IDEAS home Printed from https://ideas.repec.org/a/vrs/reoecp/v22y2022i3p219-239n3.html
   My bibliography  Save this article

Best proxy to determine firm performance using financial ratios: A CHAID approach

Author

Listed:
  • Yousaf Muhammad

    (Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, Zlin, 76001, Czech Republic)

  • Dey Sandeep Kumar

    (Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, Zlin, 76001, Czech Republic, and Czech Mathematical Society, Prague, Czech Republic)

Abstract

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.

Suggested Citation

  • Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
  • Handle: RePEc:vrs:reoecp:v:22:y:2022:i:3:p:219-239:n:3
    DOI: 10.2478/revecp-2022-0010
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/revecp-2022-0010
    Download Restriction: no

    File URL: https://libkey.io/10.2478/revecp-2022-0010?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
    ---><---

    References listed on IDEAS

    as
    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    2. Gaurav Jyoti & Ashu Khanna, 2021. "Does sustainability performance impact financial performance? Evidence from Indian service sector firms," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1086-1095, November.
    3. Mohamed Abdel-Basset & Weiping Ding & Rehab Mohamed & Noura Metawa, 2020. "An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries," Risk Management, Palgrave Macmillan, vol. 22(3), pages 192-218, September.
    Full references (including those not matched with items on IDEAS)

    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. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    2. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    3. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    4. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    5. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    6. H Seol & H Lee & S Kim & Y Park, 2008. "The impact of information technology on organizational efficiency in public services: a DEA-based DT approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 231-238, February.
    7. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    8. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    9. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    10. Adrien Ehrhardt & Christophe Biernacki & Vincent Vandewalle & Philippe Heinrich, 2019. "Feature quantization for parsimonious and interpretable predictive models," Papers 1903.08920, arXiv.org.
    11. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    12. Jae-Dong Kim & Tae-Hyeong Kim & Sung Won Han, 2023. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, MDPI, vol. 11(3), pages 1-10, January.
    13. Agapito, Dora & Mendes, Julio & Valle, Patricia, 2011. "The Sea as a Connection between Residents and Tourists in Coastal Destinations: A Case in Algarve," Spatial and Organizational Dynamics Discussion Papers 2011-13, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    14. Francisco Javier Rondán-Cataluña & Patricio E. Ramírez-Correa & Jorge Arenas-Gaitán & Muriel Ramírez-Santana & Elizabeth E. Grandón & Jorge Alfaro-Pérez, 2020. "Social Network Communications in Chilean Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
    15. Timmins, Christopher & Vissing, Ashley, 2022. "Environmental justice and Coasian bargaining: The role of race, ethnicity, and income in lease negotiations for shale gas," Journal of Environmental Economics and Management, Elsevier, vol. 114(C).
    16. A. Israëls & J. Driel, 1983. "Use of the chi-square statistic for selecting explanatory variables in multiway tables," Quality & Quantity: International Journal of Methodology, Springer, vol. 17(2), pages 103-116, April.
    17. Nocella, Giuseppe & Stefani, Gianluca & Romano, Donato, 2011. "Preferences, trust and willingness to pay for food information: An analysis of the Italian Market," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114606, European Association of Agricultural Economists.
    18. Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
    19. Marina Segura & Jorge Mello & Adolfo Hernández, 2022. "Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?," Mathematics, MDPI, vol. 10(18), pages 1-20, September.
    20. Alvarez-Gonzalez, José A. & Diaz-Perez, Flora M. & Bethencourt Cejas, Maria & Gonzalez Morales, M Olga, 2002. "The segmentation of the Canarian tourism market with regared to expenditure: an empirical study of La Palma," ERSA conference papers ersa02p251, European Regional Science Association.

    More about this item

    Keywords

    Czech firms; Decision tree; financial ratios; firm performance; return on assets;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

    Statistics

    Access and download statistics

    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:vrs:reoecp:v:22:y:2022:i:3:p:219-239:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.