Using the Beneish M-score model: Evidence from non-financial companies listed on the Warsaw Stock Exchange

  • 182 Views
  • 35 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The risk of distortion of financial statements has been growing. Following the 2008 crisis, recipients of financial information are increasingly focusing on the likelihood of financial statements being distorted through fraudulent presentation of financial information. Therefore, scientific research pays more attention to models capable of detecting financial statement manipulation.
The paper aims to present the principles of functioning and the possibility of using the Beneish M-score model in Polish realities. It analyzes the history of more than 30 companies listed on the Warsaw Stock Exchange to select those whose history indicates that they can be classified as manipulators, and to select the same number of companies from the control group that are considered as non-manipulators.
The research method involves the analysis of empirical data on companies listed on the Warsaw Stock Exchange.
The analysis showed the 8-factor Beneish model identified manipulators with 100% accuracy and succeeded in identifying non-manipulators. The effectiveness of the 5-factor model was much lower.
To serve the purpose of the study, the effectiveness of the Beneish model was tested on a small sample of Polish listed companies as an introduction to a planned larger scale research. The results obtained are consistent with the results of numerous studies by authors from various countries and confirm the effectiveness of the Beneish model in detecting financial statement manipulation.

Acknowledgment
The publication is sponsored by funds from the Cracow University of Economics for the maintenance and development of research potential.

view full abstract hide full abstract
    • Figure 1. Economic fraud frequency from 2010 to 2018
    • Figure 2. Losses due to economic fraud over the period 2010–2018
    • Table 1. The frequency of the two types of fraud and the average losses caused by them in the USA in 1996, 2002, 2010 and 2018
    • Table 2. Structure of companies tested with the Beneish model
    • Table 3. Values of indicators from the Beneish model and the number of red flags in the analyzed companies calculated for financial statements prepared for 2010
    • Table 4. Number of “red flags” in companies tested with the Beneish model
    • Table 5. Aggregate values using 5-factor and 8-factor models for the analyzed companies
    • Table 6. Number of indications of the Beneish 5-factor model for “manipulator” and “non-manipulator” companies
    • Table 7. Number of indications of the Beneish 8-factor model for “manipulator” and “non-manipulator” companies
    • Conceptualization
      Artur Hołda
    • Data curation
      Artur Hołda
    • Formal Analysis
      Artur Hołda
    • Investigation
      Artur Hołda
    • Methodology
      Artur Hołda
    • Writing – original draft
      Artur Hołda
    • Writing – review & editing
      Artur Hołda