The Key Determinants of Audit Fees: An Analysis of Russell 3000 Companies for the Period 2001–2023

1. Introduction

Audit fees generally represent the costs incurred by a business for the assurance services provided by an auditing firm (Owusu & Bekoe, 2019). Fees paid to public accounting firms for audits, audit-related matters, taxes, and other accounting services are a significant and unavoidable expense that consistently increases.

Auditor independence is critical to the performance of this quality assurance role. Recent corporate scandals (e.g., Enron and the collapse of Arthur Andersen, Wirecard’s insolvency, the negligence of EY in Germany and Carillion in the UK, and KPMG’s negligence) have raised concerns and prompted academic interest in the independence of external auditors and the conduct of quality audits (Owusu & Bekoe, 2019). Among the issues considered to impair auditor independence are audit fees and the audit firm’s economic dependence on a client (Owusu & Bekoe, 2019).

Reliability placed on a company’s financial statements depends largely on the auditors’ assertions (Owusu & Bekoe, 2019), which are summarized in their audit reports (“the final product of the audit/assurance process”). Over the years, studies have used audit fees as an agent for audit quality and auditor independence, among others (Bently, Omer, & Sharp, 2011; Hoitash, Markelevich, & Barragato, 2007; Owusu & Bekoe, 2019). Auditors’ fees can, to some extent, affect the quality of the services provided by the auditors (Owusu & Bekoe, 2019). High fees paid to auditors may “force” them to put more effort into delivering audit services, thereby increasing audit quality. Conversely, such high fees may make the auditors “economically” reliant on a particular client which may also compromise their independence (Hoitash et al., 2007; Owusu & Bekoe, 2019).

Analyzing fees by industry, company size, regulatory environment, and location can provide insights into the level of risk and the auditor effort required by diverse sectors of publicly listed companies. Studies suggest that auditors charge higher fees for these three fee determinants because larger, more complex, and riskier firms require more auditing resources.

This study aimed to determine the key determinants of audit fees and the extent to which they affect audit fees, focusing on the most important accounting variables (assets, earnings, revenue, and book value) that express the size and financial condition of an audited company. Size and financial position can be considered key information for calculating audit risk. It also checks the effect of other companies’ audit fee characteristics, such as market capitalization level, industry, and the effectiveness of internal audit controls as expressed by the existence of key audit matters (KAMs). Finally, excluding audit characteristics, this study examines how audit fees are affected by auditor size. This study addresses a gap in existing research by analyzing the combined impact of accounting indices, KAMs, and auditor size on audit fee determination over an extended period, providing a comprehensive sectoral analysis of the Russell 3000 companies.

2. Literature Review

Several factors determine audit fees, including corporate size, audit firm characteristics, industry type, and client risk profile. These determinants may vary according to context and jurisdiction.

Corporate size remains a primary determinant. Larger firms generally require more extensive audit work due to their operational complexity and volume of transactions (Simunic, 1980; Owusu & Bekoe, 2019). While effective internal controls may reduce audit testing, they do not necessarily reduce audit time and effort (Stewart & Munro, 2007). Widmann et al. (2021) confirmed that total assets and the number of business segments are significant predictors of audit fees, affirming the influence of firm size and complexity.

Audit firm size and status also affect fees. Large audit firms, particularly the Big 4, are perceived as providing higher-quality services and, as a result, charge higher fees (Owusu & Bekoe, 2019). The higher litigation risks and competitive environment faced by large firms contribute to this fee premium (Al-Harshani, 2008). Consequently, audit clients of larger firms generally face higher audit costs compared to those using smaller firms (Owusu & Bekoe, 2019).

Industry type is another critical determinant. Companies operating in highly regulated sectors, such as financial services or healthcare, tend to incur higher audit fees due to complex reporting requirements and increased audit procedures (Owusu & Bekoe, 2019). Manufacturing firms also typically pay higher fees owing to transaction complexity and public pressure regarding social and environmental reporting (Camfferman & Cooke, 2002; Tagesson et al., 2009). Industry-specific risks and reporting complexity increase audit effort and, consequently, audit fees (Owusu & Bekoe, 2019).

Client risk is widely recognized as influencing audit fees. High-risk companies, whether due to volatile market conditions, financial distress, or weak internal controls, require additional audit procedures to manage audit risk (Owusu & Bekoe, 2019; Wong, 2009). Auditors assess client-specific risks, including the likelihood of material misstatements, to determine the necessary audit scope (Chan et al., 1993). As risk increases, so too does the audit effort required, leading to higher fees. Gu (2021) demonstrated that firms with high foreign direct investment (FDI) levels and industry homogeneity face higher audit fees due to increased audit complexity and risk.

The role of profitability remains less clear. Some studies suggest that profitable companies pay higher audit fees due to increased auditor scrutiny of performance figures, especially revenue recognition and expenditure completeness (Owusu & Bekoe, 2019). Conversely, less profitable firms may also pay higher fees due to weakened internal controls and increased audit effort (Chan et al., 1993).

Further insights are provided by recent studies. Sun and Habib (2021) highlighted that auditor-provided tax services (APTS), while potentially improving audit efficiency, may compromiseauditor independence. Habib et al. (2020) emphasized that firms with significant organizational capital incur higher audit fees due to the need for more extensive audit work. Carson et al. (2022) identified that multinational audits relying on component auditors lead to higher fees and potentially higher audit quality as local insights contribute to more thorough audits.

In summary, existing literature consistently identifies corporate size, audit firm status, industry characteristics, and client risk as the primary determinants of audit fees. Profitability, organizational complexity, and market-specific factors also influence fee structures, but to a lesser extent. These findings underline the multifactorial nature of audit fee determination.

3. Data

Our sample comprises all companies in the Russel 3000 index. which represents approximately 97% of the American public equity market. Our data cover the period 2001–2023 and were extracted from the Ideagen Audit Analytics database. We excluded companies in the financial sector. The sample size is shown in Table 1.

Notes for tables:

• Significance levels are denoted by * (p < 0.1), ** (p < 0.05), and *** (p < 0.01).
• T-statistics are reported in parentheses, hereinafter referred: (T)
• Clustered standard errors, hereinafter referred: (CSE)

The following accounting variables were extracted from the database:

• Audit Fees
• Revenue
• Earnings
• Total Assets
• Book Value

We then derived the following variables:

• Audit Fees / Revenue
• Audit Fees / Assets
• Audit Fees / Earnings

Table 2 presents the variables’ descriptive statistics and Table 2a lists the relevant correlation matrices.

4. Methodology

As previously stated, this study concentrated on the level of audit fees and the factors influencing it. Specifically, this study zeroed in on the impact of a variety of variables on the level of audit fees to ascertain the way these factors are used to determine the audit fees. The following accounting variables that characterize the audit risk of audited firms (the clients of the audit company) are the primary factors considered when determining audit fees:

• Revenue
• Earnings
• Total Assets
• Book Value.

Therefore, we constructed the following models:

Audit Fees = a + b1 * Earnings 1A

Audit Fees = a + b1 * Total Assets 1B

Audit Fees = a + b1 * Book Value 1C

Audit Fees = a + b1 * Revenue 1D

Audit Fees = a + b1 * Total Assets + b2 * Earnings + b3 * Book Value + b4 * Revenue 2A

For each model, we also studied the effect of these variables on audit fees as a percentage of Total Assets, Earnings and Revenue, constructing relevant sub-models in which Audit Fees are substituted by Audit Fees / Assets, Audit Fees / Revenue, and Audit Fees / Earnings.

Next, we chose the best of all the above models and used them to split our original sample into the following subsamples:

Split A based on company size: S&P 400 – S&P 500 – S&P 600

Split B based on sector as per NAICS code

Split C existence of key audit matters (KAMs) in previous period

Split D size of audit firm (Big4 – Big6 – Other)

We examined the effect of these independent variables on the audit fee level (dependent variable) using econometric analysis, panel data analysis, and the GMM method.

5. Data Analysis

The model in Table 3 illustrates how an audit client’s earnings influence the audit fees charged.

Model 1A_1 shows a strong positive relationship between client earnings and audit fees. The Earnings coefficient is positive and statistically significant. Furthermore, the model’s predictability at the audit fee level is very high, with an Adj R2 value of approximately 34%. This indicates that a company’s earnings affect audit fees similarly.

Consequently, we may conclude that during the client acceptance phase, audit organizations consider that audits for clients with higher profits result in more audit work, increasing the audit cost; hence, the audit price must be increased. This is rational and consistent with audit accounting theory, which states that higher earnings frequently indicate higher audit risk (as inherent and detection risks grow).

This is because larger companies with higher risk characteristics typically generate higher earnings through more transactions, higher turnover, more complex and valuable assets, investments in associates and subsidiaries, large bank loans, issued bonds, related transactions, and public interest companies. All these qualities have a major effect on audit risk (particularly inherited risk).

Consequently, audit organizations must execute more audit procedures to in anticipation of increasing audit risk, resulting in longer audit hours and higher expenses.

This positive link may be due to commercial criteria in addition to the audit firm’s appraisal of audit risk. Audit firms may charge higher rates to financially wealthy organizations because such organizations do not especially care to to pay high audit fees. Audit costs are immaterial to companies and CFOs do not want to waste time negotiating discounts. Consequently, audit businesses demand high audit costs because they believe there is little or no resistance to them.

The statistically substantial negative association between Audit Fees as a percentage of Client Revenue and Client Earnings, as provided by Model 1A_2, is consistent with the findings of Model 1A_1, and was expected. In most circumstances, revenue exceeds earnings and an increase in revenue results in substantially smaller earning gains. Therefore, based on mathematics, it is natural that Audit Fees / Revenues and Earnings are negatively associated, considering that Audit Fees are insignificant compared with Revenue and Earnings. Consequently, the more lucrative a firm, the less important audit fees are connected to its revenues, even if the audit costs increase in currency terms. Audit fees have become increasingly insignificant. This could be an argument auditing firms use to encourage clients to pay higher audit rates.

For almost the same mathematical rationale, Model 1A_4 shows a negative relationship between Audit Fees as a proportion of Client Earnings. Since Audit Fees are small compared with Earnings, an increase in Earnings results in a drop in the Audit Fees / Earnings variable. The increase in the denominator is significantly greater than that in the numerator.

Model 1A_3 shows a statistically significant association between a client’s earnings and audit fees as a percentage of Total Assets. Given that audit fees are nearly always a small percentage of a company’s overall assets, any change in audit fees due to profitability is negligible compared with total assets. The indication of a negative association can be explained.

Model 1A_3 shows a negative association between client earnings and audit fees as a percentage of total assets. First, we must remember that Assets and Earnings are often positively connected. Companies facing financial difficulties typically have outdated assets with lower net book values (due to cumulative depreciation) or devalued assets because of impairment losses, or sell their assets in anticipation of cash flow problems or to generate working capital. Conversely, financially healthy corporations tend to invest in new assets (with high net values) and in long-term equity investments and subsidiaries, and do not anticipate impairment losses. Audit fees are always small compared with assets. Therefore, based on this (positive correlation and small levels of audit fees compared with total assets) and for the same logical reason explained earlier, this negative correlation is rational and conforms to the findings of Model 1A_1.

Finally, the predictive ability of Models 1A_2, 1A_3, and 1A_4 was significantly lower than that of Model 1A_1. This suggests that the client’s earning level can reliably explain fluctuations in Audit Fee behavior; however, the behavior of other variables (e.g., Audit Fees as a percentage of another accounting variable) cannot be fully explained by earnings. This was predicted and rational because the other variables were considerably affected by other factors, except for Earnings, which affected their denominators, as previously mentioned.

The model in Table 4 shows how an audit client’s net asset value affects the audit fees charged by the auditor.

Model 1B_1 shows a strong and beneficial correlation between a client’s net asset value and audit fees. The coefficient of asset net value is positive and statistically significant. Furthermore, the model’s prediction of audit fee levels is excellent, with an Adj R2 value of approximately 59%. This suggests that a company’s assets have a significant positive correlation with audit fees.

During the client acceptance process, audit companies assume that clients with higher-value assets require more thorough audit work, resulting in higher audit charges. Consequently, audit fees must be increased. This is consistent with audit accounting theory, which states that a large number or high value of assets typically correlates with increased audit risk due to increased inherent and detection risks. To prepare for increased audit risk, audit firms must perform additional audit procedures, resulting in the increased number of hours necessary to perform the audit, and concomitant higher costs.

The other three variables, which express Audit Fees as percentages of Revenues, Assets, and Earnings, have coefficients that are not statistically significant and nearly zero, and the model’s predictability, as expressed by the Adj R-squared, is nearly zero. This suggests that assets have almost no effect on these factors.

Regarding book value as a driver of audit fees, Model 1C in Table 5 clearly shows no effect on audit fee levels. The beta coefficients in all sub-models are either statistically nonsignificant or almost zero. Furthermore, all models have immaterial explanatory capacities, as indicated by Adj R2.

However, is this discovery rational and predictable? Can we conclude that book value is not a factor in audit risk? Can we conclude that a company with a negative book value has the same level of risk as one with a positive book value? Based on common logic and corporate finance theory, the answer is no.

A corporation with a negative book value cannot be classified as having typical audit risk. A negative book value indicates accumulated losses, lack of cash, high leverage, and even potential going-concern troubles. All these elements influence audit risk.

Consequently, we conclude that a negative book value may be a significant variable negatively associated with audit fees. This notion is strongly supported by Panel B of Table 5. Panel B shows athat the Book Value coefficient is statistically significant. Additionally, the model’s explanatory power (including only negative book values) is over 1460% higher than that of the previous model in Panel A, indicating that the more negative the book value (the greater the absolute value), the higher the audit costs as risk increases.

However, we find that a positive book value is associated with higher audit fees. The larger the company (as measured by its book value), the greater the inhered risk and and thus, the higher the costs. We must state that the correlation between audit fees and negative book values is over 400% greater than that between audit fees and positive book values. This indicates that negative book values are far more important than positive book values in determining audit fees. This is understandable, as large negative book values contain the same size complexity as positive book values and audit risk due to the company’s financial difficulties.

Consequently, the effect of negative values on audit fees is offset by the opposite effect of positive values, making book values statistically negligible.

Regarding revenue (turnover) as a predictor of audit fees, Model 1D in Table 6 illustrates how client income affects audit fees. Greater revenue results in higher audit fees, which is rational and consistent with audit theory. The revenue level influences audit risk. Revenue level influences inherent risk because more sales transactions typically result in more complex transactions, a large number of diverse clients, larger trade receivable amounts, bad debts, and fraud risk. Furthermore, the revenue level may influence control risk because a revenue cycle with a high turnover level and many transactions is more difficult to monitor through internal audits. Finally, the enormous volume of transactions increases detection risk.

Table 7 displays the coefficient of each variable and the model’s explanatory power when each variable is the sole independent variable in the audit fee model.

According to Table 7, assets have the greatest influence on audit fees, followed by revenue (turnover). This is logical and to be expected. Both variables are perfect indicators of company size.

Asset level is a reliable indicator of a company’s size. As a company grows, audit risk increases, necessitating more audit work. Additionally, a company’s revenue volume serves as a reliable indicator of transaction volume. Generally, the more transactions, the higher the inherent risk, entailing more audit work. Thus, the audit fees increase.

In Table 8, the results of Model 2A regarding the effect of each accounting factor on audit fees are consistent with those of Models 1A, 1B, 1C, and 1D.

The only difference lies in revenue. Specifically, while the revenue coefficient in Model 1D_1 is statistically significant and has a non-zero value, the revenue coefficient in Model 2A_1 is also statistically significant but has a nearly zero value, implying that the model has little effect on audit fees. In this approach, revenue adds nothing to the explanatory words. This means that when one or more of the other variables in the model are present, the Revenue variable becomes unimportant. In model 2B, we discovered that assets are the variable that makes revenue irrelevant in subsequent models.

The most significant conclusion is that the model’s explanatory power, as measured by Adj R2, is 64% (for sub-models with Audit Fees as the dependent variable), which is greater than the explanatory power of all preceding models. The model containing all accounting variables appears to be the most effective in terms of explanatory power.

In summary, all variables (net assets, book value, earnings, and revenue) seem to have a considerable impact on audit fees.

When Book Value includes all values (positive and negative), it apparently is inconsequential. However, when the variable contains only positive or negative variables, it becomes statistically significant, with negative values having a greater impact on Audit Fees.

Book Value, when containing both negative and positive variables, is statistically indifferent. Therefore, it is logical to not include it in a model with other variables.

To include Book Value, we must create two sub-models for our model (Model 2A): one model with only an absolute value of Book Values to capture the effect of company size as expressed by book value on audit fees, and one that includes Book Value using dummy variables to detect the entire effect of book value on audit fees.

Table 9 and Table 10 present these models.

The positive book value, which indicates a company’s size and its impact on audit risk, is statistically nonsignificant. The other variables likely provide a better explanation of the information (company size) than this value. Regarding negative book value, except for company size, expressing audit risk because of going-concern issues is statistically significant. Given the statistical insignificance of positive book values, we conclude that book value only serves as a useful tool to explain audit fees when it incorporates going-concern risk, which only occurs when it is negative.

The next model confirms this conclusion, stating that absolute book value, which solely represents company size, is statistically nonsignificant.

Next, we created a model (Model 3A in Table 11) that includes only the variables that remained statistically significant for all values, that is, Assets, Earnings, and Revenue.

The model’s results are consistent with those of earlier models, and all variables that affect audit fees are statistically significant.

Table 12 summarizes the explanatory power of each model.

Therefore, we conclude that the model that best describes the audit fees is Model 2.

We avoided including Book Value in our model because we must use only negative book values. This may lead to biased results because companies with negative book values are not equally shared between groups.

This is somewhat perplexing in terms of the outcomes and analysis. Therefore, we based our research on F(x) = (Assets, Earnings, and Revenue). Furthermore, as Table 12 illustrates, the explanatory powers of both models are nearly identical (a 0.97% difference).

Thus, using Model 4, we proceeded to the cluster analysis, which examined the effects of sector, market, KAMs, and auditors.

Table 13 presents the regression results of our model for each industry sector. Industry classification was based on two-digit NAICS codes.

Table 14 shows the significance (V) of the independent variables for audit fees in each industry with the order of explanatory power.

A regression analysis conducted across industries indicates that the determinants of audit fees significantly vary by sector.

Assets appeared to be the most consistent driver of audit fees, showing statistical significance across nearly all sectors, except Transportation, where only Revenue was significant (R-squared 0.6293).

The significance of fixed assets is logical and expected. Fixed assets are the key indicators of a company’s complexity and audit risk. Typically, company size is described by its asset value. The larger the company, the greater the audit risk; therefore, more audit work must be done. Additionally, large-value fixed assets are usually difficult to audit. Impairment loss is usually a key audit risk, and any possible impairment loss may significantly affect financial statements. Auditing this possibility is usually difficult and time-consuming for auditors.

The transportation industry is heavily reliant on physical infrastructure and revenue rather than asset size, which plays a larger role in determining audit complexity. This is logical and expected because these industries rely on fixed assets. Fixed assets are the most important element on the balance sheet and, in most cases, constitute over 80% of a company’s total assets. The profitability of these industries is closely related to fixed assets. The condition of fixed assets and their ability to create revenue for a company are best described by their fair value rather than their relative static accounting value. Therefore, users of financial statements (including auditors) are primarily interested in the fair value of these assets. The fair value, rather than the book value, of these assets best describes a company’s audit risk.

Earnings were statistically significant only in the Mining, Utilities, and Construction, and Information sectors. The limited significance indicates that profitability is essential for deciding audit fees in these businesses, probably because of increased scrutiny of financial performance and the risks linked to earnings volatility. Conversely, profitability did not appear to have a significant impact on audit fees in most sectors. Gains and losses may be inversely correlated with audit fees such as book value.

Revenue proved substantial in four sectors: Mining, Utilities, and Construction; Manufacturing; Retail; and Information. The importance of revenue in various sectors indicates that elevated sales volumes, which potentially result in more intricate revenue-recognition challenges, necessitate more comprehensive audit procedures, thereby augmenting audit effort and costs.

The explanatory capacity of the regression models, denoted by the R-squared values, also fluctuated. The Information sector scored the highest with an R-squared value of 0.7406, signifying that the model accounted for approximately 74% of the variance in audit fees. The significant explanatory power may indicate the intricacy of financial reporting in this industry, encompassing intangible assets and swift technology advancements that necessitate more thorough audits. The Retail and Manufacturing sectors ranked second and third, respectively, owing to their massive operations and complicated inventory management, which require thorough audits. Conversely, sectors such as Scientific, Management, and Administrative Services exhibited the lowest explanatory power (R-squared = 0.3387), indicating that additional, unconsidered factors may substantially affect audit fees in these fields.

Table 14a depicts the average ratios of audit fees to assets, earnings, and revenue across industries, emphasizing significant variations. The “Scientific, Management, and Administrative Services” sector demonstrates the highest audit fees in relation to assets (0.1914%) and earnings (5.9825%), indicating increased audit difficulties and regulatory requirements. Conversely, the “Information” sector recorded the lowest audit fees relative to assets (0.0668%), although it possessed one of the greatest ratios in relation to earnings (5.8140%), suggesting diminished asset bases but potentially elevated profitability influencing audit fees. The “Real Estate” sector incurred the greatest audit fees relative to revenue (0.3025%), which is likely attributable to intricate revenue recognition challenges. Conversely, the “Retail” sector consistently exhibited some of the lowest ratios across all parameters, particularly in audit fees relative to sales (0.0631%), potentially indicating simpler financial arrangements. These variances highlight that industry-specific factors, including financial complexity, regulatory conditions, and intrinsic risk profiles, profoundly affect audit fee structures.

Regarding the average audit fee level, the Manufacturing and Information sectors incurred the largest average fees, around $4,170,301 and $4,063,637, respectively. Conversely, the “Other services” category exhibited the lowest average fee at $1,739,366. Fluctuations in fees may indicate sector-specific intricacies or regulatory mandates. Sectors such as mining, utilities, and construction as well as scientific, management, and administrative services recorded fees nearing $3 million, signifying modest audit cost levels.

Next, Table 15 presents the analysis for each market cluster: S&P 400, S&P 500 and S&P 600.

In all clusters, assets and revenue had a statistically significant impact on audit fee levels. However, earnings did not appear to have any influence. In more uniform market capitalization samples, earnings do not appear to substantially affect audit fees. Revenue appears to be a better risk indicator than earnings, which is logical. Market capitalization is strongly tied to earnings or earnings movements. Companies in the same market cluster have the same market capitalization range, suggesting that their earnings levels and movements are similar. Consequently, earnings for these organizations have become a less essential aspect of their diversification in terms of financial performance and audit risk, prompting financial statement users to focus on other criteria. Finally, the model’s explanatory power increases with company size (as measured by market capitalization). This is because auditors in larger organizations can quantify or estimate audit risk using financial statements. Smaller organizations face more complex risk-detection challenges, with auditors requiring more information than financial statements alone. Additionally, based on the value of the constant c, we observed that audit firms’ fee levels (in dollar terms) rose congruent with increases in the size of the corporation, which is logical. As firms grow, auditors must perform more work.

Table 15a presents the audit fee levels as a percentage of firm size between market clusters.

Table 15a. Audit Fees level in between Market Clusters.

	S&P 600	S&P 400	S&P 500
E( Audit Fees)	1,676,307	2,768,582	7,247,867
E( Audit Fees / Assets)	0.156868%	0.112287%	0.060250%
s ( Audit Fees / Assets)	0.142285%	0.106318%	0.067297%
E( Audit Fees / Earnings)	5.298458%	3.297788%	1.456946%
s ( Audit Fees / Earnings)	11.486491%	7.972432%	4.699062%
E( Audit Fees / Revenue)	0.178635%	0.142383%	0.082978%
s ( Audit Fees / Revenue)	0.185377%	0.149813%	0.088555%

Table 15a. Audit Fees level in between Market Clusters.

	S&P 600	S&P 400	S&P 500
E( Audit Fees)	1,676,307	2,768,582	7,247,867
E( Audit Fees / Assets)	0.156868%	0.112287%	0.060250%
s ( Audit Fees / Assets)	0.142285%	0.106318%	0.067297%
E( Audit Fees / Earnings)	5.298458%	3.297788%	1.456946%
s ( Audit Fees / Earnings)	11.486491%	7.972432%	4.699062%
E( Audit Fees / Revenue)	0.178635%	0.142383%	0.082978%
s ( Audit Fees / Revenue)	0.185377%	0.149813%	0.088555%

According to Table 15a, the larger the company, the lower its audit fees as a percentage of its size. Although audit fees increase in monetary terms with client size, as shown in the first line, the audit fee-to-client size ratio cannot remain constant. This is because there are minimum audit fees; for smaller organizations, a fee of $100,000 may be equivalent to 5% of total assets. However, if a company’s total assets exceed $1 billion, it is illogical to assume that it must pay an inordinate audit cost of $50,000,000 (5% of its assets). Audit work and risk do not increase linearly with client size.

Table 16 shows the analysis for each audit company cluster.

As reflected in Table 16, the outcomes for the Big 4 and Big 6 groups were nearly identical. This was expected, given the modest differences between the groupings in terms of the included companies. The Big4 audit firms service the majority of companies; hence, both clusters contained a large proportion of the same companies. Additionally, BDO and Grant Thornton are large firms that adhere to the same policies as the Big4. The distinction between the Big4 (or Big6) and “Other” clusters is interesting.

We observed that large audit firms relied more on financial statements than smaller firms. This can be due to the following reasons:

·

Big4/6 enterprises have larger clients and base audits more on financial statements.

·

Big4/6 organizations can better assess audit risk using financial statements.

· Big4/6 firms base pricing on client accounting factors and use automated audit methods. Conversely, smaller organizations price their clients based on characteristics and criteria other than accounting. For example, a small company prices a customer based on the time of year in which the audit work must be completed. As smaller companies do not have many employees, the timing of the audit is critical to their annual work calendar. Furthermore, smaller organizations, which have considerably fewer clients than larger ones, use individualized pricing for each consumer.

Regarding the level of audit fees among clusters, Table 16a illustrates the audit fee level as a percentage of firm size and auditor size .

Table 16a. Audit Fees level in between Auditors' Clusters.

	BIG4	BIG6	Other	BIG4 – Other Difference%
E( Audit Fees)	3,890,868	3,656,338	543,500	616%
E( Audit Fees / Assets)	0.125080%	0.133450%	0.181692%	-31%
s ( Audit Fees / Assets)	0.139568%	0.148367%	0.198530%
E( Audit Fees / Earnings)	4.194599%	4.417831%	5.168981%	-19%
s ( Audit Fees / Earnings)	10.429624%	10.799593%	11.930431%
E( Audit Fees / Revenue)	0.157860%	0.166266%	0.250280%	-37%
s ( Audit Fees / Revenue)	0.189193%	0.195718%	0.281399%

Table 16a. Audit Fees level in between Auditors' Clusters.

	BIG4	BIG6	Other	BIG4 – Other Difference%
E( Audit Fees)	3,890,868	3,656,338	543,500	616%
E( Audit Fees / Assets)	0.125080%	0.133450%	0.181692%	-31%
s ( Audit Fees / Assets)	0.139568%	0.148367%	0.198530%
E( Audit Fees / Earnings)	4.194599%	4.417831%	5.168981%	-19%
s ( Audit Fees / Earnings)	10.429624%	10.799593%	11.930431%
E( Audit Fees / Revenue)	0.157860%	0.166266%	0.250280%	-37%
s ( Audit Fees / Revenue)	0.189193%	0.195718%	0.281399%

Large audit firms charged their clients less per unit size (asset value, earnings, or revenue) than small audit firms. This does not imply that large corporations pay less for audits than small corporations. The costs charged by large audit companies are typically significantly greater in dollar terms, as shown in the first line. One probable explanation is that smaller audit firms focus on the market for smaller businesses (clients), which is not the primary market for larger audit firms. Furthermore, stronger human connections exist between auditors and audited organizations in this industry. Audits are easier in this industry, and corporations value personal relationships more than consultant skills because they do not have to deal with complex challenges. Consequently, smaller audit firms demand higher fees (percentage-wise) than larger firms.

Finally, regarding the KAMs in the previous year, Table 17 presents the regression findings for each cluster.

As seen in both cases, the model produced essentially identical outcomes.

The explanatory power in both scenarios is practically the same. Furthermore, audit fees are affected by asset value, but earnings have no effect. The main distinction is that when KAMs are present, revenue appears to have no effect on audit fees. A noteworthy discovery is the value of the constant – the constant value for the KAM existence cluster is greater than that of the No-KAM cluster, which suggests that the audit fee level for the KAM cluster is higher. Table 17a presents the audit fee level as a percentage of firm size and auditor size in the KAM clusters.

Table 17a. Audit Fees level in between KAM Clusters.

	KAM	No KAM	KAM - No KAM Difference%
E( Audit Fees)	4,527,783	3,099,652	46%
E( Audit Fees / Assets)	0.1071908%	0.1459421%	-27%
s ( Audit Fees / Assets)	0.1118888%	0.1624167%
E( Audit Fees / Earnings)	4.0109658%	4.6117265%	-13%
s ( Audit Fees / Earnings)	10.9036897%	10.8778866%
E( Audit Fees / Revenue)	0.1598929%	0.1760952%	-9%
s ( Audit Fees / Revenue)	0.1735905%	0.2125549%

Table 17a. Audit Fees level in between KAM Clusters.

	KAM	No KAM	KAM - No KAM Difference%
E( Audit Fees)	4,527,783	3,099,652	46%
E( Audit Fees / Assets)	0.1071908%	0.1459421%	-27%
s ( Audit Fees / Assets)	0.1118888%	0.1624167%
E( Audit Fees / Earnings)	4.0109658%	4.6117265%	-13%
s ( Audit Fees / Earnings)	10.9036897%	10.8778866%
E( Audit Fees / Revenue)	0.1598929%	0.1760952%	-9%
s ( Audit Fees / Revenue)	0.1735905%	0.2125549%

Table 17a shows that audit fees were approximately 50% higher when KAMs were present than when they are not. This can be explained by the fact that the KAMs make audits riskier, entailing more work for auditing firms, with concomitant charge higher audit fees. Similarly, as with market clusters, the ratio of audit fees to business size decreases.

6. Conclusions

Our analysis concludes that Assets, Earnings, and Revenues are statistically significant and explain audit fees for their entire range of values (positive and negative).

Book Value appears to have a statistically insignificant impact. This is because positive values have the opposite effect on audit fees compared with negative values. Specifically, for positive book values, the relationship between book values and audit fees is positive, whereas for negative book values, the correlation is negative. Additionally, the correlation for negative book values is stronger. Therefore, as book value increases, audit fees also increase, and when book value is negative and decreases (increases in absolute values), audit fees also increase. Moreover, a stronger correlation exists between negative book value and audit fees. This is because in addition to the size effect on risk, negative book values also include the going-concern risk, which increases audit risk, consequently leading to higher audit fees.

Due to the opposing impacts of both positive and negative book values on audit fees, no discernible effect of book value exists.

Earnings, which include assets and revenues, should also exhibit similar behavior, with positive values influencing audit fees differently to negative values. For instance, a company that generates $1 million in net gains faces a different audit risk than a company that experiences $1 million in losses. However, earnings are only indicative of a single period; therefore, a year with normal losses will not significantly increase the audit risk. Conversely, book value represents the company’s cumulative results. A negative book value signifies the number of years of losses, or a single or few years of extreme losses, indicating a generally poor state of the company, which in turn increases audit risk.

Therefore, we conclude that the best model to describe audit fees based on accounting variables is:

Audit Fees(i,t)=a₀+β₁ Assets_i,t + β₂ Revenue _i,t + β₃ Earnings _i,t + β₄ Book Value (positive) _i,t *D₁ + β₅ BOOK VALUE (Negative) *D₂ + ε_i,t

In our analysis of industry clusters, we observe that the determinants of audit fees varied considerably among sectors. In most industries, assets consistently influenced audit fees, indicating company complexity and audit risk. However, in Transportation, revenue had a more significant impact. The importance of fixed assets corresponded to their influence on audit risk and effort. Earnings influenced audit fees exclusively in specific sectors, such as Mining and Utilities, whereas Revenue was essential in industries characterized by intricate sales frameworks. R-squared values varied, with the Information sector demonstrating the greatest explanatory capacity. We also identified industry-specific variations in audit fee structures, emphasizing the impacts of financial complexity, regulatory conditions, and risk profiles.

An examination of market clusters indicates that assets and revenue substantially affected audit fees, whereas earnings exerted minimal influence. In uniform market capitalization samples, revenues serve as superior risk indicators compared with earnings owing to the homogeneity of earnings levels within clusters. The model’s explanatory capacity increases according to company size, suggesting that auditors can more effectively evaluate risk for larger entities through financial statements. Larger companies incur lower audit fees relative to their size, even though fees increase in absolute terms. This non-linear growth indicates that audit activities and risk do not increase in direct proportion to company size.

The examination of audit firm clusters illustrates that the Big 4 and Big 6 firms employed analogous methodologies, predominantly depending on financial statements, to evaluate audit risk, which is attributable to their extensive client portfolios and more dependable data. Conversely, smaller audit firms applied distinct pricing criteria, such as audit timing, owing to their constrained personnel and tailored client pricing. Furthermore, larger audit firms imposed lower fees relative to firm size compared with smaller firms. Nonetheless, in absolute terms, larger firms continued to impose higher fees, whereas smaller firms could secure a higher percentage of fees owing to personal client relationships.

An examination of KAMs indicates that their existence substantially influenced audit fees. Table 17 demonstrates that the presence of KAMs correlates with audit fees that were approximately 50% higher than in its absence, signifying elevated audit risk and effort. In both the KAM and non-KAM scenarios, asset value influenced audit fees, whereas earnings did not. Revenue diminished the impact of fees in the presence of KAM. An increased constant value in the KAM cluster indicates increased fees. Analogous to market clusters, the ratio of audit fees to firm size decreased as company size increased.

Other variables, such as the market share of the audit firm and the economic conditions of the country need to be included in the “regression model(s)” in future research. The outcome(s) of such studies can be used by audit firms to determine audit fees. Companies’ management can also use the results of such studies to predict the amount of audit fees that they would pay. Finally, based on all the above-mentioned, a topic for further research can be the construction of an AI model that, based on all the above, predicts a normal level of audit fees. Audit companies and their clients can use this as reference

This study is subject to certain limitations. The analysis focused exclusively on Russell 3000 companies, which may restrict the generalizability of the findings to smaller firms or companies operating outside the U.S. Additionally, the exclusion of financial sector firms means that the determinants of audit fees in highly regulated industries remain unexamined. Furthermore, although the use of panel data over an extended period enhances the robustness of the findings, regulatory changes and evolving audit standards during the period may not have been fully accounted for. Future research may address these limitations by expanding the analysis to non-U.S. firms, incorporating the financial sector, and employing models that explicitly account for regulatory developments over time.

The findings of this study have practical implications for auditors and companies. Understanding the principal factors influencing audit fees enables audit firms to improve fee estimation processes and resource planning, while allowing companies to enhance audit budgeting and negotiation strategies. In addition, this study contributes to the existing literature by offering a comprehensive sectoral analysis of audit fee determinants, which may serve as a foundation for future research.