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Predicting Accounting Fraud: Evidence from Japan

Author

Listed:
  • Mingzi Song

    (Financial Technology Research Institute Inc., Tokyo, JAPAN)

  • Naoto Oshiro

    (Financial Technology Research Institute Inc., Tokyo, JAPAN)

  • Akinobu Shuto

    (Graduate School of Economics, The University of Tokyo, JAPAN)

Abstract

This study develops a prediction model for identifying accounting fraud by analyzing the accounting information for Japanese firms. In particular, we (1) explore the characteristics of accounting fraud firms by analyzing financial information obtained from annual reports (yukashoken-houkokusho in Japanese) and (2) develop a model for predicting accounting fraud based on the characteristics of Japanese fraud firms. To identify the characteristic of fraud firms, we focus on 39 variables for the eight factors of “accruals quality,” “performance,” “nonfinancial measures,” “off-balance-sheet activities,” “market-related incentives,” “conservatism,” “real-activities manipulation,” and “Japanese-specific factors.” Through our univariate analysis and model building process, we find that “accrual quality,” “market-related incentives,” “real-activities manipulation,” “conservatism” and “Japanese-specific factors” are generally useful for detecting accounting fraud. We also conduct several analyses that test the predictive ability of our models, including (1) the detection rates of fraud firms, (2) Type I and Type II error rates, (3) marginal effect analysis on independent variables, and (4) robustness tests on time periods and industry clustering. We find that our models have generally higher predictive power in detecting accounting fraud. We expect that our models can be used widely in various accounting and finance practices.

Suggested Citation

  • Mingzi Song & Naoto Oshiro & Akinobu Shuto, 2016. "Predicting Accounting Fraud: Evidence from Japan," The Japanese Accounting Review, Research Institute for Economics & Business Administration, Kobe University, vol. 6, pages 17-63, December.
    • Handle: RePEc:kob:tjrevi:dec2016:v:6:p:17-63
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    References listed on IDEAS

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    Cited by:

    1. KONDO Satoshi & MIYAKAWA Daisuke & SHIRAKI Kengo & SUGA Miki & USUKI Teppei, 2019. "Using Machine Learning to Detect and Forecast Accounting Fraud," Discussion papers 19103, Research Institute of Economy, Trade and Industry (RIETI).
    2. Kusano, Masaki & Sakuma, Yoshihiro, 2019. "Effects of recognition versus disclosure of finance leases on audit fees and costs: Evidence from Japan," Journal of Contemporary Accounting and Economics, Elsevier, vol. 15(1), pages 53-68.
    3. Slavko ?odan, 0000. "Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments," Proceedings of Economics and Finance Conferences 14416287, International Institute of Social and Economic Sciences.

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    More about this item

    Keywords

    Earnings Quality; Accounting Fraud; Accrual Quality; F-Score; Japan;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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