The relationship between accounting information quality and market value in Brazilian companies

Abstract

Purpose  This study investigates the relationship between accounting information quality and market value in publicly traded Brazilian companies, highlighting the impact of high-quality financial reporting on valuation and investor decision-making.

Theoretical framework  Building on international research, the study examines how accounting information quality, firm size, growth, leverage, and dividends influence market valuation.

Design/methodology/approach  The analysis is based on an unbalanced panel of 317 non-financial publicly traded companies listed on the Brazilian Stock Exchange (B3) from 2011 to 2020, totaling 2,246 firm-year observations. Short panel regression with firm fixed effects, year dummies, and clustered robust standard errors was employed using the market-to-book ratio as a proxy for market value.

Findings  The results show a significant positive relationship between accounting information quality and market value. Market value is also positively affected by firm size and growth, but negatively affected by leverage and dividend yield. Capital expenditures and return on equity were not statistically significant.

Practical & social implications of research  The findings underscore the importance of transparent and reliable financial reporting to foster investor confidence and improve valuation. The study suggests that enhancing corporate governance and incentives for accounting quality can further strengthen the Brazilian capital market.

Originality/value  This research contributes to the literature on emerging markets by providing robust empirical evidence of the impact of accounting information quality on market valuation in Brazil, complementing international studies and evidencing advances in Brazilian corporate governance practices.

Keywords:
Earnings quality; market valuation; accruals; corporate governance

1 Introduction

Financial data is fundamental to capital markets because it provides critical insights into the financial health of companies. The accuracy and reliability of this information are essential for market participants to make well-informed decisions (Easley & O’Hara, 2004). Moreover, such information shapes expectations, influencing investors' perceptions and actions, which in turn affect the valuation of the companies in which they invest (Leuz & Verrecchia, 2005).

However, episodes such as the accounting scandals at Enron, WorldCom, Lehman Brothers, and more recently the Brazilian company Americanas, demonstrate that despite standardized disclosure requirements, financial results can still be manipulated to influence market valuations. These cases highlight the vital role of information quality for market participants, as reliable data supports more realistic and effective investment decisions (Sucuahi and Cambarihan, 2016). Additionally, Easley and O’Hara (2004) highlight that investors with better access to information have an advantage in allocating their resources efficiently.

Several authors have proposed methods to evaluate the quality of financial information by measuring the quality of accruals, which serve as proxies for information reliability (Burgstahler et al., 2006; Dechow & Dichev, 2002; Martins, 2007; Paulo, 2006).

Meanwhile, market value reflects investors' expectations about the future, as it incorporates all the information conveyed through financial reports (La Porta, 1996). Damodaran (1999) emphasizes that asset valuation is central to corporate finance, as most decisions, whether personal or organizational, aim to maximize value.

Fernández (2001) further explains that business valuation serves multiple purposes, such as: (i) determining the value of shares at their initial offering, (ii) comparing companies, (iii) quantifying value creation attributable to management, and (iv) guiding strategic decision-making within firms.

Several studies have examined the link between accounting information quality and market value. For instance, Francis et al. (2004), Easley and O’Hara (2004), and Bushman and Smith (2001) found that a firm's cost of capital is closely related to its market value. Specifically, a higher cost of capital, reflecting greater risk, tends to reduce market value (Easley & O’Hara, 2004).

Further research by Bao and Bao (2004), Gaio and Raposo (2011), Gao and Zhang (2015), and Dang et al. (2020), conducted in diverse contexts and using different methodologies, corroborates the existence of a relationship between accounting information quality and market value.

This study addresses a key gap in the literature by focusing on the Brazilian capital market, which has unique characteristics compared to more mature markets, such as high ownership and control concentration, fewer listed firms, and a relatively small investor base. Unlike most prior research, which has centered on developed economies, this paper explores how accounting information quality impacts market value in this emerging market context.

The central research question is: Does the quality of accounting information significantly influence the market valuation of listed companies in Brazil? By employing robust panel data methodologies with firm and year fixed effects and clustering standard errors at the firm level, the analysis controls for unobserved heterogeneity and temporal shocks, thereby reinforcing the reliability of the findings.

From a theoretical perspective, this study expands the understanding of how accounting information qualities operates in emerging markets, where institutional environments and market dynamics differ from those in developed countries. In practice, the results offer valuable insights for regulators, investors, and corporate managers aiming to enhance financial transparency and market efficiency, ultimately supporting better decision-making and resource allocation.

2 Literature review

2.1 Quality of financial information and its impact

Sucuahi and Cambarihan (2016) emphasizes that the quality of financial information is crucial to profitability and significantly influences investment decisions. Gaio and Raposo (2011) further highlight that reliable financial data provides more accurate insights into investment performance. However, Kamel and Elbanna (2009) argue that managers may manipulate financial information to artificially enhance a company's credibility and market value. Easley and O’Hara (2004) demonstrate that information asymmetry between public and private investors has economic consequences, underscoring the market's need for high-quality and uniformly distributed information to support effective investment decisions.

Dechow et al. (2010) define accounting information quality as a combination of attributes such as persistence, conservatism, accrual measurement quality, transparency, and the correlation between accounting figures and market performance. Additionally, lower variability in cash flow enhances decision-making confidence.

According to Burgstahler et al. (2006), a low level of earnings management indicates higher quality financial information, as earnings management reflects firms' responses to information incentives. Given the broad range of disclosure alternatives and measurement criteria, managers may opportunistically select accounting policies that favorably influence stakeholders' perceptions. Consequently, earnings management involves choosing accounting policies to achieve specific objectives.

To assess the reliability of financial reporting, Chan et al. (2006) performed cross-sectional regressions using data from companies listed on the New York Stock Exchange, Amex, and Nasdaq. They found evidence suggesting that managers employ earnings manipulation techniques to maximize reported profits, thereby distorting company results.

It is important to note that small variations in reported results do not necessarily translate into higher company value (Beaver, 2002).

2.2 Market value determinants and theoretical models

McConnell and Muscarella (1985) argue that a firm's market value results from two key components: the expected present value of the cash flow generated by current assets and the expected present value of the cash flows from new investment opportunities. Similarly, Modigliani and Miller (1963) state that a firm's value depends on the market rate of return for comparable assets, the firm's ability to generate cash from its assets, and its prospects for future investments.

Falcini (1995) defines market value as a company's share price multiplied by the number of outstanding shares. In a related approach, Ohlson (1995) proposes a residual earnings valuation model based on the dividend discount framework, which estimates a company's value by linking current earnings to the book value of equity.

Bushman and Smith (2001) examined three types of accounting information that can influence a firm's cost of capital and, consequently, its market value. Two of these factors reduce perceived risk by improving the usability of information for decision-makers and strengthening corporate control mechanisms. The third factor helps narrow the information gap among investors, thereby lowering liquidity risk.

Hermuningsih (2014) analyzed the effects of profitability, capital structure, and growth opportunities on firm value. Using a structural equation model (SEM) and data from over 150 companies listed in Indonesia, she concluded that these attributes directly impact market value.

Mule et al. (2015) examined the relationship between company size, profitability, and market value using panel data from firms listed on the Kenyan Stock Exchange between 2010 and 2014. Their results indicate that firm value is related to profitability but not to size. Both studies highlight the critical role of profitability in assessing firm value.

2.3 Empirical evidence across different markets

Recent studies have increasingly highlighted the importance of accounting information quality in determining firm value and the effectiveness of corporate decisions. For example, Perotti and Wagenhofer (2014) found a positive association between income smoothing and excess returns. In contrast, other quality proxies, including earnings persistence and abnormal accruals, show a negative association. These findings suggest that the market selectively values higher accounting quality.

Similarly, Bao and Bao (2004) found a positive correlation between smoothing and changes in multiples, employing the price-to-earnings ratio as a proxy for firm value. However, they acknowledged limitations related to firm-specific characteristics.

Other studies, such as those by Gaio and Raposo (2011), Gao and Zhang (2015), and Dang et al. (2020), employed Tobin's Q as a valuation metric and confirmed a positive relationship between income smoothing and the quality of accounting information, especially in different institutional contexts. Gao and Zhang (2015) emphasize that the impact of smoothing depends on its interaction with corporate social responsibility (CSR) practices and tends to be stronger when linked to substantial CSR initiatives. Dang et al. (2020), analyzing Vietnam, further reinforce the idea that accounting regulation and the institutional environment shape the effectiveness of accounting information quality.

However, this association is not universal. Hutagaol-Martowidjojo et al. (2019) found no significant relationship between earnings quality and market value when studying Indonesian firms. They did, however, identify direct effects of dividends, political factors, and share concentration on firm value, suggesting that national context moderates such relationships.

In an Asian context, Zhang et al. (2024) demonstrate that the quality of accounting information positively impacts technological innovation. Stronger effects are observed in private companies than in state-owned enterprises. Thus, the ownership structure emerges as an important moderator. Expanding on this discussion, Lei et al. (2022) indicate that big data adoption enhances the quality of accounting information, reduces financial barriers, and increases the efficiency of investments in innovation. This highlights the role of emerging technologies in transforming accounting practices.

Technology also plays a key role in governance and control. Abbaszadeh et al. (2024) found that using information technology (IT) strengthens internal controls in Iranian public agencies, thereby promoting transparency and traceability. Similarly, Dehghan et al. (2024) show that IT fosters organizational resilience by facilitating communication, decision-making, and sustainable performance.

From an informational structure perspective, Qatawneh (2023) argues that organizational culture significantly influences the success of (AIS). Managerial engagement and openness to change are critical for effectiveness. Tran Thanh Thuy (2025) further emphasizes that AIS quality positively impacts decision-making and non-financial performance, especially when combined with high-quality non-financial information.

The impact of audit quality on accounting information quality has also gained attention. Liu et al. (2024) found that robust internal audits improved the comparability of financial information in Chinese firms. Ndubuisi et al. (2023) demonstrate that high-quality audits increase profits and investor confidence when analyzing Nigerian companies.

Gender diversity has become a noteworthy factor in accounting information quality research. Monteiro et al. (2024) show that greater female representation in management positions amplifies the impact of internal control systems on non-financial performance, fostering inclusive governance and social value creation.

Recent studies also highlight digital transformation and blockchain technology as drivers of accounting quality. Alkafaji et al. (2023) conducted a case study in Iraq and demonstrated that blockchain enhances the reliability, timeliness, and integrity of accounting information. Similarly, Kong et al. (2025) found that Chinese companies with greater big data maturity have better quality accounting information and are more innovative, particularly in the private sector.

From a strategic standpoint, Kwakye and Ahmed (2023) demonstrated that accounting information quality mediates the link between business strategy and the cost of equity, acting as a bridge between strategic decisions and how market risk is perceived.

Finally, Widiastuti and Rahmawati (2022) reiterate that transparent and accurate accounting information affects firm value, even in developing economies. Their study of Indonesian firms provides evidence of this.

3 Methods

This study uses an unbalanced panel dataset sourced from Economatica^®, covering the period from 2010 to 2020. However, since some variables required lagged values, data from 2010 were excluded from the panel regressions. Thus, the final sample includes 2,246 observations from 317 non-financial, publicly traded Brazilian companies. Due to their distinct regulatory environment, financial institutions were excluded from the sample, as were firms with incomplete data or with negative equity, cash, or total assets (Supplementary Data 1 - STATA Database; Supplementary Data 2 - Excel Database).

3.1 Quality of accounting information

This study measures accounting information quality primarily through discretionary accruals estimated using the Modified Jones Model (Dechow et al., 1995). This model is an improvement over the Standard Jones Model (Jones, 1991). The Modified Jones Model adjusts for potential revenue manipulation by excluding changes in accounts receivable, providing a more accurate estimation of earnings management.

The Standard Jones Model estimates total accruals (TA) as a function of lagged total assets, changes in revenues, and property, plant, and equipment (PPE), as shown in Equation 1:

$T{A}_{i,t}={\alpha }_1\frac{1}{A_{i,t-1}}+{\alpha }_2\Delta Re{v}_{i,t}+{\alpha }_{3}PP{E}_{i,t}+{\epsilon }_{i,t}$ (1)

where: $T{A}_{i,t}$ is total accruals for firm i at time t; $A_{i,t-1}$ is total assets lagged by one period; $\Delta Re{v}_{i,t}$ is the change in revenues; $PP{E}_{i,t}$ is property, plant, and equipment; ${\epsilon }_{i,t}$ is the error term.

$T{A}_{i,t}=\Delta C{A}_{i,t}-\Delta Cas{h}_{i,t}-\Delta C{L}_{i,t}+-DA{E}_{i,t}$ (2)

where: $T{A}_{i,t}$is total accruals in year t for firm i; CA is current assets in year t minus current assets in year t – 1 for firm i; ΔCash is cash in year t minus cash in year t – 1 for firm i; ΔCL is current liabilities in year t minus current liabilities in year t − 1 for firm i; $DA{E}_{i,t}$ is the depreciation and amortization expense in year t for firm i, as defined in Equation 2.

Following Dechow et al. (1995), this study further refines the model by incorporating changes in short-term debt within current liabilities as an additional explanatory variable, as specified in Equation 3:

$\begin{array}{c}T{A}_{i,t}=\Delta C{A}_{i,t}-\Delta Cas{h}_{i,t}-\Delta C{L}_{i,t}\\ +\Delta ST{D}_{i,t}-DA{E}_{i,t}+{\epsilon }_{i,t}\end{array}$ (3)

where: $\Delta ST{D}_{i,t}$ is the change in short-term debt included in current liabilities.

The Modified Jones Model with a return on assets (ROA) adjustment, proposed by Kothari et al. (2005), improves upon the original model by incorporating firm performance (ROA) to better control for the effects of performance on accruals. This specification is presented in Equation 4.

$\begin{array}{c}T{A}_{i,t}=\alpha +{\beta }_1\frac{1}{A_{i,t-1}}+{\beta }_2(\Delta Re{v}_{i,t}-\Delta A{R}_{i,t})\\ +{\beta }_{3}PP{E}_{i,t}+{\beta }_{4}RO{A}_{i,t-1}+{\epsilon }_{i,t}\end{array}$ (4)

where: $RO{A}_{i,t-1}$ is return on assets lagged by one period; $\Delta A{R}_{i,t}$ is the change in accounts receivable.

The Modified Jones Model, which considers cash flows and accrual reversals and was proposed by Pae (2005), adds cash flow variables and lagged accruals to account for accrual reversals. This aims to reduce omitted variable bias and improve reliability, as specified in Equation 5:

$\begin{array}{c}T{A}_{i,t}=\alpha +{\beta }_1\frac{1}{A_{i,t-1}}+{\beta }_2(\Delta Re{v}_{i,t}-\Delta A{R}_{i,t})\\ +{\beta }_{3}PP{E}_{i,t}+{\beta }_{4}CF{O}_{i,t}+{\beta }_{4}CF{O}_{i,t-1}+{\beta }_{6}T{A}_{i,t-1}+{\epsilon }_{i,t}\end{array}$ (5)

where: $CF{O}_{i,t}$ and $CF{O}_{i,t-1}$ are cash flows from operations at times t and t−1, respectively; $T{A}_{i,t-1}$ is total accruals lagged by one period.

To control for differences in firm size and enable comparability, all total accruals (TA) are scaled by lagged total assets, as specified in Equation 6:

$EQ=\frac{T{A}_{i,t}}{A_{i,t-1}}$ (6)

where: $EQ$ is the normalized measure of accrual quality used as the main proxy for accounting information quality in this study.

The normalized measure of accrual quality (EQ) is the primary proxy used in this study to assess accounting information quality, while alternative measures, including the Standard Jones Model, are employed solely for robustness checks. All calculations followed the step-by-step procedures outlined in the tutorial by Costa and Soares (2022), which details the implementation of both the Standard and Modified Jones Models for estimating earnings management.

3.2 Measuring firm value

Firm value is measured using the market-to-book (MtB) ratio as an alternative to Tobin's Q. Tobin's Q was initially considered based on La Rocca (2010), who reviewed multiple studies measuring firm value using profitability proxies (e.g., ROA, ROE) and market-based indicators (e.g., Tobin's Q, EVA, EPS, MtB). However, since Tobin's Q relies on the replacement cost of assets, which is often unavailable, this study adopts the MtB ratio, as supported by Chung and Pruitt (1994). The MtB ratio is defined in Equation 7:

$MtB=\frac{TotalLiabilities+MarketValueofEquity}{BookValueofTotalAssets}$ (7)

where: MtB is market-to-book.

3.3 Variables and model specification

This study aims to analyze the impact of accounting information quality on the market value of Brazilian listed companies. The dependent variable is the market-to-book ratio (MtB), which is calculated by adding total liabilities and the market value of equity, then dividing that sum by the book value of total assets. The main explanatory variable is accounting information quality (EQ), which is estimated using the Modified Jones Model. The computation of this variable followed the step-by-step procedures outlined in the tutorial by Costa and Soares (2022), which details the implementation of the Standard and Modified Jones Models for earnings management estimation. Additional control variables include Size, Indebt, Growth, Capex, Dividend Yield (DY), and Return on Equity (ROE).

The econometric model is specified in Equation 8:

$$\begin{array}{c}Mt{B}_{i,t}={\beta }_0+{\beta }_1{\left(EQ\right)}_{i,t}+{\beta }_2{\left(Size\right)}_{i,t}\\ +{\beta }_3{\left(Indebt\right)}_{i,t}+{\beta }_4{\left(Growth\right)}_{i,t}+{\beta }_5{\left(Capex\right)}_{i,t}\\ +{\beta }_6{\left(DY\right)}_{i,t}+{\beta }_7{\left(ROE\right)}_{i,t}+{\epsilon }_{i,t}\end{array}$$ (8)

where: $i$ indexes firms, $t$ indexes years, and $\epsilon$ is the error term.

Table 1 summarizes the variables and their sources (Supplementary Data 3 - Variable Dictionary).

Table 1
Variable description

	Variable	Description	Source
MtB	Market to book	(Total liabilities + market value) / Book value of equity	Dang et al. (2020) and Hutagaol-Martowidjojo et al. (2019)
EQ	Accounting Information Quality	Total Accruals(i,t) / Total Assets(i,t-1)	Dechow et al. (1995) and Costa and Soares (2022)
Size	Company Size	Logarithm of total assets	Dang et al. (2020) and Hutagaol-Martowidjojo et al. (2019)
Indebt	Liabilities	Total Liabilities/ Total Assets	Dang et al. (2020)
Growth	Revenue Growth	(Net Revenue(i, t) / Net Revenue(i, t-1)) - 1	Dang et al. (2020)
Capex	Investment in fixed assets	Variation in property, plant and equipment plus depreciation, relative to total assets.	Hutagaol-Martowidjojo et al. (2019)
DY	Dividend Yield	Amount of distributed profits / Market Value	Dang et al. (2020)
ROE	Return on investment	Net income / shareholders’ equity	La Rocca (2010)

• Note: own elaboration.

3.4 Panel data regression

The main econometric model specified in Equation 8 is estimated using panel data methods that are suitable for short panels, as discussed by Fávero (2013). The objective of the regression analysis is to examine the impact of accounting information quality (EQ) on the market value (MtB) of Brazilian listed companies.

To select the most appropriate data model, a sequential testing strategy was adopted. First, the Breusch-Pagan Lagrange Multiplier (LM) test was applied to choose between the pooled ordinary least squares (POLS) model and the random effects (RE) model. Subsequently, the Chow F-test was used to compare the POLS model with the fixed effects (FE) model. Finally, the Hausman test was used to assess whether the RE model provides consistent estimates compared to the FE model, guiding the final model selection.

This sequence ensures that the selected model produces unbiased and efficient parameter estimates. The regression aims to verify whether the coefficients of key explanatory variables, especially the accounting quality measure (EQ), are statistically significant and in the expected direction, allowing us to draw inferences about the influence of earnings management on firm valuation.

The methodological approach follows prior empirical studies such as those by Bao and Bao (2004), Gaio and Raposo (2011), Perotti and Wagenhofer (2014), Gao and Zhang (2015), and Dang et al. (2020).

3.5 Robustness and diagnostic tests

To ensure the robustness and validity of the panel data analysis, several diagnostic tests and procedures were performed in sequence. First, basic summary statistics were obtained to understand the distribution of the variables and identify potential outliers. To mitigate the influence of extreme values, the variables were winsorized at the 1% and 99% percentiles. This procedure preserved most of the data variability while limiting distortions caused by outliers. Subsequently, simple correlation matrices were analyzed to examine the relationships among the variables, revealing low to moderate correlations and indicating no major multicollinearity issues (Supplementary Data 4 - Stata Script; Supplementary Data 5 - STATA output).

Following this, initial model specifications were tested using POLS, RE, and FE estimations. The LM test indicated a preference for the RE model over the POLS model, the Chow F-test favored the FE model over the POLS model, and the Hausman test confirmed the appropriateness of the FE model. To further ensure the independence of the explanatory variables, the variance inflation factor (VIF) was calculated. The average value was around 1.05, confirming the absence of multicollinearity.

The presence of heteroscedasticity in the FE model was detected using the Modified Wald test (p < 0.001), highlighting the need for robust standard errors. Therefore, standard errors were clustered by firm to correct for heteroscedasticity and possible autocorrelation within firms, ensuring reliable statistical inference. To control for unobserved temporal shocks and common time trends, dummy variables for years were included in the FE model. Finally, the residuals were tested for autocorrelation. Positive serial correlation was detected (coefficient approximately 0.36, p < 0.001), reinforcing the justification for clustered robust standard errors. The final model, which combines fixed effects, clustered standard errors, and year dummies, is statistically valid and robust, as supported by these diagnostic procedures.

4 Results

4.1 Descriptive analysis

Table 2 presents descriptive statistics for the original (O) and winsorized (W) datasets. Winsorization adjusts extreme values by replacing the lowest 1% of observations with the value at the 1st percentile and the highest 1% with the value at the 99th percentile. This procedure reduces the influence of outliers that could distort the analysis, thus improving the reliability of the results. The approach follows the methods used in previous studies by Verdi (2006), Colla et al. (2013), Eça and Albanez (2022), and Santos (2023).

Table 2
Descriptive analysis

Variable		Obs	Mean	Std. Dev.	Min	Max
MB	O	2,246	1.288	0.930	0.031	8.743
	W	2,246	1.288	0.928	0.037	8.374
DY	O	2,246	0.025	0.052	0.000	1.114
	W	2,246	0.025	0.049	0.000	0.814
Capex	O	2,246	0.064	0.115	-1.411	0.867
	W	2,246	0.064	0.114	-1.368	0.850
ROE	O	2,246	0.043	1.381	-54.515	17.910
	W	2,246	0.051	0.802	-21.276	2.632
Indebt	O	2,246	0.562	0.189	0.031	0.996
	W	2,246	0.562	0.189	0.037	0.995
Size	O	2,246	6.484	0.762	3.078	8.995
	W	2,246	6.484	0.762	3.303	8.967
Growth	O	2,246	0.137	0.424	-0.967	7.356
	W	2,246	0.136	0.411	-0.966	5.393
EQ	O	2,246	-0.094	2.602	-102.733	1.818
	W	2,246	-0.079	2.033	-68.033	1.317

• Note: own elaboration.

The summary statistics show that the market-to-book ratio (MtB) has an average value of approximately 1.29, with a minimum of 0.031 and a maximum of 8.74, in the original data. After winsorization, the range narrows slightly, indicating that only the most extreme values were adjusted. Dividend yield (DY) averages about 2.5%, with the maximum value decreasing from 1.11 to 0.81 after winsorization. Capital expenditure (Capex) has an average of around 6.4%, though the wide range of values indicates substantial variation among firms. The average return on equity (ROE) is close to 4.3%, with significant decreases in the maximum and minimum values after winsorization, reflecting a reduction of outliers. Indebtedness (Indebt) averages close to 56%, with minimal changes in central tendency and slight adjustments to extremes. Firm size (Size), measured as the logarithm of total assets, averages 6.48. The minimum and maximum values show some variability but remain stable after winsorization. Growth shows an average annual increase of approximately 13.7%, and the variable EQ has an average close to zero, though it has wide dispersion that narrows following winsorization.

Overall, the minimal differences between the original and winsorized data confirm that only a small number of observations were modified. This supports the robustness of the dataset used in this study. For this reason, all subsequent analyses were conducted using the winsorized data. However, the final model was also tested using the original, non-winsorized data. The results showed consistent coefficients and significance levels across variables and models. Therefore, only the results based on the winsorized data are presented in the following sections.

Table 3 shows that the correlations between all variables except Indebt and Size are low, below 0.2. Therefore, there is no evidence of multicollinearity among the independent variables. This is further confirmed by the results of the VIF test presented in the following sections.

Table 3
Correlation matrix

	MtB	DY	Capex	ROE	Indebt	Size	Growth	EQ
MtB	1
DY	0.049	1
Capex	0.066	-0.061	1
ROE	0.070	0.071	0.057	1
Indebt	-0.046	-0.149	0.092	-0.112	1
Size	0.068	0.018	0.052	0.024	0.297	1
Growth	0.012	-0.043	0.283	0.071	0.036	0.000	1
EQ	0.022	0.012	-0.200	0.006	-0.021	0.006	-0.044	1

• Note: own elaboration.

4.2 Regression results

Table 4 presents the estimation results of panel data regression models that examine the relationship between accounting information quality (EQ), as measured by the Modified Jones Model (Dechow et al., 1995), and the market-to-book ratio (MtB). Four model specifications are reported: pooled OLS (POLS), random effects (RE), fixed effects (FE), and fixed effects with clustered robust standard errors (FE Robust). These models were estimated using 2,246 observations from 317 publicly listed Brazilian firms throughout the sample period.

Table 4
Short panel data regression model

Variables	POLS	RE	FE	FE Rob
DY	0.7194	-0.4168	-0.5350*	-0.5350**
	(0.5767)	(0.2813)	(0.2846)	(0.2326)
Capex	0.6145***	0.1906	0.1344	0.1344
	(0.2077)	(0.1224	(0.1252)	(0.1234)
Size	0.0984	0.2847***	0.4529***	0.4529***
	(0.0638)	(0.0446)	(0.0647)	(0.1549)
ROE	0.0629	0.0064	0.0011	0.0011
	(0.0526	(0.0155)	(0.0156)	(0.0121)
Indebt	-0.3134	-0.6668***	-0.7740***	-0.7740**
	(0.2954)	(0.1291)	(0.1476)	(0.3436)
Growth	-0.0175	0.0630**	0.0750**	0.0750**
	(0.0389)	(0.0315)	(0.0318)	(0.0351)
EQ	0.0158***	0.0121*	0.0131**	0.0131***
	(0.0026)	(0.0062)	(0.0062)	(0.0026)
cons	0.7682*	-0.2192	-1.2183***	-1.2183
	(0.3984)	(0.2872)	(0.419)	(0.8946)
N	2246	2246	2246	2246
n	317	317	317	317
R2	0.0197		0.042	0.042
R2 (ove)		0.01	0.009	0.009
R2 (bet)		0.01	0.01	0.01
R2 (wit)		0.04	0.042	0.042
F	10.047		12.11	5.369
Prob > F	0.000		0.000	0.000
Wald chi2(7)		71.461
Prob > chi2		0.0000
Breusch and pagan
chibar2(01)	3582.34	3582.34
Prob > chibar2	0.0000	0.0000
F Chow
F(316, 1922)	15.63		15.63
Prob > F	0.0000		0.0000
Hausman
chi2(7)		31.63	31.63
Prob>chi2		0.0000	0.0000

• Note: Standard errors are shown in parentheses. Statistical significance is indicated by ***, **, and * at the 1%, 5%, and 10% levels, respectively. The variables N and n represent the total number of observations and the number of firms, respectively. The variables and their descriptions are detailed in Table 1. The results presented in this table correspond to estimations using the Pooled OLS (POLS), Random Effects (RE), Fixed Effects (FE), and Fixed Effects with clustered robust standard errors (FE Rob) models. All models include firm-level data from 317 firms and 2,246 observations over the sample period. Own elaboration.

Several specification tests were performed to select the most appropriate model. The Breusch-Pagan Lagrange Multiplier test strongly favored the RE model over the POLS model (χ² = 3582.34, p < 0.001). The Chow F-test indicated that the FE model is preferable to the POLS model (F(316, 1922) = 15.63, p < 0.001). Finally, the Hausman test confirmed that the FE model yields more consistent and efficient estimates than the RE model (χ² = 31.63, p < 0.001), thus supporting its adoption as the baseline specification. Additionally, the FE robust model corrects for potential heteroscedasticity and within-firm autocorrelation by clustering standard errors at the firm level, thus ensuring robust inference.

The results show a negative and significant relationship between dividend yield (DY) and the market-to-book ratio (MtB) in the fixed effects (FE) models. This suggests that firms with lower dividend payouts tend to reinvest earnings, potentially promoting growth and increasing valuation, as proposed by Dempsey et al. (2019). However, these results contrast with those of studies by Hutagaol-Martowidjojo et al. (2019) and Dang et al. (2020), which reported a positive association. The coefficients for investment in fixed assets (Capex) and return on equity (ROE) are not statistically significant, indicating that these variables do not explain variations in market valuation within this sample. These findings contrast with the results reported by Dang et al. (2020) and La Rocca (2010).

Firm size (Size) exhibits a positive and highly significant effect, consistent with the notion that larger firms benefit from greater access to capital markets and lower risk premiums (Hutagaol-Martowidjojo et al., 2019). Leverage (Indebt) negatively influences market valuation, aligning with the literature on the costs associated with higher debt levels (Dang et al., 2020; Bao and Bao, 2004). Growth positively influences MtB, consistent with the idea that firms with greater growth prospects receive higher valuations, as supported by Fama and French (2001) and Durnev and Kim (2005).

Importantly, accounting information quality (EQ) is positively and significantly associated with market value across all model specifications. This reinforces the hypothesis that high-quality financial reporting enhances firm valuation, consistent with previous studies by Bao and Bao (2004), Gaio and Raposo (2011), Perotti and Wagenhofer (2014), Gao and Zhang (2015), and Dang et al. (2020).

4.3 Robustness checks: FE with year controls and alternative accounting quality measures

To strengthen the robustness of the findings, Table 5 reports fixed effects regressions that incorporate year fixed effects as additional control variables. The inclusion of year dummies accounts for macroeconomic shocks, policy changes, and common time trends that impact all firms during specific years. This enhances the model's explanatory power, as evidenced by the higher overall R² compared to Table 4.

Table 5
Fixed effects robust model with year fixed effects and alternative accounting quality measures

Variables	MtB (FE Rob Y)	MtB (FE Rob Y)	MtB (FE Rob Y)	MtB (FE Rob Y)
DY	-0.1494	-0.1783	-0.1606	-0.1606
	(0.1763)	(0.1828)	(0.1798)	(0.1782)
Capex	0.1245	0.0973	0.051	0.0464
	(0.111)	(0.1102)	(0.105)	(0.1063)
Size	0.1079	0.2278	0.1915	0.1807
	(0.1512)	(0.15020)	(0.1449)	(0.1425)
ROE	0.0123	0.0085	0.0122	0.0104
	(0.0112)	(0.01110	(0.011)	(0.0109)
Indebt	-0.5289	-0.7866**	-0.7513**	-0.7411**
	(0.3587)	(0.3345)	(0.3311)	(0.331)
Growth	0.0543*	0.0706**	0.0556*	0.0531*
	(0.0312)	(0.0334)	(0.0307)	(0.0295)
EQ (Jones Standard)	-0.0002
	(0.0003)
EQ (Jones Modified)		0.0081***
		(0.003)
EQ (ROA)			0.3008**
			(0.1476)
EQ (PAE)				0.3276*
				(0.1804)
2012.Year	0.1096***	0.0941***	0.1000***	0.0995***
	(0.0316)	(0.0312)	(0.0315)	(0.0315)
2013.Year	0.0144	-0.0074	-0.0056	-0.0069
	(0.0373)	(0.0373)	(0.0372)	(0.0371)
2014.Year	-0.1235***	-0.1429***	-0.1329***	-0.1341***
	(0.0419)	(0.0423)	(0.043)	)0.043)
2015.Year	-0.2333***	-0.2483***	-0.2415***	-0.2383***
	(0.0416)	(0.0426)	(0.0426)	(0.0434)
2016.Year	-0.1587***	-0.1742***	-0.1969***	-0.1708***
	(0.0509)	(0.0516)	(0.0503)	(0.0522)
2017.Year	0.0459	0.0218	0.0315	0.033
	(0.0588)	(0.0598)	(0.0606)	(0.061)
2018.Year	-0.0119	-0.0485	-0.0389	-0.0365
	(0.0568)	(0.0597)	(0.0601)	(0.0601)
2019.Year	0.2193***	0.1462**	0.1679**	0.1710**
	(0.0641)	(0.0662)	(0.0666)	(0.0668)
2020.Year	0.2381***	0.2498***	0.1989***	0.1743**
	(0.077)	(0.0771)	(0.0759)	(0.0802)
Cons	0.8856	0.2454	0.4321	0.4977
	(0.8522)	(0.8535)	(0.8262)	(0.8093)
N	2081	2246	2246	2246
n	239	317	317	317
R2	0.101	0.113	0.119	0.121
R2 (ove)	0.03	0.027	0.031	0.033
R2 (bet)	0.004	0.004	0.003	0.003
R2 (wit)	0.101	0.113	0.119	0.121
F(16,316)	11.354	10.589	10.138	10.245
Prob > F	0.0000	0.0000	0.0000	0.0000

• Note: This model controls unobserved temporal shocks and common macroeconomic trends via year dummies, improving estimation precision of the impact of EQ and other variables on MtB. Results are presented for 317 firms over 2,246 observations, with clustered robust standard errors. Statistical significance: *** p<0.01; ** p<0.05; * p<0.10. Own elaboration.

Accounting information quality (EQ) is assessed using multiple methodologies: the Standard Jones Model (Jones, 1991), the Modified Jones Model (Dechow et al., 1995), and its extensions, which adjust for return on assets (Kothari et al., 2005) and cash flow reversals (Pae, 2005). All models were estimated following the step-by-step algorithm proposed by Costa and Soares (2022).

While the Standard Jones Model does not yield statistically significant results, the Modified Jones Model and the performance-based measures (ROA and PAE) show positive and significant associations with market-to-book ratios. This suggests that these alternative approaches better capture accounting information quality and firm performance, offering greater explanatory power in relation to firm valuation.

Year fixed effects reveal statistically significant impacts in 2012, 2014, 2015, 2016, 2019, and 2020. These impacts reflect major macroeconomic and political events that influenced market valuations during those years. The positive coefficient in 2012 aligns with the Dilma Rousseff administration's stimulus policies in response to the global financial crisis (Dempsey et al. 2019). The negative coefficients between 2014 and 2016 coincide with the Great Brazilian Recession, which was characterized by austerity measures, fiscal tightening, and high interest rates (Oreiro, 2017; Banco Central do Brasil, 2016). The political shift in 2016, marked by President Rousseff's impeachment and the passage of Constitutional Amendment 95/2016, which capped public expenditure growth, signaled a new economic orientation (Montani & Busato, 2023).

In 2019, positive and significant effects reemerged, reflecting improved macroeconomic stability, controlled inflation, historically low interest rates, and the approval of pension reform (Brasil, 2020). Despite the shock of the unprecedented 2020 COVID-19 pandemic, which was characterized by large fiscal stimulus and the lowest Selic rate on record (Banco Central do Brasil, 2020), the significant positive coefficient captures the resilience and complexity of the Brazilian economy amid a crisis.

In summary, these year-specific effects highlight the influence of broader economic cycles and policy shocks on firm market valuations. Incorporating such controls strengthens the robustness of the primary finding, that higher accounting information quality is positively and significantly associated with the market valuation of Brazilian publicly listed companies. This relationship remains robust when using alternative methodologies to measure accounting information quality (EQ).

5 Discussions

The empirical results reaffirm the critical role of accounting information quality in explaining the market valuation of Brazilian companies. This is demonstrated by the consistent, positive, and statistically significant relationship between EQ and the market-to-book ratio across all model specifications. These findings are consistent with international literature, including the studies by Bao and Bao (2004), Gaio and Raposo (2011), Perotti and Wagenhofer (2014), Gao and Zhang (2015), and Dang et al. (2020), which emphasize that reliable, relevant, and timely accounting information reduces information asymmetry and strengthens investor confidence, thereby positively affecting firm valuation.

The analysis of year-specific effects highlights the relevance of economic and institutional contexts in shaping the impact of accounting quality on market value. This aligns with the studies by Hutagaol-Martowidjojo et al. (2019) and Dang et al. (2020), which underscore the moderating influence of economic cycles and regulatory policies. For instance, the positive effect observed in 2012 occurred during a period of growth stimulus following the slowdown in 2011. Conversely, the negative effects observed during 2014–2016 reflect the challenges posed by the Great Brazilian Recession and the related austerity measures, which weakened the capacity of accounting information to sustain investor confidence.

The political transition in 2016 reshaped the institutional landscape, influencing risk perception and company valuations (Montani & Busato, 2023). The lack of statistical significance in 2017 and 2018 suggests a transitional phase or policy inertia. In contrast, positive and significant coefficients in 2019 reflect renewed macroeconomic stability and pension reform, signaling fiscal responsibility (Brasil, 2020).

Notably, even amid the COVID-19 crisis in 2020, accounting information quality remained positively associated with market value, indicating that transparency is especially valuable during periods of heightened uncertainty.

As expected, structural variables such as company size and growth correlate positively with market valuation, consistent with the findings of Fama and French (2001), Durnev and Kim (2005), and Hutagaol-Martowidjojo et al. (2019). Larger firms tend to have better reputations, greater risk diversification, and easier access to capital. These factors reduce risk premiums and boost valuations. High-growth firms are viewed as innovative and capable of generating future cash flows, which enhances their attractiveness to investors.

Conversely, the negative relationship between dividend yield and market value indicates that investors prefer profit retention and reinvestment over distribution, particularly in contexts involving growth or innovation. This finding aligns with the work of Dempsey, Gunasekarage, and Truong (2019), but is inconsistent with that of Hutagaol-Martowidjojo et al. (2019), who linked dividends to financial strength. Additionally, leverage negatively impacts market valuation, confirming the results of Bao and Bao (2004) and Dang et al. (2020), as high indebtedness raises financial risk and capital costs, potentially undermining future profitability and incurring market penalties.

Overall, the study confirms the positive impact of accounting information quality on market valuations in Brazil. This suggests that, on average, the Brazilian market values high-quality accounting information, reflecting a relatively mature institutional and governance environment. However, the study neither directly compares Brazilian practices to international benchmarks nor examines public policies. While plausible, recommendations for enhanced incentives and mechanisms to improve accounting quality extend beyond the study's empirical scope and are best considered as proposals for future policy development.

6 Concluding considerations

The main objective of this study was to analyze the relationship between the quality of accounting information of companies listed in Brazil and their respective market values. To this end, an unbalanced panel of 2,246 observations from 317 non-financial public companies listed on the Brazilian Stock Exchange was used. The data covered financial statements from 2011 to 2020. As discussed in the previous section, the results show a positive relationship, confirming findings from studies in other countries based on similar theoretical frameworks.

The primary findings reinforce the importance of accounting information quality as a determinant of firm value, while also highlighting the influence of structural factors such as size, growth, leverage, and dividends. Additionally, the inclusion of year fixed effects captures the significant impact of macroeconomic conditions and policy changes, underscoring the need to consider the broader economic context when analyzing value creation dynamics in the Brazilian market.

These results have practical implications for corporate governance and investor decision-making. The positive association between accounting quality and market valuation suggests that transparent and reliable financial reporting can bolster investor confidence, potentially improving access to capital and reducing the cost of equity.

The robustness of this study is enhanced by the use of multiple accounting quality measures and controls for temporal macroeconomic shocks, which provides greater confidence in the validity of the findings. However, some limitations remain. For example, the study relies on proxies for accounting information quality and market value. Additionally, the selection of independent variables is based on international literature, which may not fully capture Brazil's unique institutional characteristics.

Future research could build on these findings by exploring alternative measures of accounting quality and firm value, as well as by expanding the set of explanatory variables. Comparative studies involving countries with similar emerging market profiles or regional proximity could also offer valuable insights into the generalizability of these results. Moreover, further investigation into the role of specific regulatory and institutional factors would deepen the understanding of how these influence the relationship between accounting quality and market valuation.

Supplementary material

Supplementary material accompanies this paper.

Supplementary Data 1 - STATA Database

Supplementary Data 2 - Excel Database

Supplementary Data 3 - Variable Dictionary

Supplementary Data 4 - Stata Script

Supplementary Data 5 - STATA Output

Supplementary data to this article can be found online at https://doi.org/10.7910/DVN/FBQRQO

• Evaluation process:
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• How to cite:
  Leite, J. P. P. C., Novais, F. M. O., Rossetti, N., & Carvalho, F. L. (2025). The relationship between accounting information quality and market value in Brazilian companies. Revista Brasileira de Gestão de Negócios, 27(3), e20240213. https://doi.org/10.7819/rbgn.v27i03.4309
• Financial support:
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• Open Science:
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• Responsible editor:
  Dr. Prof. Ibrahim Nandom Yakubu

• Reviewers:
  Hung Dang ; Mahdi Salehi

Data availability

Padial da Costa Leite, João Pedro; Oliveira Novais, Felipe Miguel; Rossetti, Nara; Carvalho, Flavio Leonel de, 2025, "The Relationship Between Accounting Information Quality and Market Value in Brazilian Companies", https://doi.org/10.7910/DVN/FBQRQO, Harvard Dataverse, V1, UNF:6:X0aLA7Lj/O+pnY+4RAC5HA== [fileUNF]

Supplementary material

Supplementary material accompanies this paper.

Supplementary Data 1 - STATA Database

Supplementary Data 2 - Excel Database

Supplementary Data 3 - Variable Dictionary

Supplementary Data 4 - Stata Script

Supplementary Data 5 - STATA Output

Supplementary data to this article can be found online at https://doi.org/10.7910/DVN/FBQRQO

Publication Dates

• Publication in this collection
  20 Oct 2025
• Date of issue
  2025

History

• Received
  16 Jan 2025
• Accepted
  06 Aug 2025