Audit Report Timeliness Before and During the COVID-19 Pandemic: Evidence from the Market Reaction

Spence (1973) first put forward the signalling theory, which states that signalling is the company’s effort — as a provider of information — to convey an accurate picture of the problem to other parties outside the company so that they are willing to invest even though they are under uncertainty. Signalling theory applies when the company distributes signals to users of financial statements outside the company. Then, Ross (1977) developed the theory, stating that for a company’s stock price to increase, information must be conveyed to potential investors by its executives. Therefore, through financial reports, the company provides information to parties outside the company that the company is making a profit and has implemented accounting policies according to applicable standards.

Audit report timeliness¹ is the total number of days the auditor takes to complete the audited financial report after the closing date of the company’s books. Ashton, Graul and Newton (1989) define ARL as the duration in which the audited financial report is processed, which is calculated from the closing date of the book until the date of the independent auditor’s report. Several factors may influence audit report timeliness. According to Ashton, Willingham and Elliott (1987), the incompatibility between the auditor and management to agree on the audit results is one of the communication processes that may take longer, resulting in delay in finishing the audit in a timely manner.

In the era of the COVID-19 pandemic, in 2020, OJK decided to relax the deadline for submitting annual financial reports after considering the prevailing conditions due to the COVID-19 pandemic in Indonesia. Therefore, through official announcements and press releases, OJK stated that the date of submission of audited financial statements be no later than May 31, 2020 (previously, the deadline was on Mar. 30), and the submission of annual reports was to be before Jun. 30, 2020 (previously, the deadline was Apr. 30).

The studies of Herdiana (2017), Handoko and Sudarno (2015) and Paramita (2014) concluded that audit report timeliness has a positive influence on investor reactions. Their studies found that the higher the timeliness of audit reports, the higher the market will react because this is good news for investors. In addition, Handoko and Sudarno (2015) suggested that entities that disclose their financial statements timely have better performance and greater ARs.

In other studies, Shulthoni (2013) and Syafruddin (2006) found that audit delay significantly affects investor reactions. Shulthoni (2013) tested the market reaction using two proxies and showed the same results on AR proxies and trading volume activity. These results also align with those of Girsang et al. (2017), who found that audit delay negatively affects investor reactions. Their study results showed that the longer the audit delay, the greater is the investors’ sense of uncertainty in making decisions.

Meanwhile, a research study by Diputra and Anna (2014), using the Kompas 100 Index, concluded that audit delay does not significantly affect investor reactions for 2010–2012 issuers. They argued that the companies listed on Kompas 100 are specifically selected companies and, on average, have good performance. Therefore, investors no longer consider the timeliness of submitting audited financial statements. In addition, research studies by Dewi et al. (2019), Lestari and Nuryatno (2018) and Dwiyani et al. (2017) also found consistent results that audit delay does not have a significant effect on the market reaction. These studies signify that ARL does not affect stock price fluctuations, and there are no differences in the market reactions towards the timely submission of audited financial statements.

Based on the literature review, we conclude that the findings of previous studies are mixed. We argue that longer time taken for submission of audited financial statements by the auditors can have a negative market reaction, considering that the market requires timely information for making investment decisions. In other words, the information content conveyed to the market through late financial statement information can negatively affect the market reactions, measured by the ERC. Therefore, Hypothesis H1 to be tested is as follows:

H1: The market will react negatively to delay in audited financial statements, ceteris paribus.

To the authors’ knowledge, no research studies have examined the market reaction to ARL in the era of the COVID-19 pandemic. We only found previous research studies that examined the level of audit delay during the COVID-19 pandemic, namely research studies from Wijasari and Wirajaya (2021). Wijasari and Wirajaya (2021) found a significant difference in the audit delay before and during the COVID-19 pandemic. They found that the audit delay during the COVID-19 pandemic was much greater. This significant difference was due to the limitations faced by auditors in obtaining audit evidence due to social and physical distancing. Before the pandemic, auditors met face-to-face with clients, tracked records directly and confirmed what needed to be confirmed in person to obtain and collect audit evidence. However, during the pandemic in 2020, all audit procedures significantly changed because auditors could not trace, obtain and collect evidence directly on audit fields and meet with the clients but had to go through virtual meetings and use online media.

Based on the arguments above, we suspect that audit report timeliness during the COVID-19 pandemic will be higher than in pre-pandemic times, and the market will react more negatively to audit report timeliness. Thus, our Hypothesis H2 to be tested is as follows:

H2: The market will react more negatively to delays in audited financial reports in the COVID-19 pandemic era, ceteris paribus.

The study population includes all publicly listed companies on the IDX, except companies in the financial sector, with an observation period of 2018–2020. The sources of the study data are the secondary data obtained from the audited financial reports published on the companies’ official websites, the IDX website (www.idx.co.id) and the Standard and Poor’s (S&P) Global Market Intelligence website (www.spglobal.com/marketintelligence/en/). Statistical data on company stocks of Individual Stock Price Index (Indeks Harga Saham Individual or IHSI) and Composite Stock Price Index (Indeks Harga Saham Gabungan or IHSG) were obtained through the IDX website and Yahoo Finance (www.finance.yahoo.com). We use a non-probabilistic sampling method in collecting our quantitative financial data from the above sources and apply a purposive sampling technique using certain criteria. We obtain 977 firm-year observations as our final sample. Table 1 shows the sample selection process of the study.

IDR, Indonesian Rupiah (Rp); IDX, Indonesia Stock Exchange; S&P, Standard and Poor’s.

The data were processed using the Statistical Software for Data Science (STATA), version 15.0 manufactured by StataCorp LLC. The data analysis methods include descriptive statistical analysis, correlation analysis, classical assumption test, model specification test and hypothesis testing.

Our study develops the information content model using multiple regression models from previous studies, such as those by Collins and Kothari (1989), Balsam, Krishnan and Yang (2003) and Dewi and Herusetya (2015). The empirical research model for testing Hypothesis H1 is as follows:

Model 1 CARit=α0+α1 UEit+α2 UE*ARLit+α3 UE*Sizeit+α4 UE*LEVit+α5 UE*Growit++α6 UE*Lossit+α7 UE*Big4it+α8 ARLit+α9 Sizeit+α10 LEVit++α11 Growit+α12 LOSSit+α13 Big4it+εit $$\matrix{ {\matrix{ {{\rm{CAR}}it = \alpha 0 + \alpha 1\,{\rm{UE}}it + \alpha 2\,{\rm{UE*ARL}}it + \alpha 3\,{\rm{UE*Size}}it + \alpha 4\,{\rm{UE*LEV}}it + \alpha 5\,{\rm{UE*Grow}}it + } \hfill \cr {{\rm{ + \alpha }}6\,{\rm{UE*Loss}}it + \alpha 7\,{\rm{UE*Big4}}it + \alpha 8\,{\rm{ARL}}it + \alpha 9\,{\rm{Size}}it + \alpha 10\,{\rm{LEV}}it + } \hfill \cr } } \hfill \cr { + \alpha 11\,{\rm{Grow}}it + \alpha 12\,{\rm{LOSS}}it + \alpha 13\,{\rm{Big4}}it + \varepsilon it} \hfill \cr } $$

Model 1 is used to test Hypothesis H1. The main variable of our concern is UE*ARL, the interaction variable between unexpected earnings (UE) and the timeliness of audit reports (in terms of ARL). Coefficient α2 is the ERC and is negatively predicted and statistically significant. The prediction is supported if coefficient α2 < 0, which implies that the market reacts negatively to the timeliness of audit reports (ARL). The predictions for each control variable are as follows: α3 > 0, α4 < 0, α5 > 0, α6 < 0 and α7 > 0.

The definitions of the variables for Models 1 and 2, except for the interaction variables, are as follows:

CAR = cumulative abnormal return is the number of ARs of stock i for 12 months after adjusting the market return, from April 1 of the year t to March end of year t + 1; UE = unexpected earnings calculated by earnings per share (EPS) in t minus EPS in year t – 1, then divided by year-end closing price in t – 1; ARL = audit report lag/audit delay/audit report timeliness, calculated using natural logarithm from the number of days after the date of the balance sheet to the date of the audit report; COVID = dummy variable, assigned ‘1’ if the year is 2020 (when the pandemic began); ‘0,’ otherwise; Size = natural logarithm of total assets; LEV = leverage, that is, the debt-to-equity ratio; Grow = sales growth (Sales in year t – Sales in year [t – 1])/Sales in t – 1; Loss = dummy variable, assigned ‘1’ if the company experiences loss in year t; ‘0,’ otherwise; Big4 = dummy variable, assigned ‘1’ if one of the Big Four audit firms (i.e., Deloitte, PricewaterhouseCoopers (PwC), Ernst & Young, and KPMG…) audits the company; and ‘0,’ otherwise; α0 and β0 = constants; εit = residual error; subscripts i and t = identification for company i and year t, respectively.

The empirical research model (Model 2) for testing Hypothesis H2 is as follows:

Model 2 CARit=β0+β1UEit+β2UE*ARLit+β3UE*ARL*COVIDit+β4UE*COVIDit++β5ARL*COVIDit+β6UE*Sizeit+β7UE*LEVit+β8UE*Growit++β9UE*Lossit+β10UE*Big4it+β11ARLit+β12COVIDit+β13Sizeit++β14LEVit+β15Growit+β16Lossit+β17Big4it+εi,t $$\matrix{ {\matrix{ {{\rm{CAR}}it = \beta 0 + \beta 1\,{\rm{UE}}it + \beta 2\,{\rm{UE*ARL}}it + \beta 3\,{\rm{UE*ARL*COVID}}it + \beta 4\,{\rm{UE*COVID}}it + } \hfill \cr { + \beta 5\,{\rm{ARL*COVID}}it + \beta 6\,{\rm{UE*Size}}it + \beta 7\,{\rm{UE*LEV}}it + \beta 8\,{\rm{UE*Grow}}it + } \hfill \cr { + \beta 9\,{\rm{UE*Loss}}it + \beta 10\,{\rm{UE*Big4}}it + \beta 11\,{\rm{ARL}}it + \beta 12\,{\rm{COVID}}it + \beta 13\,{\rm{Size}}it + } \hfill \cr { + \beta 14\,{\rm{LEV}}it + \beta 15\,{\rm{Grow}}it + \beta 16\,{\rm{Loss}}it + \beta 17\,{\rm{Big4}}it + \varepsilon i,t} \hfill \cr } } \hfill \cr } $$

Model 2 is used to test Hypothesis H2. The main variable of our concern is EU*ARL*COVID. Coefficient b3 of the variable EU*ARL*COVID represents the market reaction of the ARL during the COVID-19 pandemic. Therefore, we predict that coefficient b3 is negative and statistically significant. The prediction is supported if coefficient β3 < 0 and is significant, which implies that the market is reacting more negatively to audit report timeliness (ARL) in the COVID-19 era, indicated by the incremental negative coefficient of EU*ARL*COVID towards coefficient β2 (UE*ARL). The expectations for each control variable are as follows: β6 > 0, β7 < 0, β8 > 0, β9 < 0 and β10 > 0.

Table 2 presents the descriptive statistics of all variables used in this study with 977 observations, except for the interaction variable. All continuous variables are winsorised 1% and 99% to avoid data outliers. CAR has a mean value of 0.2832, with a standard deviation of 9.1412. The average company in our sample profile has a CAR rate of 28.32% for 1 year, with a minimum rate of −234.55% and a maximum rate of 182.67%. This large maximum value could be attributed to the year 2018; the average CAR in 2018 was very high due to the possibility of a market error on the IDX in May 2018, which caused the IHSI of all companies in May 2018 to be very high, thus affecting the 2018 CAR. In Table 2, the UE has a mean value of 0.0001, with a standard deviation of 0.1565.

Audit report lag in days (ARLD) has a mean value of 97 days, indicating that, on average, an auditor takes 97 days to issue an audit report for all observations. The minimum number of days for auditors to issue the audit report is 29, and the maximum is 330 days for all observations in 2018–2020. Specifically, in 2020, the mean and maximum numbers of days taken byauditors to issue audit reports were 106 days and 272 days, respectively, while in 2018–2019, the same were 92 days and 330 days, respectively. The mean ARLD for 2020 was higher than the ARLD for 2018 and 2019 (pre-COVID-19). We suspect that it was due to the COVID-19 pandemic in 2020 that auditors needed more time and more audit efforts to collect appropriate and sufficient evidence, considering the health protocol regulations due to the spread of COVID-19. The OJK extended the deadline in the COVID-19 pandemic era to 150 days for 2020 (OJK, 2020). Assets is the total number of assets (in millions of Indonesian Rupiah [IDR], symbolRp) in the observations of the study sample, with a mean of Rp 9,975,840 million. The sample has a minimum value of Rp 3,266 million for the smallest company and Rp 352,000,000 million for the largest company. The mean, minimum and maximum for the other variables can be seen in Table 2.

In Table 3, the results of the correlation analysis with pairwise correlation show that UE positively correlates with CAR (ρ = 0.0543), which is significant at the 10% level. ARL positively correlates with CAR and is significant at the 1% level. The control variable LEV has a negative correlation with CAR at the 10% level, and LEV also has a positive correlation at the 1% level with Size. Meanwhile, the variable Big4 positively correlates with CAR at 10%. Big4 also has a negative correlation with ARL and Loss and a positive correlation with Size, significant at the 1% level. COVID negatively correlates with the significant variable CAR at the 1% level. COVID also has a positive and significant correlation at the 1% level with UE, ARL and Loss and a negative correlation with the variable Grow. Other variables do not appear to correlate with the other independent variables in the empirical model.

We performed preliminary tests to meet the classical assumption of best-unbiased estimators and model specifications concerning the regression models used. Our preliminary tests passed the required assumptions to proceed with the hypothesis testing for Models 1 and 2. Table 4 presents the results of hypothesis testing for empirical Model 1, and Table 5 reports the results for empirical Model 2 with 977 firm-year observations as our full sample.

Hypothesis H1 testing was carried out to test whether the timeliness of audit reports has a negative effect on the market reaction. Table 4 summarises the regression results of testing Hypothesis H1, along with robust standard errors. These results indicate that the main variable of our concern, namely UE*ARL, has a coefficient of −5.28, not significant at 10% (t-test= −1.26, probability = 0.206 > 10%) with a two-tailed test. These results indicate that the timeliness of audit reports (ARL) does not affect the market reaction measured by the ERC. Thus, we conclude that Hypothesis H1 is rejected because there is no significant difference in the market reaction towards the audit report timeliness (ARL).

Using the 2018–2020 observations as our full sample, our results are in line with the results of previous studies by Dewi et al. (2019), Lestari and Nuryatno (2018), Dwiyani et al. (2017) and Diputra and Anna (2014). These studies found no evidence of a market reaction towards ARL. However, our results are not in line with the findings of Girsang et al. (2017), Herdiana (2017), Handoko and Sudarno (2015), Paramita (2014), Shulthoni (2013) and Syafruddin (2006). In several test results of control variables, among others, LEV has a negative effect on the market reaction (t-test = −2.50, probability = 0.013), significant at the 5% level. However, Size, Grow, Loss and Big4 are insignificant in our 2018–2020 observation period.

Based on these results, we conclude that, for capital market players, differences in the number of days taken for submitting audit reports are not very relevant and do not affect stock price fluctuations (e.g. Dewi et al., 2019; Dwiyani et al., 2017; Lestari & Nuryatno, 2018). Another alternative explanation is that the delay in submitting the audited financial statements by the auditor is not too long in relation to the expected deadline, so the market and users of financial statements can still tolerate it. Furthermore, the capital market player has also realised that a regulation regarding late submission of audited financial reports, which brings consequences to the listed firms in the form of warning letters from the OJK and being subjected to sanctions in the form of fines, is not in place. Therefore, the market is not concerned about the delay of audited financial statements.

Hypothesis H2 testing was carried out to test whether ARL has an increasingly negative influence on the market reactions in the COVID-19 pandemic era. Table 5 summarises the regression results with robust standard errors that have met the requirements to test the hypothesis.

The main variable of concern in Model 1 is UE*ARL*COVID, which has a coefficient of 36.60 and a significance level of 0.092 with a two-tailed test. The result shows that the market reacted more positively to ARL in the COVID-19 era at 10% (probability = 0.092 < 0.10) than in the pre-COVID-19 period. In other words, the positive incremental information provided by capital market players is reflected in the ERC of the EU*ARL*COVID interaction variable. These results are different from the expectations of Hypothesis H2. Thus, Hypothesis H2 is rejected because it is not in line with the initial prediction, whereby ARL has a positive influence on the market reactions in the COVID-19 era. Finally, the test results of control variables show that all control variables, such as LEV, Grow, Size, Loss and Big4, were insignificant at the 10% level towards the market reaction, indicating that these interaction variables are not significant.

Based on the test results above, we conclude that the market reacted positively to the delay in audited financial reports in the 2020 COVID-19 pandemic era in Indonesia. The results of our study may indicate that the market understands the situation and conditions of the COVID-19 pandemic, which affects all aspects of life, including the timeliness of submitting audited financial statements. Therefore, the market participants may tolerate auditors requiring more audit efforts and a longer period during a pandemic to issue independent audit reports than in normal conditions. Our results align with the findings of Wijasari and Wirajaya (2021), who reported a significant difference in audit delay before the pandemic and in the COVID-19 pandemic era. The audit delay during the COVID-19 pandemic is greater than in the era before the pandemic. More audit efforts are needed because there are limitations in obtaining audit evidence due to a series of health protocols and social distancing rules. The COVID-19 condition in 2020 implies that the market can be more tolerant of time extensions for submitting audited financial statements so that the market reacts positively to the delays of audited financial statements.

This study finds no significant difference in the association between the audit report timeliness and the market reaction measured by the ERC using a full sample from 2018 to 2020. Therefore, our results indicate that the market does not react to the timely submission of audited financial statements, or there is no significant difference in the market reaction whether auditors submit audited financial statements timely or not. These results are consistent with the results of previous studies, for example Dewi et al. (2019), Lestari and Nuryatno (2018) and Dwiyani et al. (2017).

However, our study finds weak evidence that the market reacted more positively to the audit report timeliness during Indonesia’s COVID-19 pandemic in 2020 than in the pre-COVID-19 period (2018–2019). The results show that the market tolerates the conditions during the pandemic where auditors need more audit efforts to obtain sufficient and appropriate evidence to publish audited financial reports. Our study implies that the market assesses information content from the submission date of audited financial statements due to the company’s sustainable performance and the quality of audited financial reports, which comprise very important information for capital market players, especially during the COVID-19 pandemic.

This study has some limitations. First, this study’s CAR calculation is suspected of containing market error data on individual stock price indexes in 2018, affecting the study results. The trade war between the United States and its counterparts had made the world financial markets chaotic, thus affecting the statistical data on the stock transactions in Indonesia. Second, the observation period for the COVID-19 pandemic is only 1 year, namely 2020, which could also affect and coincide with the study results, compared to observations of the pre-COVID-19 years. Future studies may consider these limitations.