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Financial Analysis Management of Companies in a Region of Mexico: the Need of a Financial Ratios Annual Directory

Author

Listed:
  • Deyanira Bernal Dom¨ªnguez

    (Faculty of Accounting and Administration, Universidad Aut¨®noma de Sinaloa Blvd, Mexico)

  • Mar¨ªa Luisa Saavedra Garc¨ªa

    (Faculty of Accounting and Administration, Universidad Nacional Aut¨®noma de M¨¦xico)

  • Lydia Mar¨ªa L¨®pez Barraza

    (Department of Economic and Administrative Sciences, Universidad de Occidente, Mexico)

Abstract

Decisions of the organizational policies by business managers, based on ideal financial indicators, can be taken with the support of the analysis of financial ratios like predictors of business solvency, profitability and growth. This is an important financial tool to support decision making and for understanding the economic contexts where a company operates. For these reasons, the objective of this research is to identify the financial ratio usage level and to determine the relevance of elaborating an annual directory as support in defining the future of companies in Mexico. A qualitative methodology with a descriptive cross-sectional approach was used. To obtain the information a questionnaire with 43 items was successfully applied to 120 entrepreneurs in Culiac¨¢n, Sinaloa, Mexico. In this context it can be affirmed that the usage level of financial ratios is related to the firm¡¯s size. That is to say, the bigger the size of the company, the higher the level of usage is. The results support the relevance of elaborating the directory with advantages: its availability to businesses of all sizes at low cost.

Suggested Citation

  • Deyanira Bernal Dom¨ªnguez & Mar¨ªa Luisa Saavedra Garc¨ªa & Lydia Mar¨ªa L¨®pez Barraza, 2014. "Financial Analysis Management of Companies in a Region of Mexico: the Need of a Financial Ratios Annual Directory," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 64-78, August.
  • Handle: RePEc:bap:journl:140306
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    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Sonia Ondo-Ndong & Sandra Rigot, 2011. "The Aggregated Leverage Ratio and the Detection of Financial Vulnerability :Evidence from the United States and European Countries," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 54(1), pages 5-20.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    4. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    5. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    6. Nina Ponikvar & Maks Tajnikar & Ksenja Pušnik, 2009. "Performance ratios for managerial decision‐making in a growing firm," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(2), pages 109-120, February.
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    More about this item

    Keywords

    Ratios; Benchmarking; Financial analysis;
    All these keywords.

    JEL classification:

    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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