Summary:
Information disclosure significantly affects listed companies' market value and investors' decisions. After years of practice, regulators have recognized that the public listing system should focus on information disclosure, rather than evaluating issuance quality, which should be left for the market to assess. Furthermore, the deferred income tax data associated with corporate profit and management flexibility has become an arena for gaming between regulators and listed companies. This paper highlights the important role of deferred income tax in the early alerting of fraudulent activities. It reveals what motivates companies to abuse their deferred income tax data, both theoretically and empirically. A sample of A-share companies from 2000 to 2017 is analyzed, controlling for partial observability and endogeneity problems. The deferred income tax indicator is significantly positively correlated with the tendency of listed companies to commit fraud and significantly negatively correlated with the probability of fraud detection. This result implies that regulators focus on monitoring the financial indicators of A-share companies, but fail to recognize the relationship between deferred income tax data and corporate disclosure fraud. Such oversight may incentivize listed companies to manipulate deferred income tax data to boost their financial performance. According to Spence's signaling theory, listed companies transmit information about their corporate value to the market through signals, such as financial performance. Thus far, deferred income tax adjustment has served as a convenient tool for listed companies in managing earnings at low costs. It constitutes a “soft signal”, which bears a low threshold to mimic but conveys less reliable information on companies' fundamental value. Three factors motivate financial fraud: financial distress, avoidance of negative performance reporting and excessive valuation upon corporate growth. The first two reflect competitive pressures and the third reflects the rewarding effect of the capital market. Separating accounting and tax standards yields differences between pre-tax accounting profits and taxable income. Factors such as the confirmation time and amount of deferred income tax assets fall within the scope of management discretion. This accounting-tax discrepancy provides an opportunity for companies to abuse their deferred income tax data for earnings management. This paper makes three contributions to the literature. First, it reveals the anomaly of extensive occurrences of disclosure fraud among A-share companies and the lagging regulatory inspections upon fraud. Furthermore, it offers an effective solution based on a solid analysis of the mechanisms underlying fraud. The literature on disclosure fraud detection is limited. Such academic neglect may explain the regulatory insufficiency regarding corporate fraud and the lack of public monitoring of this regulatory insufficiency. Second, this paper demonstrates the informational value of income tax data in detecting disclosure fraud and its “hard signal” value in revealing the true financial status of a listed company. Third, it showcases the role of machine learning in improving capital market governance. The Guotaian CSMAR database conveys information on public announcements of regulatory measures on corporate disclosure fraud from authorities. The financial data of A-share listed companies is from the Wind database. A binary probit model is constructed, controlling for endogeneity and partial observability, to analyze the relationship between the deferred income tax indicator and companies' tendency to commit fraud. In the interest of accurately identifying companies with instances of fraudulent activity, a decision tree model is built to implement out-of-sample prediction and form investment portfolios based on the prediction results. Among the out-of-sample firms, the decision tree model identifies 39% of the fraud-free firms with 95% accuracy and identifies 2% of the fraud-committing firms with 100% accuracy. The annual return on assets of the fraud-free firms is 2.80% higher than that of the fraud-committing firms, and the average annual stock return of the fraud-free firms is 3.69% higher than that of the fraud-committing firms. This paper analyzes the mechanism underlying financial manipulation, shedding light on the predictive power of the deferred income tax indicator in detecting disclosure fraud. For regulators, the timely identification of disclosure fraud is of great significance for combating violations and maintaining healthy stock market development. For investors, risk management is a primary concern. Avoiding companies with major alleged fraud (e.g., LeTV) is a primitive demand for risk control. The decision tree model in this paper provides an analytic framework for assessing the likelihood of listed companies to commit fraud, with parameter settings that address the accuracy and coverage of such assessment.
[1]薄仙慧和吴联生,2009,《国有控股与机构投资者的治理效应:盈余管理视角》,《经济研究》第2期,第81～91页。 [2]贺小刚、邓浩、吴诗雨和梁鹏,2015,《赶超压力与公司的败德行为——来自中国上市公司的数据分析》,《管理世界》第9期,第104～124页。 [3]廖理、刘碧波和郦金粱,2008,《道德风险、信息发现与市场有效性——来自于股权分置改革的证据》,《金融研究》第4期,第146～160页。 [4]陆蓉和常维,2018,《近墨者黑:上市公司违规行为的“同群效应”》,《金融研究》第8期,第172～189页。 [5]陆瑶、朱玉杰和胡晓元,2012,《机构投资者持股与上市公司违规行为的实证研究》,《南开管理评论》第15期,第13～23页。 [6]孟庆斌、邹洋和侯德帅,2019,《卖空机制能抑制上市公司违规吗?》,《经济研究》第6期,第89～105页。 [7]权小锋、吴世农和文芳,2010,《管理层权力、私有收益与薪酬操纵》,《经济研究》第11期,第73～87页。 [8]魏芳和耿修林,2018,《高管薪酬差距的阴暗面——基于公司违规行为的研究》,《经济管理》第3期,第57～73页。 [9]Altman, E.I.. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance, 23(4):189～209. [10]Amiram, D., Z. Bozanic, J. Cox, Q. Dupont, J. Karpoff, and R. Sloan. 2018. “Financial Reporting Fraud and Other Forms of Misconduct: A Multidisciplinary Review of the Literature”, Review of Accounting Studies, 23(2):732～783. [11]Barth, M., J. Elliott, and M. Finn. 1999. “Market Rewards Associated with Patterns of Increasing Earnings”, Journal of Accounting Research, 37(2):367～413. [12]Baucus, M. S.. 1994. “Pressure, Opportunity and Predisposition: A Multivariate Model of Corporate Illegality”, Journal of Management, 20(4):699～721. [13]Beasley, M. S.. 1996. “An Empirical Analysis of the Relation between the Board of Director Composition and Financial Statement Fraud”, The Accounting Review, 71(4):443～65. [14]Bell, T., and J. Carcello. 2000. “A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting”, Auditing: A Journal of Practice & Theory, 9(1):169～178. [15]Beneish, M. D.. 1999. “The Detection of Earnings Manipulation”, Financial Analysts Journal, 155(5):24～36. [16]Bernard, V.L., and D. J. Skinner. 1996. “What Motivates Managers' Choice of Discretionary Accruals?” Journal of Accounting and Economics, 22(1-3):313～325. [17]Chen, T., and C. Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System”, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining. [18]Dechow, P., W. Ge, C. Larson, and R. Sloan. 2011. “Predicting Material Accounting Misstatements”, Contemporary Accounting Research, 28(1):1～16. [19]Dhaliwal, D., C. Gleason, and L. Mills. 2004. “Last Chance Earnings Management: Using the Tax Expense to Achieve Earnings Targets”, Contemporary Accounting Research, 21(2):431～458. [20]Efendi, J., A. Srivastava, and E. P. Swanson. 2007. “Why Do Corporate Managers Misstate Financial Statements? The Role of Option Compensation and Other Factors”, Journal of Financial Economics, 85(3):667～708. [21]Guay, W. R., S. P. Kothari , and R. L. Watts . 1996. “A Market-based Evaluation of Discretionary Accrual Models.” Journal of Accounting Research, 34:83～105. [22]Hoberg, G., and G. Phillips. 2010. “Real and Financial Industry Booms and Busts”, The Journal of Finance, 65(1):45～86. [23]Kim, E. H., and Y. Lu. 2011. “CEO Ownership, External Governance, and Risk-taking”, Journal of Financial Economics, 102(2):272～292. [24]Kini, O., and R. Williams. 2012. “Tournament Incentives, Firm Risk, and Corporate Policies”, Journal of Financial Economics, 103(2):350～376. [25]Li, J., and C. Wu. 2006. “Daily Return Volatility, Bid-Ask Spreads, and Information Flow: Analyzing the Information Content of Volume”, The Journal of Business, 79(5):2697～2739. [26]Mills, L., and K. Newberry. 2001. “The Influence of Tax and Nontax Costs on Book-tax Reporting Differences: Public and Private Firms”, The Journal of the American Taxation Association, 23(1):1～19. [27]Persons, O. S.. 1995. “Using Financial Statement Data to Identify Factors Associated with Fraudulent Financial Reporting”, Journal of Applied Business Research, 11(3):38～46. [28]Phillips, J., M. Pincus, and S. Rego. 2003. “Earnings Management: New Evidence Based on Deferred Tax Expense”, The Accounting Review, 78(2):491～521. [29]Poirier, D. J.. 1980. “Partial Observability in Bivariate Probit Models”, Journal of Econometrics, 12(2):209～217. [30]Povel, P., R. Singh, and A. Winton. 2007. “Booms, Busts, and Fraud”, Review of Financial Studies, 20(4):1219～1254. [31]Skinner, D., and R. Sloan. 2002. “Earnings Surprises, Growth Expectations, and Stock Returns or Don't Let an Earnings Torpedo Sink Your Portfolio”, Review of Accounting Studies, 7(2/3):289～312. [32]Spence, A. M.. 1973. “Job Market Signaling”, Quarterly Journal of Economics, 87(3):355～374. [37]Stice, J. D.. 1991. “Using Financial and Market Information to Identify Pre-Engagement Factors Associated with Lawsuits against Auditors”, The Accounting Review, 66:516～533. [33]Wang, T.Y., A. Winton, and X. Yu. 2010. “Corporate Fraud and Business Conditions: Evidence from IPOs”, The Journal of Finance, 65(6):2255～2292.
