Accounting Manipulation and Value Creation: An Empirical Study of EVA and Accounting Quality in NYSE and NASDAQ Companies
Abstract
1. Introduction
2. Literature Review
2.1. Accounting Quality
Characteristics of Accounting Quality According to the IASB
- 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.
2.2. Economic Value Added
2.3. Accounting Quality and Firm Value
2.4. Development of Hypotheses
3. Materials and Methods
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variables | AQ_2017 | AQ_2018 | AQ_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_2017 | P. corr. | — | ||||||||||||||
p-value | — | |||||||||||||||
AQ_2018 | P. corr. | −0.187 | — | |||||||||||||
p-value | 0.164 | — | ||||||||||||||
AQ_2019 | P. corr. | −0.223 | 0.181 | — | ||||||||||||
p-value | 0.095 | 0.177 | — | |||||||||||||
∆P_2017_Q1 | P. corr. | −0.011 | −0.634 *** | −0.077 | — | |||||||||||
p-value | 0.936 | <0.001 | 0.569 | — | ||||||||||||
∆P_2017_Q2 | P. corr. | −0.089 | −0.533 *** | −0.213 | 0.711 *** | — | ||||||||||
p-value | 0.510 | <0.001 | 0.111 | <0.001 | — | |||||||||||
∆P_2017_Q3 | P. corr. | −0.167 | −0.455 *** | −0.151 | 0.315 * | 0.564 *** | — | |||||||||
p-value | 0.214 | <0.001 | 0.262 | 0.017 | <0.001 | — | ||||||||||
∆P_2017_Q4 | P. corr. | −0.120 | −0.477 *** | −0.127 | 0.330 * | 0.495 *** | 0.664 *** | — | ||||||||
P. corr. | 0.375 | <0.001 | 0.347 | 0.012 | < 0.001 | < 0.001 | — | |||||||||
∆P_2018_Q1 | p-value | −0.246 | −0.491 *** | 0.172 | 0.551 *** | 0.671 *** | 0.823 *** | 0.595 *** | — | |||||||
P. corr. | 0.065 | <0.001 | 0.200 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||||||
∆P_2018_Q2 | p-value | −0.234 | −0.508 *** | 0.194 | 0.541 *** | 0.666 *** | 0.821 *** | 0.587 *** | 0.990 *** | — | ||||||
P. corr. | 0.080 | <0.001 | 0.149 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||||||
∆P_2018_Q3 | p-value | −0.284 ** | −0.443 *** | −0.264 * | 0.581 *** | 0.666 *** | 0.768 *** | 0.559 *** | 0.983 *** | 0.974 *** | — | |||||
P. corr. | 0.032 | <0.001 | 0.047 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||||
∆P_2018_Q4 | p-value | −0.275 * | −0.397 ** | −0.375 ** | 0.538 *** | 0.626 *** | 0.700 *** | 0.506 *** | 0.948 *** | 0.953 *** | 0.974 *** | — | ||||
P. corr. | 0.039 | 0.002 | 0.004 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||||
∆P_2019_Q1 | p-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 | <0.001 | 0.006 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||
∆P_2019_Q2 | p-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 | <0.001 | 0.005 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||
∆P_2019_Q3 | p-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 | <0.001 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||
∆P_2019_Q4 | p-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. | 0.100 | 0.001 | 0.003 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — |
Variables | AQ_2017 | AQ_2018 | AQ_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_2017 | P. corr. | — | ||||||||||||||
p-value | — | |||||||||||||||
AQ_2018 | P. corr. | −0.187 | — | |||||||||||||
p-value | 0.164 | — | ||||||||||||||
AQ_2019 | P. corr. | −0.223 | 0.181 | — | ||||||||||||
p-value | 0.095 | 0.177 | — | |||||||||||||
∆V_2017_Q1 | P. corr. | −0.077 | 0.366 ** | −0.046 | — | |||||||||||
p-value | 0.571 | 0.005 | 0.735 | — | ||||||||||||
∆V_2017_Q2 | P. corr. | −0.082 | 0.403 ** | 0.013 | 0.976 *** | — | ||||||||||
p-value | 0.544 | 0.002 | 0.923 | <0.001 | — | |||||||||||
∆V_2017_Q3 | P. corr. | −0.075 | 0.398 ** | −0.001 | 0.978 *** | 0.998 *** | — | |||||||||
p-value | 0.581 | 0.002 | 0.991 | <0.001 | <0.001 | — | ||||||||||
∆V_2017_Q4 | P. corr. | −0.057 | 0.312 * | −0.082 | 0.982 *** | 0.983 *** | 0.983 *** | — | ||||||||
P. corr. | 0.672 | 0.018 | 0.546 | <0.001 | <0.001 | <0.001 | — | |||||||||
∆V_2018_Q1 | p-value | −0.082 | 0.407 ** | 0.012 | 0.969 *** | 0.999 *** | 0.995 *** | 0.981 *** | — | |||||||
P. corr. | 0.543 | 0.002 | 0.932 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||||||
∆V_2018_Q2 | p-value | −0.068 | 0.375 ** | −0.013 | 0.986 *** | 0.995 *** | 0.996 *** | 0.993 *** | 0.993 *** | — | ||||||
P. corr. | 0.614 | 0.004 | 0.923 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||||||
∆V_2018_Q3 | p-value | −0.086 | 0.406 ** | 0.019 | 0.957 *** | 0.996 *** | 0.991 *** | 0.975 *** | 0.999 *** | 0.987 *** | — | |||||
P. corr. | 0.525 | 0.002 | 0.889 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||||
∆V_2018_Q4 | p-value | −0.053 | 0.325 ** | −0.094 | 0.977 *** | 0.988 *** | 0.987 *** | 0.996 *** | 0.987 *** | 0.993 *** | 0.982 *** | — | ||||
P. corr. | 0.696 | 0.014 | 0.489 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||||
∆V_2019_Q1 | p-value | −0.068 | 0.376 ** | −0.028 | 0.979 *** | 0.998 *** | 0.996 *** | 0.987 *** | 0.996 *** | 0.996 *** | 0.992 *** | 0.994 *** | — | |||
P. corr. | 0.615 | 0.004 | 0.838 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||||
∆V_2019_Q2 | p-value | −0.097 | 0.404 ** | 0.010 | 0.970 *** | 0.994 *** | 0.990 *** | 0.978 *** | 0.993 *** | 0.990 *** | 0.990 *** | 0.984 *** | 0.994 *** | — | ||
P. corr. | 0.472 | 0.002 | 0.942 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | |||
∆V_2019_Q3 | p-value | −0.086 | 0.401 ** | −0.009 | 0.976 *** | 0.999 *** | 0.995 *** | 0.983 *** | 0.998 *** | 0.994 *** | 0.996 *** | 0.989 *** | 0.998 *** | 0.995 *** | — | |
P. corr. | 0.526 | 0.002 | 0.949 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — | ||
∆V_2019_Q4 | p-value | −0.082 | 0.392 ** | −0.007 | 0.964 *** | 0.997 *** | 0.993 *** | 0.982 *** | 0.999 *** | 0.991 *** | 0.999 *** | 0.989 *** | 0.996 *** | 0.992 *** | 0.998 *** | — |
P. corr. | 0.542 | 0.003 | 0.960 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | — |
Appendix B
Model Name | Model Formula |
---|---|
Jones (1991) Model | |
Dechow et al. (1995) Model | |
Kasznik (1999) Model | |
Kothari et al. (2005) Model | |
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. |
Model Area | Model Formula |
---|---|
Operating cash flow | |
Production costs | |
Discretionary expenses | |
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. |
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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. |
Frequency | Percentage | |
---|---|---|
Sample | 57 | 50% |
Control Sample | 57 | 50% |
Total | 114 | 100% |
Frequency | Percentage | |
---|---|---|
NASDAQ | 48 | 42.11% |
NYSE | 66 | 57.89% |
Total | 114 | 100% |
Cases | Distribution | |
---|---|---|
Large-capitalisation | 12 | 10.53% |
Mid-capitalisation | 42 | 36.84% |
Small-capitalisation | 60 | 52.63% |
Total | 114 | 100% |
Sample Frequency | Sample Distribution | |
---|---|---|
Energy | 4 | 3.51% |
Materials | 10 | 8.77% |
Industrials | 12 | 10.53% |
Consumer Discretionary | 14 | 12.28% |
Consumer Staples | 16 | 14.04% |
Health Care | 20 | 17.54% |
Financials | 0 | 0.00% |
Information Technology | 18 | 15.79% |
Communication Services | 6 | 5.26% |
Utilities | 4 | 3.51% |
Real Estate | 10 | 8.77% |
Total | 114 | 100% |
2017 | 2018 | 2019 | Average | Sample Distribution | Sample Deviation | |
---|---|---|---|---|---|---|
Health Care | 19.24% | 23.68% | 20.54% | 21.15% | 17.54% | −3.61% |
Information Technology | 17.47% | 17.88% | 21.08% | 18.81% | 15.79% | −3.02% |
Energy, materials | 14.43% | 13.85% | 12.70% | 13.66% | 12.28% | −1.38% |
Consumer Discretionary | 12.41% | 11.34% | 10.00% | 11.25% | 12.28% | 1.03% |
Industrials | 9.11% | 6.80% | 7.03% | 7.65% | 10.53% | 2.88% |
Consumer Staples | 8.35% | 9.32% | 10.27% | 9.31% | 14.04% | 4.72% |
Utilities | 3.80% | 3.27% | 2.16% | 3.08% | 3.51% | 0.43% |
Communication Services | 4.56% | 4.28% | 4.86% | 4.57% | 5.26% | 0.70% |
Other | 10.63% | 9.57% | 11.35% | 10.52% | 8.77% | −1.75% |
100% | 100% | 100% | 100% | 100% | - |
Research Stage | Description | Sources/Tools | Output |
---|---|---|---|
Sample selection | Identified 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 database | 57 manipulation firms |
Control group matching | Each 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 checks | 57 matched controls |
Final dataset | Balanced sample of 114 firms, diverse across industries and size (large-, mid-, small-cap). | GICS classification, CAQ (2024) distribution comparison | Robustness of sample confirmed |
Data collection | Accounting 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 Online | Firm-level panel dataset (2017–2019) |
Variable measurement | AQ measured via discretionary accruals (Kasznik model); EVA = NOPAT – WACC × Invested Capital; dividends = annual payouts; prices = quarterly changes; volumes = trading activity. | Multivariate regression models; error terms | Firm-year level AQ and value metrics |
Analysis method | Compared 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 diagnostics | Exploratory associations and baseline patterns |
Limitations noted | Small N (114) limits causal inference; heterogeneity across industries; no significant real-EM results; exploratory stage only. | — | Framing for future research |
Measurement Models | 2017 | 2018 | 2019 | Average |
---|---|---|---|---|
Jones (1991) Model | 0.562 | 0.255 | 0.240 | 0.352 |
Dechow et al. (1995) Modified Jones Model | 0.709 | 0.495 | 0.290 | 0.498 |
Kasznik (1999) Model | 0.748 | 0.575 | 0.633 | 0.652 |
Kothari et al. (2005) Model | 0.723 | 0.525 | 0.365 | 0.538 |
Measurement Model | 2017 | 2018 | 2019 |
---|---|---|---|
Kasznik (1999) Model | 1.788 | 1.715 | 1.757 |
Sample | AQ_2017 | AQ_2018 | AQ_2019 |
Valid | 57 | 57 | 57 |
Median | 1.430 | 1.470 | 2.120 |
Mean | 1.446 | 1.497 | 2.066 |
Std. Deviation | 0.275 | 0.242 | 0.434 |
Skewness | 0.091 | 0.192 | −0.432 |
Kurtosis | −0.512 | −0.282 | −0.062 |
Shapiro–Wilk | 0.983 | 0.986 | 0.974 |
p-value of Shapiro–Wilk | 0.592 | 0.757 | 0.263 |
Minimum | 0.950 | 0.910 | 0.990 |
Maximum | 2.056 | 2.020 | 2.870 |
Control Sample | AQ_2017 | AQ_2018 | AQ_2019 |
Valid | 57 | 57 | 57 |
Median | 0.880 | 1.010 | 0.960 |
Mean | 0.893 | 0.966 | 0.925 |
Std. Deviation | 0.387 | 0.375 | 0.270 |
Skewness | 0.103 | −0.114 | −0.313 |
Kurtosis | −0.647 | −0.687 | −0.061 |
Shapiro–Wilk | 0.978 | 0.978 | 0.982 |
p-value of Shapiro–Wilk | 0.391 | 0.398 | 0.556 |
Minimum | 0.150 | 0.180 | 0.250 |
Maximum | 1.640 | 1.640 | 1.470 |
Variable | Calculation Basis | Levene Statistic | df1 | df2 | Significance |
---|---|---|---|---|---|
AQ_2017 | Based on Mean | 6.593 | 1 | 112 | 0.012 |
AQ_2018 | Based on Mean | 9.509 | 1 | 112 | 0.003 |
AQ_2019 | Based on Mean | 8.655 | 1 | 112 | 0.004 |
AQ_2017 | AQ_2018 | AQ_2019 | |
---|---|---|---|
Kruskal–Wallis H | 47.219 | 48.089 | 78.210 |
df | 1 | 1 | 1 |
Asymptotic Significance | p < 0.001 | p < 0.001 | p < 0.001 |
AQ_2017 | AQ_2018 | AQ_2019 | |
---|---|---|---|
Welch-t a | 77.599 | 80.711 | 283.894 |
df1 | 1 | 1 | 1 |
df2 | 101.081 | 95.870 | 93.725 |
Significance | p < 0.001 | p < 0.001 | p < 0.001 |
AQ_2017 | AQ_2018 | AQ_2019 | ∆EVA_2017 | ∆EVA_2018 | ∆EVA_2019 | ||
---|---|---|---|---|---|---|---|
AQ_2017 | Pearson Correlation | — | |||||
p-value | — | ||||||
AQ_2018 | Pearson Correlation | −0.187 | — | ||||
p-value | 0.164 | — | |||||
AQ_2019 | Pearson Correlation | −0.223 | 0.181 | — | |||
p-value | 0.095 | 0.177 | — | ||||
∆EVA_2017 | Pearson Correlation | −0.051 | −0.093 | −0.481 *** | — | ||
p-value | 0.704 | 0.490 | <0.001 | — | |||
∆EVA_2018 | Pearson Correlation | 0.058 | −0.183 | −0.259 * | −0.328 * | — | |
p-value | 0.668 | 0.174 | 0.043 | 0.013 | — | ||
∆EVA_2019 | Pearson Correlation | −0.022 | −0.059 | −0.655 *** | −0.199 | 0.458 *** | — |
p-value | 0.868 | 0.661 | <0.001 | 0.137 | <0.001 | — |
AQ_2017 | AQ_2018 | AQ_2019 | DIV_2017 | DIV_2018 | DIV_2019 | ||
---|---|---|---|---|---|---|---|
AQ_2017 | Pearson Correlation | — | |||||
p-value | — | ||||||
AQ_2018 | Pearson Correlation | −0.187 | — | ||||
p-value | 0.164 | — | |||||
AQ_2019 | Pearson Correlation | −0.223 | 0.181 | — | |||
p-value | 0.095 | 0.177 | — | ||||
DIV_2017 | Pearson Correlation | −0.019 | −0.437 *** | −0.030 | — | ||
p-value | 0.890 | <0.001 | 0.825 | — | |||
DIV_2018 | Pearson Correlation | −0.076 | −0.255 | −0.136 | 0.819 *** | — | |
p-value | 0.576 | 0.055 | 0.313 | <0.001 | — | ||
DIV_2019 | Pearson Correlation | −0.128 | −0.076 | 0.027 | 0.572 *** | 0.735 *** | — |
p-value | 0.343 | 0.574 | 0.841 | <0.001 | <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
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 StyleHegedűs, 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 StyleHegedűs, 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