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

Yousaf, Muhammad; Dey, Sandeep Kumar

dc.title	Best proxy to determine firm performance using financial ratios: A CHAID approach	en
dc.contributor.author	Yousaf, Muhammad
dc.contributor.author	Dey, Sandeep Kumar
dc.relation.ispartof	Review of Economic Perspectives
dc.identifier.issn	1213-2446 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn	1804-1663 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2022
utb.relation.volume	22
utb.relation.issue	3
dc.citation.spage	219
dc.citation.epage	239
dc.type	article
dc.language.iso	en
dc.publisher	Sciendo
dc.identifier.doi	10.2478/revecp-2022-0010
dc.relation.uri	https://sciendo.com/article/10.2478/revecp-2022-0010
dc.relation.uri	https://intapi.sciendo.com/download/file?packageId=63335cb9dd1966168bbc6dfa&downloadType=PDF
dc.subject	Czech firms	en
dc.subject	decision tree	en
dc.subject	financial ratios	en
dc.subject	firm performance	en
dc.subject	return on assets	en
dc.description.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.	en
utb.faculty	Faculty of Management and Economics
dc.identifier.uri	http://hdl.handle.net/10563/1011162
utb.identifier.obdid	43883752
utb.identifier.scopus	2-s2.0-85139490029
utb.identifier.wok	000862631900003
utb.source	J-wok
dc.date.accessioned	2022-10-18T12:15:15Z
dc.date.available	2022-10-18T12:15:15Z
dc.description.sponsorship	Internal Grant Agency (IGA) in Tomas Bata University in Zlin, Czech Republic [IGA/FAME/2022/012]
dc.description.sponsorship	Tomas Bata University in Zlin, TBU: IGA/FAME/2022/012
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.access	openAccess
utb.contributor.internalauthor	Yousaf, Muhammad
utb.contributor.internalauthor	Dey, Sandeep Kumar
utb.fulltext.affiliation	Muhammad Yousaf1 and Sandeep Kumar Dey2 1 Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, Zlin 76001, Czech Republic, e-mail: usaf880@yahoo.com 2 Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, Zlin 76001, Czech Republic, and Czech Mathematical Society, Prague, Czech Republic, e-mail: dey@utb.cz
utb.fulltext.dates	Received: 24 March 2022 Accepted: 26 July 2022 Sent for Publication: 9 September 2022
utb.fulltext.references	ABDEL-BASSET, M., DING, W., MOHAMED, R., & METAWA, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries. Risk Management, 22(3), 192–218. https://doi.org/10.1057/s41283-020-00061-4 ABDI, H, & BEATON, D. (2021). Principal component and correspondence analyses using R. Springer International Publishing. ABDI, HERVÉ, & WILLIAMS, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433–459. https://doi.org/10.1002/wics.101 AKGÜN, A. İ., & MEMIŞ KARATAŞ, A. (2020). Investigating the relationship between working capital management and business performance: evidence from the 2008 financial crisis of EU-28. International Journal of Managerial Finance. https://doi.org/10.1108/IJMF-08-2019-0294 AL-ZARAREE, A., AL-SAWALHAH, J., & SAMARA, A. (2021). The relationship between the return on equity (ROE) and the capital structure of the Jordanian public shareholding industrial companies. Academy of Accounting and Financial Studies Journal, 25(1), 1–10. ALMASKATI, N. (2022). The determinants of bank profitability and risk: A random forest approach. Cogent Economics & Finance, 10(1), 2021479. https://doi.org/10.1080/23322039.2021.2021479 AZUR, M. J., STUART, E. A., FRANGAKIS, C., & LEAF, P. J. (2011). Multiple imputation by chained equations: what is it and how does it work? Wiley Online Library, 20(1), 40-49. https://doi.org/https://doi.org/10.1002/mpr.329 Bawazir, H., Khayati, A., & AbdulMajeed, F. (2021). Corporate governance and the performance of non-financial firms: the case of Oman. Entrepreneurship and Sustainability Issues, 8(4), 595–609. https://doi.org/10.9770/jesi.2021.8.4(36) BAYRAKTAROGLU, A. E., CALISIR, F., & BASKAK, M. (2019). Intellectual capital and firm performance: an extended VAIC model. Journal of Intellectual Capital, 20(3), 406–425. https://doi.org/10.1108/JIC-12-2017-0184 BISNODE, 2021; Bisnode Albertina, https://www.bisnode.cz/produkty/albertina/. Gold Edition BLAŽKOVÁ, I., & DVOULETÝ, O. (2022). Relationship between firm total factor productivity and performance: case of the Czech high-tech industry. International Journal of Entrepreneurial Venturing. BOSE, I. (2006). Deciding the financial health of dot-coms using rough sets. Information & Management, 43(7), 835-846. CAMSKA, D., KLECKA, J., & SCHOLLEOVA, H. (2021). Influence of age on selected parameters of insolvent companies. Problems and Perspectives in Management, 19(2), 77–90. https://doi.org/10.21511/ppm.19(2).2021.07 CHANDRAPALA, P., & KNÁPKOVÁ, A. (2013). Firm-specific factors and financial performance of firms in the Czech Republic. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61(7), 2183–2190. https://doi.org/10.11118/actaun201361072183 CHICCO, D., TÖTSCH, N., & JURMAN, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14(1), 13. https://doi.org/10.1186/s13040-021-00244-z ČINČALOVÁ, S., & HEDIJA, V. (2020). Firm characteristics and corporate social responsibility: the case of Czech transportation and storage industry. Sustainability, 12(5), 1992. https://doi.org/10.3390/su12051992 DELEN, D., KUZEY, C., & UYAR, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), 3970–3983. https://doi.org/10.1016/j.eswa.2013.01.012 DÍAZ-PÉREZ, F. M., & BETHENCOURT-CEJAS, M. (2016). CHAID algorithm as an appropriate analytical method for tourism market segmentation. Journal of Destination Marketing and Management, 5(3), 275–282. https://doi.org/10.1016/j.jdmm.2016.01.006 DUNTEMAN, G. H. (1989). Sage university paper series on quantitative applications in social sciences. Principal components analysis, Newbury Park, CA: Sage. DWILITA, H., & MINGKA, A. Y. (2022). The effect of Nim, Npl, firm size and exchange rate on profitability with car as an intervening variable in the company conventional banking sector registered in Indonesia stock exchange 2015-2019 period. Accounting and Business Journal, 4(1), 28–37. https://doi.org/10.54248/ABJ.V4I1.4052 FIELD, A. P. (2005). Intraclass Correlation. Encyclopedia of Statistics in Behavioral Science. Chichester, UK: John Wiley & Sons, Ltd. https://doi.org/10.1002/0470013192.bsa313 GAYATHRI, C. S., & VIJAYALAKSHMI, S. (2021). A study on the effect of cost of capital on profitability of a company. International Research Journal on Advanced Science Hub, 3(0), 54–59. https://doi.org/10.47392/IRJASH.2021.165 HABIBI, A. & IQBAL, M. (2021). Benefits of financial ratios for financing sharia banking Indonesia. Jurnal Ekonomi Dan Keuangan Syariah, 5(1), 1–12. https://doi.org/10.29313/amwaluna.v5i1.5299 Harman, H. H. (1976). Modern Factor Analysis. University of Chicago Press. HUA, Z., WANG, Y., XU, X., ZHANG, B., & LIANG, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434-440. HUSSON, F., LÊ, S., & PAGÈS, J. (2017). Exploratory multivariate analysis by example using R. Routledge New York, NY. ISLAMIYATI, G. M., & DIANA, N. (2021). Effect of Mudarabah and Musharakah financing on return on equity (ROE) case studies on Islamic commercial banks in Indonesia for the period 2015-2019. Jurnal Ekonomi Syariah Teori Dan Terapan, 8(3), 255–263. https://doi.org/10.20473/VOL8ISS20213PP255-263 JOHNSON, R. A. & WICHERN, D. W. (2013). Applied Multivariate Statistics analysis (6th ed.). New Jersy: Pearson Education Inc. JYOTI, G., & KHANNA, A. (2021). Does sustainability perforcmance impact financial performance? Evidence from Indian service sector firms. Sustainable Development, 29(6), 1086-1095. https://doi.org/10.1002/sd.2204 KAISER, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575 KASS, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119. https://doi.org/10.2307/2986296 KAYANI, U. N., DE SILVA, T. A., & GAN, C. (2020). Working capital management and firm performance relationship: An empirical investigation of Australasian firms. Review of Pacific Basin Financial Markets and Policies, 23(3). https://doi.org/10.1142/S0219091520500265 KORENIUS, T., LAURIKKALA, J., & JUHOLA, M. (2007). On principal component analysis, cosine and Euclidean measures in information retrieval. Information Sciences, 177(22), 4893–4905. https://doi.org/10.1016/j.ins.2007.05.027 KUČERA, J., VOCHOZKA, M., & ROWLAND, Z. (2021). The ideal debt ratio of an agricultural enterprise. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13094613 LARASATI, C. I., & PURWANTO, P. (2022). How financial ratios and firm size affect profitability: evidence from food and beverages industry in Indonesia. The Winners, 23(1), 43–50. https://doi.org/10.21512/TW.V23I1.7099 LÊ, S., JOSSE, J., & HUSSON, F. (2008). FactoMineR: An R package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01 LISTIORINI, L., & PUTRI, R. A. (2022). The effect of leverage, return on asset (ROA), and company size on company value with good corporate governance as moderating variables (study on Manufacturing Companies). Accounting and Business Journal, 4(1), 38–49. https://doi.org/10.54248/ABJ.V4I1.4053 LUBIS, I., & ALFIYAH, F. N. (2021). Effect of return on equity and debt to equity ratio to stock return. Indonesian Financial Review, 1(1), 18–32. https://doi.org/10.55538/ifr.v1i1.3 MILLER, B., FRIDLINE, M., LIU, P. Y., & MARINO, D. (2014). Use of CHAID Decision Trees to formulate pathways for the early detection of metabolic syndrome in young adults. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2014/242717 NILASHI, M., BAGHERIFARD, K., RAHMANI, M., & RAFE, V. (2017). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers and Industrial Engineering, 109, 357–368. https://doi.org/10.1016/j.cie.2017.05.016 PIVOŇKOVÁ, A., & TEPPEROVÁ, J. (2021). Interest limitation rule under ATAD: case of the Czech Republic. DANUBE, 12(2), 121–134. https://doi.org/10.2478/DANB-2021-0009 REZAIE, K., RAMIYANI, S. S., NAZARI-SHIRKOUHI, S., & BADIZADEH, A. (2014). Evaluating performance of Iranian cement firms using an integrated fuzzy AHPVIKOR method. Applied Mathematical Modelling, 38(21–22), 5033–5046. https://doi.org/10.1016/j.apm.2014.04.003 SEAN, N. A. M., ANAGREH, S., AL-DALAIEN, B. O. A., ALMUGARI, F., KHALED, A. S. D., & AL-HOMAIDI, E. A. (2021). Working capital management and banks’ performance: Evidence from India. The Journal of Asian Finance, Economics and Business, 8(6), 747–758. https://doi.org/10.13106/JAFEB.2021.VOL8.NO6.0747 SHARMA, R. K., BAKSHI, A., & CHHABRA, S. (2020). Determinants of behaviour of working capital requirements of BSE listed companies: An empirical study using cointegration techniques and generalised method of moments. Cogent Economics & Finance, 8(1), 1720893. https://doi.org/10.1080/23322039.2020.1720893 SOEWARNO, N., & TJAHJADI, B. (2020). Measures that matter: an empirical investigation of intellectual capital and financial performance of banking firms in Indonesia. Journal of Intellectual Capital, 21(6), 1085–1106. https://doi.org/10.1108/JIC-09-2019-0225 THARWAT, A. (2018). Classification assessment methods. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2018.08.003 TRACY, A. (2012). Ratio analysis fundamentals: how 17 financial ratios can allow you to analyse any business on the planet. CreateSpace Independent Publishing Platform. VALASKOVA, K., KLIESTIK, T., & KOVACOVA, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105–121. https://doi.org/10.24136/oc.2018.006 VAN BUUREN, S., & GROOTHUIS-OUDSHOORN, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03 VRBKA, J. (2020). The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic. Oeconomia Copernicana, 11(2), 325–346. https://doi.org/10.24136/OC.2020.014 VRBKA, J., KALINOVÁ, E., & DVOŘÁKOVÁ, Z. (2022). Optimization of the capital structure of an agricultural company in the Czech Republic. SHS Web of Conferences, 132, 1008. https://doi.org/10.1051/SHSCONF/202213201008 WANG, T. C., & CHEN, Y. H. (2006). Applying rough sets theory to corporate credit ratings. In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics (pp. 132-136). IEEE. https://doi.org/10.1109/SOLI.2006.329050 WITTEN, I. H., & FRANK, E. (2000). Machine Learning Algorithms in Java. Machine Learning, 265–320. XU, J., & LI, J. (2020). The interrelationship between intellectual capital and firm performance: evidence from China’s manufacturing sector. Journal of Intellectual Capital. https://doi.org/10.1108/JIC-08-2019-0189/FULL/HTML YALCIN, N., BAYRAKDAROGLU, A., & KAHRAMAN, C. (2012). Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Systems with Applications, 39(1), 350-364. https://doi.org/10.1016/j.eswa.2011.07.024 YOUSAF, M. (2022a). Bankruptcy risks, firm size, and firm profitability: A dynamic panel data approach. International Journal of Sustainable Economy. https://doi.org/10.1504/IJSE.2023.10045805 YOUSAF, M. (2022b). Labour productivity and firm performance: evidence from certified firms from the EFQM Excellence Model. Total Quality Management and Business Excellence. https://doi.org/10.1080/14783363.2022.2054319 YOUSAF, M. (2021). Intellectual capital and firm performance: evidence from certified firms from the EFQM Excellence Model. Total Quality Management and Business Excellence. https://doi.org/10.1080/14783363.2021.1972800 YOUSAF, M., & BRIS, P. (2021a). Assessment of bankruptcy risks in Czech companies using regression analysis. Problems and Perspectives in Management, 19(3), 46–55. https://doi.org/10.21511/PPM.19(3).2021.05 YOUSAF, M., & BRIS, P. (2021b). Effects of working capital management on firm performance: evidence from the EFQM certified firms. Cogent Economics and Finance, 9(1). https://doi.org/10.1080/23322039.2021.1958504 YOUSAF, M., BRIS, P., & HAIDER, I. (2021). Working capital management and firm’s profitability: Evidence from Czech certified firms from the EFQM excellence model. Cogent Economics and Finance, 9(1). https://doi.org/10.1080/23322039.2021.1954318 ZAINI, B. J., & MAHMUDDIN, M. (2019). Classifying firms’ performance using data mining approaches. International Journal Supply Chain Management, 8(1), 690. ZUARDI, M. H. (2021). Measuring healthiness of islamic banks using solvabilitas financial ratios. International Journal of Islamic Economics, 3(1), 17–36. https://doi.org/10.32332/IJIE.V3I1.3279
utb.fulltext.sponsorship	This work was supported by Internal Grant Agency (IGA) in Tomas Bata University in Zlin, Czech Republic, under the project No. IGA/FAME/2022/012.
utb.wos.affiliation	[Yousaf, Muhammad; Dey, Sandeep Kumar] Tomas Bata Univ Zlin, Fac Econ & Management, Mostni 5139, Zlin 76001, Czech Republic; [Dey, Sandeep Kumar] Czech Math Soc, Prague, Czech Republic
utb.scopus.affiliation	Faculty of Management and Economics, Tomas Bata University in Zlin, Mostni 5139, Zlin, 76001, Czech Republic; Czech Mathematical Society, Prague, Czech Republic
utb.fulltext.projects	IGA/FAME/2022/012
utb.fulltext.faculty	Faculty of Management and Economics
utb.fulltext.faculty	Faculty of Management and Economics
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