and unlisted) UK companies,

together with prior theoretical and empirical research into the factors that influence reporting

timeliness. Section 3 describes our data and methods, while our main results are presented in

section 4. Section 5 concludes the paper with limitations and suggestions for further research.

2. Prior Literature and Regulatory Requirements

2.1 Prior research on the timeliness of financial reporting information

It has long been recognised in numerous standard setters’ Conceptual Frameworks that

accounting information should be timely in order for it to be useful to financial decision

makers. For example, the second Concepts Statement of the US Financial Accounting

Standards Board (FASB, 1980) included timeliness as one of the three components of the

primary decision-specific quality of relevance. It stated (para. 56) that ‘If information is not

available when it is needed or becomes available only so long after the reported events that it

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has no value for future action, it lacks relevance and is of little or no use.’ In the UK, as

pointed out by Davies et al. (1999, p. 86) the Corporate Report of the (then) Accounting

Standards Steering Committee stated that in order to fulfil their primary objective and be

useful, corporate reports should be ‘relevant, understandable, reliable, complete, timely and

comparable’ (emphasis added). The present status of timeliness in the IASB Conceptual

Framework is somewhat less prominent, though as a constraint on the main qualitative

characteristics of financial reporting information, relevant and reliable information, it remains

important.

Despite its importance to standard setters, there have been relatively few systematic

theoretical analyses of timeliness. One of the earliest examinations is by Feltham (1972), who

shows, from an information economics perspective, that an information system having a

shorter reporting delay than another is more informative provided that both systems ultimately

report the same information. The latter assumption is crucial since it assumes that the

reliability of information is constant irrespective of the timeliness, which might not hold in

practise due to timeliness/accuracy trade-offs. Hence, although it is perceived as important, it

should be noted that timeliness is not an overriding objective of financial reporting due to the

potential trade offs between the timeliness of information and its quality. Both standard setters

(e.g. FASB, 1980) and academics (e.g. Suphap, 2004) recognise that preparers of accounts

may sacrifice the reliability of information by focusing excessively on timeliness. Ultimately,

however, as noted by Bromwich (1992), a lack of timeliness may cause information to have

zero utility for decision making and/or information becoming available from other sources.1

Such late information may result in misallocation of capital where outside investors and

creditors face serious adverse selection and moral hazard problems (Leventis and Weetman,

2004). Even in the case of private companies, where there is less of a separation of ownership 1 Bromwich (1992) also points out, however, that timeliness is not always deemed an ‘absolute’ characteristic from an information economics perspective – only where a decision made conditioned upon early access to the information yields greater welfare to the decision maker than late access.

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and control, the timeliness of information is still potentially important to outside creditors and

to those (albeit less frequently) with external shareholders.. Like timely loss recognition, it

can affect the speed with which debt covenant restrictions are imposed which cause control to

shift from managers to lenders in order to limit actions such as dividend payouts and further

borrowing (Ball and Shivakumar, 2005).

To summarise, we cannot improve on Davies and Whittred’s (1980: 48-49) view that

‘Irrespective of whether one chooses to call timeliness an objective of accounting or an

attribute of useful accounting information, it is clear that both the disclosure regulations and a

large part of the accounting literature adopt the premise (either implicitly or explicitly) that

timeliness is a necessary condition to be satisfied if financial statements are to be useful.’

Although the theory in this area is not particularly well developed, several studies

have examined the factors that influence timeliness. One of the earliest studies of the factors

affecting reporting timeliness is by Dyer and McHugh (1975), who investigate determinants

of the reporting lag for 120 Australian companies listed on the Sydney Stock Exchange. Inter

alia, larger companies are hypothesised to be associated with shorter delays due to the

economies of scale in preparing financial statements and that more profitable companies are

associated with more timely reporting due to bad news taking longer to be disclosed. They

find support for the size hypothesis, but little evidence that profitability influences reporting

timeliness.

In a later study of the timeliness of Australian corporate annual reports Whittred

(1980) finds that companies receiving ‘first time’ audit qualifications are associated with

significantly longer reporting lags than companies with clean audit reports; moreover, the

more serious the qualification, the longer is the reporting delay. More recently in a study of 47

companies listed on the Zimbabwe Stock Exchange, Owusu-Ansah (2000) reports that

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corporate size, profitability and age are associated with variation in financial reporting

timeliness, but no evidence was found that gearing influences timeliness.

Leventis and Weetman (2004) argue that the decision of whether to report in a more

timely fashion is akin to voluntary disclosure and apply general disclosure theories of

Diamond (1985) and Verrecchia (1983; 1990) to the issue of timeliness. They find that

proprietary costs, information cost savings and the extent of favourable or unfavourable news

disclosures contained in the information are all factors influencing reporting the reporting lag

of Greek listed companies. Although corporate size was found to be statistically insignificant,

whether or not the firm underwent a public issue of shares, change in profitability, industry

concentration and the number of remarks in audit reports were found to be significant

determinants of financial reporting timeliness.

In a comparison of multinational and domestic companies, Lee et al. (2008) find that

the audit delay is more significant for multinational firms due to their more complex nature.

However, their overall reporting lag was shorter than for their. Furthermore, companies

reporting a loss, with high gearing, bad news, more reportable segments and with

extraordinary items (the latter two items representing complexity) were associated with a

longer reporting lag, whereas larger companies with a big 4 auditor reported more quickly

overall.

Although prior studies are based on public listed companies, where the timely release

off information is essential for the efficient operation of capital markets, the general theory

(and associated categories of explanatory variables, where relevant to private firms) relating

to the timely reporting of annual accounts applies equally to our sample of firms. As noted

above, Leventis and Weetman (2004) contend (in line with empirical evidence) that there is a

voluntary element (management discretion) to the timeliness lag. However, in recognition of

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the importance of time-relevant information, this is generally curtailed by statutory/regulatory

intervention.

The legal requirements for UK companies’ filings are set out in the 2006 Companies

Act, which represented a major overhaul of British corporate law, updating the 1985

Companies Act. However, the provisions in 2006 Act relevant to this study are only

applicable for financial years beginning on or after 06 April 2008. The UK Government made

these changes partly in response to the Company Law Review, which recommending filing

times should be reduced ‘in order to reflect improvements in technology and the increased

rate a which information becomes out of date’ (RIA, 2007 p.35). On the introduction of the

new regime, the normal time (to avoid penalties) allowed for filing accounts was reduced

from 10 months under the 1985 Act to 9 months for a private company (and from 7 months to

6 months for public companies). The lower limit for public firms is a clear reflection of their

(generally) wider responsibility to shareholders (e.g. UK firms quoted on the Main Market

must additionally disclose their annual reports and accounts to the London Stock Exchange

(and via their web site) within 4 moths to avoid market regulatory penalties). However, other

thing equal, private companies have more discretion over their timeless (at least until

penalties are incurred) than their public counterparts.

Increased penalty bands apply to accounts delivered late on or after 1 February 2009,

whether filed under the Companies Act 1985 or the Companies Act 2006 (penalties for late

submission of accounts under the Companies Act 2006 are for financial years beginning on or

after 6 April 2008). These range from £150 for being up to one month late, to £1500 for

accounts being filed more than 6 months late (the corresponding minimum and maximum for

public companies are £750 and £7,500). These amounts are doubled in cases where the

accounts are filed late under the Companies Act 2006 and the previous year’s accounts under

the 2006 Act (i. e. for a financial year beginning on or after 6 April 2008), were also late.

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Company directors may also be prosecuted (i.e., it is a criminal offence) for the non/late

submission of their annual accounts in addition to incurring the above penalties

2.2 Model development

Our main area of interest in this study is upon the extent to which reporting timeliness is

influenced by corporate governance characteristics, though our model also captures a number

of additional important accounting variables, together with a suitable vector of controls.

Recent research by Lara et al. (2009) finds, inter alia, that the timeliness of earnings, i.e.

conditional conservatism, is associated with firms’ corporate governance. They note that

improved corporate governance results in better monitoring of managers’ actions and because

conservatism results in lower litigation risk for directors, auditors and managers, they predict

(and find) a positive association between the strength of corporate governance mechanisms

and earnings timeliness. This is attributed to the prediction that those involved in the financial

reporting process regard earnings timeliness as a desirable attribute of accounting information

and will therefore favour its implementation. Although we do not study earnings (loss

recognition) timeliness, our intuition is similar to that of Lara et al. (2009) and indeed with the

implicit assumption of standard setters noted above that timeliness of accounting information

is an important and desirable qualitative characteristic (ceteris paribus) and that corporate

governance mechanisms should respond to pressures from outside providers of finance.

Even though private companies are by their very nature less widely held than public

companies, a large proportion of their finance comes in the form of debt and, as shown by

Collis (2008) and Peek et al. (2020), lenders and creditors often rely on financial statement

data for assessing the status of their claims and in contracting. That the companies we

examine are typically small with relatively few directors (median board size of 3) means that

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any marginal effect of corporate governance attributes may well be more clearly discernible

than in larger, listed companies.

Our first experimental corporate governance variable is the presence of a director on

the board with a professional accounting qualification. The companies we study are not

regulated by the same regulation as listed companies and therefore, it is not necessary for

them to have an audit committee, let alone a member of an audit committee with financial

expertise.2 The qualified board members we observe are therefore appointed by companies

without regulatory intervention. We are aware of no previous studies into the effects of

financial expertise on financial reporting timeliness, though we hypothesise that as such board

members are able to offer advice and participate in the preparation of the financial statements,

that the effect of the presence of an accountant on the board will be to reduce the time

between the company’s year end and the filing at Companies House – i.e., the date on which

the reports are made available to the public. We note, however, that related research into

information reliability (as opposed to our focus on a subset of information relevance) has

produced inconclusive results, with Jeanjean and Stolowy (2009: 379) noting ‘Some studies

actually show and inverse relationship between expertise and the likelihood of financial

reporting regularities, earnings management and restatements in the United States’.

The second corporate governance variable we study is the proportion of female

members of the board. Increasing regulatory attention is being paid to the gender balance of

boards of directors and its effects (e.g. Nielsen and Huse, 2010), especially due to concern by

policy makers that females are very poorly represented (particularly amongst very large

companies) and firms are thus missing an important pool of talent. Indeed, this has even

prompted some governments (e.g. in Norway) to impose mandatory limits for the proportion

2 For example, the Smith Committee report of 2003 contains recommendations that the audit committee contains at least one member with financial expertise: It also advises: “It is desirable that the committee member whom the board considers to have recent and relevant financial experience should have a professional qualification from one of the professional accountancy bodies.”

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of female board members, though in the UK, so far, regulatory intervention has been limited

to advice that improving board diversity (e.g. as in the 2003 Higgs Review – see Adams and

Ferreira, 2009). Despite its regulatory significance, according to Nielsen and Huse, 2010),

there remains somewhat limited knowledge of the contribution of female directors to board

decision and processes. They find that women tend to contribute to board effectiveness under

certain conditions (e.g. in oversight of firm strategy), whereas over other issues (e.g.

operational control), the relationship is less marked. There is, however, growing acceptance in

the management and governance literature that gender differences may result in different

behaviour and, since boards characteristics are critical to decision making, to variation in

accounting and financial outcomes.

In general, the research into the association between gender diversity and performance

has produced mixed results (Gulamhussen and Santa, 2010). Adams and Ferreira (2009)

investigate the impact of women board members upon firm governance and they find that it is

substantial. In particular, they find that women are more active board members (they attend

more meetings and are more likely to join monitoring committees) and that ‘female directors

appear to have a similar impact as the independent directors in governance theory do’ (2009:

308). Their results on performance effects are less emphatic, however. In an international

study of the banking industry, Gulamhussen and Santa (2010) find a negative association

between the presence and percentage of women on the board and various measures of

corporate risk-taking. Furthermore, a number of studies find women less likely than men to

break the law and to engage in unethical behaviour, such as earnings management (e.g. Betz

et al., 1989; Krishnan and Parsons, 2008).

To the extent that female representation improves corporate governance effectiveness

and reduces risk taking, we predict that financial reporting timeliness is improved by having

(more) female representation on the board. This is because the late filing of accounts is

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associated with criminal proceedings and because of prior research that finds that stronger

governance improves the timeliness of loss recognition (Lara et al., 2009).

Our third measure of corporate governance measure is board size. Although it is a

commonly used board characteristic, the relationship between board size and board

effectiveness is complex. In some cases, it is argued that larger boards are less effective at

monitoring due to difficulties in coordinating board activities and directors being more likely

to ‘free-ride’ than in smaller boards. In other cases, however, larger boards can be more

effective, particularly for more complex firms where advantages from the greater advisory

capacity may outweigh the costs of less effective monitoring than larger boards. In our case,

there is the additional consideration that, due to the size of our sample firms, larger boards

may involve bringing in more outsiders to the firms’ original owner/managers, since the

minimum number of directors is one for UK private firms. We do not therefore predict the

direction of the relationship between board size and the timeliness of financial reporting

information.

Finally, we capture the quality of corporate governance by the presence and type of

external auditor. UK companies under certain size limits are exempt from the statutory audit.

For large and small companies alike, the audit function is a crucial aspect of corporate

governance and numerous studies document that audit (and audit quality) have a positive

effect on the reliability and relevance of accounting information (e.g. Teoh and Wong, 1993).

We predict that the presence of an auditor will result in more timely information and that this

effect will be greater for big 4 auditors, where the big 4 indicator is a proxy for audit quality.

It is possible, however, that the presence of an auditor will induce further delays in filings.

Naturally, it is necessary in the empirical models to control for the large number of

additional factors that are expected to influence the timeliness of financial statements. These

include institutional factors, accounting factors and firm factors. A particularly interesting

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variable is whether or not the firm subsequently field amended accounts. This is important as

it provides some insight into the potential trade off between accounting relevance and

reliability. It is often argued that more timely information is less reliable (e.g. Davies et al.,

1999), so an interesting question is whether companies that did file amended accounts were

more timely. Alternatively, were these companies filing less timely and less relevant

information? To address this, we include an indicator variable taking a value of one if the

company subsequently amended its accounts at Companies House.

In the case of institutional factors, we include an indicator variable taking the value of

one if the firm filed under the new regime (i.e., its accounts were drawn up for a period

beginning after April 6th 2008) and zero otherwise since the 2006 Act requires accounts to be

field within 6, rather than 7 months. We also control for the fact that under the Companies

Act, firms under certain size criteria are permitted to file abbreviated accounts (i.e., only a

balance sheet), whereas medium companies need only file and income statement without a

figure for sales. These factors could well influence the timeliness of the filings, since less

information is required to be prepared. A major consequence of this is that we are required to

draw our main variables from balance sheet data alone for most of our sample. We thus

include a dummy variable for companies with a small company exemption and medium

company exemption and also report results only for those companies reporting income

statement data.

It is reasonably well established that in theory and in practice, companies with bad

news to report tend to take longer to release that information (e.g. Whittred and Davies,

1980). We thus include variables for companies with negative equity, negative working

capital, negative retained earnings (and change in retained earnings – though of course this

omits dividend effects), whether or not the company had a qualified audit report and a

composite credit score measure (Q-score – provided on FAME for all companies). In addition,

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where we have income statement data, we include a dummy variable indicating whether or

not the firm reported a loss before tax. We also include firm gearing (ratio of total assets to

total liabilities) as a measure of firm risk. Although riskier firms tend to report later on

average (e.g. Lee et al., 2008), it is possible that in our setting, gearing represents a measure

of the importance of outside finance and is thus associated with more timely disclosure.

We control for firm complexity by including measures of the (square root of) number

of subsidiaries and number of SIC codes and also whether the company changed its year end;

we also include a variable for whether or not the company reported a post-balance sheet

event, as this may involve more complexity in the preparation accounting reports. We include

measures of firm size (natural log of total assets) and firm age and also include industry

dummies.

Since it is possible that considerable diversity exists between firms in different

industrial sectors and that any systematic effects may not be well captured by a constant, we

also include industry-controlled reporting lag as a dependent variable in some of our models.

This is measured as the reporting lag for each firm minus the reporting lag for all firms in that

particular sector (measured by a two-digit SIC code). The variables included in our models

and definitions are reported in Table 1.

Insert Table 1 about here

3. Data, Sample and Estimation

3.1 Data sources

The April 2009 Bureau Van Dijk Financial Analysis Made Easy (FAME) DVD-ROM UK

database was the source of all the financial and non-financial data used in this study.

Financial (annual accounts data) and non-financial data (e.g., company location, auditor and

industrial classification) are available, as individual records for each company on the

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database. The database is based on the records placed at Companies House, which is the UK

centre for accounting and company filings. Obtaining the data in a usable format required

considerable data manipulation, including the use of text recognition programmes, as many of

the variables are downloaded as unordered blocks of strings with associated dates having to

be isolated (e.g. for the accounts filing date).

Our starting point for the sample is all active UK private companies with total assets

above £500 with reporting lag data (i.e., both a filing date at Companies House and a financial

year end date) for a full period and with a year end after or during 2008. We removed

companies that had reporting dates for the year in which they were formed and also removed

data which was clearly inaccurate, particularly a small number of companies with a negative

or zero lag and companies with a lag exceeding 18 months. Further investigation revealed that

these cases were due to differences in the years for filing accounts (e.g. a company had a year

ending 31st December 2009, but Companies House had not yet recorded the filing date for

2009, so the 2008 date was included). We winsorize continuous right hand side variables at

the 1st and 99th percentile to minimise the influence of data errors. Furthermore, since the

credit score variable may include reporting lag as a component, we use the credit score from

the previous period.

This results in a total sample of 1,032,615 companies. In our analysis, however, in

addition to this full sample (for which we only have balance sheet data) we report results for

independent companies (since it is possible that subsidiaries filing times are driven by their

parents’ deadlines, which might be shorter) and for companies for which we have profit data.

3.2 Descriptive statistics

Descriptive statistics for the full sample of companies (n = 1,032,615) and for those

companies reporting only income statement data (n = 224,294) are presented in Table 2. For

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the full sample, the statistics show that the average lag is 228 days, with a median of 257. Just

over 2% of the sample has a professionally qualified accountant as a board member, but

surprisingly, and in stark contrast to studies of listed companies that often report proportions

in single figures, the proportion of females on the boards of the sample companies is around

one third. It is possible that such membership is notional in that it is in order to exploit tax

advantages; nevertheless, it is an interesting statistic.

Companies in our sample have relatively small boards (median of 2 members),

reflecting the size of the firms (median total assets of only £81,000 and some 87% qualifying

as for the small exemption).

In general, companies in the subsample with profit data have a similar lag distribution

though they are considerably larger than the full sample (as expected), with a median size of

approximately £170k and a moderately larger board (75th percentile is 4 members, in contrast

to 3 for the full sample). They also have a higher propensity to employ accountants on the

board and to have a big 4 auditor, though they employ fewer women directors.

Insert Table 2 about here

3.3 Estimation procedures

Prior research into the timeliness of financial reporting information uses a number of different

measures as the dependent variable, largely due to issues of non-normality. For instance,

Leventis and Weetman (2004) use natural logarithm of reporting lag as a robustness test,

while Owusu-Ansah (2000) uses the square root transformation (which is similar in nature to

the natural logarithm transformation). In our study, the raw reporting lag has a very large

variance and positive skewness, leading to severe non-normality. To ameliorate these

problems, we use the natural log of the number of days between the year end and filing of

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accounts (i.e., the reporting lag). We also use an industry adjusted version of the reporting lag,

which is not as prone to normality problems as it admits the possibility of negative values.

Rather than rely purely on OLS with transformed data, we assess the robustness of our

findings by employing a regression estimator designed for our type of data, namely negative

binomial regression. Poisson regression is designed for count data (i.e., data on cardinal

ordered discrete outcomes bounded at zero, but unbounded on the right, but this assumes that

the mean and variance of the distribution are equal. In our case, the data have a very large

variance and so we rely on negative binomial (NB) regression, since this is a modified version

of Poisson regression that allows for the ‘overdispersion’ we face in our data (e.g. Verbeek,

2004).

4. Results

4.1 Main regression Results

Our main regression results are presented in Table 3, which includes reported models for the

full sample, for independents companies only and for those with income statement (profit)

data, based on both ordinary least squares (OLS) and negative binomial (NB) regression

estimators.

Insert Table 3 about here

The results support proposition that corporate governance factors have an important influence

on financial reporting timeliness. For the full sample based on OLS with lag and industry

controlled lag both indicate that boards with professional accounting experience file their

accounts in a more timely manner than those without (significant at the 0.01 level based on

White’s corrected standard errors). Furthermore, the negative binomial regression supports

this finding, though at a slightly lower level of significance (p < 0.05). The findings for the

proportion of women on the board is significant at the 0.01 level using both lag definitions

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and using negative binomial regression. Interestingly, and in contrast to suggestions in prior

research that board size is often negatively related to board effectiveness, we find strong

evidence that larger boards are associated with more timely financial reporting information. 3

Finally for our main variables of interest, the presence of a non big 4 auditor is associated

with a lower reporting lag (at p < 0.01); furthermore, companies being audited by a big 4

auditor filed their accounts more quickly than their non big 4 counterparts (this difference is

significant at p <0.01 in a Wald test).

In general, these results this hold for the other two samples – i.e., independent

companies and those reporting profits/income statement data. The one exception is that for the

sample of independent companies, the coefficient for the presence of an accountant on the

board is insignificant.

The estimated parameters for the additional variables in Table 3 also reveal some

interesting relationships. For instance, as expected, the change in regulation had a dramatic

effect (captured by the coefficient for NEW_REGIME). Also noteworthy is the finding that

companies that were eventually asked to restate their accounts (AMENDACC) were generally

significantly slower than other companies, suggesting that rather than there being a trade off

between relevance and reliability, some companies tend to produce unreliable and less timely

data. This result appears to be confined to smaller companies, however, since those with

income statement data show no statistical relationship. Also of note are the effects of bad

news on reporting lag. In addition to the credit score being consistently significant across all

models, there are positive coefficients on financial statement qualification (consistent with

Whittred and Davies, 1979), whether the company had negative equity (NEG_EQ), negative

retained earnings (NEG_RETEARN), negative working capital (LOW_LIQ) and gearing

(TLTA); the coefficient on change in retained earnings is, however, not significant, in contrast

3 We replicated this finding using untransformed board size.

19

to findings for larger companies (e.g. Leventis and Weetman, 2004). Finally, companies in the

smaller sample with profit data – i.e., those with an observed value for LOSS – also reported

their accounting information significantly later than those reporting a profit.

4.2 Regressions for different sized companies

Since our sample contains such a wide range of size (as captured by total assets), we examine

the main models across four size quartiles, where quartile 1 contains the smallest companies

and quartile 4 the largest. The results are presented in Table 4.

Insert Table 4 about here

Although our main findings are generally robust across the 4 groups, we note that the

robustness of the ACCQUAL coefficient estimate is low. It appears that the largest companies

are most affected by the presence of an accountant on the board. The proportion of women on

the board and the size of the board are more robust and in general, having an auditor tends to

result in more timely information regardless of size (except for size quartile 3). The new

regime had a similar impact across all size groups though companies restating their accounts

are only less timely if they are not amongst the largest 25% of companies. Overall, however,

the models appear to be generally stable across all size categories and the main control

variables retain the same sign as in the overall models in Table 3.

5. Discussion and Conclusions

The timeliness of financial reporting information is an important intrinsic characteristic and

an essential element of information relevance, as recognised by the conceptual frameworks of

major standard setters over decades. In this paper, we studie the effect of corporate

governance factors on the timeliness of information of a large sample of UK private

companies. The companies we examine are often closely held, though they rely heavily on

20

outsiders for debt finance. Although it has often been assumed that lenders may use

alternative sources of information (e.g. for lending contracts), recent evidence refutes this

(e.g. Collis, 2008; Peek et al., 2009) and shows that lenders rely on published financial

statements.

In line with suggestions in the literature that corporate governance characteristics may

affect the timeliness of loss recognition, and with findings in the general corporate

governance literature on board composition, we find that companies with better financial

expertise, with larger and more diverse boards and those companies that appointed an external

auditor were associated with more timely release of financial statements to the public. We

also find that companies restating their accounts were generally later filing in the first place;

moreover, the introduction of the new Companies Act which reduced filing time by one

month (with fines for late filing), unsurprisingly, had a significant effect. In line with prior

research on the determinants of timeliness in larger listed companies, we find that ‘bad news’,

such as a qualified audit report, negative equity and reporting a loss, negatively impacts upon

timeliness.

In general, our results are robust to various subsamples and variable definitions and

also to different estimators, though some of our measures are apparently not applicable to

different sized companies. Further work will be necessary to investigate the reasons for the

changes in results across the size range.

21

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24

Table 1

Variable Definitions and Expected Relationship with Timeliness

Variable Definition Label Expected Sign

Number of days between account year end and date accounts filed at Companies House

LAG N/A

LAG adjusted by median of lag for firms with the same 2 digit SIC code IALAG N/A 1 if there is a qualified accountant on the board, 0 otherwise ACCQUAL - Proportion of female directors on the board PROP_FEMS - Natural log of number of directors LNBRDSIZE - 1 if company is audited by a big 4 auditor, 0 otherwise BIG4 - 1 if company is audited by a non big 4 auditor, 0 otherwise NONBIG4 - 1 if company filed amended accounts, 0 otherwise AMENACC ? 1 if company had qualified audit report, 0 otherwise QUALIF + 1 if company had negative working capital, 0 otherwise NEGWC + 1 if company had negative equity, 0 otherwise NEGEQ + Previous year’s credit score (0-100; 100 is least risky) QSCORE - 1 if company disclosed post-balance sheet event, 0 otherwise POST_BAL + Natural log of total assets LNTA + Company age in years AGE - 1 if company filed accounts under small company exemption, 0 otherwise SM_EXEMP - 1 if company filed accounts under medium company exemption, 0 otherwise MED_CO ? 1 if accounts are filed under new April 2008 reporting regime, 0 otherwise NEW_REGIME - Total liabilities to total assets TLTA ? Square root of the number of subsidiaries SQRSUBS + Number of additional industrial SIC codes (0 indicating no additional SIC code) N_SIC + 1 if company changed its accounting year end, 0 otherwise YE_CHANGE ? 1 if company had negative retained earnings, 0 otherwise NEG_RETEARN + Change in retained earnings scaled by current year total assets CH_RETEARN - 1 if the company is independent (not held as a subsidiary), 0 otherwise INDEP ? 1 if the company had negative PBT, 0 otherwise LOSS ? 1 if company is in agriculture sector, 0 otherwise AGRIC ? 1 if company is in mining sector, 0 otherwise MINING ? 1 if company is in manufacturing sector, 0 otherwise MANUF ? 1 if company is in construction sector, 0 otherwise CONSTR ? 1 if company is in utilities sector, 0 otherwise UTIL ? 1 if company is in retail/wholesale sector, 0 otherwise RETAIL ? 1 if company is in the service sector, 0 otherwise SERVICE ? 1 if company is in financial sector, 0 otherwise FINANCE ?

Table 2 Descriptive Statistics

Full Sample

(N = 1,032,615) Sample Reporting Profit Before Tax

(N = 224,294) Variable Mean SD P25 Median P75 Mean SD P25 Median P75 LAG 228.2 87.50 162 257 297 229.7 88.58 164 254 297 IA LAG -28.89 87.29 -95 -1 36 -26.30 88.21 -92 -3 37 ACCQUAL 0.0216 0.145 0 0 0 0.0455 0.208 0 0 0 PROP FEMS 0.324 0.285 0 0.333 0.500 0.259 0.274 0 0.200 0.500 LNBRDSIZE 0.970 0.396 0.693 1.099 1.099 1.125 0.462 0.693 1.099 1.386 BRD SIZE 2.867 1.377 2 3 3 3.453 1.939 2 3 4 BIG4 0.0394 0.195 0 0 0 0.170 0.376 0 0 0 NONBIG4 0.549 0.498 0 1 1 0.633 0.482 0 1 1 AMENACC 0.00550 0.0737 0 0 0 0.00570 0.0751 0 0 0 QUALIF 0.00870 0.0927 0 0 0 0.0270 0.162 0 0 0 POST BAL 0.0110 0.104 0 0 0 0.0400 0.196 0 0 0 TA 1.303e+07 9.164e+08 20380 81080 397500 5.787e+07 1.965e+09 23990 166900 2.954e+06 LNTA 11.50 2.275 9.922 11.30 12.89 12.49 3.095 10.09 12.03 14.90 SM EXEMP 0.869 0.338 1 1 1 0.602 0.490 0 1 1 MED CO 0.00710 0.0842 0 0 0 0.0325 0.177 0 0 0 NEW REGIME 0.263 0.440 0 0 1 0.214 0.410 0 0 0 AGE 11.82 12.80 4 7 13 14.75 15.79 5 9 19 QSCORE 42.38 19.44 30 41 50 52.49 27.03 31 50 77 NEGWC 0.395 0.489 0 0 1 0.350 0.477 0 0 1 NEGEQ 0.207 0.405 0 0 0 0.211 0.408 0 0 0 NEG RETEARN 0.246 0.431 0 0 0 0.280 0.449 0 0 1 CH RETEARN -0.126 0.659 -0.117 0.000200 0.0837 -0.135 0.707 -0.0995 0.00260 0.0787 TLTA 1.056 1.798 0.399 0.743 0.987 1.083 2.001 0.341 0.701 0.983 SQRSUBS 0.116 0.456 0 0 0 0.294 0.779 0 0 0 SUBS 0.221 2.444 0 0 0 0.693 4.973 0 0 0 N SIC 0.135 0.438 0 0 0 0.188 0.547 0 0 0 YE CHANGE 0.0193 0.138 0 0 0 0.0244 0.154 0 0 0 LOSS - - - - - 0.316 0.465 0 0 1 INDEP

1

Table 3

Multivariate Regression Results Dep. variable LNLAG IALAG LAG LNLAG LAG LNLAG IALAG LAG Sample FULL FULL FULL INDEP INDEP IS DATA IS DATA IS DATA Estimator OLS OLS COUNTREG OLS COUNTREG OLS OLS COUNTREG ACCQUAL -0.0189

(5.31)*** -2.3077

(4.18)*** -0.0048 (1.97)**

-0.0204 (4.14)***

0.0029 (0.91)

-0.0243 (5.17)***

-6.3019 (7.66)***

-0.0197 (5.34)***

PROP_FEMS -0.0525 (30.03)***

-10.6752 (37.22)***

-0.0440 (33.98)***

-0.0536 (29.09)***

-0.0447 (32.80)***

-0.0324 (7.82)***

-7.0541 (10.33)***

-0.0277 (9.01)***

LNBRDSIZE -0.0422 (31.11)***

-6.2834 (27.79)***

-0.0409 (39.88)***

-0.0416 (27.61)***

-0.0397 (35.26)***

-0.0556 (20.89)***

-8.0559 (17.66)***

-0.0566 (27.39)***

BIG4 -0.1243 (45.21)***

-21.0890 (40.18)***

-0.1039 (45.85)***

-0.0988 (13.69)***

-0.0793 (13.19)***

-0.0721 (16.72)***

-11.0472 (14.50)***

-0.0621 (18.60)***

NONBIG4 -0.0766 (72.53)***

-14.7331 (86.21)***

-0.0691 (88.81)***

-0.0772 (71.22)***

-0.0696 (87.14)***

-0.0565 (18.09)***

-10.5882 (21.74)***

-0.0537 (24.49)***

AMENA CC 0.0438 (6.42)***

13.0502 (11.91)***

0.0520 (11.46)***

0.0558 (7.78)***

0.0605 (12.83)***

-0.0279 (1.76)*

-0.7930 (0.31)

-0.0044 (0.41)

QUALIF 0.1122 (28.49)***

27.8029 (32.58)***

0.1045 (32.75)***

0.0947 (13.25)***

0.0896 (15.95)***

0.1311 (27.91)***

32.4927 (30.71)***

0.1212 (30.78)***

POST_BAL 0.0837 (23.05)***

18.7841 (24.89)***

0.0748 (24.97)***

0.0810 (13.05)***

0.0659 (13.29)***

0.0843 (20.50)***

19.9565 (22.62)***

0.0763 (21.98)***

LNTA 0.0182 (59.33)***

1.7737 (35.26)***

0.0121 (53.23)***

0.0208 (59.91)***

0.0136 (54.11)***

0.0155 (26.78)***

1.0837 (10.93)***

0.0108 (24.27)***

SM_EXEMP -0.0608 (24.38)***

-12.5044 (29.28)***

-0.0573 (30.58)***

-0.0620 (19.75)***

-0.0607 (26.09)***

-0.0393 (9.05)***

-2.9793 (3.89)***

-0.0218 (6.40)***

MED_CO 0.0359 (7.76)***

7.8898 (8.81)***

0.0295 (7.74)***

0.0360 (6.12)***

0.0293 (6.04)***

0.0426 (8.66)***

9.3526 (9.86)***

0.0350 (8.55)***

NEW_REGIME -0.3798 (306.29)***

-67.6674 (381.06)***

-0.3248 (346.83)***

-0.3807 (289.72)***

-0.3247 (328.61)***

-0.4053 (137.54)***

-71.2449 (173.56)***

-0.3470 (154.96)***

AGE 0.0005 (10.85)***

0.1383 (18.12)***

0.0004 (12.51)***

0.0005 (9.83)***

0.0005 (11.89)***

0.0001 (2.12)**

0.0591 (4.69)***

0.0001 (1.15)

QSCORE -0.0013 (30.55)***

-0.2856 (38.59)***

-0.0013 (42.23)***

-0.0015 (29.01)***

-0.0015 (40.52)***

-0.0005 (7.04)***

-0.0459 (3.83)***

-0.0005 (9.74)***

NEGWC 0.0385 (33.56)***

4.7977 (25.13)***

0.0288 (33.47)***

0.0401 (32.41)***

0.0301 (32.66)***

0.0357 (14.11)***

4.0065 (9.26)***

0.0274 (14.26)***

NEGEQ 0.0495 (19.44)***

8.2747 (18.72)***

0.0354 (18.70)***

0.0407 (13.17)***

0.0285 (12.79)***

0.0501 (11.51)***

9.1698 (11.58)***

0.0416 (12.27)***

2

NEG_RETEARN 0.0423 (18.23)***

8.4620 (21.11)***

0.0378 (21.73)***

0.0564 (19.76)***

0.0482 (23.29)***

-0.0015 (0.38)

0.1032 (0.15)

-0.0007 (0.22)

CH_RETEARN -0.0018 (1.99)**

-0.4365 (3.07)***

-0.0020 (3.15)***

-0.0009 (0.91)

-0.0011 (1.58)

0.0070 (3.66)***

1.2051 (3.95)***

0.0040 (2.97)***

TLTA 0.0064 (18.46)***

0.7319 (13.08)***

0.0045 (18.87)***

0.0079 (20.21)***

0.0057 (21.46)***

0.0072 (10.46)***

0.8643 (7.65)***

0.0052 (10.78)***

SQRSUBS 0.0079 (7.65)***

1.6067 (8.29)***

0.0075 (8.92)***

0.0194 (13.48)***

0.0159 (14.29)***

0.0009 (0.70)

0.6318 (2.47)**

0.0022 (1.94)*

N_SIC 0.0175 (16.83)***

2.7022 (15.07)***

0.0146 (18.40)***

0.0200 (16.72)***

0.0165 (18.50)***

0.0124 (7.04)***

2.2168 (7.06)***

0.0112 (8.14)***

YE_CHANGE -0.0913 (23.17)***

-11.3912 (18.41)***

-0.0616 (21.04)***

-0.1285 (27.37)***

-0.0933 (26.81)***

-0.0383 (5.20)***

-0.6893 (0.57)

-0.0110 (2.05)**

INDEP -0.0065 (3.65)***

-0.0687 (0.21)

-0.0026 (1.90)*

- - 0.0032 (1.05)

1.7942 (3.16)***

0.0033 (1.33)

LOSS - - - - - 0.0548 (21.39)***

10.9721 (25.08)***

0.0438 (22.74)***

Constant 5.2978 (381.23)***

0.9787 (1.12)

5.4578 (500.16)***

5.2946 (281.20)***

5.4693 (386.17)***

5.1997 (247.98)***

-12.8708 (7.59)***

5.3561 (311.89)***

Industry dummies Yes N/A Yes Yes Yes Yes N/A Yes Observations 1032615 1032615 1032615 909324 909324 224294 224294 224294 R-squared 0.1339 0.1503 - 0.1329 - 0.1375 0.1413 - F-value 4633.46 8585.95 - 4223.59 - 967.40 1678.88 chi-2 - - 169025.23 - 150507.22 35106.49 Robust t statistics in parentheses for OLS (ordinary least squares) regression; robust z statistics in parentheses for count (negative binomial) regression. * significant at 10%; ** significant at 5%; *** significant at 1% Variable definitions are provided in Table 1

Table 4 Count Regressions Results by Size Quartiles

Size Quartile 1 Size Quartile 2 Size Quartile 3 Size Quartile 4

ACCQUAL 0.0045 -0.0003 0.0097 -0.0091 (0.67) (0.05) (1.79)* (2.72)*** PROP_FEMS -0.0492 -0.0558 -0.0461 -0.0376 (18.25)*** (21.41)*** (18.57)*** (14.80)*** LNBRDSIZE -0.0230 -0.0305 -0.0405 -0.0634 (10.10)*** (13.58)*** (19.44)*** (36.17)*** BIG4 -0.0501 -0.0472 -0.0082 -0.0643 (4.04)*** (4.06)*** (1.16) (22.98)*** NONBIG4 -0.0735 -0.0738 -0.0670 -0.0386 (46.11)*** (49.57)*** (46.92)*** (23.52)*** AMENACC 0.0744 0.0798 0.0670 -0.0036 (7.37)*** (8.82)*** (7.99)*** (0.41) QUALIF 0.1504 0.0447 0.0783 0.1224 (8.25)*** (2.73)*** (7.29)*** (35.82)*** POST_BAL 0.0658 0.0701 0.1333 0.0747 (3.32)*** (4.61)*** (17.52)*** (22.10)*** LNTA 0.0063 0.0164 0.0133 0.0143 (6.20)*** (8.57)*** (8.60)*** (23.37)*** NEW_REGIME -0.3332 -0.3292 -0.3266 -0.3118 (162.77)*** (173.62)*** (181.04)*** (178.06)*** AGE 0.0003 0.0016 0.0008 -0.0002 (2.76)*** (14.92)*** (11.03)*** (5.48)*** QSCORE -0.0009 -0.0013 -0.0014 -0.0005 (13.19)*** (18.80)*** (23.92)*** (11.72)*** NEGWC 0.0283 0.0370 0.0310 0.0177 (13.11)*** (20.60)*** (18.75)*** (11.82)*** NEGEQ 0.0220 0.0332 0.0307 0.0380 (4.36)*** (6.51)*** (7.46)*** (13.27)*** NEG_RETEARN 0.0397 0.0447 0.0400 0.0346 (8.40)*** (9.32)*** (10.58)*** (14.27)*** CH_RETEARN -0.0002 -0.0024 -0.0093 -0.0147 (0.28) (1.55) (4.42)*** (5.76)*** TLTA 0.0041 0.0070 0.0114 0.0122 (13.37)*** (10.65)*** (12.44)*** (10.60)*** SQRSUBS 0.0346 0.0329 0.0203 0.0088 (7.16)*** (8.30)*** (7.94)*** (9.12)*** N_SIC 0.0293 0.0250 0.0183 0.0047 (14.63)*** (12.89)*** (10.72)*** (4.09)*** YE_CHANGE -0.1496 -0.1006 -0.0523 0.0089 (20.83)*** (14.69)*** (8.75)*** (2.04)** Constant 5.4501 5.3591 5.4380 5.3066 (193.20)*** (172.61)*** (183.25)*** (324.31)*** Observations 229736 253930 268992 279957 chi2 36512.30 43000.92 45816.84 46791.24 The dependent variable is reporting lag (in days). Table contains negative binomial regressions by size quartiles, where quartile 1 contains the smallest companies and quartile 4 the largest. Robust z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Industry dummy variables are included in all regressions.