Integrated Reporting and Assurance in Emerging Economies: Impacts on Market Liquidity and Forecast Accuracy

Abstract

This article examines whether the presentation of integrated reports (IRs), the external assurance of non-financial information, and the use of auditing standards affect market liquidity and the accuracy of earnings per share forecasts in the Chilean market following the publication of the International IR Framework. Using ordinary least squares estimations, results show that IRs significantly reduce information asymmetry, thereby improving market liquidity. This effect is reinforced when non-financial information is externally assured, particularly under the ISAE3000 standard. However, neither IRs nor external assurance significantly impact financial analysts’ earnings forecast accuracy, suggesting that such information serves a complementary role in their evaluations. This study contributes to the literature by providing empirical evidence on the role of IRs and assurance in emerging economies, emphasizing their effectiveness in enhancing transparency and liquidity. The findings have direct implications for companies, as they suggest that adopting IRs and obtaining external assurance can strengthen market perceptions and investor confidence, particularly when using the ISAE3000 standard. For regulators, the results highlight the potential benefits of promoting standardized sustainability disclosures and assurance mechanisms to foster transparency in capital markets. Investors, in turn, can use IR quality and assurance as signals of corporate credibility and long-term value creation.

1. Introduction

The disclosure of corporate information has significantly evolved over the past two decades, driven by increasing stakeholder demand for transparency and accountability [1]. In this context, integrated reporting (IR) has emerged as a key tool to address the limitations of traditional financial and sustainability reporting by offering a unified framework that connects financial performance with sustainability and long-term value creation. The value of IR lies in its ability to integrate financial and non-financial information, enabling organizations to communicate how they create value over the short, medium, and long term [2]. The academic literature highlights several benefits associated with IR adoption, including enhanced organizational transparency, reduced information asymmetry, lower capital costs, and improved market liquidity [3,4]. Furthermore, studies suggest that IR contributes to better corporate valuation and more predictable cash flows [5]. Despite these advantages, the application of IR faces challenges, particularly in emerging economies where its adoption generally is voluntary. The lack of standardization and external verification mechanisms further complicates the consistent and effective implementation of IR [6].

Unlike financial statement audits, which are mandatory and primarily conducted by accounting professionals, non-financial information assurance operates in a broader context beyond traditional accounting practices. The role of external assurance in enhancing the credibility of sustainability information has been a recurring theme in the literature, particularly in voluntary reporting environments. Studies suggest that external assurance can strengthen trust in corporate disclosures by reducing information asymmetry and improving market perceptions [7,8]. These benefits are often linked to lower capital costs, enhanced market liquidity, and improved earnings forecast accuracy [9,10]. However, the evidence remains inconclusive, with some researchers arguing that assurance does not necessarily improve the overall quality of information or positively influence market valuation [6,11]. The choice of assurance framework further complicates this landscape, as standards like ISAE3000 [12] and AA1000AS [13] offer distinct advantages but also present challenges in implementation. This ongoing debate highlights the need for further exploration of the role and the impact of sustainability assurance, particularly in emerging markets.

This study investigates the impact of IR preparation and presentation on market liquidity and earnings forecast accuracy in the Chilean market, following the publication of the International IR Framework in 2013. Additionally, it examines whether external assurance of non-financial information and the use of auditing standards, such as ISAE3000 and AA1000, influence the market liquidity of firms and the accuracy of earnings forecasts. The evidence on its effectiveness remains scarce and mixed. Information asymmetry, commonly measured as the bid–ask spread (BAS), serves as a specific proxy for market liquidity and a general indicator of transparency. This study hypothesizes that both the BAS and earnings forecast errors are negatively associated with IR presentation, external assurance of non-financial information, and the use of ISAE3000 and AA1000 standards. Using econometric modeling, the research examines data from 2013 to 2017, capturing the effects of voluntary IR adoption and assurance practices in the Chilean market. The findings reveal that companies presenting IRs exhibit a significant negative relationship with the BAS, indicating improved market liquidity. Industry-specific analysis highlights that this effect is more pronounced in firms operating in consumer services and basic materials sectors. Additionally, companies that engage in external assurance of non-financial information tend to experience narrower spreads compared to those without assurance mechanisms. Sensitivity analysis further demonstrates that firms utilizing ISAE3000 standards exhibit a stronger positive impact on liquidity compared to those employing AA1000. Contrary to expectations, the results do not indicate a statistically significant relationship between IR presentation, external assurance, or the use of auditing standards and the accuracy of earnings forecasts. This suggests that non-financial information, while valuable for market liquidity, may be perceived as complementary rather than essential for financial analysts when forecasting earnings.

This study contributes to the existing literature by providing empirical evidence on the benefits and limitations of the IR practices and the assurance of sustainability information. The findings underscore the potential of IR and external assurance to reduce information asymmetry and foster more transparent markets, offering practical implications for companies, regulators, and investors striving to enhance sustainability and transparency in financial markets.

2. Literature Review

The integration of financial and non-financial information in corporate reporting has gained significant attention over the past two decades. IR is an innovative framework addressing the limitations of traditional reporting by promoting a holistic approach that connects financial performance with sustainability and long-term value creation [2]. Studies have shown that IR improves organizational transparency and reduces information asymmetry between companies and their stakeholders [4,9,14,15]. Furthermore, IR enables investors to make more informed decisions by lowering capital costs and strengthening trust in disclosed information [16]

Theoretical perspectives underpinning IR adoption are diverse. The legitimacy theory highlights that companies use IR to align with societal expectations and maintain their social license to operate [17,18]. Stakeholder theory emphasizes the integration of diverse stakeholder needs into corporate strategy [19]. Institutional innovation theory suggests that IR responds to normative, mimetic, and coercive pressures, especially in emerging markets [20]. However, the adoption of IR faces challenges, including the lack of standardization and clear guidelines, which hinder consistent implementation [21]. Companies may also perceive IR as an additional effort that does not always yield immediate tangible benefits. Despite these challenges, global trends toward sustainability and transparency indicate the growing importance of IR, particularly for sectors seeking to balance economic performance with social and environmental responsibility.

Empirical evidence links IR adoption to various economic and financial benefits. For instance, IR has been associated with improved financial performance, reduced cash flow volatility, and enhanced market liquidity [3,4,9,22]. Additionally, positive relationships between IR and corporate value have been observed, particularly in sectors sensitive to sustainability [5,20]. However, the voluntary nature of IR in many jurisdictions poses challenges regarding the quality of information delivered to the market. IR seeks to provide a comprehensive view of corporate performance by linking financial results with social, environmental, and governance impacts. Nonetheless, sustainability information often relies on diverse sources that may be perceived as less reliable compared to those used for financial data preparation [23]. External assurance emerges as a mechanism to enhance the credibility of sustainability data.

The Importance of Sustainable Information Assurance

Credibility plays a vital role in financial markets, enabling investors to distinguish between well-performing companies and those engaging in greenwashing practices [24]. The literature presents mixed findings on the benefits of external assurance of sustainability information, particularly in markets where such assurance is not a regulatory requirement. Some authors argue that external verification increases the credibility of reports and reduces information asymmetry [7,8]. Moreover, external assurance has been linked to lower capital costs, improved market liquidity, and reduced earnings forecast errors [9,10,22]. However, other studies suggest that this mechanism does not significantly enhance information quality or correlate with higher market value [6,11].

Recent research highlights that investors are more likely to trust companies whose sustainability disclosures are externally assured, especially when aligned with recognized frameworks, such as ISAE3000 or AA1000AS [10]. These standards are widely used for sustainability assurance, with each offering unique advantages. For instance, AA1000AS has been associated with increased market liquidity and greater stakeholder acceptance of reports [11]. Meanwhile, ISAE3000 has been found to promote significant green transformation within companies [25]. Despite these benefits, challenges persist. Greater disclosure of sustainability performance can incentivize greenwashing [24]. Furthermore, the lack of standardization in assurance requirements and the absence of mandatory regulations in many countries hinder widespread adoption. High assurance costs also represent a barrier for small- and medium-sized enterprises in emerging economies.

Some studies suggest that separate reports for financial and non-financial information may better communicate specific assurance levels, while combined reports could dilute understanding of assurance coverage [26]. This finding is particularly relevant for IR, which integrates financial and non-financial information into a single document. Therefore, this study seeks to determine whether the disclosure of IR and the assurance of sustainability information in a voluntary setting affect market liquidity and financial analysts’ earnings forecast accuracy. By using the bid–ask spread (BAS) as a specific proxy for market liquidity and the earnings forecast error (FERROR) as a general proxy for information asymmetry, this research examines the impact of IR presentation, non-financial information assurance, and auditing standards on these variables in the Chilean market following the publication of the International IR Framework.

Chile provides a relevant context for analyzing the impact of IR and the assurance of non-financial information due to its distinctive market characteristics. Unlike jurisdictions with more extensive mandatory disclosure frameworks, Chilean firms operated in a voluntary reporting environment during the period analyzed, where IR adoption was guided by strategic considerations rather than regulatory mandates. This setting offers a unique opportunity to examine how voluntary IR practices affect market liquidity and information asymmetry. Additionally, the Chilean market is characterized by concentrated ownership structures and lower levels of financial analyst coverage compared to developed economies, which amplifies the importance of voluntary disclosure mechanisms in enhancing transparency. By investigating IR and assurance practices in this context, this study contributes to a broader understanding of how voluntary sustainability disclosures can strengthen investor confidence and improve capital market efficiency in emerging economies.

This study aims to examine the relationship between IR presentation, assurance of non-financial information, and market outcomes, particularly their impact on bid–ask spreads (BASs) as a proxy for market liquidity and earnings forecast errors (FERROR) as an indicator of information asymmetry. The research seeks to determine whether IR and assurance practices contribute to reducing information asymmetry and fostering transparency in emerging financial markets.

3. Materials and Methods

This study examined whether the presentation of IR, the external assurance of non-financial information (sustainability information), and the use of auditing standards impacted market liquidity and the accuracy of earnings per share forecasts in the Chilean context. The analysis focused on the period following the publication of the International IR Framework in 2013. Two econometric models were employed to test the hypotheses.

The first model investigated market liquidity using the bid–ask spread (BAS), a widely accepted proxy for information asymmetry [4,27]. The BAS is calculated as the natural logarithm of the daily average difference between bid and ask prices at market close, divided by the midpoint price [28]. Lower information asymmetry encourages greater market participation, reducing the BAS; therefore, firms disclosing IRs are expected to exhibit a lower BAS due to increased transparency [29]

This study focused on the 2013–2017 period, with an additional six-month window before and after the evaluation period, as reports are typically published months after the fiscal year-end [29]. The study period (2013–2017) was chosen to capture the voluntary adoption of IR following the publication of the International IR Framework in 2013. This timeframe allows for an analysis of market responses in an environment where IR adoption was driven by strategic corporate decisions rather than regulatory mandates. Furthermore, while reporting frameworks in Chile evolved over time, the introduction of Norma de Carácter General 461 (NCG 461) in 2022 marked a regulatory shift that made IR reporting mandatory. Extending the study period beyond 2017 could introduce confounding effects, as firms’ reporting practices may have been shaped by changing disclosure expectations rather than voluntary strategic considerations. By focusing on the 2013–2017 period, this study provides valuable insights into the role of voluntary IR adoption and assurance in an emerging market context.

The independent variable IR indicates whether a firm disclosed integrated reports (IRs; 1 = Yes, 0 = No). To determine this, corporate reports were manually reviewed from the company’s official website and/or the Chilean regulatory authority’s database. Through this process, we identified whether firms disclosed sustainability-related information and classified their reporting approach as either an integrated report or a standalone sustainability report.

The variable AUD captures whether the firm engaged external assurance for non-financial information (1 = Yes, 0 = No). The inclusion of IR and AUD as independent variables was based on empirical evidence suggesting that IR and external assurance can reduce information asymmetry and enhance market confidence [16,22]. To determine this, the complete corporate reports were carefully reviewed to assess whether the disclosed sustainability information, or any part of it, had been independently audited by a third party. If external assurance was identified, the firm was assigned a value of 1; otherwise, it was assigned a value of 0. Although the primary independent variable AUD captured whether a firm’s non-financial information was externally assured, the specific use of auditing standards, such as ISAE3000 and AA1000, was analyzed separately as an additional robustness check rather than being explicitly incorporated into the main regression equations. This approach allowed us to first examine the general effect of assurance on market liquidity and forecast accuracy and subsequently assess whether the choice of assurance standard influenced these relationships. Given that firms voluntarily select these standards, their impact was examined through an extended analysis beyond the core models, ensuring robustness across different assurance frameworks. To conduct this analysis, additional dummy variables were introduced to assess the sensitivity of the results to the adoption of ISAE3000 and AA1000. To determine standard adoption, all reports were manually reviewed to identify whether companies explicitly adhered to any of these assurance frameworks. Since sustainability assurance was not mandatory during the analyzed period, firms had the discretion to follow a recognized standard, potentially as a means of signaling credibility and differentiation in the market. If a report explicitly stated that the sustainability information had been assured under ISAE3000 or AA1000, the corresponding dummy variable was assigned a value of 1; otherwise, it was set to 0.

Since the disclosure of sustainability information was not mandatory in the analyzed period, companies could choose whether to report such information or not. Among those that did disclose sustainability-related data, some opted for an integrated report (captured by the IR variable), while others published separate sustainability reports. To account for this distinction, the dummy variable SR was included, taking the value of 1 if the company disclosed sustainability information through an independent sustainability report and 0 otherwise.

3.1. Control Variables Model 1

To ensure robustness, control variables were incorporated. In financial markets, external investors often do not have the same level of access to detailed and high-quality information as insiders. In contrast, insiders generally possess more comprehensive knowledge, allowing them to monitor corporate activities more effectively and participate more actively in market transactions [28]. To account for this, the variable INSTITUTIONAL represents the proportion of shares held by long-term institutional investors, as their presence can significantly impact corporate oversight and market engagement [28]. The literature has found that larger firms are more likely to generate higher-quality disclosures, while companies with greater analyst coverage can help mitigate information asymmetry by providing more refined earnings forecasts [30]. Based on this, firm size (SIZE) was measured as the natural logarithm of total assets, and analyst coverage (ANALYST) was represented by the number of analysts issuing earnings forecasts. Both variables served as proxies for the quality of the information environment in this study.

Barth, Cahan [29] suggested that there is a significant and positive relationship between market liquidity and company complexity. They argued that for more complex firms, integrating the business model—an essential component of IR—can be challenging. Following Markarian and Parbonetti [31], company complexity was controlled through the market-to-book value of assets ratio (MVBVA). This is because assets, among other factors, are key drivers of business growth, and growth opportunities have been associated with company complexity [32]. To control for industry-specific effects that may influence market liquidity and earnings forecast accuracy, we included IND as a categorical variable, representing the industry classification of each firm. Industry effects are relevant, as different sectors exhibit varying levels of information asymmetry, regulatory requirements, and sustainability disclosure practices. By incorporating IND as a control variable, we accounted for sectoral differences that could systematically impact the relationships between IR, external assurance, and market outcomes. Industry fixed effects were included in the regression models to ensure robust estimations, where Ɛ represents the error term.

In summary, the econometric Model 1 is presented as follows:

$$\begin{array}{c}{\mathrm{B}\mathrm{A}\mathrm{S}}_{\mathrm{i},\mathrm{t}}=\mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{I}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{A}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}{+{\mathsf{\beta }}_3{\mathrm{S}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+\mathsf{\beta }}_4\mathrm{I}\mathrm{N}\mathrm{S}\mathrm{T}\mathrm{I}\mathrm{T}\mathrm{U}\mathrm{T}\mathrm{I}\mathrm{O}\mathrm{N}\mathrm{A}\mathrm{L}+{\mathsf{\beta }}_5{\mathrm{A}\mathrm{N}\mathrm{A}\mathrm{L}\mathrm{I}\mathrm{S}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{S}\mathrm{I}\mathrm{Z}\mathrm{E}}_{\mathrm{i}\mathrm{t}}\\ +{\mathsf{\beta }}_7{\mathrm{M}\mathrm{V}\mathrm{B}\mathrm{V}\mathrm{A}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{ℇ}}_{\mathrm{i}\mathrm{t}}\end{array}$$

(1)

3.2. Model for Forecast Accuracy (FERROR)

The second model evaluated the precision of financial analysts’ earnings forecasts. Forecast error (FERROR) was calculated as the absolute difference between forecasted and actual earnings per share (EPS), adjusted by the share price at the beginning of the fiscal year [33]. This inverse measure of forecast precision was examined for the fiscal year (FY1) and the subsequent two fiscal years (FY2 and FY3). Lower FERROR values indicated higher forecast precision (see Model 2):

$${\mathrm{F}\mathrm{E}\mathrm{R}\mathrm{R}\mathrm{O}\mathrm{R}\left(\mathrm{Y}\right)}_{\mathrm{i}\mathrm{t}}={\displaystyle \frac{\frac{1}{\mathrm{N}}\sum _{\mathrm{j}=1}^{\mathrm{N}}⌊{\mathrm{A}\mathrm{E}\mathrm{F}}_{\mathrm{i},\mathrm{t},\mathrm{j}}^{\mathrm{Y}}-{\mathrm{E}\mathrm{P}\mathrm{S}}_{\mathrm{i},\mathrm{t}}^{\mathrm{Y}}⌋}{{\mathrm{P}}_{\mathrm{i},\mathrm{t}}}}$$

(2)

where FERROR(Y)it represents the absolute error of all forecasts made during the fiscal year for reported earnings per share, adjusted by the stock price at the beginning of the year. Following [33], the indicator Y takes three values (0, 1, and 2), representing the earnings outcomes and projections for the current year and the subsequent two years, respectively. The subscripts (i, t, and j) denote the evaluated company, year, and forecast, respectively. The acronym AEF stands for “analyst earnings forecast”, while EPS refers to “earnings per share”. The indicator “P” represents the market price obtained at the time of the forecast. The study was limited to a maximum of two years because financial analysts typically do not provide forecasts beyond the second fiscal year. Extending the analysis beyond two years significantly reduces the sample size for the forecasts considered.

The independent variables IR and AUD measured the impact of IR presentation and external assurance on forecast accuracy (see Model 3). As previously discussed, these variables were manually reviewed and defined based on the disclosure practices observed in corporate reports. Dummy variables for ISAE3000 and AA1000 standards were also included to assess their effects, following the same identification process detailed earlier.

3.3. Control Variables Model 3

As in Model 1, SR was included in Model 3 to capture the effects of companies that disclose sustainability information through independent reports. As previously discussed, this variable was manually identified based on corporate disclosures. Also, ANALYST was included as a control variable. It has been argued that a higher number of financial analysts increases the incentive to narrow the gap between forecast errors and the entity’s report [33]. Firm size was controlled through the natural logarithm of total assets. Additionally, earnings volatility (VAREARN) was included as a control variable. Volatility is negatively associated with earnings predictability, making it more challenging for analysts to forecast [34]. VAREARN was measured using the natural logarithm of the standard deviation of the earnings per share time series. Ref. [35] suggested that company losses make earnings more volatile. Therefore, a dummy variable LOSS was used to control for whether the company reported losses during the analyzed period. Finally, SIZE was calculated as the natural logarithm of total assets. As previously defined in Model 1, IND represents the industry classification of each firm and was included as a control variable to account for sector-specific differences that may influence forecast accuracy. Given that industries vary in terms of financial transparency, analyst coverage, and disclosure practices, incorporating IND allowed us to control for these structural differences, where Ɛ represents the error term. All models included year and industry fixed effects. For a comprehensive description of all variables used in the study, including their definitions and measurement criteria, refer to Appendix A.

Models were tested using a balanced panel data in Stata 18.5 software by estimating the following ordinary least square (OLS) regression in equation models.

Model 3 is specified as follows:

$$\begin{array}{ll}{\mathrm{F}\mathrm{E}\mathrm{R}\mathrm{R}\mathrm{O}\mathrm{R}\left(\mathrm{y}\right)}_{\mathrm{i},\mathrm{t}}=& \mathsf{\alpha }+{\mathsf{\beta }}_1{\mathrm{I}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{A}\mathrm{U}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{S}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{A}\mathrm{N}\mathrm{A}\mathrm{L}\mathrm{I}\mathrm{S}\mathrm{T}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{V}\mathrm{A}\mathrm{R}\mathrm{E}\mathrm{A}\mathrm{R}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{L}\mathrm{O}\mathrm{S}\mathrm{S}}_{\mathrm{i}\mathrm{t}}\\ & +{\mathsf{\beta }}_6{\mathrm{S}\mathrm{I}\mathrm{Z}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_7{\mathrm{I}\mathrm{N}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{ℇ}}_{\mathrm{i}\mathrm{t}}\end{array}$$

(3)

4. Results

Table 1. Panel A: sample description (2013–2017). Panel B: industry breakdown.

Panel A. Sample Description (2013–2017)	Observations
Companies covered annually by Thomson Reuters I/B/E/S for the Chilean market	108
Total observations for companies with available data to calculate the BAS (2013–2017)	540
Total observations for companies with available data to calculate FERROR in fiscal year 1 (FY1; 2013–2017)	217
Total observations for companies with available data to calculate FERROR in FY2 (2013–2017)	215
Total observations for companies with available data to calculate FERROR in FY3 (2013–2017)	202
Companies that disclosed integrated reports (IRs)	24
Companies that disclosed sustainability reports (SRs)	205
Externally audited reports	64
Companies that used ISAE3000 standard in assurance	34
Companies that used AA1000 standard in assurance	3
Panel B. Industry Breakdown	%
Basic materials	12%
Consumer goods	19%
Consumer services	9%
Financial sector	20%
Health	4%
Industrial	14%
Gas	2%
Telecommunications	3%
Basic utilities	17%

Source: Author’s elaboration. Variable definitions are consistent with the prior discussion.

provides a detailed description of the sample. The final composition comprised 540 observations (108 companies per year) for the study related to the bid–ask spread (BAS). For the second study, the sample size decreased because not all companies were followed by financial analysts. For companies that were covered, analysts issued earnings per share (EPS) forecasts for the current fiscal year. Additionally, some analysts extended their forecasts to one or two fiscal years beyond the current period. While a minority of analysts predicted a third fiscal year, including this in the analysis would significantly reduce the sample size. Consequently, as detailed in Table 1, the study included 217 observations for the current fiscal year (FY1), 215 for the subsequent year (FY2), and 202 for the third fiscal year (FY3). Combining both studies, the total number of unique observations for non-financial information disclosures was 229, of which 24 corresponded to integrated reports (IRs), and 205 were sustainability reports (SRs). On average, 28% of the reports were externally audited, and over 57% adhered to an auditing standard. Consistent with prior research, ISAE3000 was the most frequently utilized standard among auditing providers.

An industry analysis revealed that the majority of the sample was concentrated in sectors such as consumer goods, financial services, industrials, and basic utilities. These sectors demonstrated a prominent representation in the dataset, reflecting their relevance within the economic structure of the analyzed market.

Table 2. Statistical description (N = 540 BAS; N = 217 FERROR).

	Minimum	Maximum	Mean	Median	Std. Dev.
BAS	−7.60	7.13	1.46	1.58	2.47
FERROR1	0.000039	6.86	0.12	0.05	0.68
FERROR2	0.000036	8.00	0.12	0.06	0.77
FERROR3	0.000060	5.32	0.06	0.06	0.38
IR	0	1	0.04	0.08	0.21
SR	0	1	0.38	0.64	0.49
AUD	0	1	0.12	0.25	0.32
INSTITUTIONAL	0.00	0.49	0.03	0.02	0.07
ANALYST	0.00	2.81	0.59	1.51	0.84
SIZE	15.18	24.35	20.53	21.67	1.86
MVBVA	0.00	0.01	0.00	0.00	0.00
VAREARN	−13.82	6.26	−0.05	0.94	4.93

Source: Author’s elaboration. Variable definitions are consistent with the prior discussion.

provides a statistical summary of the analyzed sample. The mean (median) value of the dependent variable BAS was −1.46 (1.58), with minimum and maximum values of −7.60 and 7.13, respectively. Similarly, FERROR exhibited minimum (maximum) average values of 0.000045 (6.73) across the three fiscal years analyzed. Regarding disclosure practices, only 4% of firms reported integrated reports (IRs), indicating that IR adoption remained limited in Chile during the analyzed period. In contrast, 38% of firms disclosed sustainability reports (SRs), showing a higher prevalence of standalone sustainability disclosures. External assurance (AUD) was observed in 12% of the sample, suggesting that independent verification of sustainability data was not a common practice.

Among the control variables, firm size (SIZE) showed a mean of 20.53, indicating a sample composed mainly of medium-to-large firms. Institutional ownership (INSTITUTIONAL) had a low mean of 3%, consistent with concentrated ownership structures in emerging markets. The number of analysts covering firms (ANALYST) varied from 0 to 2.81, with a mean of 0.59, reflecting limited analyst coverage in the Chilean market.

The inclusion of these descriptive statistics demonstrated the heterogeneity within the sample and provided a foundation for subsequent analyses of the relationship between IR, SR, and financial performance metrics.

Table 3. Market liquidity (BAS), integrated reporting, and audit. Period 2013–2017.

		C1	C2	C3
Variables	Predictive Sign	IR	AUD	AUDIT STANDARD
IR	-	−1.48 ***	−1.42 ***	−1.45 ***
AUD	-		−0.78 **
ISAE3000	-			−1.19 **
AA1000	-			1.595
SR		0.005	0.215	0.131
INSTITUTIONAL		−1.97	−1.84	−1.91
ANALYST		−0.22	−0.2	−0.22 **
SIZE		0.156 **	0.160 **	0.180 **
MVBVA		681.9 ***	737.2 ***	788.4 ***
Constant		−1.95	−2.09	−2.51 *
Metrics
Observations (N)		540	540	540
R-squared		0.08	0.9	0.1
Adjusted R-squared		0.07	0.8	0.8

Authors’ elaboration: ***, **, and * indicate significance levels at 1%, 5%, and 10%, respectively. Variable definitions are consistent with the prior discussion.

displays the regression results analyzing the variables under study. Using ordinary least squares (OLS) to estimate the parameters proposed in Model 1, Column 1 coefficients revealed a negative and significant association between the BAS and IR (−1.48; p < 0.003), indicating that firms adopting IR experienced greater market liquidity, as reflected in a narrower bid–ask spread. The economic significance of this effect suggests that firms issuing IRs exhibited a substantial reduction in information asymmetry, which aligns with prior research emphasizing IRs’ role in enhancing market efficiency. An industry-specific analysis (not tabulated) revealed that this effect was particularly strong in the consumer services sector (−5.11; p < 0.001) and basic materials (−1.25; p < 0.1).

To assess potential multicollinearity in the main regressions, variance inflation factor (VIF) and tolerance (1/VIF) tests were conducted. These are widely used methods to identify multicollinearity issues [36]. In Model 1, all associated values (VIF and tolerance) were below 2 (1.36 and 0.7, respectively), indicating no multicollinearity problems.

To further explore whether external verification of non-financial information enhanced market liquidity, Column 2 incorporated the variable AUD, which captured whether sustainability disclosures were subject to external assurance. Results indicated that external verification was statistically significant, reducing the BAS (−0.788; p < 0.034), confirming that assurance mechanisms contributed to improving market confidence. Moreover, IRs continued to exhibit a negative and significant association with the BAS (−1.426; p < 0.005), suggesting that both reporting and assurance played complementary roles in reducing information asymmetry. An industry-specific analysis of the AUD variable (not tabulated) showed that the basic materials sector (−0.64; p < 0.10) and consumer goods sector (−1.128; p < 0.04) drove this significance.

In Column 3, a robustness check examined the role of auditing standards (ISAE3000 and AA1000) in shaping market perceptions. Findings indicated that firms applying ISAE3000 experienced a negative and significant effect on the BAS (−1.191; p < 0.012). Conversely, the AA1000 standard did not yield significant results (1.595; p < 0.252), which may be attributed to its lower adoption in the analyzed sample. Multicollinearity tests (VIF and tolerance) confirmed no issues, with average values of 1.4 and 0.7, respectively, ensuring robust estimates.

This study evaluated whether the presentation of IR was positively associated with the accuracy of earnings forecasts. Results in

Table 4. Earnings forecasts (FERROR), integrated reporting, and audit. Period 2013–2017.

		Column 1—IR			Column 2—IR/AUD
Variables	Predictive Sign	FY1	FY2	FY3	FY1	FY2	FY3
IR	-	−0.11	−0.11	−0.02	−0.09	−0.1	−0.02
AUD	-				−0.06	−0.06	−0.01
SR	-	−0.12	−0.11	−0.04	−0.08	−0.08	−0.03
ANALYST		0.014	0.014	0.00	0.011	0.011	0.00
SIZE		0.00	0.00	0.00	0.00	0.00	0.00
VAREARN		−0.12 ***	−0.12 ***	−0.03	−0.12 ***	−0.12 ***	−0.03
LOSS		−0.02	−0.03	0.069	−0.03	−0.04	0.067
Constant		2.814 ***	2.874 ***	0.821 *	2.886 ***	2.947 ***	0.843
Observations (N)		217	215	202	217	215	202
R-squared		0.09	0.07	0.03	0.09	0.07	0.03
Adjusted R-squared		0.06	0.04	0.01	0.06	0.04	0.01

Authors’ elaboration: ***, and * indicate significance levels at 1%, and 10%, respectively. Variable definitions are consistent with the prior discussion.

, Column 1, indicated that there was no significant relationship between IR disclosure and forecast accuracy across the three fiscal years analyzed. Similarly, as shown in Column 2, the external assurance of non-financial information did not appear to improve the precision of financial analysts’ forecasts. One possible explanation for these results lies in the characteristics of the Chilean market. Unlike more developed financial markets, Chile has a relatively shallow capital market with limited analyst coverage. As a result, financial analysts may prioritize traditional financial indicators over non-financial disclosures when forming their earnings projections. This contrasts with evidence from other contexts, where higher-quality sustainability disclosures have been linked to reduced forecast dispersion [5]. However, these studies did not specifically analyze the role of IR or its assurance in forecast accuracy. Additionally, the explanatory power of the models (R-squared and adjusted R-squared) was relatively low, which suggests that other factors not captured in this study played a more significant role in explaining forecast accuracy. Future research could explore whether the quality of IR disclosure—rather than its mere presence—affects analysts’ ability to refine their projections. Prior literature suggested that higher-quality reports provide more useful decision information, which could influence analysts’ earnings estimates in contexts where sustainability disclosures are more integrated into investment decision-making.

Table 5. Sensitivity Analysis of Earnings Forecasts (FERROR) Considering Assurance Standards (ISAE3000 and AA1000), Period 2013–2017.

	IR
Variables	FY1	FY2	FY3
IR	−0.1	−0.11	−0.02
ISAE3000	−0.06	−0.05	−0.01
AA1000	−0.09	−0.09	−0.02
SR	−0.09	−0.09	−0.03
ANALYST	0.009	0.01	0.00
SIZE	0.00	0.00	0.00
VAREARN	−0.12	−0.12	−0.03
LOSS	−0.02	−0.03	0.069
Constant	2.857	2.916	0.832
Observations (N)	217	215	202
R-squared	0.09	0.07	0.03
Adjusted R-squared	0.06	0.04	0.01

Author’s elaboration. Variable definitions are consistent with the prior discussion.

refines the analysis presented in Table 4 by distinguishing between the assurance standards used—ISAE3000 and AA1000—instead of considering external assurance (AUD) as a single category. This differentiation allowed us to evaluate whether the choice of assurance framework influenced the forecast accuracy. Although these standards represent subsets of AUD, they were analyzed separately to assess whether different assurance approaches yielded distinct effects. The results indicated that neither ISAE3000 nor AA1000 significantly impacted forecast accuracy across the three fiscal years analyzed. These findings align with the results observed for AUD in Table 4, suggesting that in the Chilean market, external assurance—whether in general or under a specific standard—does not systematically improve analysts’ earnings forecast precision. Thus, external assurance did not appear to play a significant role in shaping financial analysts’ expectations.

An additional observation was the variation in coefficient estimates for SRs. In Table 4, SR exhibited coefficients of −0.12, −0.11, and −0.04 across FY1 to FY3, while in Table 5, these estimates stabilized at −0.09 across all periods. This discrepancy arose due to the inclusion of additional assurance-related variables in Table 5 (ISAE3000 and AA1000), which marginally adjusted the estimated effects of SR. However, the overall negative sign remained consistent, reinforcing the notion that firms disclosing standalone sustainability reports, rather than integrating them within IR, did not experience improved forecast accuracy. These results suggest the need for further research into whether the quality and integration of sustainability disclosures enhance their usefulness for financial analysts, particularly in emerging markets.

5. Discussion and Conclusions

This study contributed to the ongoing discussion on the impact of IR and external assurance on market liquidity and earnings forecast accuracy. The findings indicated that IR significantly reduced information asymmetry, improving market liquidity. This aligns with prior research suggesting that IR enhances transparency and investor confidence [4,14,22]. Additionally, the positive role of external assurance—particularly under the ISAE3000 standard—reinforced previous findings on the credibility-enhancing effects of sustainability assurance [8,10]. However, the study did not find a significant impact of IR and external assurance on the accuracy of earnings forecasts. This result contrasts with studies in developed markets, where analysts benefit from structured non-financial disclosures [29,33]. One possible explanation is that, in the Chilean context, financial analysts may already rely on other sources of information, such as direct engagement with firms or industry reports, rather than voluntary sustainability disclosures. Moreover, the discretionary nature of IR in the analyzed period may have led to variability in reporting quality, affecting its usefulness for forecast accuracy. Prior studies also suggested that the extent to which IR influences analysts’ forecasts depends on the maturity of capital markets and the regulatory environment [23,32]. Unlike mandatory settings, where reporting quality is standardized, the discretionary nature of IR in Chile allowed for significant variation in reporting practices. This variation may explain why the impact on market liquidity was evident, yet no clear effect was found on analysts’ earnings forecasts. From a practical perspective, the results suggested that firms seeking to enhance investor confidence should consider not only adopting IR but also ensuring that their non-financial information is externally assured under recognized standards. Regulators, in turn, may benefit from policies that encourage standardized sustainability reporting to improve capital market efficiency.

Future Research Directions

Future research could delve deeper into the content and quality of audited non-financial information to assess its materiality and relevance to stakeholders. This opens a series of critical questions that remain unanswered and could attract significant attention from researchers. For instance:

• To what extent does the proportion of audited information relative to the total disclosed information influence its perceived credibility and market impact?
• How does the level of assurance provided—limited or reasonable—affect stakeholders’ confidence, especially among financial analysts and institutional investors?
• What are the perceptions of financial analysts regarding the role of auditing non-financial information? Does it serve as a signal of enhanced transparency, or could it be interpreted as an overcompensation for weak internal governance?
• How do different auditing standards (e.g., ISAE3000 vs. AA1000) shape the market’s perception of the credibility of sustainability disclosures?

Moreover, future studies could explore the cross-cultural dimensions of these issues by comparing markets with different regulatory environments and levels of adoption of IR and sustainability audits. For example, investigating whether mandatory audit requirements in more regulated markets produce different effects on market liquidity or earnings forecast accuracy could provide valuable insights.

Another avenue for research could focus on longitudinal studies assessing the long-term impacts of sustainability audits and IR on corporate value, capital costs, and market behavior. The interplay between the materiality of non-financial information and its assurance could also be analyzed to provide practical guidance for both firms and regulators seeking to optimize transparency and accountability in financial markets.