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An investigation into the measurement of graph distortion in financial reports

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  • Dineli Mather
  • Paul Mather
  • Alan Ramsay

Abstract

The Graph Discrepancy Index (GDI), which originates from the lie factor introduced by Tufte (1983), is the mechanism commonly used in the financial graphics literature to determine whether graphs are distorted and to quantify the extent of such distortion. Although the GDI is critical to the financial graphics literature, little or no attention has been paid to its robustness and accuracy. We critically examine the mathematical characteristics of the GDI and show its limitations as a measure of graph distortion. We review a number of cases to demonstrate these limitations and present an alternative measure of graph distortion—the Relative Graph Discrepancy index (RGD). Numerous simulations suggest that the RGD overcomes the problems associated with the GDI. The RGD is also tested on data presented in earlier research and the results are compared to those obtained using the GDI. In comparison with the GDI, we find that the RGD is more consistent and produces slightly stronger results. We stress, however, that this is not a best or definitive measure but is intended to start a research process that leads to a generally accepted measure.

Suggested Citation

  • Dineli Mather & Paul Mather & Alan Ramsay, 2005. "An investigation into the measurement of graph distortion in financial reports," Accounting and Business Research, Taylor & Francis Journals, vol. 35(2), pages 147-160.
    • Handle: RePEc:taf:acctbr:v:35:y:2005:i:2:p:147-160
    DOI: 10.1080/00014788.2005.9729670
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    References listed on IDEAS

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    1. Paul Mather & Alan Ramsay & Alan Serry, 1996. "The Use and Representational Faithfulness of Graphs in Annual Reports: Australian Evidence," Australian Accounting Review, CPA Australia, vol. 6(12), pages 56-63, September.
    2. Beattie, Vivien & Jones, Michael John, 2001. "A six-country comparison of the use of graphs in annual reports," The International Journal of Accounting, Elsevier, vol. 36(2), pages 195-222, May.
    3. Graves, O. Finley & Flesher, Dale L. & Jordan, Robert E., 1996. "Pictures and the bottom line: The television epistemology of U.S. annual reports," Accounting, Organizations and Society, Elsevier, vol. 21(1), pages 57-88, January.
    4. Jayne Godfrey & Paul Mather & Alan Ramsay, 2003. "Earnings and Impression Management in Financial Reports: The Case of CEO Changes," Abacus, Accounting Foundation, University of Sydney, vol. 39(1), pages 95-123, February.
    5. Gibbins, M & Richardson, A & Waterhouse, J, 1990. "The Management Of Corporate Financial Disclosure - Opportunism, Ritualism, Policies, And Processes," Journal of Accounting Research, Wiley Blackwell, vol. 28(1), pages 121-143.
    6. Preston, Alistair M. & Wright, Christopher & Young, Joni J., 1996. "IMag[in]ing annual reports," Accounting, Organizations and Society, Elsevier, vol. 21(1), pages 113-137, January.
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    3. Shaio Yan Huang & Tung-Hsien Wu & An-An Chiu & David C. Yen, 2015. "Measurements of mislead threshold of company graph distortion," Information Systems Frontiers, Springer, vol. 17(5), pages 1111-1132, October.
    4. Simone Aresu, 2015. "Graphical Reporting in Italian Annual Reports during the Financial Crisis: Impression Management or Incremental Information?," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2015(1), pages 77-106.
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    8. Parsons, Linda M. & Tinkelman, Daniel, 2013. "Testing the feasibility of small multiples of sparklines to display semimonthly income statement data," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 58-76.

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