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Analysis of earnings management influence on the investment efficiency of listed Chinese companies

Author

Listed:
  • Shen, Chung-Hua
  • Luo, Fuyan
  • Huang, Dengshi

Abstract

This study examines the “EM (earnings management) and excess investment hypothesis,” which posits that upward EM increases investment spending. Two types of EM proxies and two types of investment proxies are calculated to ensure the robustness of results. The two types of EM proxies are case studies (firms with three fraudulent activities) and econometric estimation of regression residuals (of revenues and accrual functions). The two investment proxies are industry-adjusted investment and investment function residuals. Considering that outliers are common in panel data but are often ignored in estimation, the aforementioned proxies are determined with and without considering the outliers using listed companies in China from 1998 to 2010. Empirical results demonstrate that the hypothesis is supported when the outliers are considered, whereas the opposite result is obtained when the outliers are not considered. The analysis and findings of this study indicate that considering the influence of the outliers is crucial to support the hypothesis.

Suggested Citation

  • Shen, Chung-Hua & Luo, Fuyan & Huang, Dengshi, 2015. "Analysis of earnings management influence on the investment efficiency of listed Chinese companies," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 60-78.
  • Handle: RePEc:eee:empfin:v:34:y:2015:i:c:p:60-78
    DOI: 10.1016/j.jempfin.2015.08.003
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    Keywords

    Earnings management; Investment efficiency; Outlier; Robust regression;

    JEL classification:

    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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