Abstract
The need for an effective comprehensive financial performance score of the firm derived from accounting-based variables is increasingly felt in the stream of empirical research on relationships between financial performance and other dimensions of corporate performance. The solution to this problem must be pursued in literature on statistical-mathematical techniques to synthesize financial performance through financial ratios derived from financial statements. Until now, however, studies have mainly focused on mathematical modeling and ranking of companies, without using appropriate benchmarks to verify the relevance of the scores obtained and to establish, from a comparative perspective between different techniques, which one provides the assessment that best summarizes financial performance. To make a contribution to this research gap, using a sample of 845 companies observed from 2014 to 2020, we compared Data Envelopment Analysis and Principal Component Analysis with two other applications based on new methodologies derived from the Multi-Criteria Decision Methods: The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis. We developed the 4 techniques on a set of 9 financial ratios expressive of profitability and operating efficiency, solvency, and liquidity, and Tobin's q was used as a benchmark to compare the results provided by the 4 techniques and identify the best performing one. We found that TOPSIS is the most effective methodology for synthesizing an effective accounting-based score of the firm’s financial performance.
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Notes
Some authors (Jessop 2004; Xu 2004; Ma et al. 1999) emphasized the process of determining the weights of the financial ratios involved in the analysis. Identification of the weights is a prerogative of the scholar and it seems obvious that it should not be subjective. In this regard, Zou et al. (2006) proposed to use entropy (Shannon 1948), considered as a measure of the degree of disorder in the evaluation system.
The degree of concordance between the rankings obtained from the calculation of FPS and Tobin's q (benchmark variable) can be assessed using one of the non-parametric statistic measures of rank correlation (Spearman 1904). In our case, to measure the degree of similarity between rankings, we used Spearman's ρ. Coefficient ρ is appropriate for both continuous and discrete ordinal variables and allows us to evaluate the relationship between two variables using a monotone function. Spearman’s correlation coefficient is given by \({\rho }=1-\frac{6{\sum }_{{i}}{l{d}}_{{i}}^{2}}{{n}({{n}}^{2}-1)}\), where: d is the difference between paired ranks; n is the number of paired items; ρ ranges from –1 to 1.
References
Adalessossi, K.: Prediction of corporate bankruptcy: evidence from west African’s SMEs. J. Econ. Financ. Account. 2, 331--352 (2015)
Adegbite, E., Guney, Y., Kwabi, F., Tahir, S.: Financial and corporate social performance in the UK listed firms: the relevance of non-linearity and lag effects. Rev. Quant. Financ. Account. 52, 105–158 (2019). https://doi.org/10.1007/s11156-018-0705-x
Adler, N., Golany, B.: Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe. Eur. J. Oper. Res. 132, 260–273 (2001). https://doi.org/10.1016/S0377-2217(00)00150-8
Adler, N., Golany, B.: Including principal component weights to improve discrimination in data envelopment analysis. J. Oper. Res. Soc. 53, 985–991 (2002). https://doi.org/10.1057/palgrave.jors.2601400
Adler, N., Yazhemsky, E.: Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. Eur. J. Oper. Res. 202, 273–284 (2010). https://doi.org/10.1016/j.ejor.2009.03.050
Ahn, T., Charnes, A., Cooper, W.W.: Using data envelopment analysis to measure the efficiency of not-for-profit organizations: a critical evaluation—comment. Manag. Decis. Econ. 9, 251–253 (1988). https://doi.org/10.1002/mde.4090090310
Akkoc, S., Vatansever, K.: Fuzzy performance evaluation with AHP and Topsis methods: evidence from Turkish banking sector after the global financial crisis. Eurasian J. Bus. Econ. 6, 53–74 (2013)
Alam, S.M.S., Chowdhury, M.A.M., Razak, D.B.A.: Research evolution in banking performance: a bibliometric analysis. Futur. Bus. J. 7, 66 (2021). https://doi.org/10.1186/s43093-021-00111-7
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23, 589–609 (1968). https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Arce, M., Mora, A.: Empirical evidence of the effect of European accounting differences on the stock market valuation of earnings and book value. Eur. Account. Rev. 11, 573–599 (2002). https://doi.org/10.1080/09638180220125616
Bayaraa, B., Tarnoczi, T., Fenyves, V.: Corporate performance measurement using an integrated approach: a Mongolian case. Monten. J. Econ. 16, 123–134 (2020)
Bellovary, J.L., Giacomino, D.E., Akers, M.D.: A review of bankruptcy prediction studies: 1930 to present. J. Financ. Educ. 33, 1–42 (2007)
Bellovary, J.L., Giacomino, D.E., Akers, M.D.: A Review Of Going Concern Prediction Studies: 1976 to Present. J. Bus. Econ. Res. 5, 9–28 (2007). https://doi.org/10.19030/jber.v5i5.2541
Blasius, J., Gower, J.C.: Multivariate prediction with nonlinear principal components analysis: application. Qual. Quant. 39, 373–390 (2005). https://doi.org/10.1007/s11135-005-3006-0
Bogetoft, P., Otto, L.: Benchmarking with DEA, SFA, and R. Springer, New York (2011)
Bolch, B.W., Huang, C.T.: Multivariate statistical methods for business and economics. Prentice-Hall, Englewood Cliffs (1974)
Brainard, W.C., Tobin, J.: Pitfalls in financial model building. Am. Econ. Rev. 58, 99–122 (1968)
Bruna, M.G., Lahouel, B. Ben: CSR & financial performance: Facing methodological and modeling issues commentary paper to the eponymous FRL article collection. Financ. Res. Lett. (2021). https://doi.org/10.1016/j.frl.2021.102036
Bruna, M.G., Loprevite, S., Raucci, D., Ricca, B., Rupo, D.: Investigating the marginal impact of ESG results on corporate financial performance. Financ. Res. Lett. (2022). https://doi.org/10.1016/j.frl.2022.102828
Bulgurcu, B.K.: Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia—Soc. Behav. Sci. 62, 1033–1040 (2012). https://doi.org/10.1016/j.sbspro.2012.09.176
Bulgurcu, B.K.: Financial performance ranking of automotive industry firms in Turkey: evidence from entropy weighted technique. Int. J. Econ. Financ. Issues. 3, 844–851 (2013).
Cahan, S.F., De Villiers, C., Jeter, D.C., Naiker, V., Van Staden, C.J.: Are CSR disclosures value relevant? Cross-country evidence. Eur. Account. Rev. 25, 579–611 (2016). https://doi.org/10.1080/09638180.2015.1064009
Celik, M., DehaEr, I., Ozok, A.F.: Application of fuzzy extended AHP methodology on shipping registry selection: the case of Turkish maritime industry. Expert Syst. Appl. 36, 190–198 (2009). https://doi.org/10.1016/j.eswa.2007.09.004
Chang, C.-P.: Establishing a performance prediction model for insurance companies. J. Am. Acad. Bus. 8, 73–77 (2006)
Chang, K.-P., Guh, Y.-Y.: Linear production functions and the data envelopment analysis. Eur. J. Oper. Res. 52, 215–223 (1991). https://doi.org/10.1016/0377-2217(91)90082-7
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978). https://doi.org/10.1016/0377-2217(78)90138-8
Chung, K.H., Pruitt, S.W.: A simple approximation of Tobin’s q. Financ. Manag. 23, 70–74 (1994). https://doi.org/10.2307/3665623
Chung, Y.H., Färe, R., Grosskopf, S.: Productivity and undesirable outputs: a directional distance function approach. J. Environ. Manage. 51, 229–240 (1997). https://doi.org/10.1006/jema.1997.0146
Ciavolino, E., Calcagnì, A.: A generalized maximum entropy (GME) approach for crisp-input/fuzzy-output regression model. Qual. Quant. 48, 3401–3414 (2014). https://doi.org/10.1007/s11135-013-9963-9
Condello, S., Del Pozzo, A., Loprevite, S., Ricca, B.: Potential and limitations of D.E.A. as a bankruptcy prediction tool in the light of a study on Italian listed companies. Appl. Math. Sci. 11, 2185–2207 (2017)
Cultrera, L., Croquet, M., Jospin, J.: Predicting bankruptcy of Belgian SMEs: a hybrid approach based on factorial analysis. Int. Bus. Res. 10, 33–41 (2017)
Damodaran, A.: Applied corporate finance. Wiley (2010)
Daugaard, D.: Emerging new themes in environmental, social and governance investing: a systematic literature review. Account. Financ. 60, 1501–1530 (2020). https://doi.org/10.1111/acfi.12479
Demireli, E.: TOPSIS multicriteria decision making method: an examination on state owned commercial banks In Turkey. J. Entrep. Dev. 5, 101–112 (2010)
Deng, J.: Introduction to grey system theory. J. Grey Syst. 1, 1–24 (1989)
Diakoulaki, D., Mavrotas, G., Papayannakis, L.: Determining objective weights in multiple criteria problems: the critic method. Comput. Oper. Res. 22, 763–770 (1995). https://doi.org/10.1016/0305-0548(94)00059-H
Doğan, M.: Measuring bank performance with gray relational analysis: the case of Turkey. Ege Acad. Rev. 13, 215–226 (2013)
De Lucia, C., Pazienza, P., Bartlett, M.: Does good ESG lead to better financial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability 12, 5317 (2020). https://doi.org/10.3390/su12135317
Eliwa, Y., Aboud, A., Saleh, A.: ESG practices and the cost of debt: Evidence from EU countries. Crit. Perspect. Account. (2019). https://doi.org/10.1016/j.cpa.2019.102097
Elzahar, H., Hussainey, K., Mazzi, F., Tsalavoutas, I.: Economic consequences of key performance indicators’ disclosure quality. Int. Rev. Financ. Anal. 39, 96–112 (2015). https://doi.org/10.1016/j.irfa.2015.03.005
Fairfield, P.M., Yohn, T.L.: Using asset turnover and profit margin to forecast changes in profitability. Rev. Account. Stud. 6, 371–385 (2001). https://doi.org/10.1023/A:1012430513430
Falah, A.: A novel application of grey principal component analysis to determine stockholder’s approach towards financial ratios. Bus. Econ. Horiz. 11, 41–50 (2015). https://doi.org/10.22004/ag.econ.246140
Feng, C.-M., Wang, R.-T.: Performance evaluation for airlines including the consideration of financial ratios. J. Air Transp. Manag. 6, 133–142 (2000). https://doi.org/10.1016/S0969-6997(00)00003-X
Fuertes, G., Alfaro, M., Vargas, M., Gutierrez, S., Ternero, R., Sabattin, J.: Conceptual framework for the strategic management: a literature review—descriptive. J. Eng. 2020, 6253013 (2020). https://doi.org/10.1155/2020/6253013
Gao, S., Meng, F., Gu, Z., Liu, Z., Farrukh, M.: Mapping and clustering analysis on environmental, social and governance field a bibliometric analysis using scopus. Sustain. 13, 7304 (2021). https://doi.org/10.3390/su13137304
Gillan, S.L., Koch, A., Starks, L.T.: Firms and social responsibility: a review of ESG and CSR research in corporate finance. J. Corp. Financ. 66, 101889 (2021). https://doi.org/10.1016/j.jcorpfin.2021.101889
Huang, D.Z.X.: Environmental, social and governance (ESG) activity and firm performance: a review and consolidation. Account. Financ. 61, 335–360 (2021). https://doi.org/10.1111/acfi.12569
Hwang, C.-L., Yoon, K.: Methods for multiple attribute decision making. Presented at the multiple attribute decision making: methods and applications a state-of-the-art survey (1981a)
Hwang, C.L., Yoon, K.: Multi objective decision making: methods and application. Springer, Berlin (1981b)
Jahmane, A., Gaies, B.: Corporate social responsibility, financial instability and corporate financial performance: linear, non-linear and spillover effects—The case of the CAC 40 companies. Financ. Res. Lett. 34, 101483 (2020). https://doi.org/10.1016/j.frl.2020.101483
Jessop, A.: Minimally biased weight determination in personnel selection. Eur. J. Oper. Res. 153, 433–444 (2004). https://doi.org/10.1016/S0377-2217(03)00163-2
Joint Research Centre-European Commission: Handbook on constructing composite indicators: methodology and user guide. Secretary-General of the OECD (2008)
Ju-Long, D.: Control problems of grey systems. Syst. Control Lett. 1, 288–294 (1982). https://doi.org/10.1016/S0167-6911(82)80025-X
Kaldor, N.: Marginal productivity and the macro-economic theories of distribution: comment on Samuelson and Modigliani. Rev. Econ. Stud. 33, 309–319 (1966). https://doi.org/10.2307/2974428
Keskin, A.İ, Dincer, B., Dincer, C.: Exploring the impact of sustainability on corporate financial performance using discriminant analysis. Sustainability. 12, 2346 (2020). https://doi.org/10.3390/su12062346
Knox Lovell, C.A., Pastor, J.T.: Units invariant and translation invariant DEA models. Oper. Res. Lett. 18, 147–151 (1995). https://doi.org/10.1016/0167-6377(95)00044-5
Kocmanova, A., Dočekalová, M.P., Simanavičienė, Ž: Corporate sustainability measurement and assessment of Czech manufacturing companies using a composite indicator. Eng. Econ. 28, 88–100 (2017)
Kountur, R., Aprilia, L.: A factor analysis of corporate financial performance: prospect for new dimension. ACRN J. Financ. Risk Perspect. 9, 113–119 (2020). https://doi.org/10.35944/jofrp.2020.9.1.009
Kung, C.-Y.: Using fuzzy sets and grey decision-making to construct the performance evaluation model of firm’s outsourcing management—a case study of avionics manufacturer in Taiwan. Qual. Quant. 40, 577–593 (2006). https://doi.org/10.1007/s11135-005-3265-9
Lahouel, B.B., Zaied, Y.B., Song, Y., Yang, G.: Corporate social performance and financial performance relationship: a data envelopment analysis approach without explicit input. Financ. Res. Lett. 39, 101656 (2021). https://doi.org/10.1016/j.frl.2020.101656
Li, Y., Zhang, Q.: The application of principal component analysis on financial analysis in real estate listed company. Procedia Eng. 15, 4499–4503 (2011). https://doi.org/10.1016/j.proeng.2011.08.845
Lin, C.-T., Tsai, M.-C.: Location choice for direct foreign investment in new hospitals in China by using ANP and TOPSIS. Qual. Quant. 44, 375–390 (2010). https://doi.org/10.1007/s11135-008-9199-2
Lin, W.L., Law, S.H., Ho, J.A., Sambasivan, M.: The causality direction of the corporate social responsibility—corporate financial performance Nexus: application of panel vector autoregression approach. North Am. J. Econ. Financ. 48, 401–418 (2019). https://doi.org/10.1016/j.najef.2019.03.004
Liu, J.S., Lu, L.Y.Y., Lu, W.-M., Lin, B.J.Y.: Data envelopment analysis 1978–2010: a citation-based literature survey. Omega 41, 3–15 (2013). https://doi.org/10.1016/j.omega.2010.12.006
Ma, J., Fan, Z.-P., Huang, L.-H.: A subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 112, 397–404 (1999). https://doi.org/10.1016/S0377-2217(98)00141-6
Morrison, D.F.: Multivariate statistical methods. Thomson/Brooks/Cole, Belmont (2005)
Nuber, C., Velte, P., Hörisch, J.: The curvilinear and time-lagging impact of sustainability performance on financial performance: Evidence from Germany. Corp. Soc. Responsib. Environ. Manag. 27, 232–243 (2020). https://doi.org/10.1002/csr.1795
Ohlson, J.A.: Earnings, book values, and dividends in equity valuation*. Contemp. Account. Res. 18(1), 661–687 (1995). https://doi.org/10.1111/j.1911-3846.1995.tb00461.x
Omrani, S., Jafari, M., Mansori, A.: Analysis of financial performance of cement industry manufacturing companies in tehran stock exchange using the FAHP technique and the TOPSIS method. Indep. J. Manag. Prod. 10, 512–536 (2019)
OualaidJanah, O., Sassi, H.: The ESG impact on corporate financial performance in developing countries: a systematic literature review. Int. J. Acc., Financ. Audit. Manag. Econ. 2, 391–410 (2021). https://doi.org/10.5281/zenodo.5706449
Paradi, J., Wilson, D., Yang, X.: Data envelopment analysis of corporate failure for non-manufacturing firms using a slacks-based measure. J. Serv. Sci. Manag. 7, 277–290 (2014)
Pastor, J.T., Ruiz, J.L.: Variables with negative values in dea. Presented at the modeling data irregularities and structural complexities in data envelopment analysis (2007)
Popa, D.C.S., Popa, D.N., Bogdan, V., Simut, R.: Composite financial performance index prediction–a neural networks approach. J. Bus. Econ. Manag. 22, 277–296 (2021). https://doi.org/10.3846/jbem.2021.14000
Portela, M.C.A.S., Thanassoulis, E., Simpson, G.: Negative data in DEA: a directional distance approach applied to bank branches. J. Oper. Res. Soc. 55, 1111–1121 (2004). https://doi.org/10.1057/palgrave.jors.2601768
Portela, S.M.C., Borges, P.C., Thanassoulis, E.: Finding closest targets in non-oriented DEA models: the case of convex and non-convex technologies. J. Product. Anal. 19, 251–269 (2003). https://doi.org/10.1023/A:1022813702387
Premachandra, I.M., Chen, Y., Watson, J.: DEA as a tool for predicting corporate failure and success: a case of bankruptcy assessment. Omega 39, 620–626 (2011). https://doi.org/10.1016/j.omega.2011.01.002
Price, J., Haddock, M.D., Brock, H.R.: College accounting. Macmillan/McGraw-Hill, New York (1993)
Qureshi, M.A., Akbar, M., Akbar, A., Poulova, P.: Do ESG endeavors assist firms in achieving superior financial performance? A case of 100 best corporate citizens. SAGE Open 11, 21582440211021600 (2021). https://doi.org/10.1177/21582440211021598
Rapposelli, A.: route-based performance evaluation using data envelopment analysis combined with principal component analysis. Presented at the advanced statistical methods for the analysis of large data-sets (2012)
Rapposelli, A., Za, S.: Quality and efficiency evaluation of airlines services. Presented at the exploring service science (2020)
Rodríguez-Pérez, G., Slof, J., Solà, M., Torrent, M., Vilardell, I.: Assessing the impact of fair-value accounting on financial statement analysis: a data envelopment analysis approach. Abacus 47, 61–84 (2011). https://doi.org/10.1111/j.1467-6281.2011.00331.x
Roy, P., Patro, B.: Financial performance analysis of NBFC-MFIs in India using TOPSIS and IV-TOPSIS. Int. J. Math. Eng. Manag. Sci. 6, 1423 (2021)
Sarkis, J., Cordeiro, J.J.: An empirical evaluation of environmental efficiencies and firm performance: pollution prevention versus end-of-pipe practice. Eur. J. Oper. Res. 135, 102–113 (2001). https://doi.org/10.1016/S0377-2217(00)00306-4
Shannon, C.E.: A mathematical theory of communications. Bell Syst. Tech. J. 27, 379–423 (1948)
Simionescu, L.N., Dumitrescu, D.: Empirical study towards corporate social responsibility practices and company financial performance. Evidence for companies listed on the bucharest stock exchange. Sustain. 10, 3141 (2018). https://doi.org/10.3390/su10093141
Spearman, C.: Reprinted: The proof and measurement of association between two things (2010). Int. J. Epidemiol. 39, 1137–1150 (1904)
Sueyoshi, T., Goto, M.: Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment. Eur. J. Oper. Res. 199, 561–575 (2009). https://doi.org/10.1016/j.ejor.2008.11.030
Sueyoshi, T., Goto, M.: Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: a use of data envelopment analysis with strong complementary slackness condition. Eur. J. Oper. Res. 207, 1742–1753 (2010). https://doi.org/10.1016/j.ejor.2010.07.024
Sun, C.C.: Combining grey relation analysis and entropy model for evaluating the operational performance: an empirical study. Qual. Quant. 48, 1589–1600 (2014). https://doi.org/10.1007/s11135-013-9854-0
Suvvari, A., Goyari, P.: Financial performance assessment using Grey relational analysis (GRA). Grey Syst. Theory Appl. 9, 502–516 (2019). https://doi.org/10.1108/GS-05-2019-0010
Taliento, M., Favino, C., Netti, A.: Impact of environmental, social, and governance information on economic performance: evidence of a corporate ‘sustainability advantage’ from Europe. Sustainability. 11, 1738 (2019). https://doi.org/10.3390/su11061738
Tebini, H., M’Zali, B., Lang, P., Méndez-Rodrı́guez, P.: Social Performance and financial performance: a controversial relationship. In: Ballestero, E., Pérez-Gladish, B., Garcia-Bernabeu, A. (eds.) Socially responsible investment, pp. 53–73. Springer, Cham (2015)
Tobin, J.: A general equilibrium approach to monetary theory. J. Money, Credit Bank 1, 15–29 (1969). https://doi.org/10.2307/1991374
Tobin, J.: Monetary policies and the economy: the transmission mechanism. South. Econ. J. 44, 421–431 (1978). https://doi.org/10.2307/1057201
Tobin, J., Brainard, W.C.: Asset markets and the cost of capital. In: Nelson, R., Balassa, B. (eds.) Economic progress private values and public policy: essays in honour of W. Fellner, pp. 235–262. North-Holland Publishing Company, Amsterdam (1977)
Tomkins, C., Green, R.: An experiment in the use of data envelopment analysis for evaluating the efficiency of uk University Departments of accounting. Financ. Account. Manag. 4, 147–164 (1988). https://doi.org/10.1111/j.1468-0408.1988.tb00296.x
Tone, K., Chang, T.-S., Wu, C.-H.: Handling negative data in slacks-based measure data envelopment analysis models. Eur. J. Oper. Res. 282, 926–935 (2020). https://doi.org/10.1016/j.ejor.2019.09.055
Tung, C.-T., Lee, Y.-J.: A novel approach to construct grey principal component analysis evaluation model. Expert Syst. Appl. 36, 5916–5920 (2009). https://doi.org/10.1016/j.eswa.2008.07.007
Velte, P.: Does ESG performance have an impact on financial performance? Evidence from Germany. J. Glob. Responsib. 8, 169–178 (2017). https://doi.org/10.1108/JGR-11-2016-0029
Wang, Y.-J.: Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Syst. Appl. 34, 1837–1845 (2008). https://doi.org/10.1016/j.eswa.2007.02.029
Wang, Y.-J.: Combining grey relation analysis with FMCGDM to evaluate financial performance of Taiwan container lines. Expert Syst. Appl. 36, 2424–2432 (2009). https://doi.org/10.1016/j.eswa.2007.12.027
Wang, Y.-J., Lee, H.-S.: A clustering method to identify representative financial ratios. Inf. Sci. (NY) 178, 1087–1097 (2008). https://doi.org/10.1016/j.ins.2007.09.016
Wen, K.-L., Chang, T.-C., You, M.-L.: The grey entropy and its application in weighting analysis. In: SMC’98 conference proceedings. 1998 IEEE international conference on systems, man, and cybernetics (Cat. No. 98CH36218), vol. 2, pp. 1842–1844 (1998)
West, R.C.: A factor-analytic approach to bank condition. J. Bank. Financ. 9, 253–266 (1985). https://doi.org/10.1016/0378-4266(85)90021-4
Wu, C.-R., Lin, C.-T., Tsai, P.-H.: Evaluating business performance of wealth management banks. Eur. J. Oper. Res. 207, 971–979 (2010). https://doi.org/10.1016/j.ejor.2010.04.034
Xu, X.: A note on the subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 156, 530–532 (2004). https://doi.org/10.1016/S0377-2217(03)00146-2
Yujie, S.: Evaluation of Enterprise Investment Value Based on Principal Component Analysis: Information Transmission, Software and Information Technology Services in China. Acad. J. Eng. Technol. Sci. 3, 132–144 (2020). https://doi.org/10.25236/AJETS.2020.030714
Yurdakul, M., Ic, Y.T.: An illustrative study aimed to measure and rank performance of Turkish automotive companies using TOPSIS. J. Fac. Eng. Archit. Gazi Univer. 18, 1–18 (2003)
Zhou, P., Ang, B.W., Poh, K.L.: A mathematical programming approach to constructing composite indicators. Ecol. Econ. 62, 291–297 (2007). https://doi.org/10.1016/j.ecolecon.2006.12.020
Zhu, J.: Data envelopment analysis vs. principal component analysis: an illustrative study of economic performance of Chinese cities. Eur. J. Oper. Res. 111, 50–61 (1998). https://doi.org/10.1016/S0377-2217(97)00321-4
Zhu, J.: Quantitative models for performance evaluation and benchmarking. Springer, Boston (2009)
Zou, Z., Yun, Y., Sun, J.: Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J. Environ. Sci. 18, 1020–1023 (2006). https://doi.org/10.1016/S1001-0742(06)60032-6
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Ricca, B., Ferrara, M. & Loprevite, S. Searching for an effective accounting-based score of firm performance: a comparative study between different synthesis techniques. Qual Quant 57, 3575–3602 (2023). https://doi.org/10.1007/s11135-022-01522-6
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DOI: https://doi.org/10.1007/s11135-022-01522-6