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Cluster Analysis of Financial Strategies of Companies

Author

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  • Sergey Dzuba

    (FEFU Campus, Far Eastern Federal University, 10 Ajax Bay, Russky Island, 690922 Vladivostok, Russia)

  • Denis Krylov

    (FEFU Campus, Far Eastern Federal University, 10 Ajax Bay, Russky Island, 690922 Vladivostok, Russia)

Abstract

Measuring the value of companies and assessing their risk often relies on econometric methods that consider companies as a set of objects under study, homogeneous in the sense of their use of financial strategies. This paper shows that cluster analysis methods can divide companies into classes according to financial strategies that they employ. This indicates that homogeneity can be considered within these classes, while between-class companies should rather be perceived as heterogeneous. The clustering of companies has to be performed on quite a dense set of strategies, which requires a combination of formal and heuristic methods. To divide companies into classes, we used financial coefficients characterizing strategies for the 2030 largest non-financial companies within the time period from 2006 to 2018. As a result, a stable division into seven clusters/strategies was obtained. We revealed that some strategies were more characteristic for the companies of high-tech economy, while others were typical for the companies in basic industries. The dynamics of clusters is characterized by an increase in the share of risky strategies. A good meaningful interpretation of the resulting clustering confirms its consistency. The identified clusters can be used as dummy variables in econometric studies of companies to improve the quality of the results.

Suggested Citation

  • Sergey Dzuba & Denis Krylov, 2021. "Cluster Analysis of Financial Strategies of Companies," Mathematics, MDPI, vol. 9(24), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3192-:d:699850
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    References listed on IDEAS

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    1. Ramanna, Karthik, 2008. "The implications of unverifiable fair-value accounting: Evidence from the political economy of goodwill accounting," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 253-281, August.
    2. 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.
    3. Capece, Guendalina & Cricelli, Livio & Di Pillo, Francesca & Levialdi, Nathan, 2010. "A cluster analysis study based on profitability and financial indicators in the Italian gas retail market," Energy Policy, Elsevier, vol. 38(7), pages 3394-3402, July.
    4. 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.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    6. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    7. Bonaimé, Alice A. & Hankins, Kristine W. & Jordan, Bradford D., 2016. "The cost of financial flexibility: Evidence from share repurchases," Journal of Corporate Finance, Elsevier, vol. 38(C), pages 345-362.
    8. Ali Seyed Shirkhorshidi & Saeed Aghabozorgi & Teh Ying Wah, 2015. "A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
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    Cited by:

    1. Jin Kuang & Tse-Chen Chang & Chia-Wei Chu, 2022. "Research on Financial Early Warning Based on Combination Forecasting Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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