Principal component analysis of financial statements. A compositional approach
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.3580Keywords:
financial ratio, compositional data analysis (CoDa), biplot, grocery retail sector, data visualizationAbstract
Financial ratios are often used in principal component analysis and related techniques for the purposes of data reduction and visualization. Besides the dependence of results on ratio choice, ratios themselves pose a number of problems when subjected to a principal component analysis, such as skewed distributions. In this work, we put forward an alternative method drawn from compositional data analysis (CoDa), a standard statistical toolbox for use when data convey information about relative magnitudes, as financial ratios do. The method, referred to as the CoDa biplot, does not rely on any particular choice of financial ratio but allows researchers to visually order firms along the pairwise financial ratios for any two accounts. Non-financial magnitudes and time evolution can be added to the visualization as desired. We show an example of its application to the top chains in the Spanish grocery retail sector and show how the technique can be used to depict strategic management differences in financial structure or performance, and their evolution over time.
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References
Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44, 139-177. DOI: 10.1111/j.2517-6161.1982.tb01195.x.
Aitchison, J. (1983). Principal component analysis of compositional data. Biometrika, 70, 57-65. DOI:10.1093/biomet/70.1.57.
Aitchison, J. (1986). The Statistical Analysis of Compositional Data. Monographs on Statistics and Applied Probability. London: Chapman and Hall.
Aitchison, J., & Greenacre, M. (2002). Biplots of compositional data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51, 375-392. DOI:10.1111/1467-9876.00275.
Allen, R.S., & Helms, M.M. (2006). Linking strategic practices and organizational performance to Porter’s generic strategies. Business Process Management Journal, 12, 433-455. DOI: 10.1108/14637150610678069.
Azevedo, L., Daunis-i-Estadella, J., Mateu-Flgueras, G., & Thió-Henestrosa, S. (2011). Flying in compositional morphospaces: evolution of limb proportions in flying vertebrates. Compositional Data Analysis. Theory and Applications (V. Pawlowsky-Glahn & A. Buccianti, eds). New York, NY: Wiley. pp. 235-254.
Banker, R.D., Mashruwala, R., & Tripathy, A. (2014). Does a differentiation strategy lead to more sustainable financial performance than a cost leadership strategy? Management Decision, 52, 872-896. DOI: 10.1108/MD-05-2013-0282.
Barceló-Vidal, C., & Martín-Fernández, J.A. (2016). The mathematics of compositional analysis. Austrian Journal of Statistics, 45, 57-71. DOI: 10.17713/ajs.v45i4.142.
Barnes, P. (1987). The analysis and use of financial ratios: A review article. Journal of Business Finance & Accounting, 14, 449-461. DOI: 10.1111/j.1468-5957.1987.tb00106.x.
Batista-Foguet, J.M., Ferrer-Rosell, B., Serlavós, R., Coenders, G., & Boyatzis, R.E. (2015). An alternative approach to analyze ipsative data. Revisiting Experiential Learning Theory. Frontiers in Psychology, 6, 1742. DOI: 10.3389/fpsyg.2015.01742.
Belles-Sampera, J., Guillen, M., & Santolino, M. (2016) Compositional methods applied to capital allocation problems. Journal of Risk, 19, 1-15. DOI: 10.21314/JOR.2016.345.
Blanco-Oliver, A.J., Irimia-Diéguez, A.I., & Vázquez-Cueto, M.J. (2016) Design of a specific model for predicting micro-entities failure. Journal of Quantitative Methods for Economics and Business Administration, 22, 3-18. https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2336
Boonen, T., Guillén, M., & Santolino, M. (2019). Forecasting compositional risk allocations. Insurance: Mathematics and Economics, 84, 79-86. DOI: 10.1016/j.insmatheco.2018.10.002.
Buccianti, A., Mateu-Figueras, G., & Pawlowsky-Glahn, V. (2006) Compositional Data Analysis in the Geosciences: From Theory to Practice. London: Geological Society.
Caro, N.P., Arias, V., & Ortiz, P. (2017). Prediction of failure in Latin-American companies using the nearest-neighbor method to predict random effects in mixed models. Journal of Quantitative Methods for Economics and Business Administration, 24, 5-24. https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2878.
Chen, K.H., & Shimerda, T.A. (1981). An empirical analysis of useful financial ratios. Financial Management, 10, 51-60. DOI: 10.2307/3665113.
Cowen, S.S., & Hoffer, J.A. (1982). Usefulness of financial ratios in a single industry. Journal of Business Research, 10, 103-118. DOI: 10.1016/0148-2963(82)90020-0.
Davis, B.C., Hmieleski, K.M., Webb, J.W., & Coombs, J.E. (2017). Funders' positive affective reactions to entrepreneurs' crowdfunding pitches: The influence of perceived product creativity and entrepreneurial passion. Journal of Business Venturing, 32, 90-106. DOI:10.1016/j.jbusvent.2016.10.006.
Deakin, E.B. (1976). Distributions of financial accounting ratios: some empirical evidence. The Accounting Review, 51, 90-96.
Dimitropoulos, P.E., Asteriou, D., & Koumanakos, E. (2010). The relevance of earnings and cash flows in a heavily regulated industry: Evidence from the Greek banking sector. Advances in Accounting, 26, 290-303. DOI: 10.1016/j.adiac.2010.08.005.
Egozcue, J.J. Pawlowsky-Glahn, V. Mateu-Figueras, G., & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35, 279-300. DOI: 10.1023/A:1023818214614.
European Union (2016). Competition in the Food Retail Sector Proceedings of the Workshop. Policy Department A: Economic and Scientific Policy. www.europarl.europa.eu/supporting-analyses.
Evans, J. R., & Mathur, A. (2014). Retailing and the period leading up to the Great Recession: a model and a 25-year financial ratio analysis of US retailing. The International Review of Retail, Distribution and Consumer Research, 24, 30-51. DOI:10.1080/09593969.2013.801360.
Ferrer-Rosell, B., & Coenders, G. (2018). Destinations and crisis. Profiling tourists’ budget share from 2006 to 2012. Journal of Destination Marketing & Management, 7, 26-35. DOI:10.1016/j.jdmm.2016.07.002.
Ferrer-Rosell, B., Coenders, G., & Martínez-Garcia, E. (2015). Determinants in tourist expenditure composition-the role of airline types. Tourism Economics, 21, 9-32. DOI:10.5367/te.2014.0434.
Ferrer-Rosell, B., Coenders, G., & Martínez-Garcia, E. (2016a). Segmentation by tourist expenditure composition. An approach with compositional data analysis and latent classes. Tourism Analysis, 21, 589-602. DOI: 10.3727/108354216X14713487283075.
Ferrer-Rosell, B., Coenders, G., Mateu-Figueras, G., & Pawlowsky-Glahn, V. (2016b). Understanding low cost airline users’ expenditure pattern and volume. Tourism Economics, 22, 269-291. DOI:10.5367/te.2016.0548.
Filzmoser, P., Hron, K. & Templ, M. (2018). Applied Compositional Data Analysis with Worked Examples in R. New York: Springer.
Frecka, T.J., & Hopwood, W.S. (1983). The effects of outliers on the cross-sectional distributional properties of financial ratios. The Accounting Review, 58, 115-128.
Gabriel, K.R. (1971). The biplot-graphic display of matrices with application to principal component analysis. Biometrika, 58, 453-467. DOI:10.1093/biomet/58.3.453.
Glassman, D.A., & Riddick, L.A. (1996). Why empirical international portfolio models fail: evidence that model misspecification creates home asset bias. Journal of International Money and Finance, 15, 275-312. DOI: 10.1016/0261-5606(95)00046-1.
Grant, R.M. (1991). The resource-based theory of competitive advantage: implications for strategy formulation. California Management Review, 33, 114-135. DOI: 10.2307/41166664.
Grant, R.M. (2008). Contemporary Strategy Analysis. Malden, MA: Blackwell Publishing.
Greenacre, M. (2018). Compositional Data Analysis in Practice. Boca Raton, FL: CRC Press.
Hoque, Z. (2004). A contingency model of the association between strategy, environmental uncertainty and performance measurement: impact on organizational performance. International Business Review, 13, 485-502. DOI:10.1016/j.ibusrev.2004.04.003.
Horrigan, J.O. (1968). A short history of financial ratio analysis. The Accounting Review, 43, 284-294.
Joueid, A., & Coenders, G. (2018). Marketing innovation and new product portfolios. A compositional approach. Journal of Open Innovation: Technology, Market and Complexity, 4, 19. DOI: 10.3390/joitmc4020019.
Linares-Mustarós, S., Coenders, G., & Vives-Mestres, M. (2018). Financial performance and distress profiles. From classification according to financial ratios to compositional classification. Advances in Accounting, 40, 1-10. DOI: 10.1016/j.adiac.2017.10.003.
Lovell, D., Pawlowsky-Glahn, V., Egozcue, J.J., Marguerat, S., & Bähler, J. (2015). Proportionality: a valid alternative to correlation for relative data. PLoS Computational Biology, 11, e1004075. DOI: 10.1371/journal.pcbi.1004075.
Lukason, O., & Laitinen, E.K. (2016) Failure processes of old manufacturing firms in different European countries. Investment Management and Financial Innovations, 13, 310-321.
Mariné-Roig, E., & Ferrer-Rosell, B (2018). Measuring the gap between projected and perceived destination images of Catalonia using compositional analysis. Tourism Management, 68, 236-249. DOI: 10.1016/j.tourman.2018.03.020.
Martín-Fernández, J.A., Palarea-Albaladejo, J., & Olea, R.A. (2011). Dealing with zeros. Compositional Data Analysis. Theory and Applications (V. Pawlowsky-Glahn & A. Buccianti, eds). New York, NY: Wiley. pp. 47-62.
Martín-Oliver, A., Ruano, S., & Salas-Fumás, V. (2017). The fall of Spanish cajas: Lessons of ownership and governance for banks. Journal of Financial Stability, 33, 244-260. DOI:10.1016/j.jfs.2017.02.004.
Mateu-Figueras, G., Daunis-i-Estadella, J., Coenders, G., Ferrer-Rosell, B., Serlavós, R., & Batista-Foguet, J.M. (2016). Exploring the relationship between two compositions using canonical correlation analysis. Metodološki Zvezki, 13, 131-150. http://ibmi.mf.uni-lj.si/mz/2016/no-2/p5MZ13_2.pdf
McGee, J., & Thomas, H. (1986) Strategic groups: theory, research and taxonomy. Strategic Management Journal, 7, 141-160. DOI: 10.1002/smj.4250070204.
Morais, J., Thomas-Agnan, C., & Simioni, M. (2018). Using compositional and Dirichlet models for market-share regression. Journal of Applied Statistics, 45, 1670-1689. DOI: 10.1080/02664763.2017.1389864.
Norman, P.M. (2018). An exercise to integrate strategic and financial analysis. Management Teaching Review, 3, 252-264. DOI: 10.1177/2379298117752680.
OECD (2013). Competition Issues in the Food Chain Industry. https://www.oecd.org/daf/competition/CompetitionIssuesintheFoodChainIndustry.pdf
Ortells, R., Egozcue, J.J., Ortego, M.I., & Garola, A. (2016). Relationship between popularity of key words in the Google browser and the evolution of worldwide financial indices. Compositional Data Analysis. Springer Proceedings in Mathematics & Statistics, Vol. 187 (J.A. Martín-Fernández & S. Thió-Henestrosa, eds). Cham, CH: Springer. pp. 145-166.
Pablos, R.R., Vara, A.J., & Roche, I.C. (2013). Estrategias de las principales empresas de distribución minorista de gran consumo ante la crisis económica. Distribución y Consumo, 23, 5-17.
Palarea-Albaladejo, J., & Martín-Fernández, J.A. (2008). A modified EM alr-algorithm for replacing rounded zeros in compositional data sets. Computers & Geosciences, 34, 902-917. DOI: 10.1016/j.cageo.2007.09.015.
Palarea-Albaladejo, J., & Martín-Fernández, J.A. (2015) zCompositions-R package for multivariate imputation of left-censored data under a compositional approach. Chemometrics and Intelligent Laboratory Systems, 143, 85-96. DOI: j.chemolab.2015.02.019.
Pawlowsky-Glahn, V. & Buccianti, A. (eds) (2011). Compositional Data Analysis. Theory and Applications. New York, NY: Wiley.
Pawlowsky-Glahn, V., Egozcue, J.J., & Tolosana-Delgado, R. (2015). Modeling and Analysis of Compositional Data. Chichester, UK: Wiley.
Pinches, G.E., Mingo, K.A., & Caruthers, J.K. (1973). The stability of financial patterns in industrial organizations. The Journal of Finance, 28, 389-396. DOI: 10.1111/j.1540-6261.1973.tb01782.x.
Porter, M. (1980). Competitive Strategy: Techniques for Analysing Industries and Competitors. New York, NY: The Free Press.
Ross, S.A., Westfield, R.W., & Jordan, B.D. (2003). Fundamentals of Corporate Finance. 6th ed. (Vol.1). New York, NY: McGraw-Hill.
Sanz, J.Á., Bedate, A.M., & Durántez, M. (2018). Determining some factors of the financial situation in the European Union publishing sector. Review of Economic Perspectives, 18, 25-43. DOI: 10.2478/revecp-2018-0002.
Sharma, S., Shebalkov, M., & Yukhanaev, A. (2016). Evaluating banks performance using key financial indicators-a quantitative modeling of Russian banks. The Journal of Developing Areas, 50, 425-453. DOI: 10.1353/jda.2016.0015.
Smith, M. (2005). Performance Measurement & Management: a Strategic Approach to Management Accounting. London: SAGE.
Sudarsanam, P.S., & Taffler, R.J. (1995). Financial ratio proportionality and inter-temporal stability: An empirical analysis. Journal of Banking & Finance, 19, 45-60. DOI: 10.1016/0378-4266(94)00044-4.
Templ, M., Hron, K., & Filzmoser P. (2011). robCompositions: an R-package for robust statistical analysis of compositional data. Compositional Data Analysis. Theory and Applications (V. Pawlowsky-Glahn & A. Buccianti, eds). New York, NY: Wiley. pp. 341-355.
Thió-Henestrosa, S., & Martín-Fernández, J.A. (2005). Dealing with compositional data: The freeware CoDaPack. Mathematical Geology, 37, 773-793. DOI: 10.1007/s11004-005-7379-3.
Van den Boogaart, K.G., & Tolosana-Delgado, R. (2013). Analyzing Compositional Data with R. Berlin: Springer.
Van Eijnatten, F.M., van der Ark, L.A., & Holloway, S.S. (2015). Ipsative measurement and the analysis of organizational values: an alternative approach for data analysis. Quality & Quantity, 49, 559-579. DOI: 10.1007/s11135-014-0009-8.
Vives-Mestres, M., Daunis-i-Estadella, J., & Martín-Fernández, J.A. (2014). Out-of-control signals in three-part compositional T2 control chart. Quality and Reliability Engineering International, 30, 337-346. DOI: 10.1002/qre.1583.
Vives-Mestres, M., Martín-Fernández, J.A., & Kenett, R. (2016a). Compositional data methods in customer survey analysis. Quality and Reliability Engineering International, 32, 2115-2125. DOI: 10.1002/qre.2029.
Vives-Mestres, M., Daunis-i-Estadella, J., & Martín-Fernández, J.A. (2016b). Signal interpretation in Hotelling’s T2 control chart for compositional data. IIE Transactions, 48, 661-672. DOI: 10.1080/0740817X.2015.1125042.
Voltes-Dorta, A., Jiménez, J.L., & Suárez-Alemán, A. (2014). An initial investigation into the impact of tourism on local budgets: A comparative analysis of Spanish municipalities. Tourism Management, 45, 124-133. DOI: 10.1016/j.tourman.2014.02.016
Wang, H., Lu, S., & Zhao, J. (2019). Aggregating multiple types of complex data in stock market prediction: A model-independent framework. Knowledge-Based Systems, 164, 193-204. DOI: 10.1016/j.knosys.2018.10.035.
Yap, B.C.F., Mohamed, Z., & Chong, K.R. (2014). The effects of the financial crisis on the financial performance of Malaysian companies. Asian Journal of Finance & Accounting, 6, 236-248. DOI: 10.5296/ajfa.v6i1.5314.
Yoshino, N., & Taghizadeh-Hesary, F. (2015). Analysis of credit ratings for small and medium-sized enterprises: Evidence from Asia. Asian Development Review, 32, 18-37. DOI: 10.1162/ADEV_a_00050.
Yoshino, N., Taghizadeh-Hesary, F., Charoensivakorn, P., & Niraula, B. (2016). Small and medium-sized enterprise (SME) credit risk analysis using bank lending data: An analysis of Thai SMEs. Journal of Comparative Asian Development, 15, 383-406. DOI: 10.1080/15339114.2016.1233821.
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