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Indian Journal of Finance

ISSN: 0973-8711 Frequency: Monthly Peer Review: Double-blind Published since: 2007 Language: English
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Original Article

Open Access Original Article

Predicting Earnings Manipulation Using Beneish M - Score of Selected Companies in India

Charmi Shah1,  Mehul Saraswat2,  Akshay Mehta3

1 Assistant Professor, IFIM B School, 8P &9 P KIADB Industrial Area, Electronics City Phase 1, Bangalore - 560 100

2 PGDM Student, IFIM B School, 8P &9 P KIADB Industrial Area, Electronics City Phase 1, Bangalore - 560 100

3 PGDM Student, IFIM B School, 8P &9 P KIADB Industrial Area, Electronics City Phase 1, Bangalore - 560 100

Volume 12
Issue 4
Pages 54–66
Year 2018
Received: Oct. 18, 2017 Accepted: March 26, 2018 Published: April 1, 2018
Abstract

In the field of global competition, the corporate world is witnessing manipulation of financial statements to achieve the desired outcomes. According to India Forensic Consultancy Services, at least 1200 companies listed on domestic stock exchanges counterfeited their financial statements. The increasing rate of such undesired acts laid path for the investigation into such practices. This empirical research work was conducted to predict the signs of earnings manipulation of companies listed with the BSE 100 index by using the probabilistic Beneish M score eight variable model. Time frame for the investigation of financial statements' data was confined to 2011 to 2016 to calculate the M score. Based on M score results of each year, the companies were classified into two groups: likely manipulator and non-likely manipulator. Then multinomial regression technique was used to find the most significant variables that affected manipulation. The outcomes of the results revealed that three ratios, namely TATA (total accruals to total assets), DSRI (daily sales in receivables index), and SGI (sales growth index) variables could be considered as signals of earnings manipulation by companies. The present work contributes to the literature through identification of probable earnings manipulators in BSE 100 index companies which investors prefer the most for their equity investments. The research findings may be an eye-opener for regulators /policy makers to implement stringent checks on auditing of financial results of the companies. Furthermore, the research would be useful for the investors to go for face validation of companies instead of relying on misrepresentation of their true value.

Keywords Earnings Manipulation Beneish M-Score Indian Companies
How to Cite

Charmi Shah, Mehul Saraswat, Akshay Mehta (2018). Predicting Earnings Manipulation Using Beneish M - Score of Selected Companies in India. Indian Journal of Finance, 12(4), 54–66. https://doi.org/10.17010/ijf/2018/v12i4/122796

References
  1. Ahmed, T., & Naima, J. (2016). Detection and analysis of probable earnings manipulation by firms in a developing country. Asian Journal of Business and Accounting, 9 (1), 59 - 81. DOI: 10.1016/j.sbspro.2014.06.032
  2. Ahuja, B. R., & Singhal, N. (2014). Assessing the financial soundness of companies with special reference to the Indian textile sector: An application of the Altman Z score model. Indian Journal of Finance, 8 (4), 38 - 48. DOI: 10.17010/ijf/2014/v8i4/71922
  3. Alleyne, P., & Howard, M. (2005). An exploratory study of auditors’ responsibility for fraud detection in Barbados. Managerial Auditing Journal, 20 (3), 284 - 303. DOI: <a href=https://doi.org/10.1108/02686900510585618 target="_blank">https://doi.org/10.1108/02686900510585618</a>
  4. Anh, N. H., & Linh, N. H. (2016).Using the M-score Model in detecting earnings management: Evidence from non-financial Vietnamese listed companies. VNU Journal of Science : Economics and Business, 32 (2), 14 - 23.
  5. Apparao, G, Singh, A., Rao, G. S, Bhavani, B. L., Eswar, K., & Rajani, D. (2009).Financial statement fraud detection by data mining. International Journal of Advanced Networking and Applications, 1(3), 159 - 163.
  6. Aris, N. A., Arif, S. M. M., Othman, R., & Zain, M. M. (2015). Fraudulent financial statement detection using statistical techniques: The case of small medium automotive enterprise. Journal of Applied Business Research, 31(4), 1469 - 1478.
  7. Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24 - 36.
  8. Beneish, M. D., Lee, C. M., & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69 (2), 57 - 82.
  9. Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28 (1), 17 - 82.
  10. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13 (1), 1-36. DOI: <a href=https://doi.org/10.1111/j.1911-3846.1996.tb00489.x target="_blank">https://doi.org/10.1111/j.1911-3846.1996.tb00489.x</a>
  11. Dikmen, B., & Küçükkocaoğlu, G. (2010). The detection of earnings manipulation: The three - phase cutting plane algorithm using mathematical programming. Journal of Forecasting, 29 (5), 442 - 466. DOI: <a href=https://doi.org/10.1002/for.1138 target="_blank">https://doi.org/10.1002/for.1138</a>
  12. Harrington, C. (2005, March/April). Formulas for detection : Analysis ratios for detecting financial statement fraud. Fraud Magazine - March/April 2005. Retrieved from <a href=http://www.fraud-magazine.com/article.aspx?id=4294967726 target="_blank">http://www.fraud-magazine.com/article.aspx?id=4294967726</a>
  13. Kamal, M. E. M., Salleh, M. F. M., & Ahmad, A. (2016). Detecting financial statement fraud by Malaysian Public Listed Companies: The reliability of the Beneish M-Score model. Journal Pengurusan (UKM Journal of Management), 46, 23-32.
  14. Kaur, R., Mehra, S., & Khanna, A. (2015).The state of earnings management in India: An empirical analysis. Indian Journal of Finance, 9 (12), 41-50. DOI: 10.17010/ijf/2015/v9i12/84383
  15. Kaur, R., Sharma, K., & Khanna, A. (2014). Detecting earnings management in India: A sector-wise study. European Journal of Business and Management, 6 (11), 11-18.
  16. Kaushal, M. (2013). An analysis of earnings management in India: An empirical study. Indian Journal of Finance, 7 (6), 45 - 55. DOI:10.17010/ijf/2013/v7i6/72108
  17. Kumari, P., & Pattanayak, J. K. (2015). The trend of earnings management practices in the Indian financial sector. Indian Journal of Finance, 9 (9), 44 - 54. DOI: 10.17010/ijf/2015/v9i9/77197
  18. Lobo, G. J., & Zhou, J. (2001). Disclosure quality and earnings management. Asia-Pacific Journal of Accounting & Economics, 8 (1), 1 - 20.
  19. Mahama, M. (2015).Detecting corporate fraud and financial distress using the Atman and Beneish models. International Journal of Economics, Commerce and Management, 3 (1), 1-18.
  20. Mavengere, K. (2016). Predicting corporate bankruptcy and earnings manipulation using the Altman Z-Score and Beneish M Score. The case of Z manufacturing firm in Zimbabwe. International Journal of Management Sciences and Business Research, 4(10), 8 - 14.
  21. Mehta, A., & Bhavani, G. (2017). Application of forensic tools to detect fraud : The case of Toshiba. Journal of Forensic and Investigative Accounting, 9 (1), 692-710.
  22. Mollik, A. T., Mir, M., McIver, R., & Bepari, M. K. (2013, December). Earnings management during the global financial crisis: Evidence from Australia. In Proceedings of International Business and Social Sciences and Research Conference, 16 - 17.
  23. Persons, O. S. (1995). Using financial statement data to identify factors associated with fraudulent financial reporting. Journal of Applied Business Research, 11(3), 38. DOI: <a href=https://doi.org/10.19030/jabr.v11i3.5858 target="_blank">https://doi.org/10.19030/jabr.v11i3.5858</a>
  24. Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. Retrieved from <a href=https://arxiv.org/ftp/arxiv/papers/1009/1009.6119.pdf target="_blank">https://arxiv.org/ftp/arxiv/papers/1009/1009.6119.pdf</a>
  25. Pustylnick, I. (2011). Empirical algorithm of detection of manipulation with financial statements. Journal of Accounting, Finance, and Economics, 1(2), 54 - 67.
  26. Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50 (2), 491-500.
  27. Razali, W. A. A. W. M., & Arshad, R. (2014).Disclosure of corporate governance structure and the likelihood of fraudulent financial reporting. Procedia - Social and Behavioral Sciences, 145, 243 - 253. DOI: 10.1016/j.sbspro.2014.06.032
  28. Repousis, S., & Repousis, S. (2016). Using Beneish model to detect corporate financial statement fraud in Greece. Journal of Financial Crime, 23 (4), 1063 - 1073. DOI: <a href=https://doi.org/10.1108/JFC-11-2014-0055 target="_blank">https://doi.org/10.1108/JFC-11-2014-0055</a>
  29. Roshchina, K. (2016). Analysis of earnings manipulation practices of Finnish and German companies using Beneish M-score model (Thesis). JAMK University of Applied Sciences, Finland.
  30. Roy, C., & Debnath, P. (2015). Earnings management practices in financial reporting of public enterprises in India: An empirical test with M - score. DOI : <a href=https://dx.doi.org/10.2139/ssrn.2551713 target="_blank">https://dx.doi.org/10.2139/ssrn.2551713</a>
  31. Selim A. (2006). Comparative evaluation of models used in the determination of earning manipulation. Journal of Academic Studies, 8(31), 35 - 48.
  32. Summers, S. L., & Sweeney, J. T. (1998).Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review, 73 (1), 131-146.
  33. Tarjo & Herawati, N. (2015). Application of Beneish M-Score Models and data mining to detect earning manipulation. Procedia-Social and Behavioral Sciences, 211, 924-930. DOI: <a href=https://doi.org/10.1016/j.sbspro.2015.11.122 target="_blank">https://doi.org/10.1016/j.sbspro.2015.11.122</a>
  34. Warshavsky, M. (2012). Analyzing earnings quality as a financial forensic tool. Financial Valuation and Litigation Expert Journal, 39, 16-20.
  35. Wyrobek, J. M., & Stanczyk, Z. (2015). Manipulations of financial statements in the polish accounting and role of financial audit in their detection. DOI: <a href=http://dx.doi.org/10.2139/ssrn.2649021 target="_blank">http://dx.doi.org/10.2139/ssrn.2649021</a>
  36. Zaki, M., & Theodoulidis, B. (2013). Analyzing financial fraud cases using a linguistics-based text mining approach. DOI: 10.13140/2.1.1127.3607
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