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Article

Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies

Department of Accounting, Faculty of Finance and Accountancy, Budapest University of Economics and Business, 1149 Budapest, Hungary
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Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(10), 584; https://doi.org/10.3390/jrfm18100584
Submission received: 15 August 2025 / Revised: 30 September 2025 / Accepted: 9 October 2025 / Published: 15 October 2025
(This article belongs to the Special Issue Accounting Ethics and Financial Management)

Abstract

Accounting manipulation undermines the integrity of financial reporting and can distort key performance indicators, yet its quantitative effects on accounting quality (AQ) and value-related metrics remain underexplored. This study analyses U.S. publicly traded firms involved in accounting manipulation between 2017 and 2019, comparing them with matched non-manipulative industry peers to assess differences in AQ. It also examines potential links between manipulation-related AQ distortions and changes in Economic Value Added (EVA), stock prices, trading volumes, and dividend payouts. The sample includes 57 manipulation-affected firms and 57 matched controls, identified through SEC enforcement filings and the Violation Tracker database. Financial and stock data were sourced from EDGAR, ORBIS, and Morningstar. AQ was measured using discretionary accruals estimated via the Kasznik model. Correlation analysis tested associations between AQ and the selected performance indicators. Results show that firms involved in accounting manipulations had significantly lower AQ than their peers. However, no consistent correlations were found between AQ and EVA, dividends, stock prices, or volumes during the manipulation period. These findings suggest that the performance effects of manipulations are case-specific and shaped by additional factors, underscoring the importance of strong regulatory oversight and high-quality accounting practices. Ethically, our evidence underscores that misreporting corrodes investor trust and the public-interest mandate of financial reporting; accordingly, we stress the duties of boards, executives, auditors, and regulators to uphold faithful representation and timely disclosure, and to remediate misreporting when detected.

1. Introduction

Over the past two decades, numerous academic studies have investigated the causes, consequences, and detection of accounting manipulation. Researchers have explored these phenomena from ethical, economic, and organizational perspectives, analysing both individual and systemic risk factors that contribute to the occurrence of fraud and manipulations. Numerous studies have developed predictive models and conceptual frameworks for manipulation and fraud detection and prevention, frequently assessing their applicability across industries and firm sizes (Rezaee, 2005; Giroux, 2008; Richardson et al., 2022). In parallel, growing attention has been paid to the use of emerging technologies such as artificial intelligence, big data analytics, and machine learning in enhancing manipulation and fraud detection and financial risk assessment (Bao et al., 2020; Cockcroft & Russell, 2018; Craja et al., 2020; Jan, 2018, 2021). These advancements have not only facilitated earlier identification of manipulation but have also underscored the severe implications of accounting misconduct on firm-level financial health and broader capital market trust. An ethics-centred perspective is employed to frame these questions in terms of the profession’s public-interest mandate. Despite extensive work on manipulation, fraud detection, and value relevance, prior studies rarely link enforcement-verified manipulation to matched non-manipulative peers over a multi-year horizon while jointly examining EVA, dividend policy, prices, and trading volumes. Most rely on proxy screens for misreporting or focus on single market outcomes, making it difficult to isolate manipulation-related reporting frictions from underlying fundamentals. This leaves a clear research gap on whether and how accrual-based accounting-quality deterioration associated with manipulation maps into multiple value-creation metrics.
In this study, we use the term value-creation gap to denote a divergence between contemporaneous changes in accrual-based accounting quality (AQ) and movements in value metrics—EVA, dividend payouts, stock prices, and trading volumes. Such a gap is theoretically plausible because EVA deducts a capital charge and therefore may not rise with short-horizon earnings tactics unless underlying economic profit improves; dividend policies are typically smoothed, which limits near-term adjustments; and capital market reactions often unfold with disclosure lags and gradual investor learning. Moreover, operational window-dressing—such as pulling sales forward or deferring expenses—can buoy reported performance without creating durable value, so AQ may deteriorate while EVA and payouts appear temporarily resilient and prices reprice only as credibility shocks are absorbed. Real-world enforcement records illustrate these timing frictions: the SEC’s Under Armour order describes repeated revenue pull-forwards and disclosure failures with enforcement arriving years after the practices were used; Kraft Heinz faced charges for a long-running expense-management scheme that culminated in restatements; and General Electric settled disclosure-violation charges following protracted revelations in power and insurance, with substantial repricing over 2017–2018. Together, these episodes show how manipulation-related AQ deterioration can precede or exceed the immediate response of EVA, payouts, and market variables, thereby motivating our empirical tests of a value-creation gap.
The phenomenon of fraud and manipulation in financial markets cannot be reduced merely to issues of efficiency; it is imperative to recognise that these practices erode the very foundation upon which trust is built, thereby imposing costs on stakeholders who lack the necessary information to make informed decisions. In accordance with the qualitative characteristics established by the International Accounting Standards Board (IASB) and the Financial Accounting Standards Board (FASB), the concept of faithful representation functions as both a measurement ideal and an ethical obligation. Leadership ‘tone at the top’, robust assurance, and credible oversight are therefore central to sustaining decision-usefulness and curbing opportunism. Consequently, we interpret adverse movements in accounting quality (AQ) as signals of ethical hazard and governance failure, with implications for investor protection and market integrity.
In corporate finance research, firm value and its determinants have been examined extensively, particularly through the lens of capital structure, corporate governance, and financial performance indicators (Aydoğmuş et al., 2022; Biehl et al., 2024; Brown & Caylor, 2006; Y. Chen et al., 2023; Fatemi et al., 2018). While significant attention has been given to the role of sustainability and economic indicators in shaping firm value, comparatively less is known about the consequences of poor accounting quality—especially when rooted in manipulative or fraudulent behaviour—on firm valuation (Donelson et al., 2021). Existing literature has mostly explored accounting fraud’s and manipulation’s macroeconomic and reputational effects, or its detection and enforcement, without fully addressing how fraud- and manipulation-induced deterioration in accounting quality affects key value indicators such as Economic Value Added (EVA), dividend payouts, stock price and volume movements (Bhuiyan & Ahmad, 2022; Donelson et al., 2022; Erragragui et al., 2023; Karajian & Ullah, 2022; Kapons et al., 2023; Richardson et al., 2022). This gap is especially pronounced in longitudinal, firm-level analyses that connect accounting quality with capital market behaviour before, during, and after manipulation incidents.
Accounting quality refers to the degree to which financial statements reflect a firm’s actual economic condition, comply with applicable standards, and reduce information asymmetry among stakeholders (Dechow et al., 2010; Francis et al., 2004; Barth et al., 2008). High-quality reporting enables better valuation accuracy, promotes investor confidence, and supports efficient capital allocation. Conversely, low accounting quality, often associated with accounting manipulation or fraud, increases the risk of misvaluation, weakens investor trust, and may precipitate adverse market reactions (Hribar & Nichols, 2007; Tiron-Tudor & Achim, 2019). Despite this, empirical investigations that explicitly quantify these effects using integrated value-based metrics remain limited.
This study seeks to address that research gap by examining the relationship between accounting quality and firm valuation in the context of accounting manipulation. Using a sample of U.S. publicly traded firms involved in accounting manipulation between 2017 and 2019, the paper investigates whether firms that engaged in manipulation exhibited lower accounting quality than comparable, non-manipulative industry peers. Furthermore, it explores how changes in accounting quality relate to EVA, dividend policy, stock price trajectories, and trading volume in the years surrounding manipulation detection.
Beyond classical accounting-quality frameworks, recent practice-oriented research highlights institutional mechanisms that shape misreporting risk and the credibility of financial statements. Board structure and audit committees’ attributes are central: greater independence and financial expertise are associated with lower accrual-based earnings management and constrained real earnings management, while board co-option can erode audit quality and oversight effectiveness (Zadeh et al., 2023; Alquhaif & Alobaid, 2024). In parallel, the salience of sustainability reporting has increased. Capital-market evidence from the EU’s non-financial disclosure mandate indicates that ESG-related transparency can affect investor reactions and perceived credibility, yet rating-agency disagreement underscores measurement challenges that matter for valuation and assurance (Grewal et al., 2019; D. M. Christensen et al., 2022). We therefore position our U.S. evidence within this broader governance- and ESG-aware landscape and return to cross-jurisdictional comparability in the discussion.
Recent studies reaffirm that the deterioration of accrual-based accounting quality (AQ) and manipulation are closely linked to firms’ value creation capacity and market reactions. Evidence from regulatory reforms shows that firm profitability enhances, while losses diminish, reporting quality, and that larger firms and IFRS adoption contribute to higher AQ and stronger investor confidence (Al-Shehri, 2025). Dividend policy and corporate governance mechanisms have also been shown to moderate managers’ propensity for earnings manipulation, while in emerging markets, incentives for accrual-based earnings management remain strong and can be traced in both stock price movements and dividend payouts (Mlawu et al., 2025).
Further research highlights that manipulation significantly reduces the value relevance of financial statements (Burlacu et al., 2024; Al-Shattarat, 2021), and that dividend policy can act either as a reinforcing or weakening factor in the relationship between AQ and market value (Markonah et al., 2020; Koo et al., 2017). These findings support the “value-creation gap” concept, whereby changes in AQ do not fully align with movements in value metrics such as EVA, dividends, stock prices, and trading volumes.
The research is guided by the following research questions:
Q1: Is there a statistically significant difference in the accounting quality of firms engaged in accounting manipulation compared to their industry-matched, non-manipulative peers during the study period?
Q2: What is the relationship between accounting quality and changes in the Economic Value Added indicators of listed firms involved in manipulation during the research period?
Q3: What is the relationship between the accounting quality of the listed firms involved in manipulation and the dividends paid during the research period?
Q4: What is the relationship between the accounting quality of listed firms involved in manipulation and the change in their stock price over the research period?
Q5: What is the relationship between the accounting quality of the listed firms involved in manipulation and the change in their trading volume over the research period?
This paper contributes to the existing literature in four key ways: (1) It offers empirical evidence on the interplay between accounting manipulation and firm valuation using multiple financial and market-based indicators. (2) It enriches the accounting quality literature by applying a dynamic, multi-year perspective that tracks how reporting reliability correlates with shifts in firm value over time. (3) It expands the understanding of how different dimensions of valuation—economic (EVA), distributional (dividends), and market-based (price and volume)—are affected by deteriorations in accounting quality. (4) It provides practical implications for investors, regulators, and corporate governance stakeholders in identifying early warning signs of misreporting and assessing their valuation consequences.
The remainder of the paper is structured as follows. Section 2 reviews the relevant literature on accounting quality and valuation metrics. Section 3 describes the data, sample construction, variables, and analytical methods. Section 4 presents the empirical results and hypothesis testing. Section 5 discusses the implications of the findings, limitations, and potential alternative explanations. Section 6 concludes by summarizing the key contributions and suggesting directions for future research.

2. Literature Review

2.1. Accounting Quality

Our review focused on studies from the 2000s that examined financial reporting quality. Database searches using the queries “accounting quality” and “financial reporting quality” yielded numerous publications. From these, we selected the most relevant literature. Table 1 provides a structured overview of the key concepts identified in these studies.
A review of professional publications suggests that there is no uniform approach and explanation of what constitutes accounting quality. Thus, the quality of accounting statements is not an exact concept. It is not directly observable, and measuring it requires the use of estimates and abstractions. Therefore, there are several approaches to assessing the quality of accounting statements, whether theoretical or practical. Even if researchers do not declare it, the methodology they use can be traced back to how they have approached the quality of reporting in their research. Some researchers have used methodologies that look at the issue from a user perspective (e.g., value relevance of reported data, Elbakry et al. (2017), Uwuigbe et al. (2017)), while others have used models that are more able to measure compliance with regulations (e.g., use of discretionary accruals, Fields et al. (2018), Chowdhury et al. (2018)), but there are also studies where the scope of the methodology used is not separable, measuring a characteristic that is both important for users and for regulation (e.g., the use of bankruptcy models to compare the quality of accounting reports, Bodle et al. (2016)).
In the course of the research, it was concluded that, basically, when discussing the quality of accounting reporting, the quality of reporting is identified with the quality characteristics formulated by the IASB. Some of the above definitions refer to the IASB’s discussion of quality as a measure of decision usefulness that improves the quality of financial reporting (FRQ). Jorissen (2015) and Pacter (2017) acknowledge that ‘high quality’ accounting information is the lifeblood of the capital market. The IASB’s mission creates transparency by improving the international comparability and ‘quality’ of accounting information, enabling investors and other market participants to make economic decisions. The literature outlines that many researchers identify the quality of accounting with the quality of accruals, which leads to an examination of earnings management. According to some researchers, legal culture, controls (auditing), and future cash flows are the factors that determine the quality of accounting reporting. Some literature identifies the quality of accounting with compliance with the basic principles, and it is considered as quality if it contains reliable and true information on the financial, income and wealth situation of the enterprise. Overall, it can be said that the qualitative grouping of the IASB is the starting point for the literature review, and the researcher adds additional factors and characteristics to these qualities.
The recent literature emphasizes that AQ is shaped not only by firm-level attributes but also by institutional environments and regulatory mechanisms (Cabán, 2024). Consequently, manipulation risk is directly connected to capital market responses and investor trust (Elrayah & Makhmudov, 2023).

Characteristics of Accounting Quality According to the IASB

According to the International Accounting Standards Board (IASB), the fundamental principle for assessing accounting quality relates to the accuracy of the objectives and the quality of the information disclosed in an entity’s financial statements. These qualitative characteristics enhance the decision-usefulness of financial statements and thus overall reporting quality. Consistent with contemporary frameworks, high-quality financial information is relevant and faithfully represented—that is, complete, neutral, and free from error—and is further enhanced by comparability, verifiability, timeliness, and understandability (IFRS Foundation, 2021; FASB, 2024). Recent evidence also indicates that such characteristics improve usefulness and reduce information asymmetry (Tran, 2022; Farshadfar & Monem, 2023). Based on the above definitions, it can be concluded that the quality of accounting reporting is defined as a financial reporting process that produces useful information for users and that is complete—i.e., it includes all transactions and information relating to the financial year—transparent and not misleading, and that meets the quality characteristics of accounting information, namely relevance, reliability, comparability, timeliness and understandability. The usefulness of a given concept is predicated on two fundamental characteristics: relevance and faithful representation. These characteristics are themselves augmented by additional features, namely understandability, timeliness, comparability, and verifiability.
The concept of relevance in information theory pertains to the ability of information to influence decisions through its predictive and confirmatory value. It assists users in formulating expectations regarding future conditions while validating or revising prior assessments of past events. The effectiveness of this approach is contingent upon materiality, defined as the potential impact of omission or misstatement on decision-making processes. Additionally, the concept of utility plays a pivotal role in determining the relevance of information. The relevance of information is determined by its consideration in the interpretation of past or present events. The timeliness of information is a crucial factor in its utility, as it enables the influence of information on decision-making processes to be exerted during the relevant time period.
In order to ensure faithful representation, it is imperative that statements depicting underlying economic phenomena are complete, neutral, and free from error. In instances where estimates are unavoidable, the nature and uncertainty of these estimates must be transparent.
It is imperative that the material is comprehensible; for this, clarity and brevity are paramount, presuming that the users have a reasonable degree of financial knowledge and diligence. The concept of timeliness entails the ability to respond promptly without compromising the integrity of measurement. Speed is valuable only to the extent that it does not compromise the accuracy and fairness of the assessment.
Comparability is a process that enables users to relate information across entities and periods without enforcing uniformity. This in turn provides meaningful benchmarks that strengthen relevance. The concept of verifiability is realised when informed, autonomous observers can reach reconcilable conclusions from the same evidence, even if perfect consensus is not a requirement. Collectively, these characteristics provide the conceptual foundation for evaluating the quality of financial statements.
To summarise, under the IASB Conceptual Framework, the IASB has defined the essential attributes as reliability (faithful representation) and relevance. The hierarchy of accounting attributes defines these two primary qualities and how they relate to decision makers and decision making.
It can be concluded that these factors are interrelated systemic elements, but that a number of other factors may also affect the state of accounting quality, such as changes in the external and internal regulatory environment, the independence of auditors and the quality of the service they provide, and certain technologies used in the preparation of financial statements. Factors contributing to the deterioration of accounting quality include distortions caused by accounting and financial manipulations.
Although a large body of work has examined the measurement, determinants and consequences of earnings quality, prior research has rarely positioned accounting fraud and manipulation as a distinct independent variable within literature reviews and empirical designs. Instead, misreporting is often treated as a contextual backdrop or proxied indirectly through restatements, enforcement actions, or abnormal accruals (Dechow et al., 2010; Hennes et al., 2008; Donelson et al., 2021). A more explicit treatment of accounting fraud and manipulation as an exogenous shock or firm-level condition is conceptually warranted because the theoretical channels by which misreporting degrades accounting information are direct: it undermines faithful representation and relevance, thereby reducing the decision-usefulness of reported numbers and mechanically lowering accruals-based proxies of accounting quality (Dechow et al., 2010). Moreover, modelling accounting fraud and manipulation as an independent variable clarifies its ramifications for other outcomes. Capital-market studies show that lower earnings/accruals quality is priced via a higher cost of equity and debt (Francis et al., 2004, 2006), while misstatement events trigger economically large market penalties and reputational losses (Karpoff et al., 2008) and negative abnormal returns around restatement announcements (Palmrose et al., 2004). Beyond contemporaneous price impacts, lower reporting quality is associated with frictions in price discovery—slower incorporation of information and higher required returns—which trace back to information risk (Callen et al., 2013). Importantly, explicit misreporting constructs sharpen empirical identification: fraud-screening models and misstatement prediction frameworks (Dechow et al., 2011) help distinguish accounting fraud and manipulation from mere estimation noise, reducing Type I/II errors in research designs (Donelson et al., 2021). Framed this way, accounting fraud and manipulation is expected to exert first-order effects on AQ and, through the information-risk channel, on value-creation metrics and market-based variables that this study analyses (EVA, dividends, prices, volumes). Treating misreporting as a dedicated explanatory construct therefore complements the IASB/FASB quality framework and aligns with agency-theoretic and value-relevance perspectives that link credibility of reporting to firms’ financing costs, investment efficiency, and investor reactions (Biddle et al., 2009; Francis et al., 2004, 2006; Karpoff et al., 2008; Palmrose et al., 2004).
A plethora of models and frameworks have been developed for the purpose of measuring accounting quality and evaluating financial statements and financial reports. The predominant conceptual approaches can be classified as follows (ElMoatasem Abdelghany, 2005; Ewert & Wagenhofer, 2012; Nikolaev, 2018; Hribar & Nichols, 2007; Penman & Zhang, 2002):
  • earnings management-based models;
  • earnings conservatism-based models;
  • relevance and market context models;
  • earnings quality-based models;
  • models also related to financial and accounting manipulation detection.
The approaches presented herein can be divided into two categories: quantitative and qualitative. Quantitative methods include regression models and time series analyses, which are the most commonly used mathematical and statistical methods. However, more recent research has also incorporated more sophisticated data mining and machine learning techniques, which can address the parametric limitations of many statistical tests. In the case of accounting manipulation analysis methods, studies focus not only on identifying the areas of interest, but also on developing prevention and detection procedures. Qualitative and other methods include the analysis and comparison of audit opinions, analysts’ opinions, content analysis of certain accounting reports, financial reports, the use of text mining techniques and questionnaire research. In our current research, we used earnings management-based multivariate estimator models to examine the value of accounting quality.

2.2. Economic Value Added

The Economic Value Added (EVA) model is a financial performance measurement tool developed by Stern Stewart & Co. to determine the economic value creation of a firm (Stewart, 1991). The EVA model compares the firm’s Net Operating Profit After Taxes (NOPAT) with the cost of capital invested to show the ability of the firm to generate value above investor expectations (Young & O’Byrne, 2001).
The EVA is positive if the firm’s profit exceeds the cost of capital invested, whereas if it is negative, the firm is unable to cover the return expected by investors, i.e., it depreciates in value (Drucker, 1995).
As demonstrated in the extant literature, Economic Value Added (EVA) is a prevalent tool in the analysis of corporate performance and the design of financial decision-making and management incentive schemes (Biddle et al., 1997). Research indicates that EVA exhibits a stronger correlation with shareholder value than other traditional measures, as it eliminates the distorting effects of accounting earnings and the possibility of accounting manipulation (S. Chen & Dodd, 2001).
The utilization of EVA is particularly pertinent for firms that engage in substantial capital investments, as it facilitates the comprehension of whether these investments contribute to the augmentation of shareholder value (Grant, 2003). Nevertheless, the use of EVA is not without its limitations. These include the intricacy of the calculation process and the necessity for numerous adjustments to the financial statements to ensure the accuracy of the firm’s performance assessment (Worthington & West, 2001).
Defined by formula:
E V A = N O P A T C o s t   o f   I C =   N O P A T I C × W A C C =   ( R O I W A C C ) × I C =   E B I T ×   ( 1 t ) I C × W A C C
where
IC = Invested Capital
EBIT = Earnings Before Interest and Taxes
NOPAT = Net Operating Profit After Taxes
ROI = Return on Investment
t = Tax rate
WACC = Weighted Average Cost of Capital

2.3. Accounting Quality and Firm Value

The relationship between accounting quality and firm value is fundamentally grounded in several core theoretical frameworks, including signaling theory, agency theory, and the efficient market hypothesis. According to signaling theory, as discussed by Spence (1973), financial statements act as signals to investors regarding the firm’s future prospects. High-quality earnings—as part of the accounting quality concept characterized by relevance, reliability, and accuracy—serve as a credible signal of sound management and sustainable profitability, thus enhancing investor confidence and ultimately increasing firm value. This is echoed in the work of Imbiri and Sjarief (2018), who emphasized that optimal earnings signal favorable firm outlooks, acting as market cues that can lead to valuation premiums.
From an agency theory perspective, the quality of earnings mitigates information asymmetry between managers and shareholders. Jensen and Meckling (1976) argue that one of the core challenges in firm valuation arises from agency conflicts, where managers may engage in earnings management to serve their own interests, thus distorting reported profitability. High-quality, audited, and conservatively prepared financial statements help bridge this gap and align managerial actions with shareholder interests.
Empirical studies further affirm these theoretical positions. Dang et al. (2020) found a statistically significant and positive association between earnings quality and firm value, particularly when measured using Tobin’s Q and economic value added (EVA). Their research demonstrates a time-lagged effect where improved earnings quality in one period correlates with higher firm valuation in subsequent periods, indicating that markets gradually incorporate the credibility of reported earnings into pricing mechanisms.
Moreover, research by Bao et al. (2020) and Craja et al. (2020) introduces a modern dimension to the analysis through the lens of technology. They demonstrate how artificial intelligence (AI)-based fraud and manipulation detection systems can identify inconsistencies and manipulations in financial reporting. Their findings not only validate earlier concerns about the reliability of reported earnings but also support the assertion that manipulation significantly affects market perception and contributes to volatility, thereby undermining firm value.
In international contexts, the relationship between earnings quality and firm value has been extensively studied but remains complex due to regulatory, institutional, and cultural differences. For instance, Gharaibeh and Qader (2017), using data from the Saudi Stock Exchange, report that volatility in earnings significantly reduces investor confidence and firm value, especially in less transparent markets. Similarly, Sarun (2016), analysing Malaysian firms, reveals that corporate governance mechanisms moderate the relationship between accounting quality and firm valuation, suggesting that structural factors play a role in how financial information is interpreted by investors.
Simanullang et al. (2021) provide further evidence from the Indonesian banking sector, highlighting that both return on assets (ROA) and return on equity (ROE) positively affect firm value, especially when the capital structure is controlled. Consistent with recent literature, the valuation consequences of earnings quality are context dependent—varying with firm—and market-specific conditions that shape information asymmetry and contracting frictions (Dechow et al., 2010); moreover, stronger corporate governance amplifies the positive association between earnings quality and firm value/performance (Intara et al., 2024).
While many studies converge on the positive effects of earnings quality on firm value, some scholars report more nuanced or even contradictory results. Wibisono and Andesto (2023) observe that in certain economic environments, especially where governance is weak or financial literacy is low, the relationship between accounting quality and firm value is either insignificant or negative. These findings suggest that high-quality reporting alone may not suffice to enhance firm value unless accompanied by a broader ecosystem of transparency and regulatory enforcement. Adding to the theoretical depth, the efficient market hypothesis (Fama, 1970) posits that all publicly available information, including high-quality accounting data, is immediately reflected in stock prices. This theoretical standpoint supports the idea that earnings quality directly affects market valuation, assuming markets function efficiently. However, in real-world markets with behavioural biases and varying levels of information dissemination, the impact of accounting quality may be delayed or diffused, explaining the mixed empirical findings.
In conclusion, the connection between accounting quality and firm value is well-established both theoretically and empirically, though mediated by contextual and methodological factors. A comprehensive understanding requires integrating classic financial theories with modern empirical insights, while also accounting for international variability in institutional settings. New evidence suggests that accrual management undermines the performance of earnings-based valuation models (Courteau et al., 2015), thereby directly affecting firm valuation. Studies on AQ and dividend policy show that higher reporting quality is associated with different payout strategies and reduced agency costs (Koo et al., 2017). Dividend policy may also strengthen the link between earnings quality and market value, justifying a multidimensional approach to the analysis (Markonah et al., 2020). When considered as a whole, the extant evidence gives rise to a pivotal question: namely, how do verified discrepancies in accounting quality influence firm value when measured not only by market prices but also by EVA, dividends, and trading volumes? This unresolved link forms the foundation of our hypotheses.

2.4. Development of Hypotheses

The present paper tests five hypotheses concerning the connection between manipulation-related accounting quality and value metrics. The hypotheses are drawn from the literature reviewed in Section 2.1, Section 2.2 and Section 2.3, which encompasses research on accrual-based AQ, value relevance, and market reactions.
H1. 
Firms engaged in accounting manipulation exhibit significantly lower accounting quality than their non-manipulative counterparts during the years analysed.
It is evident from previous research that the quality of earnings is considered to be pivotal in determining the usefulness of decisions and in the reduction of information asymmetry. It has been demonstrated that manipulation, by its very nature, is detrimental to these properties. Accrual-based models have been developed for the purpose of detecting such misreporting; accordingly, it is expected that firms which have been subject to manipulation will display systematically larger discretionary components in comparison to industry controls. This expectation is also consistent with the IASB/FASB framework (faithful representation, relevance) and the agency-theoretic link between opportunism and reporting distortions outlined in Section 2.1, Section 2.2 and Section 2.3.
H2. 
During the period of accounting manipulation, firms involved in the misconduct exhibit a relationship between accounting quality and changes in EVA.
The agency theory posits that substandard reporting practices can lead to an escalation in information risk and the cost of capital. As the Economic Value Added (EVA) model explicitly calculates operating returns by subtracting the capital charge based on the weighted average cost of capital (WACC), an augmentation in information risk is predicted to result in a compression of EVA. As demonstrated in Section 2.3, the value-relevance arguments similarly predict weaker value creation when reported numbers are less credible.
H3. 
During the period of accounting manipulation, firms involved in the misconduct exhibit a relationship between accounting quality and dividend payout.
Two competing mechanisms are observed to motivate a non-directional test. It is evident that reduced AQ has the potential to engender heightened frictions and liquidity constraints, which may result in a diminution of payouts. Conversely, managers encountering credibility deficits may opt to augment dividends, thereby conveying a sense of financial robustness. In light of the varied theoretical appeal and the cross-market heterogeneity emphasised in Section 2.3, a test for correlation is conducted without an a priori commitment to a positive outcome.
H4. 
During the period of accounting manipulation, firms involved in the misconduct exhibit a relationship between changes in stock price and accounting quality indicators.
From the perspectives of signalling and value-relevance, credible earnings support price formation, whereas lower quality heightens information risk and dampens valuation. As stated in Section 2.3, it is important to note that markets have the capacity to incorporate credibility signals with lags. Therefore, in order to capture both immediate and delayed reactions, it is necessary to assess quarterly price changes relative to the year’s baseline. Consequently, a correlation between AQ and price changes is anticipated.
H5. 
During the period of accounting manipulation, firms involved in the misconduct exhibit a relationship between changes in trading volumes and accounting quality indicators.
Declining reporting quality has been shown to engender a state of heightened uncertainty and discord among investors, a phenomenon that is commonly characterised by an escalation in trading activity upon the arrival of news or the updating of beliefs. In accordance with the market behaviour lens outlined in Section 2.3, it is therefore anticipated that AQ will demonstrate a correlation with within-year fluctuations in trading volume.

3. Materials and Methods

The sample selection process aimed to identify U.S.-listed firms on the NYSE or NASDAQ that were involved in accounting manipulation during 2017–2019, a window chosen as the last period of economic normalcy prior to COVID-19, thereby enabling an undistorted analysis of corporate performance and accounting quality. We define accounting manipulation as conduct that produced misstated GAAP financial statements and is credibly documented by authoritative sources. In practice, cases were identified through U.S. Securities and Exchange Commission (SEC) enforcement materials—such as administrative orders, civil complaints, and AAER-related documents—corroborated by court decisions and government records, and public restatements filed on EDGAR (Form 8-K Item 4.02 or amended 10-K/10-Q) where the stated reason was an accounting impropriety rather than a clerical correction or disclosure-only change. The Violation Tracker public database served as an auxiliary lead generator; all such leads were verified against the underlying SEC, court, and EDGAR documentation. Only firms for which there was clear and verifiable evidence of misconduct directly related to accounting or financial reporting irregularities—such as revenue inflation, accrual manipulation, or the presentation of falsified audited financial statements—were retained. To ensure comparability in regulatory and reporting environments, we excluded firms in the financial sector (NAICS 52/SIC 6000–6999), firms not headquartered in the United States, and firms with non-standard fiscal year-ends.
Applying these criteria yielded 57 manipulation-affected firms. For cases spanning multiple fiscal years, we mapped the first misstated fiscal year within 2017–2019 as the event year for matching and measurement, while accounting-quality variables reflect the relevant misstated year or years within that window. Financial statement variables and narrative disclosures were drawn from EDGAR, fundamentals were cross-checked with ORBIS, and market variables (prices, trading volumes, and dividends) were obtained from Morningstar. Each manipulation-affected firm was paired with a single non-manipulative peer, producing 57 matched controls. Matching proceeded on industry at the most granular feasible level (targeting exact four-digit NAICS classifications, with three-digit fallback when necessary), on the same fiscal year as the event year, and on size using nearest-neighbour proximity in the logarithm of total assets at the start of the fiscal year, with a caliper to avoid extreme mismatches; where multiple candidates remained, proximity in market-to-book and pre-event return on assets was used to break ties. Candidate controls were screened using the same enforcement and restatement sources to confirm the absence of manipulation over the sample window and were required to have complete accounting and market data in the event year. The resulting balanced sample of 57 manipulation-affected firms and 57 matched controls provides a coherent basis for the study’s comparative analyses of accounting quality and related value metrics.
Further filtration was applied prior to the finalisation of the sample. Firms were excluded if their exposure stemmed from events occurring outside the review period or if no suitable industry-year-size control could be allocated. In industries characterised by a substantial number of otherwise indistinguishable competitors who all met the matching thresholds, a simple random sampling method was employed among the eligible candidates to select the control group. This approach was adopted to preserve industry relevance while limiting researcher discretion and reducing the scope for selection bias. While the resulting sample is not intended to be fully representative of the entire capital market, its composition exhibits sufficient diversity in industry and size to support the validity of the empirical comparisons.
To enhance transparency, we include descriptive statistics (e.g., industry classification, firm size) for the sampled firms so that readers can understand the context and robustness of the analysis.
We analysed the selected firms’ financial and accounting data for 2017–2019, examining multiple relevant dimensions. The item counts for the final sample and the control sample of the survey are presented in Table 2. The item counts and distributions by stock exchange are presented in Table 3. Table 4 shows the number and distribution of the firms involved in the study according to market capitalisation categories.
The samples have been classified according to the Global Industry Classification Standard created by Morgan Stanley Capital International and S&P Global, which is presented in Table 5.
The empirical investigation of accounting manipulation is constrained by the paucity of widely accessible, reliable, and detailed databases concerning the industry distribution of firms involved in such misconduct. Case studies and legal proceedings related to accounting manipulation typically focus on individual incidents, which do not lend themselves to the establishment of industry-level statistical inferences. Furthermore, firms frequently do not admit to intentional accounting manipulation, as a result, such cases often remain concealed or are disclosed under alternative classifications (e.g., accounting errors, compliance deficiencies).
Consequently, in our research, we utilize the industry distribution of financial statement restatements published by the Center for Audit Quality (CAQ, 2024) for the period 2017–2019 as a proxy for declining accounting quality and potential manipulation. A significant proportion of restatements are indicative of irregularities or errors that emanate from a deterioration in accounting quality, and are often suggestive of deliberate distortion or fraud. In this respect, the CAQ report provides a suitable reference point for estimating industry-level risk and assessing the representativeness of the sample.
The Financials sector was excluded from the comparison because it is characterized by a different regulatory environment and is not included in the research sample. Similarly, the category of “business services” was not included in the sample, resulting in its industry breakdown not being presented in the industry breakdown of the CAQ report.
Consequently, the corresponding distributional ratios could not be compared. The comparative table presents the average industry distribution of restatements for the years 2017–2019 based on the CAQ (2024) data, alongside the actual distribution in the sample and the deviation between the two, thereby aiding the interpretation of potential distortions (Table 6).
The financial and accounting reporting data were collected using the Securities and Exchange Commission’s Electronic Data Gathering, Analysis, and Retrieval System during the designated data collection period. For additional data collection, the ORBIS database of Bureau van Dijk was utilized. The Morningstar platform provided historical data on stock prices, dividend payments, and changes in the volume of securities issued over the period for the firms under study. The Damodaran Online database was instrumental in calculating the weighted average cost of capital for the EVA indicator.
Most variables examined (and the model results) were measured on a continuous (metric) scale.
To estimate accounting quality, we evaluated the multivariate regression models described earlier and used the model with the highest explanatory power (Appendix B Table A3). We then compared the sample’s average accounting quality (AQ) values with those of the control group. Furthermore, we examined the relationship between AQ and the sample’s EVA, average dividends, and changes in average trading volume and stock price.
The use of correlation analysis in this study was driven primarily by the exploratory nature of the research and the goal of identifying preliminary patterns between key variables, namely accounting quality, EVA, dividend distributions, and stock market performance. This methodological choice was also influenced by the limited sample size (N = 114), which restricts the statistical power of more complex multivariate or causal models. The objective at this stage was to map baseline associations that could later serve as a foundation for more advanced causal modeling in future studies. It is worth noting that correlation-based methods are commonly used as a first-step approach in studies involving rare events, such as accounting manipulation, especially when sample sizes are constrained. Accordingly, while we acknowledge the limitations of correlational inference, we argue that this methodological approach was appropriate for the current stage of research and sample structure, and we recommend expanding the model in future work using more rigorous econometric tools.
Table 7 presents the methodological framework applied in this study, outlining the steps from sample selection to variable measurement and analysis. The table summarizes how manipulation-affected firms and their matched controls were identified, the sources of financial and market data, and the operationalization of key constructs such as AQ, EVA, dividends, stock prices, and trading volumes. This structured overview highlights the transparency of the research process and clarifies the exploratory scope of the analysis.

4. Results

As part of our analysis, we also applied the model developed by Roychowdhury (2006), which investigates real earnings management through three sub-models: abnormal operating cash flows, abnormal production costs, and abnormal discretionary expenses (Appendix B Table A4). These components capture manipulation via operational decisions rather than accruals. However, in our dataset, the application of these models did not yield statistically significant results. This may be attributed to the limited sample size and the heterogeneous industry characteristics of the firms involved. In the interest of methodological transparency, we note that these analyses were conducted, but since the findings did not materially support our hypotheses, we opted not to report them in detail in the final manuscript.
After defining the variables and collecting the financial statement data, we conducted multivariate linear regression analyses using the Enter method. We calculated the error terms (εi,t) for each year’s regression, which enabled us to estimate the direction and magnitude of any discretionary influence on the outcomes
In these models, a zero error term indicates high-quality, unbiased financial reporting. Conversely, a non-zero error term signals distorted, biased accounting information, which increases information asymmetry and undermines the reliability of information provided to stakeholders.
Prior literature suggests that researchers should assess not only the direction of the error terms but also their magnitude. Therefore, we analysed the absolute values of the error terms to enable comparisons across firms. This approach ensures that the model’s outputs are comparable across different companies and suitable for further analysis. This will ensure that the statistical models can be applied in practice and that the values derived from the methodology used are accurate and unbiased for further analysis.
Table 8 presents the results of four different accounting-quality measurement models (Jones, 1991; Dechow et al., 1995; Kasznik, 1999; Kothari et al., 2005). Based on the average R2 values, the Kasznik model had the highest explanatory power (0.652) and the Jones model the lowest (0.352). This finding suggests that the Kasznik model is better at detecting accounting mismatches, likely due to a more sensitive approach to assessing accounting quality.
Across most models, explanatory power declines consistently over the years. For example, the Jones model’s R2 fell from 0.562 in 2017 to 0.240 in 2019, indicating fewer accounting mismatches over that period. The Modified Jones model also dropped (from 0.709 to 0.290). In contrast, the Kasznik model showed less volatility (0.748 in 2017 vs. 0.633 in 2019), suggesting its sensitivity remained more stable. The Kothari model likewise declined (0.723 in 2017 to 0.365 in 2019).
A comparison of the models indicates that the Kasznik model consistently demonstrates higher values, suggesting that it may exhibit heightened sensitivity to accounting mismatches. The Modified Jones and Kothari models exhibit medium sensitivity, yielding comparable results, while the Jones model exhibits the lowest values annually, indicating potential reduced sensitivity. However, these differences underscore that perceptions of accounting quality depend on which measurement model is used. Therefore, any comparisons or interpretations should be framed in the context of the specific model. In light of these findings, we used the Kasznik model in subsequent stages of the research to estimate accounting quality.
The results presented in Table 9 demonstrate that the degree of autocorrelation was minimal in all the years studied, as the values were close to the benchmark of 2. This finding suggests that the estimated values of the Kasznik model are reliable over the years and that there is no significant systematic bias or time dependence in the data series.
The results for the Kasznik model sample and the control sample are displayed in Table 10.
The analysis of the evolution of AQ (accounting quality) based on the data of the sample analysed and the control sample showed significant differences between 2017 and 2019 based on descriptive statistics. An increase in the AQ value indicates poorer accounting quality; therefore, the evolution of the indicator provides important insights for comparing the accounting practices and quality of the two groups. The manipulated sample’s median and mean AQ values rose each year, indicating a deterioration in quality over time. In contrast, the control group’s AQ values were much lower and nearly flat, indicating better and more stable accounting quality in the controls. The trends in standard deviation of AQ also differed notably between the groups. For the manipulated sample, AQ variance was low in 2017–2018 but jumped higher in 2019, indicating much more variability in that final year. By contrast, the control sample’s AQ variance stayed small and stable, suggesting a more homogeneous distribution of quality. According to the Shapiro–Wilk test, both samples’ AQ values were approximately normally distributed (with only a slight deviation for the manipulated sample in 2019). Large differences were also observed for the minimum and maximum values. The minimum AQ values for the analysed sample exhibited a similarity in 2017, 2018 and 2019, while the minimum values for the control sample were characterized as well below in all years, indicating that some firms possessed exceptionally good accounting quality. The maximum values for the analysed sample increased significantly in 2019, while they remained much lower and more stable in the control sample.
The estimated values related to accounting quality in the analysed sample exhibited a steady deterioration over time, particularly in 2019, when the quality values became more extreme and there was a higher dispersion. In contrast, the accounting quality of the control sample remained more stable and higher throughout the period. These results suggest that the sample analysed demonstrates a quality difference in the model used for firms involved in accounting manipulation.
Figure 1 shows the evolution of the sample means over time and the differences between the samples. It can be seen that there are large differences in the proportions and further statistical tests are required to examine significant differences. As the data in Table 10 show that there are different variances between the samples, Kruskal–Wallis (based on median) and Welch’s t-test (based on mean) were used to test for differences between the samples.
To examine the homogeneity of variances across the accounting quality indicators over the observed years, a Levene’s test was performed. The results indicate significant differences in variance among the annual data series, suggesting that the dispersion of accounting quality measures varied notably between years. These findings highlight potential structural changes in data consistency during the examined period (Table 11).
Both the Kruskal–Wallis test (Table 12) and the Welch’s t-test (Table 13) indicate that there are significant differences in accounting quality between the analysed sample and the control sample in all three years at the 5% significance level. The results of both tests indicate that the analysed sample has lower accounting quality, as the means and medians of accounting quality are significantly higher and the variances are also larger. These findings confirm Hypothesis 1. The firms involved in accounting manipulation consistently show significantly lower accounting quality than the control firms in every year examined (p < 0.01). The robust results from both the Welch and Kruskal–Wallis tests provide strong empirical evidence that accounting manipulation leads to measurable degradation in accounting quality.
In our further analyses, we examined the relationship between accounting manipulation and changes in economic value added through accounting quality, based on the sample item data, the correlation results of which are reported in Table 14.
The results of the correlation matrix demonstrate that there is no general trend-like relationship between accounting quality and changes in the economic value added indicator over the observed years in periods affected by accounting manipulation. Nevertheless, it is important to emphasise that the bias in accounting quality in 2019 exhibited a negative weak correlation with the change in economic value added in 2018. These findings offer partial support for Hypothesis 2. In addition, a negative moderately strong correlation is also observed in 2019, which may indicate that accounting manipulation in the financial year may have reduced the change in the economic value added indicator, and thus, high accounting quality may be able to increase the change in the value of this indicator.
It is also important to note that a multitude of factors unrelated to accounting manipulation heavily influence EVA. For instance, revenue growth driven by market expansion or higher product demand can have a substantial impact on EVA. In addition, the optimisation of the cost structure (through efficiency improvements and lower procurement costs) plays a pivotal role. The efficacy of investment decisions and capital allocation (particularly the reduction of the cost of capital) is another pivotal factor. Furthermore, macroeconomic conditions (for example, inflation, GDP growth, or regulatory changes) have been shown to have a substantial impact on a firm’s economic performance, in addition to other financial strategic decisions. The intricate interplay among these factors exerts a more substantial influence on EVA’s trajectory than manipulation alone could.
The results of the correlation matrix examining the relationship between average annual dividends paid by the firms in the sample and accounting quality are presented in Table 15.
Our findings do not support Hypothesis 3. In every period we tested, there was no statistically significant relationship between accounting quality and dividend payout levels. Consistently low, insignificant correlations suggest that dividend policy is largely independent of accounting quality in these firms. Moreover, the average dividends paid during the manipulation period are likely driven by broader factors such as the firm’s financial performance, general market conditions, management decisions and strategy, and shareholder expectations. Next, we analysed the relationship between the firms’ accounting quality and their average stock price changes. For this analysis, we considered each year’s starting average stock price and then tracked the quarterly price changes relative to that baseline. The detailed results of this analysis are provided in Appendix A Table A1.
The correlation analysis (see Appendix A Table A1) shows several significant relationships between AQ and quarterly stock price changes. In 2018, we found a moderate, statistically significant negative correlation between AQ and stock price changes; in 2019, a weaker negative correlation was also present. In practical terms, during those years lower accounting quality (due to fraud/manipulation) tended to accompany smaller positive stock price changes. Overall, Hypothesis 4 receives only limited support—the negative AQ–price relationship appears in 2018 but is not consistent in other years. This suggests poor accounting quality might hurt short-term stock performance, but broader market forces likely outweigh its effect over time. Notably, prior studies have not observed similar multi-year patterns. Delayed investor reactions and varying market expectations could explain why the impact of accounting quality on stock prices is inconsistent across periods.
Finally, we analysed another aspect of market capitalisation changes: the relationship between average quarterly trading volume changes and accounting manipulation (see Appendix A Table A2 for detailed results). Our findings show significant associations between accounting quality and fluctuations in trading volume, particularly in 2018 when manipulation was most prevalent. These volume–quality relationships appeared in 2018–2019 and resemble patterns noted in our earlier studies (e.g., regarding exchange rate changes), which partially supports Hypothesis 5. However, the observed correlation is positive but modest. This suggests that during the manipulation period, firms increased their trading volumes (perhaps to offset declines in market capitalisation), thereby amplifying the average volume changes each quarter.

5. Discussion

The present study is distinct from previous research in two key ways. Firstly, it combines SEC enforcement and restatement evidence, a matched control design, and a multi-metric value lens. This methodological approach facilitates the documentation of not only the anticipated decline in accounting quality but also the heterogeneous manner in which such a decline manifests in value outcomes.
Our findings could be further explored by incorporating additional measurement methods, such as using different accounting-quality models and comparing their results, which may open up new research avenues. Applying alternative discretionary-accrual and real earnings management (REM) proxies side-by-side enhances robustness (e.g., comparing Kasznik with Jones or real-EM models) (Jamadar et al., 2022). Moreover, analytical tools based on artificial intelligence could be employed to extend the analysis—for example, by adding more explanatory or control variables and examining a wider range of factors (Kang & Park, 2021). Recent studies indicate that machine learning techniques (e.g., decision trees or neural networks) can improve the detection of manipulated financial reporting. By incorporating more explanatory variables and broader datasets, these AI-driven methods may uncover additional factors influencing accounting quality. Embracing such diverse models and AI tools would not only open new research avenues but also enhance the robustness of our conclusions (Hernandez Aros et al., 2024; Ranta et al., 2023; Zhu et al., 2025).
The study’s limitations include the absence of transparency surrounding material on fraud and manipulation in listed firms, as well as the lack of a central database from which these facts could be readily retrieved by external stakeholders. Other limitations pertain to the availability of a small sample due to data constraints and the limited comparability of adjustment factors and occupational components, which narrows the sample.
Our primary conclusion is that accounting manipulation significantly degrades the accounting quality of firms. We observed a clear disparity in accrual-based accounting quality metrics between firms that engaged in manipulative practices and their non-manipulating peers, consistent with the notion that the very presence of earnings manipulation compromises the integrity and reliability of financial reports (Burlacu et al., 2024). Such manipulated financial data can obfuscate a firm’s true financial position and performance, making key indicators like earnings and assets appear healthier than they really are. This distortion not only skews internal financial ratios but can also mislead investors—evidence shows that when misstatements eventually come to light, investor confidence is severely undermined and stock prices often decline sharply as the market corrects for the earlier misinformation (Ahmad et al., 2021). In our analysis, firms with manipulated accounts did not exhibit a stable, generalizable pattern in Economic Value Added (EVA) changes compared to control firms. This lack of a consistent relationship suggests that EVA’s trajectory is heavily influenced by other factors beyond accounting figures—for example, broader macroeconomic conditions or firm-specific strategies can overshadow the impact of earnings quality on EVA. Similarly, we did not find a uniform link between shifts in accounting quality and dividend payouts or stock price trends. This implies that the market effects of accounting manipulation are context-dependent and may vary over time, rather than being immediately or universally reflected in shareholder returns.
We formulated a theoretical expectation that companies engaging in accounting manipulation would display significantly lower accounting quality than comparable firms, which in turn could affect value-based outcomes (like EVA) and market behaviour. Our empirical tests—notably the accrual analysis using the Kasznik model (1999)—indeed confirmed a pronounced gap in accounting quality between the manipulated firms and their matched controls. This finding aligns with the broader literature that uses discretionary accrual models to successfully flag earnings management and quality issues. However, our study did not uncover any consistent, generalizable relationship between the level of accounting quality and EVA or other market indicators such as stock returns. This outcome suggests that firm value is multifaceted and influenced by many variables beyond accounting metrics, echoing recent observations that macroeconomic forces, industry trends, and corporate strategies can heavily modulate performance and investor valuation (Ibrahimov et al., 2025). In other words, while poor accounting quality is clearly associated with misrepresentation of financial health, its impact on overall firm value metrics can be diluted or masked by these other factors. By replacing the previously cited older studies, we now ground this conclusion in contemporary research, which similarly emphasizes a multi-factor perspective on corporate value creation.
This study contributes to a deeper understanding of the relationship between accounting quality and corporate value, especially during periods of manipulation. It highlights how distorted financial reporting can misrepresent internal performance and undermine investor confidence and market valuation—restatement episodes show significant stock price reversals and eroded trust (Cahan et al., 2024), underscoring the real costs of poor reporting.
From a practical perspective, our findings emphasize that stakeholders (regulators, investors, analysts) should treat accounting quality as a critical risk indicator to help anticipate and prevent value erosion.
The study is subject to practical constraints related to data access, sample size, and industry heterogeneity. Limited access shaped the breadth and granularity of available variables, which narrowed the set of complementary indicators and robustness checks that could be implemented. The sample is relatively small, bounding the complexity of feasible empirical designs and encouraging parsimonious specifications. Meaningful differences across industries also remain; although we account for these through matching and standard controls, sector-specific reporting practices and capital structures may moderate some relationships. These considerations delineate the study’s scope and suggest natural extensions, including broader data coverage, larger multi-year panels, and sector-focused analyses. Future research should aim to expand the dataset, include industry-specific segmentation, and integrate AI-based analytical frameworks to refine prediction accuracy and uncover deeper insights.

6. Conclusions

The conclusions of the present study are consistent with the international literature on the economic consequences of accounting fraud and manipulations. Prior evidence has suggested that EVA may provide a more reliable gauge of corporate performance than traditional accounting indicators, owing to its closer association with shareholder value and market reactions. The present analysis, however, uncovers no consistent relationship between changes in EVA and instances of accounting manipulation, indicating that firm- and industry-specific factors materially shape the evolution of EVA.
Relative to the alternative specifications examined, the Kasznik model exhibits the highest sensitivity to accounting irregularities. These researchers found that the modified Jones model and the Kasznik model can detect the effects of earnings management with greater accuracy. The results presented in this study corroborate the finding that accounting quality can have a significant impact on a firm’s financial performance and its perception in the capital market.
Further research is required to facilitate a more comprehensive understanding of the long-term consequences of accounting fraud and manipulation. It is imperative to investigate the differences between industries and the potential of new technologies, such as artificial intelligence and big data, in fraud and manipulation prevention and identification.
From a policy perspective, the results underscore the critical role of accounting quality in preserving financial market transparency. Accounting manipulation, as demonstrated, not only distorts internal reporting but also erodes investor confidence and market efficiency. These outcomes provide empirical support for enhanced regulatory frameworks, including stricter audit requirements, improved financial disclosure standards, and the promotion of transparent corporate governance.
When evaluated from an ethical perspective, the primary contribution of this study is to demonstrate how even minor declines in AQ can erode the trust that underpins market efficiency. In order to ensure the preservation of this trust, it is essential that managerial incentives are aligned, that assurance mechanisms are robust, and that oversight is credible.
At the managerial level, the study highlights that the manipulation of short-term financial results can ultimately diminish long-term shareholder value. Declining accounting quality may damage corporate reputation, increase the cost of capital, and elevate valuation discounts. Thus, corporate leadership should be held accountable not only for financial outcomes but also for upholding accounting integrity and transparency.
From an academic standpoint, the study contributes to the evolving discourse on the relationship between accounting quality and firm value. It illustrates methodological challenges associated with analysing manipulated data and supports the validity of accrual-based detection models. The research invites further empirical studies, particularly those that explore sector-specific effects and the integration of artificial intelligence in fraud and manipulation detection and financial analysis.
In conclusion, the phenomena of accounting manipulation—along with their determinants and relationships to financial indicators—are open to multiple interpretations. Many questions remain that require further research and clarification by future experts and scholars.

Author Contributions

Conceptualization, S.H. and E.D.; methodology, S.H.; software, Á.L.B.; validation, S.H., E.D. and Á.L.B.; formal analysis, S.H.; investigation, S.H.; resources, S.H.; data curation, Á.L.B.; writing—original draft preparation, S.H.; writing—review and editing, S.H., E.D. and Á.L.B.; visualization, Á.L.B.; supervision, S.H.; project administration, S.H.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from licensed databases (e.g., ORBIS, Yahoo Finance). Restrictions apply to the availability of these data, which were used under institutional license.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Correlation coefficients between accounting quality and average security price change values based on sample data.
Table A1. Correlation coefficients between accounting quality and average security price change values based on sample data.
VariablesAQ_2017AQ_2018AQ_2019∆P_2017_Q1∆P_2017_Q2∆P_2017_Q3∆P_2017_Q4∆P_2018_Q1∆P_2018_Q2∆P_2018_Q3∆P_2018_Q4∆P_2019_Q1∆P_2019_Q2∆P_2019_Q3∆P_2019_Q4
AQ_2017P. corr.
p-value
AQ_2018P. corr.−0.187
p-value0.164
AQ_2019P. corr.−0.2230.181
p-value0.0950.177
∆P_2017_Q1P. corr.−0.011−0.634 ***−0.077
p-value0.936<0.0010.569
∆P_2017_Q2P. corr.−0.089−0.533 ***−0.2130.711 ***
p-value0.510<0.0010.111<0.001
∆P_2017_Q3P. corr.−0.167−0.455 ***−0.1510.315 *0.564 ***
p-value0.214<0.0010.2620.017<0.001
∆P_2017_Q4P. corr.−0.120−0.477 ***−0.1270.330 *0.495 ***0.664 ***
P. corr.0.375<0.0010.3470.012< 0.001< 0.001
∆P_2018_Q1p-value−0.246−0.491 ***0.1720.551 ***0.671 ***0.823 ***0.595 ***
P. corr.0.065<0.0010.200<0.001<0.001<0.001<0.001
∆P_2018_Q2p-value−0.234−0.508 ***0.1940.541 ***0.666 ***0.821 ***0.587 ***0.990 ***
P. corr.0.080<0.0010.149<0.001<0.001<0.001<0.001<0.001
∆P_2018_Q3p-value−0.284 **−0.443 ***−0.264 *0.581 ***0.666 ***0.768 ***0.559 ***0.983 ***0.974 ***
P. corr.0.032<0.0010.047<0.001<0.001<0.001<0.001<0.001<0.001
∆P_2018_Q4p-value−0.275 *−0.397 **−0.375 **0.538 ***0.626 ***0.700 ***0.506 ***0.948 ***0.953 ***0.974 ***
P. corr.0.0390.0020.004<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆P_2019_Q1p-value−0.266 *−0.433 ***−0.359 **0.562 ***0.649 ***0.676 ***0.494 ***0.955 ***0.960 ***0.974 ***0.989 ***
P. corr.0.045<0.0010.006<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆P_2019_Q2p-value−0.239−0.440 ***−0.368 **0.569 ***0.659 ***0.651 ***0.485 ***0.941 ***0.944 ***0.960 ***0.983 ***0.993 ***
P. corr.0.073<0.0010.005<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆P_2019_Q3p-value−0.223−0.427 ***−0.400 **0.569 ***0.622 ***0.588 ***0.427 ***0.910 ***0.915 ***0.931 ***0.967 ***0.975 ***0.991 ***
P. corr.0.096<0.0010.002<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆P_2019_Q4p-value−0.220−0.416 **−0.381 **0.537 ***0.648 ***0.628 ***0.459 ***0.913 ***0.923 ***0.931 ***0.967 ***0.975 ***0.991 ***0.992 ***
P. corr.0.1000.0010.003<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table A2. Correlation coefficients between accounting quality and average security volume change values based on sample data.
Table A2. Correlation coefficients between accounting quality and average security volume change values based on sample data.
VariablesAQ_2017AQ_2018AQ_2019∆V_2017_Q1∆V_2017_Q2∆V_2017_Q3∆V_2017_Q4∆V_2018_Q1∆V_2018_Q2∆V_2018_Q3∆V_2018_Q4∆V_2019_Q1∆V_2019_Q2∆V_2019_Q3∆V_2019_Q4
AQ_2017P. corr.
p-value
AQ_2018P. corr.−0.187
p-value0.164
AQ_2019P. corr.−0.2230.181
p-value0.0950.177
∆V_2017_Q1P. corr.−0.0770.366 **−0.046
p-value0.5710.0050.735
∆V_2017_Q2P. corr.−0.0820.403 **0.0130.976 ***
p-value0.5440.0020.923<0.001
∆V_2017_Q3P. corr.−0.0750.398 **−0.0010.978 ***0.998 ***
p-value0.5810.0020.991<0.001<0.001
∆V_2017_Q4P. corr.−0.0570.312 *−0.0820.982 ***0.983 ***0.983 ***
P. corr.0.6720.0180.546<0.001<0.001<0.001
∆V_2018_Q1p-value−0.0820.407 **0.0120.969 ***0.999 ***0.995 ***0.981 ***
P. corr.0.5430.0020.932<0.001<0.001<0.001<0.001
∆V_2018_Q2p-value−0.0680.375 **−0.0130.986 ***0.995 ***0.996 ***0.993 ***0.993 ***
P. corr.0.6140.0040.923<0.001<0.001<0.001<0.001<0.001
∆V_2018_Q3p-value−0.0860.406 **0.0190.957 ***0.996 ***0.991 ***0.975 ***0.999 ***0.987 ***
P. corr.0.5250.0020.889<0.001<0.001<0.001<0.001<0.001<0.001
∆V_2018_Q4p-value−0.0530.325 **−0.0940.977 ***0.988 ***0.987 ***0.996 ***0.987 ***0.993 ***0.982 ***
P. corr.0.6960.0140.489<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆V_2019_Q1p-value−0.0680.376 **−0.0280.979 ***0.998 ***0.996 ***0.987 ***0.996 ***0.996 ***0.992 ***0.994 ***
P. corr.0.6150.0040.838<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆V_2019_Q2p-value−0.0970.404 **0.0100.970 ***0.994 ***0.990 ***0.978 ***0.993 ***0.990 ***0.990 ***0.984 ***0.994 ***
P. corr.0.4720.0020.942<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆V_2019_Q3p-value−0.0860.401 **−0.0090.976 ***0.999 ***0.995 ***0.983 ***0.998 ***0.994 ***0.996 ***0.989 ***0.998 ***0.995 ***
P. corr.0.5260.0020.949<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
∆V_2019_Q4p-value−0.0820.392 **−0.0070.964 ***0.997 ***0.993 ***0.982 ***0.999 ***0.991 ***0.999 ***0.989 ***0.996 ***0.992 ***0.998 ***
P. corr.0.5420.0030.960<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
* p < 0.05, ** p < 0.01, *** p < 0.001.

Appendix B

Table A3. Formulas and variables of the earnings management models used in the research.
Table A3. Formulas and variables of the earnings management models used in the research.
Model NameModel Formula
Jones (1991) Model T A i , t A i , t 1 = α 1 1 A i , t 1 + β 1 , i   Δ R E V i , t A i , t 1 + β 2 , i P P E i , t A i , t 1 + ε i , t
Dechow et al. (1995) Model T A i , t A i , t 1 = α 1 1 A i , t 1 + β 1 , i   Δ R E V i , t Δ R E C i , t A i , t 1 + β 2 , i P P E i , t A i , t 1 + ε i , t
Kasznik (1999) Model T A i , t A i , t 1 = α 1 1 A i , t 1 + β 1 , i Δ R E V i , t A i , t 1 + β 2 , i P P E i , t A i , t 1 + β 3 , i Δ C F O i , t A i , t 1 + ε i , t
Kothari et al. (2005) Model T A i , t A i , t 1 = α 1 1 A i , t 1 + β 1 , i Δ S A L E i , t Δ R E C i , t A i , t 1 + β 2 , i P P E i , t A i , t 1 + β 3 , i Δ R O A i , t A i , t 1 + ε i , t
Variables
TAi,t = ΔCurrent Assetsi,t − ΔCashi,t − ΔCurrent Liabilitiesi,t − Depreciationi,t
where
TAi,t = total accruals in year t for firm i;
CurrentAssetsi,t = current assets in year t less current assets in year t − 1 for firm i;
ΔCashi,t = cash in year t less cash in year t − 1 for firm i;
ΔCurrentLiabilitiesi,t = current liabilities in year t less current liabilities in year t − 1 for firm i;
Depreciationi,t = depreciation and amortization expense in year t for firm i;
ΔREVi,t = change in revenues of firm i in year t and t − 1;
ΔRECi,t = change in receivables of firm i in year t and t − 1;
ΔSALEi,t = change in sales revenue of firm i in year t and t − 1;
ΔCFOi,t = change in operating cash flow of firm i in year t and t − 1;
PPEit = gross property, plant, and equipment in year t for firm i;
ROAi,t = the return on assets of firm i in year t;
Ai,t−1 = total assets of firm i over year t − 1;
εi,t = error term in year t;
i = the indices for the firms;
t = indices for the periods under examination;
α, β = firm-specific parameters.
Table A4. Formulas and variables of the Roychowdhury (2006) model used in the research.
Table A4. Formulas and variables of the Roychowdhury (2006) model used in the research.
Model AreaModel Formula
Operating cash flow C F O i , t A i , t 1 = α 0 + β 1 , i 1 A i , t 1 + β 2 , i S A L E i , t A i , t 1 + β 3 , i   Δ S A L E i , t A i , t 1 + ε i , t
Production costs P R O D i , t A i , t 1 = α 0 + β 1 , i 1 A i , t 1 + β 2 , i S A L E i , t A i , t 1 + β 3 , i   Δ S A L E i , t A i , t 1 + β 4 , i   Δ S A L E i , t 1 A i , t 1 + ε i , t
Discretionary expenses D I S E X P i , t A i , t 1 = α 0 + β 1 , i 1 A i , t 1 + β 2 , i   Δ S A L E i , t 1 A i , t 1 + ε i , t
Variables
CFOi,t = operating cash flow of firm i in year t;
PRODi,t = production costs of firm i in year t = COGSi,t + ΔINVi,t;
COGSi,t = cost of goods sold of firm i in year t;
ΔINVi,t = change in inventories of firm i in year t and t − 1;
DISEXPi,t = discretionary expenses of firm i in year t;
SALEi,t = sales revenue of firm i in year t;
ΔSALEi,t = change in sales revenue of firm i in year t and t − 1;
ΔSALEi,t−1 = change in sales revenue of firm i in year t − 1 and t − 2;
Ai,t−1 = total assets of firm i over year t − 1;
εi,t = error term in year t;
i = the indices for the firms;
t = indices for the periods under examination;
α, β = firm-specific parameters.

References

  1. Achim, M. A., & Chis, O. A. (2014). Financial accounting quality and its defining characteristics. Practical Application of Science, 2(3), 93–98. [Google Scholar]
  2. Agana, J. A., Zori, S. G., & Alon, A. (2023). IFRS adoption approaches and accounting quality. The International Journal of Accounting, 58(03), 2350009. [Google Scholar] [CrossRef]
  3. Ahmad, S. R., Senan, N. A. M., Ali, I., Ali, K., Khan, I. A., & Baig, A. (2021). Investor reaction to the discovery of accounting fraud: The period from the discovery of the fraud to the completion of the correction. Academic Journal of Interdisciplinary Studies, 10(6), 171–190. [Google Scholar] [CrossRef]
  4. Alquhaif, A. S., & Alobaid, R. O. (2024). Audit committee financial expertise and real earnings management via accretive repurchases: Does CEO power matter? Humanities and Social Sciences Communications, 11(1), 1–17. [Google Scholar] [CrossRef]
  5. Al-Shattarat, B. (2021). The consequence of earnings management through discretionary accruals on the value relevance in Saudi Arabia. Cogent Business & Management, 8(1), 1886473. [Google Scholar] [CrossRef]
  6. Al-Shehri, A. M. (2025). Earnings quality and its determinants in the Saudi capital market: Evidence from accrual-based models under IFRS reform. Journal of Accounting Research, 12, 1–26. [Google Scholar] [CrossRef]
  7. Amer, A. M. M., Azimli, A., & Adedokun, M. W. (2024). Can IFRS adoption mitigate earnings management in an emerging market? Heliyon, 10(19), e38226. [Google Scholar] [CrossRef]
  8. Aydoğmuş, M., Gülay, G., & Ergun, K. (2022). Impact of ESG performance on firm value and profitability. Borsa Istanbul Review, 22, S119–S127. [Google Scholar] [CrossRef]
  9. Ball, R., & Nikolaev, V. V. (2022). On earnings and cash flows as predictors of future cash flows. Journal of Accounting and Economics, 73(1), 101430. [Google Scholar] [CrossRef]
  10. Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235. [Google Scholar] [CrossRef]
  11. Barth, M. E., Landsman, W. R., & Lang, M. H. (2008). International accounting standards and accounting quality. Journal of Accounting Research, 46(3), 467–498. [Google Scholar] [CrossRef]
  12. Bhuiyan, M. B. U., & Ahmad, F. (2022). Dividend payment and financial restatement: US evidence. International Journal of Accounting & Information Management, 30(3), 427–453. [Google Scholar] [CrossRef]
  13. Biddle, G. C., Bowen, R. M., & Wallace, J. S. (1997). Does EVA beat earnings? Evidence on associations with stock returns and firm values. Journal of Accounting and Economics, 24(3), 301–336. [Google Scholar] [CrossRef]
  14. Biddle, G. C., Hilary, G., & Verdi, R. S. (2009). How does financial reporting quality relate to investment efficiency? Journal of Accounting and Economics, 48(2–3), 112–131. [Google Scholar] [CrossRef]
  15. Biehl, H., Bleibtreu, C., & Stefani, U. (2024). The real effects of financial reporting: Evidence and suggestions for future research. Journal of International Accounting, Auditing and Taxation, 54, 100594. [Google Scholar] [CrossRef]
  16. Blanco, B., Dhole, S., & Gul, F. A. (2023). Financial statement comparability and accounting fraud. Journal of Business Finance & Accounting, 50(7–8), 1166–1205. [Google Scholar]
  17. Bodle, K. A., Cybinski, P. J., & Monem, R. M. (2016). Effect of IFRS adoption on financial reporting quality: Evidence from bankruptcy prediction. Accounting Research Journal, 29(3), 292–312. [Google Scholar] [CrossRef]
  18. Boulhaga, M., Bouri, A., & Elbardan, H. (2022). The effect of internal control quality on real and accrual-based earnings management: Evidence from France. Journal of Management Control, 33(4), 545–567. [Google Scholar] [CrossRef]
  19. Bourveau, T., Chen, J. V., Elfers, F., & Pierk, J. (2023). Public peers, accounting comparability, and value relevance of private firms’ financial reporting. Review of Accounting Studies, 28(4), 2642–2676. [Google Scholar] [CrossRef]
  20. Brown, L. D., & Caylor, M. L. (2006). Corporate governance and firm valuation. Journal of Accounting and Public Policy, 25(4), 409–434. [Google Scholar] [CrossRef]
  21. Burlacu, G., Robu, I. B., & Munteanu, I. (2024). Exploring the influence of earnings management on the value relevance of financial statements: Evidence from the Bucharest Stock Exchange. International Journal of Financial Studies, 12(3), 72. [Google Scholar] [CrossRef]
  22. Cabán, D. (2024). Principles versus rules based standards: Differential impact on accounting quality and relevance. Journal of Corporate Accounting & Finance, 35(3), 45–62. [Google Scholar] [CrossRef]
  23. Cahan, S. F., Chen, C., & Chen, L. (2024). In financial statements we trust: Institutional investors’ stockholdings after restatements. The Accounting Review, 99(2), 143–168. [Google Scholar] [CrossRef]
  24. Callen, J. L., Khan, M., & Lu, H. (2013). Accounting quality, stock price delay, and future stock returns. Contemporary Accounting Research, 30(1), 269–295. [Google Scholar] [CrossRef]
  25. Center for Audit Quality (CAQ). (2024). Financial restatement trends in the United States: 2013–2022. Center for Audit Quality. [Google Scholar]
  26. CFA. (2019). Financial reporting and analysis—Study manual level 1: Chartered financial analyst: Author. CFA. [Google Scholar]
  27. Chen, H., Tang, Q., Jiang, Y., & Lin, Z. (2010). The role of international financial reporting standards in accounting quality: Evidence from the European Union. Journal of International Financial Management & Accounting, 21(3), 220–278. [Google Scholar] [CrossRef]
  28. Chen, S., & Dodd, J. L. (2001). Operating income, residual income, and EVA: Which metric is more value relevant? Journal of Managerial Issues, 13(1), 65–86. [Google Scholar]
  29. Chen, Y., Jin, Z., & Qin, B. (2023). Economic Value Added in performance measurement: A simulation approach and empirical evidence. Accounting & Finance, 63(1), 109–140. [Google Scholar] [CrossRef]
  30. Chowdhury, A., Mollah, S., & Al Farooque, O. (2018). Insider-trading, discretionary accruals and information asymmetry. The British Accounting Review, 50(4), 341–363. [Google Scholar] [CrossRef]
  31. Christensen, D. M., Serafeim, G., & Sikochi, A. (2022). Why is corporate virtue in the eye of the beholder? The case of ESG ratings. The Accounting Review, 97(1), 147–175. [Google Scholar] [CrossRef]
  32. Christensen, T. E., D’Adduzio, J., & Nelson, K. K. (2023). Explaining accruals quality over time. Journal of Accounting and Economics, 76(1), 101575. [Google Scholar] [CrossRef]
  33. Cockcroft, S., & Russell, M. (2018). Big Data opportunities for accounting and finance practice and research. Australian Accounting Review, 28(3), 323–333. [Google Scholar] [CrossRef]
  34. Courteau, L., Kao, J. L., & Tian, Y. (2015). Does accrual management impair the performance of earnings-based valuation models? Journal of Business Finance & Accounting, 42(7–8), 855–896. [Google Scholar] [CrossRef]
  35. Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. [Google Scholar] [CrossRef]
  36. Dang, H. N., Nguyen, T. T. C., & Tran, D. M. (2020). The impact of earnings quality on firm value: The case of Vietnam. The Journal of Asian Finance, Economics and Business, 7(3), 63–72. [Google Scholar] [CrossRef]
  37. D’Augusta, C., & Prencipe, A. (2024). Accruals quality, shocks to macro-uncertainty, and investor response to earnings news. European Accounting Review, 33(3), 1051–1074. [Google Scholar] [CrossRef]
  38. Dechow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of accruals estimation errors. The Accounting Review, 77, 35–59. [Google Scholar] [CrossRef]
  39. Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82. [Google Scholar] [CrossRef]
  40. Dechow, P. M., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2–3), 344–401. [Google Scholar] [CrossRef]
  41. Dechow, P. M., Larson, C. R., & Resutek, R. J. (2022). The effect of accrual heterogeneity on accrual quality inferences. The Accounting Review, 97(5), 245–273. [Google Scholar] [CrossRef]
  42. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193–225. [Google Scholar]
  43. Donelson, D. C., Flam, R. W., & Yust, C. G. (2022). Spillover effects in disclosure-related securities litigation. The Accounting Review, 97(5), 275–299. [Google Scholar] [CrossRef]
  44. Donelson, D. C., Kartapanis, A., McInnis, J. M., & Yust, C. G. (2021). Measuring accounting fraud and irregularities using public and private enforcement. The Accounting Review, 96(6), 183–213. [Google Scholar] [CrossRef]
  45. Drucker, P. F. (1995). The information executives truly need. Harvard Business Review, 73(1), 54–62. [Google Scholar]
  46. Dumitru, G. (2011). The accounting information quality concept. Journal of Academic research in Economics, 3(3), 559–569. [Google Scholar]
  47. Elbakry, A. E., Nwachukwu, J. C., Abdou, H. A., & Elshandidy, T. (2017). Comparative evidence on the value relevance of IFRS-based accounting information in Germany and the UK. Journal of International Accounting, Auditing and Taxation, 28, 10–30. [Google Scholar] [CrossRef]
  48. ElMoatasem Abdelghany, K. (2005). Measuring the quality of earnings. Managerial Auditing Journal, 20(9), 1001–1015. [Google Scholar] [CrossRef]
  49. Elrayah, M., & Makhmudov, M. (2023). Market-based accounting measures and accrual manipulation in Saudi Arabia. International Journal of Economics and Finance Studies, 15(4), 815–836. [Google Scholar]
  50. Erragragui, E., Peillex, J., Benlemlih, M., & Bitar, M. (2023). Stock market reactions to corporate misconduct: The moderating role of legal origin. Economic Modelling, 121, 106197. [Google Scholar] [CrossRef]
  51. Ewert, R., & Wagenhofer, A. (2012). Earnings management, conservatism, and earnings quality. Foundations and Trends in Accounting, 6(2), 65–186. [Google Scholar] [CrossRef]
  52. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. [Google Scholar] [CrossRef]
  53. Fan, Q., & Zhang, B. X. (2012). Accounting conservatism, aggregation, and information quality. Contemporary Accounting Research, 29(1), 38–56. [Google Scholar] [CrossRef]
  54. Farshadfar, S., & Monem, R. (2023). Financial statement comparability and the usefulness of earnings. Journal of International Accounting, Auditing and Taxation, 52, 100560. [Google Scholar] [CrossRef]
  55. Fatemi, A., Glaum, M., & Kaiser, S. (2018). ESG performance and firm value: The moderating role of disclosure. Global Finance Journal, 38, 45–64. [Google Scholar] [CrossRef]
  56. Fields, L. P., Gupta, M., Wilkins, M. S., & Zhang, S. (2018). Refinancing pressure and earnings management: Evidence from changes in short-term debt and discretionary accruals. Finance Research Letters, 25, 62–68. [Google Scholar] [CrossRef]
  57. Financial Accounting Standards Board (FASB). (2024). Conceptual framework for financial reporting (Chapter 3: Qualitative Characteristics of Useful Financial Information). Financial Accounting Standards Board. [Google Scholar]
  58. Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Costs of equity and earnings attributes. The Accounting Review, 79(4), 967–1010. [Google Scholar] [CrossRef]
  59. Francis, J., Olsson, P., & Schipper, K. (2006). Earnings quality. Foundations and Trends in Accounting, 1(4), 259–340. [Google Scholar] [CrossRef]
  60. García-Teruel, P. J., Martinez-Solano, P., & Sanchez-Ballesta, J. P. (2010). Accruals quality and debt maturity structure. A Journal of Accounting, Finance and Business Studies, 46(2), 188–210. [Google Scholar] [CrossRef]
  61. Gharaibeh, A. M. O., & Qader, A. A. A. A. (2017). Factors influencing firm value as measured by Tobin’s Q: Empirical evidence from the Saudi stock exchange. International Journal of Applied Business and Economic Research, 15(6), 333–358. [Google Scholar]
  62. Giroux, G. (2008). What went wrong?: Accounting fraud and lessons from the recent scandals. Social Research: An International Quarterly, 75(4), 1205–1238. [Google Scholar] [CrossRef]
  63. Grant, J. L. (2003). Foundations of economic value added. John Wiley & Sons. [Google Scholar]
  64. Grewal, J., Riedl, E. J., & Serafeim, G. (2019). Market reaction to mandatory nonfinancial disclosure. Management Science, 65(7), 3061–3084. [Google Scholar] [CrossRef]
  65. Hennes, K. M., Leone, A. J., & Miller, B. P. (2008). The importance of distinguishing errors from irregularities in restatement research. The Accounting Review, 83(6), 1487–1519. [Google Scholar] [CrossRef]
  66. Herath, S. K., & Albarqi, N. (2017). Financial reporting quality: A literature review. International Journal of Business Management and Commerce, 2(2), 1–14. [Google Scholar]
  67. Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11(1), 1–22. [Google Scholar] [CrossRef]
  68. Hinson, L. A., Pündrich, G. P., & Zakota, M. (2024). The decision-usefulness of ASC 606 revenue disaggregation. The Accounting Review, 99(3), 225–258. [Google Scholar] [CrossRef]
  69. Hribar, P., & Nichols, D. C. (2007). The use of unsigned earnings quality measures in tests of earnings management. Journal of Accounting Research, 45(5), 1017–1053. [Google Scholar] [CrossRef]
  70. Hribar, P., & Wilson, R. (2011). A new measure of accounting quality. The University of Iowa, Todd Kravet, University of Texas at Dallas. [Google Scholar]
  71. Ibrahimov, O., Vancsura, L., & Parádi-Dolgos, A. (2025). The impact of macroeconomic factors on the firm’s performance—Empirical analysis from Türkiye. Economies, 13(4), 111. [Google Scholar] [CrossRef]
  72. IFRS Foundation. (2021). Conceptual framework for financial reporting. IFRS Foundation. [Google Scholar]
  73. Imbiri, W., & Sjarief, J. (2018). The effect of international control disclosure on financial information quality and market performance distinguished by the corporate governance index. International Journal of Accounting and Financial Reporting, 8(1), 241–260. [Google Scholar] [CrossRef]
  74. Intara, P., Sangwichitr, K., & Sattayarak, O. (2024). Earnings quality and firm value: Does corporate governance matter? Cogent Business & Management, 11(1), 2386158. [Google Scholar] [CrossRef]
  75. Jamadar, Y., Ong, T. S., Abdullah, A. A., & Kamarudin, F. (2022). Earnings and discretionary accruals. Managerial and Decision Economics, 43(2), 431–439. [Google Scholar] [CrossRef]
  76. Jan, C. L. (2018). An effective financial statements fraud detection model for the sustainable development of financial markets: Evidence from Taiwan. Sustainability, 10(2), 513. [Google Scholar] [CrossRef]
  77. Jan, C. L. (2021). Detection of financial statement fraud using deep learning for sustainable development of capital markets under information asymmetry. Sustainability, 13(17), 9879. [Google Scholar] [CrossRef]
  78. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behaviour, agency costs, and ownership structure. Journal of Financial Economics, 3(4), 305–360. [Google Scholar] [CrossRef]
  79. Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. [Google Scholar] [CrossRef]
  80. Jorissen, A. (2015). The IASB: From high quality accounting information towards information to foster trust and stability in global markets. Revista Contabilidade & Finanças, 26, 243–246. [Google Scholar]
  81. Kang, S., & Park, S. (2021). Artificial intelligence-based detection and prediction of corporate earnings management. In Fintech with artificial intelligence, big data, and blockchai (pp. 191–203). Springer. [Google Scholar]
  82. Kapons, M., Kelly, P., Stoumbos, R., & Zambrana, R. (2023). Dividends, trust, and firm value. Review of Accounting Studies, 28(3), 1354–1387. [Google Scholar] [CrossRef]
  83. Karajian, S., & Ullah, S. (2022). Consequences of fraud and overcoming negative market reaction. Global Finance Journal, 52, 100635. [Google Scholar] [CrossRef]
  84. Karpoff, J. M., Lee, D. S., & Martin, G. S. (2008). The cost to firms of cooking the books. Journal of Financial and Quantitative Analysis, 43(3), 581–611. [Google Scholar] [CrossRef]
  85. Kasznik, R. (1999). On the association between voluntary disclosure and earnings management. Journal of Accounting Research, 37(1), 57–81. [Google Scholar] [CrossRef]
  86. Kim, D., & Qi, Y. (2010). Accruals quality, stock returns, and macroeconomic conditions. The Accounting Review, 85(3), 937–978. [Google Scholar] [CrossRef]
  87. Koo, D. S., Ramalingegowda, S., & Yu, Y. (2017). The effect of financial reporting quality on corporate dividend policy. Review of Accounting Studies, 22(1), 169–214. [Google Scholar] [CrossRef]
  88. Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163–197. [Google Scholar] [CrossRef]
  89. Lam, K. C., Sami, H., Yao, J., & Yao, Y. (2023). Mandatory IFRS adoption and earnings management: The role of culture. Journal of International Accounting, Auditing and Taxation, 50, 100527. [Google Scholar] [CrossRef]
  90. Lindahl, F. W., & Schadewitz, H. J. (2014). The effect of legal quality on accounting quality in the European Union: Has east met west? SSRN Working Paper Series. [Google Scholar]
  91. Markonah, M., Siladjaja, M., & Simu, N. (2020). The impact of real earnings quality on the future market value moderated by the dividend policy. Management Research Studies Journal, 5(2), 34–47. [Google Scholar] [CrossRef]
  92. McNichols, M. F. (2002). Discussion of the quality of accruals and earnings: The role of accruals estimation errors. The Accounting Review, 7, 61–69. [Google Scholar] [CrossRef]
  93. Miculescu, C., & Miculescu, M. N. (2012). Quality of accounting information to optimize the decisional process. The Journal of Economics, 21(2), 694–699. [Google Scholar]
  94. Mlawu, L., Matenda, F. R., & Sibanda, M. (2025). Incentives for accrual-based earnings management in emerging economies—A systematic literature review with bibliometric analysis. Administrative Sciences, 15, 209. [Google Scholar] [CrossRef]
  95. Nanda, D., & Wysocki, P. (2011). The relation between trust and accounting quality. University of Miami School of Business. [Google Scholar]
  96. Nikolaev, V. V. (2018). Identifying accounting quality. Chicago booth research paper No. 14–28. The University of Chicago Booth School of Business. [Google Scholar]
  97. Pacter, P. (2017). Pocket guide to IFRS standards: The global financial reporting language. IFRS Foundation. [Google Scholar]
  98. Palmrose, Z.-V., Richardson, V. J., & Scholz, S. (2004). Determinants of market reactions to restatement announcements. Journal of Accounting and Economics, 37(1), 59–89. [Google Scholar] [CrossRef]
  99. Penman, S. H., & Zhang, X. J. (2002). Accounting conservatism, the quality of earnings, and stock returns. The Accounting Review, 77(2), 237–264. [Google Scholar] [CrossRef]
  100. Ranta, M., Ylinen, M., & Järvenpää, M. (2023). Machine learning in management accounting research: Literature review and pathways for the future. European Accounting Review, 32(3), 607–636. [Google Scholar] [CrossRef]
  101. Rep, A. (2021). Notes to the financial statements: Current state and improvement. Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, 12(2), 60–78. [Google Scholar] [CrossRef]
  102. Rezaee, Z. (2005). Causes, consequences, and deterrence of financial statement fraud. Critical Perspectives on Accounting, 16(3), 277–298. [Google Scholar] [CrossRef]
  103. Richardson, G., Obaydin, I., & Liu, C. (2022). The effect of accounting fraud on future stock price crash risk. Economic Modelling, 117, 106072. [Google Scholar] [CrossRef]
  104. Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42(3), 335–370. [Google Scholar] [CrossRef]
  105. Salewski, M. (2013). Accounting quality under IFRS essays on value relevance, earnings management and disclosure quality [Doctoral dissertation, HHL Leipzig Graduate School of Management Leipzig]. [Google Scholar]
  106. Sarun, A. (2016). Corporate governance, earnings quality and firm value: Evidence from Malaysia [Doctoral dissertation, Victoria University]. [Google Scholar]
  107. Siladjaja, M., & Jasman, J. (2024). The role of earnings quality and future returns: An illustrative simulation of rational decision model. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100191. [Google Scholar] [CrossRef]
  108. Silvers, R. (2021). The effects of cross-border cooperation on disclosure enforcement, earnings attributes, and transparency. Journal of Accounting and Public Policy, 40(4), 106875. [Google Scholar] [CrossRef]
  109. Simanullang, C. D., Edward, Y. R., Ginting, R. R., & Simorangkir, E. N. (2021). The effect of return on assets (ROA) and return on equity (ROE) on company value with capital structure as moderating variables in banking companies listed on the Indonesia stock exchange. International Journal of Business, Economics and Law, 24(6), 129–134. [Google Scholar]
  110. Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. [Google Scholar] [CrossRef]
  111. Stenheim, T., & Madsen, D. Ø. (2017). The shift of accounting models and accounting quality: The case of norwegian GAAP. Corporate Ownership & Control, 14, 289–300. [Google Scholar]
  112. Stewart, G. B. (1991). The quest for value: The EVA management guide. Harper Business. [Google Scholar]
  113. Tiron-Tudor, A., & Achim (Nasca), A. M. (2019). Accounting quality and stock price informativeness: A cross-country study. Economic Research–Ekonomska Istraživanja, 32(1), 2481–2499. [Google Scholar] [CrossRef]
  114. Tran, L. T. H. (2022). Reporting quality and financial leverage: Are qualitative characteristics or earnings quality more important? Research in International Business and Finance, 60, 101578. [Google Scholar] [CrossRef]
  115. Uwuigbe, U., Uwuigbe, O. R., Durodola, M. E., Jafaru, J., & Jimoh, R. (2017). International financial reporting standard adoption and value relevance of accounting information in Nigeria. International Journal of Economics and Financial Issues, 7(3), 1–8. [Google Scholar]
  116. Vafeas, N., & Vlittis, A. (2024). Earnings quality and board meeting frequency. Review of Quantitative Finance and Accounting, 62(3), 1037–1067. [Google Scholar] [CrossRef]
  117. Wibisono, M. G., & Andesto, R. (2023). Determinant earnings quality and its impact on firm value. Devotion: Journal of Research & Community Service, 4(12), 2321–2329. [Google Scholar]
  118. Worthington, A. C., & West, T. (2001). Economic Value-Added: A review of the theoretical and empirical literature. Asian Review of Accounting, 9(1), 67–86. [Google Scholar]
  119. Young, S. D., & O’Byrne, S. F. (2001). EVA and value-based management: A practical guide to implementation. McGraw-Hill. [Google Scholar]
  120. Zadeh, F. N., Askarany, D., Shirzad, A., & Faghani, M. (2023). Audit committee features and earnings management. Heliyon, 9(10), e20825. [Google Scholar] [CrossRef] [PubMed]
  121. Zhu, S., Ma, T., Wu, H., Ren, J., He, D., Li, Y., & Ge, R. (2025). Expanding and interpreting financial statement fraud detection using supply chain knowledge graphs. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 26. [Google Scholar] [CrossRef]
Figure 1. The average of the estimated values of accounting quality by samples and years.
Figure 1. The average of the estimated values of accounting quality by samples and years.
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Table 1. Concepts of Accounting Quality.
Table 1. Concepts of Accounting Quality.
Author(s)Concepts of Accounting Quality
(D’Augusta & Prencipe, 2024; Dechow & Dichev, 2002; Dechow et al., 2022)Accounting quality/earnings quality is related to the magnitude of the accrual-based estimation errors.
(McNichols, 2002)Earnings quality is understood as the relationship between accruals and cash flows.
(Dechow & Dichev, 2002; T. E. Christensen et al., 2023; Francis et al., 2004, 2006; Kim & Qi, 2010; García-Teruel et al., 2010; Vafeas & Vlittis, 2024)It identifies the quality of accounting with the quality of accruals.
(Amer et al., 2024; Barth et al., 2008; Lam et al., 2023)The accounting quality shows less income smoothing, more timely loss recognition, and a higher matching of net income to book equity.
(Francis et al., 2006; Barth et al., 2008; Dechow et al., 2010; H. Chen et al., 2010; Callen et al., 2013; Siladjaja & Jasman, 2024)Accounting quality depends on market influences.
(Dechow et al., 2010; Rep, 2021)Earnings quality is affected by both the underlying performance of the business and the measurement of performance.
(Dumitru, 2011; Miculescu & Miculescu, 2012)The quality of the accounts is determined by the parts of the accounts: the balance sheet, the profit and loss account and the notes to the accounts.
(Barth et al., 2008; Hinson et al., 2024)Accounting quality should be defined so that revenue measures economic performance.
(Agana et al., 2023; Fan & Zhang, 2012; Zadeh et al., 2023)The accounting system affects the quality of accounting information.
(Nanda & Wysocki, 2011; Salewski, 2013; Silvers, 2021; Lindahl & Schadewitz, 2014)The quality of accounting is influenced by the legal culture of the country.
(Boulhaga et al., 2022; Hribar & Wilson, 2011)Increased control efforts will improve the quality of accounting.
(Achim & Chis, 2014; Ball & Nikolaev, 2022)Accounting quality can be defined as the accuracy with which investors receive information about their holdings and future cash flows.
(Blanco et al., 2023; Bourveau et al., 2023; Stenheim & Madsen, 2017)Accounting quality is a measure against which accounting information can be assessed.
(Herath & Albarqi, 2017)The quality of accounting reports means that they contain accurate and fair information about the financial position and economic performance of firms.
(CFA, 2019)The Financial Reporting Quality (FRQ) refers to a characteristic of a firm’s financial reporting. The primary criterion for judging FRQ is compliance with generally accepted accounting principles (GAAP) in the jurisdiction in which the firm operates. Given that GAAP allows for a choice of methods and specific treatment of many items, compliance with GAAP alone does not necessarily result in the highest quality accounting reports. Good quality accounting reports should be useful for decision making. Two characteristics of useful accounting reports for decision making are relevance and faithful representation.
Table 2. The number and distribution of the firms involved in the study by samples.
Table 2. The number and distribution of the firms involved in the study by samples.
FrequencyPercentage
Sample5750%
Control Sample5750%
Total114100%
Table 3. The number and distribution of the firms involved in the study by stock exchange markets.
Table 3. The number and distribution of the firms involved in the study by stock exchange markets.
FrequencyPercentage
NASDAQ4842.11%
NYSE6657.89%
Total114100%
Table 4. The number and distribution of the firms involved in the study according to market capitalisation categories.
Table 4. The number and distribution of the firms involved in the study according to market capitalisation categories.
CasesDistribution
Large-capitalisation1210.53%
Mid-capitalisation4236.84%
Small-capitalisation6052.63%
Total114100%
Table 5. The number and distribution of the firms involved in the study according to GICS sectors.
Table 5. The number and distribution of the firms involved in the study according to GICS sectors.
Sample FrequencySample Distribution
Energy43.51%
Materials108.77%
Industrials1210.53%
Consumer Discretionary1412.28%
Consumer Staples1614.04%
Health Care2017.54%
Financials00.00%
Information Technology1815.79%
Communication Services65.26%
Utilities43.51%
Real Estate108.77%
Total114100%
Table 6. Comparison of industry-level restatement frequencies with research sample distribution (2017–2019).
Table 6. Comparison of industry-level restatement frequencies with research sample distribution (2017–2019).
201720182019AverageSample DistributionSample Deviation
Health Care19.24%23.68%20.54%21.15%17.54%−3.61%
Information Technology17.47%17.88%21.08%18.81%15.79%−3.02%
Energy, materials14.43%13.85%12.70%13.66%12.28%−1.38%
Consumer Discretionary12.41%11.34%10.00%11.25%12.28%1.03%
Industrials9.11%6.80%7.03%7.65%10.53%2.88%
Consumer Staples8.35%9.32%10.27%9.31%14.04%4.72%
Utilities3.80%3.27%2.16%3.08%3.51%0.43%
Communication Services4.56%4.28%4.86%4.57%5.26%0.70%
Other10.63%9.57%11.35%10.52%8.77%−1.75%
100%100%100%100%100%-
Table 7. Methodological design of the study.
Table 7. Methodological design of the study.
Research StageDescriptionSources/ToolsOutput
Sample selectionIdentified U.S.-listed firms (NYSE, NASDAQ) with proven accounting manipulation (2017–2019). Excluded financial firms, non-U.S. HQ, and non-standard fiscal years.SEC enforcement filings, AAERs, court rulings, EDGAR restatements, Violation Tracker database57 manipulation firms
Control group matchingEach manipulation firm matched with one non-manipulative peer by industry (NAICS 4-digit), fiscal year, and size (log assets, market-to-book, ROA). Random selection applied if multiple candidates.EDGAR, ORBIS, enforcement checks57 matched controls
Final datasetBalanced sample of 114 firms, diverse across industries and size (large-, mid-, small-cap).GICS classification, CAQ (2024) distribution comparisonRobustness of sample confirmed
Data collectionAccounting and reporting data from EDGAR; fundamentals from ORBIS; market data (prices, volumes, dividends) from Morningstar; WACC for EVA from Damodaran Online.SEC EDGAR, ORBIS, Morningstar, Damodaran OnlineFirm-level panel dataset (2017–2019)
Variable measurementAQ measured via discretionary accruals (Kasznik model); EVA = NOPAT – WACC × Invested Capital; dividends = annual payouts; prices = quarterly changes; volumes = trading activity.Multivariate regression models; error termsFirm-year level AQ and value metrics
Analysis methodCompared AQ between groups; correlation analysis of AQ with EVA, dividends, stock prices, trading volumes. Supplementary tests with Roychowdhury (2006) real earnings management models.Pearson correlations, regression diagnosticsExploratory associations and baseline patterns
Limitations notedSmall N (114) limits causal inference; heterogeneity across industries; no significant real-EM results; exploratory stage only.Framing for future research
Table 8. R2 coefficients of the models used to measure accounting quality by year examined.
Table 8. R2 coefficients of the models used to measure accounting quality by year examined.
Measurement Models201720182019Average
Jones (1991) Model0.5620.2550.2400.352
Dechow et al. (1995) Modified Jones Model0.7090.4950.2900.498
Kasznik (1999) Model 0.7480.5750.6330.652
Kothari et al. (2005) Model 0.7230.5250.3650.538
Table 9. The values of the Kasznik model related to the Durbin-Watson tests during the examined years.
Table 9. The values of the Kasznik model related to the Durbin-Watson tests during the examined years.
Measurement Model201720182019
Kasznik (1999) Model1.7881.7151.757
Table 10. Descriptive statistics of the estimated values related to the accounting quality of the sample and control sample elements for the years examined.
Table 10. Descriptive statistics of the estimated values related to the accounting quality of the sample and control sample elements for the years examined.
SampleAQ_2017AQ_2018AQ_2019
Valid575757
Median1.4301.4702.120
Mean1.4461.4972.066
Std. Deviation0.2750.2420.434
Skewness0.0910.192−0.432
Kurtosis−0.512−0.282−0.062
Shapiro–Wilk0.9830.9860.974
p-value of Shapiro–Wilk0.5920.7570.263
Minimum0.9500.9100.990
Maximum2.0562.0202.870
Control SampleAQ_2017AQ_2018AQ_2019
Valid575757
Median0.8801.0100.960
Mean0.8930.9660.925
Std. Deviation0.3870.3750.270
Skewness0.103−0.114−0.313
Kurtosis−0.647−0.687−0.061
Shapiro–Wilk0.9780.9780.982
p-value of Shapiro–Wilk0.3910.3980.556
Minimum0.1500.1800.250
Maximum1.6401.6401.470
Table 11. Levene statistics on accounting quality variables by year.
Table 11. Levene statistics on accounting quality variables by year.
VariableCalculation BasisLevene Statisticdf1df2Significance
AQ_2017Based on Mean6.59311120.012
AQ_2018Based on Mean9.50911120.003
AQ_2019Based on Mean8.65511120.004
Table 12. The results of the Kruskal–Wallis H-test related to the samples in the examination of differences in accounting quality.
Table 12. The results of the Kruskal–Wallis H-test related to the samples in the examination of differences in accounting quality.
AQ_2017AQ_2018AQ_2019
Kruskal–Wallis H47.21948.08978.210
df111
Asymptotic Significancep < 0.001p < 0.001p < 0.001
Table 13. The results of the Welch’s t-test related to the samples in the examination of differences in accounting quality.
Table 13. The results of the Welch’s t-test related to the samples in the examination of differences in accounting quality.
AQ_2017AQ_2018AQ_2019
Welch-t a77.59980.711283.894
df1111
df2101.08195.87093.725
Significancep < 0.001p < 0.001p < 0.001
a. Asymptotically F distributed.
Table 14. Correlation coefficients between values of the accounting quality and economic value added indicators based on sample data.
Table 14. Correlation coefficients between values of the accounting quality and economic value added indicators based on sample data.
AQ_2017AQ_2018AQ_2019∆EVA_2017∆EVA_2018∆EVA_2019
AQ_2017Pearson Correlation
p-value
AQ_2018Pearson Correlation−0.187
p-value0.164
AQ_2019Pearson Correlation−0.2230.181
p-value0.0950.177
∆EVA_2017Pearson Correlation−0.051−0.093−0.481 ***
p-value0.7040.490<0.001
∆EVA_2018Pearson Correlation0.058−0.183−0.259 *−0.328 *
p-value0.6680.1740.0430.013
∆EVA_2019Pearson Correlation−0.022−0.059−0.655 ***−0.1990.458 ***
p-value0.8680.661<0.0010.137<0.001
Note: * p < 0.05, *** p < 0.001.
Table 15. Correlation coefficients between accounting quality and average dividend paid based on sample data.
Table 15. Correlation coefficients between accounting quality and average dividend paid based on sample data.
AQ_2017AQ_2018AQ_2019DIV_2017DIV_2018DIV_2019
AQ_2017Pearson Correlation
p-value
AQ_2018Pearson Correlation−0.187
p-value0.164
AQ_2019Pearson Correlation−0.2230.181
p-value0.0950.177
DIV_2017Pearson Correlation−0.019−0.437 ***−0.030
p-value0.890<0.0010.825
DIV_2018Pearson Correlation−0.076−0.255−0.1360.819 ***
p-value0.5760.0550.313<0.001
DIV_2019Pearson Correlation−0.128−0.0760.0270.572 ***0.735 ***
p-value0.3430.5740.841<0.001<0.001
Note: *** p < 0.001.
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Hegedűs, S.; Denich, E.; Baracsi, Á.L. Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies. J. Risk Financial Manag. 2025, 18, 584. https://doi.org/10.3390/jrfm18100584

AMA Style

Hegedűs S, Denich E, Baracsi ÁL. Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies. Journal of Risk and Financial Management. 2025; 18(10):584. https://doi.org/10.3390/jrfm18100584

Chicago/Turabian Style

Hegedűs, Szilárd, Ervin Denich, and Áron Lajos Baracsi. 2025. "Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies" Journal of Risk and Financial Management 18, no. 10: 584. https://doi.org/10.3390/jrfm18100584

APA Style

Hegedűs, S., Denich, E., & Baracsi, Á. L. (2025). Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies. Journal of Risk and Financial Management, 18(10), 584. https://doi.org/10.3390/jrfm18100584

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