IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v20y2021i2d10.1057_s41272-021-00299-x.html
   My bibliography  Save this article

Discriminant model of revenue prediction: a study of selected top performing companies in India

Author

Listed:
  • Jayant Hooda

    (C.H.L. Government College Chhara)

  • Vijay Singh

    (Indira Gandhi University)

  • Amit Dangi

    (SGT University)

Abstract

A business enterprise, striving for maximum revenue generation and sustainable profitability, comprehensively and continuously engages in exploring the key factors to expand the market base aiming to clinch the top seat. Owing to plurality of financial variables, it has always been an issue of academic and practical importance to arrive at a smaller but reliable set of financial indicators. Post Great Depression, empirical literature emphasized largely upon predicting bankruptcy of companies in dichotomous categories through linear combination of a few variables, e.g., Altman Z, Zeta, and Discriminant functions, etc. With the recent advancements in the IT, Data Analysis and Research, newer and robust statistical techniques are being used to obtain linear vectors of multiple groups to explain the financial performance. The present study selected twenty-eight financial variables representing major financial dimensions of a business to derive a variate or discriminant function of the significant variables to predict the revenue of the top performing companies (three pre-defined groups). Analysis was made separately for each of the 2 years (2016 and 2017). The obtained functions accurately classified 70% of the companies in each year. The results have implications for investors, management, credit rating agencies, mergers and acquisitions, etc., as their decisions depend upon sound prediction and forecasting.

Suggested Citation

  • Jayant Hooda & Vijay Singh & Amit Dangi, 2021. "Discriminant model of revenue prediction: a study of selected top performing companies in India," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 185-193, April.
  • Handle: RePEc:pal:jorapm:v:20:y:2021:i:2:d:10.1057_s41272-021-00299-x
    DOI: 10.1057/s41272-021-00299-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00299-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1057/s41272-021-00299-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Johnson, W. Bruce, 1979. "The Cross-Sectional Stability of Financial Ratio Patterns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(5), pages 1035-1048, December.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Joy, O. Maurice & Tollefson, John O., 1975. "On the Financial Applications of Discriminant Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 10(5), pages 723-739, December.
    4. Nisansala Wijekoon & A. Abdul Azeez, 2015. "An Integrated Model to Predict Corporate Failure of Listed Companies in Sri Lanka," International Journal of Business and Social Research, LAR Center Press, vol. 5(7), pages 1-14, July.
    5. Liang, Qi, 2003. "Corporate Financial Distress Diagnosis in China : Empirical Analysis Using Credit Scoring Models," Hitotsubashi Journal of commerce and management, Hitotsubashi University, vol. 38(1), pages 13-28, October.
    6. Edward I. Altman, 1973. "Predicting Railroad Bankruptcies in America," Bell Journal of Economics, The RAND Corporation, vol. 4(1), pages 184-211, Spring.
    7. 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.
    8. Nisansala Wijekoon & A. Abdul Azeez, 2015. "An Integrated Model to Predict Corporate Failure of Listed Companies in Sri Lanka," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(7), pages 1-14, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nagy Marek & Valaskova Katarina, 2023. "An Analysis of the Financial Health of Companies Concerning the Business Environment of the V4 Countries," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 170-193, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John W. Pacey & Toan M. Pham, 1990. "The Predictiveness of Bankruptcy Models: Methodological Problems and Evidence," Australian Journal of Management, Australian School of Business, vol. 15(2), pages 315-337, December.
    2. Nurul Izzaty Hasanah Azhar & Norziana Lokman & Md. Mahmudul Alam & Jamaliah Said, 2021. "Factors determining Z-score and corporate failure in Malaysian companies," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 370-386.
    3. Ali DERAN & Omer ISKENDEROGLU & Incilay ERDURU, 2014. "Regional Differences and Financial Ratios: A Comparative Approach on Companies of ISE City Indexes," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 946-955.
    4. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    5. McGurr, Paul T. & DeVaney, Sharon A., 1998. "Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models," Journal of Business Research, Elsevier, vol. 43(3), pages 169-176, November.
    6. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    7. Jones, Stewart & Hensher, David A., 2007. "Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes," The British Accounting Review, Elsevier, vol. 39(1), pages 89-107.
    8. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    9. Beverly L. Hadaway & Samuel C. Hadaway, 1981. "An Analysis Of The Performance Characteristics Of Converted Savings And Loan Associations," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 4(3), pages 195-206, September.
    10. Su-Han Woo & Min-Su Kwon & Kum Fai Yuen, 2021. "Financial determinants of credit risk in the logistics and shipping industries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 268-290, June.
    11. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    12. Carling, Kenneth & Jacobson, Tor & Lindé, Jesper & Roszbach, Kasper, 2002. "Capital Charges under Basel II: Corporate Credit Risk Modelling and the Macro Economy," Working Paper Series 142, Sveriges Riksbank (Central Bank of Sweden).
    13. Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019. "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers 2019/292, International Monetary Fund.
    14. Harvey R. Crapp & Maxwell Stevenson, 1987. "Development of a Method to Assess the Relevant Variables and the Probability of Financial Distress," Australian Journal of Management, Australian School of Business, vol. 12(2), pages 221-236, December.
    15. Wen-Wen Chien & Roger W. Mayer & John T. Sennetti, 2010. "Audit Committee Effectiveness In The Largest Us Public Hospitals: An Empirical Study," Accounting & Taxation, The Institute for Business and Finance Research, vol. 2(1), pages 107-127.
    16. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    17. Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    18. Akarsh Kainth & Ranik Raaen Wahlstrøm, 2021. "Do IFRS Promote Transparency? Evidence from the Bankruptcy Prediction of Privately Held Swedish and Norwegian Companies," JRFM, MDPI, vol. 14(3), pages 1-15, March.
    19. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    20. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:20:y:2021:i:2:d:10.1057_s41272-021-00299-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.