CHAPTER II - REVIEW OF LITERATURE In the history of FRA it is common that professional journals and academic papers do not recognize each other. An early paper on financial ratio distributions was published in Management Accounting by Mecimore (1968)(1). It is interesting to recognize that all ingredients of modern distribution analysis already appear incumbent in Mecimore's paper. Using descriptive statistical measures (average and relative deviations from the median) he observes cross-sectional non-normality and positive skewness for twenty ratios in a sample of randomly selected forty-four Fortune-500 firms. The paper most often referred to in literature as the seminal paper in this field is, however, the much later published article by Deakin (1976)(2). His chi-square findings reject (with one exception) the normality of eleven financial ratios in a sample of 1114 Compustat companies for 1954-72. Less extreme deviations from normality were observed when square-root and logarithmic transformations were applied, but normality was still not supported. Likewise, while not statistically significantly, industry grouping made the distributions less non-normal. Concomitant results are obtained by Lee (1985)(3) using a stronger test (Kolmogorov-Smirnov) for a different set of data. Bird and McHugh (1977)(4) adopt an efficient Shapiro-Wilk small-sample test for the normality of financial ratios for an Australian sample of five ratios over six years. Like Deakin they find in their independent study that normality is transient across financial ratios and time. They also study the adjustment of the financial ratios towards industry means which is a different area of FRA research. Bougen and Drury (1980)(5) also suggest non-normality based on a cross-section of 700 UK firms. The results indicating non-normality of financial ratio distributions have led researchers into looking for methods of restoring normality to warrant standard parametric statistical analyses. Frecka and Hopwood (1983)(6) observe that removing outliers and applying transformations in a large Compustat sample covering 1950-79 restored normality in the same financial ratios as tackled by Deakin (1976)(7). They point out that if the ratios follow the gamma distribution, the square root transformation makes the distribution approximately normal. The gamma distribution is compatible with ratios having a technical lower limit of zero. There is, however, a certain degree of circularity in their approach, since instead of identifying the underlying causes of the outliers they employ a mechanistic statistical approach to identify and remove the outliers from the tails of the financial ratio distributions. A varying and often a considerable number of outliers has to be removed for different financial ratios in order to achieve normality. The empirical results are supported by later papers such as So (1987). Ezzamel, Mar-Molinero and Beecher (1987)(8) and Ezzamel and Mar-Molinero (1990)(9) review and replicate the earlier analyses on UK firms with a particular emphasis on small samples and outliers, respectively. One of the avenues taken is to study new industries. Kolari, McInish and Saniga (1989)(10) take on the distribution of financial ratios in banking. Buckmaster and Saniga (1990)(11) report on the shape of the distributions for 41 financial ratios in a Compustat sample of more than a quarter million observations. Foster (1978)(12) points out the outlier problem in FRA. Later, he presented in Foster (1986)(13) a list of alternatives for handling outliers in FRA. The list includes deleting true outliers, retaining the outlier, adjusting the underlying financial data, winsorizing that is equating the outliers to less extreme values, and trimming by dropping the tails. Foster also puts forward accounting, economic and technical reasons for the emergence of outliers in FRA. While improving the statistical results trimming and transformations can pose a problem for the theoretical rigor in FRA research. Instead of deleting or adjusting the observations McLeay (1986a)(14) proposes using a better fitting distribution with fat tails for making statistical inferences in FRA. He seeks for a best fitting t-distribution for a cross-section of 1634 UK and Irish firms. Also his empirical results confirm non-normality. The best-fitting (in the maximum-likelihood sense) t-distribution varies across financial ratios (the t-distribution can be considered a family of distributions defined by its degrees of freedom). McLeay (1986b)(15) also tackles the choice between equally weighted and value weighted aggregated financial ratios in terms of ratio distributions on a sample of French firms. Also the results by Martikainen (1991)(16) demonstrate that normality can be approached by other procedures than removing outliers. In a sample of 35 Finnish firms, four ratios and fifteen years about half of the non-normal distributions became normal if economy-wide effects were first controlled for using the so-called Accounting-index model. Martikainen (1992)(17) uses a time-series approach to 35 Finnish firms in turn observing that controlling for the economy factor improves normality.

Typically, many later papers tackle the same basic question of ratio distributions using different samples and expanding on the methodologies. Buijink and Jegers (1986)(18) study the financial ratio distributions from year to year from 1977 to 1981 for 11 ratios in Belgian firms corroborating the results of the earlier papers in the field. Refined industry classification brings less extreme deviation from normality. They also point to the need of studying the temporal persistence of cross-sectional financial ratio distributions and suggest a symmetry index for measuring it. Virtanen and Yli-Olli (1989)(19) studying the temporal behavior of financial ratio distributions observe in Finnish financial data that the business cycles affect the cross-sectional financial ratio distributions. The question of the distribution of a ratio format variable (financial ratio) has been tackled mathematically as well as empirically. Barnes (1982)(20) shows why the ratio of two normally distributed financial variables does not follow the normal distribution (being actually skewed) when ratio proportionality does not hold. Tippett (1990)(21) models financial ratios in terms of stochastic processes. The interpretation in terms of implications to financial ratio distributions are not, however, immediately evident, but the general inference is that "normality will be the exception rather than the rule". Because of these results bringing forward the significance of the distributional properties of financial ratios many later papers report routinely about the distributions of financial ratios in connection with some other main theme. Often these themes are related to homogeneity and industry studies such as Ledford and Sugrue (1983)(22). The distributional properties of the financial ratios also have a bearing in testing proportionality as can be seen, for instance, in McDonald and Morris (1984)(23). In a bankruptcy study Karels and Prakash (1987)(24) put forward that in applying the multivariate methods (like discriminant analysis) the multivariate normality is more relevant than the (univariate) normality of individual financial ratios. They observe that deviations from the multivariate normality are not as pronounced as the deviations in the earlier univariate studies. Watson (1990)(25) examines the multivariate distributional properties of four financial ratios from a sample of approximately 400 Compustat manufacturing firms for cross-sections of 1982, 1983 and 1984. Multivariate normality is rejected for all the four financial ratios. Multivariate normality is still rejected after applying Box's and Cox's modified power transformations. However, when multivariate outliers are removed, normality is confirmed.

Multivariate normality has particular bearing on research using multivariate methods, for example on bankruptcy prediction. It also has implications on univariate research, since while univariate normality does not imply multivariate normality, the opposite is true. Susan Ward (2008)(26) emphasis that financial analysis using ratios between key values help investors cope with the massive amount of numbers in company financial statements. For example, they can compute the percentage of net profit a company is generating on the funds it has deployed. All other things remaining the same, a company that earns a higher percentage of profit compared to other companies is a better investment option. Jonas Elmerraji (2005)(27) tries to say that ratios can be an invaluable tool for making an investment decision. Even so, many new investors would rather leave their decisions to fate than try to deal with the intimidation of financial ratios. The truth is that ratios aren't that intimidating, even if you don't have a degree in business or finance. Using ratios to make informed decisions about an investment makes a lot of sense, once you know how use them. `

REFERENCE1. Mecimore, C.D. (1968), "Some empirical distributions of financial ratios", Management Accounting 50/1, 13-16.2. Deakin, E.B. (1976), "Distributions of financial accounting ratios: some empirical evidence", Accounting Review, January 1976, 90-96.3. Lee, C.-W.J. (1985), "Stochastic properties of cross-sectional financial data", Journal of Accounting Research 23/1, 213-227.4. Bird, R.G., and McHugh A.J. (1977), "Financial ratios - an empirical study", Journal of Business Finance and Accounting 4/1, 29-45.5. Bougen, P.D., and Drury, J.C. (1980), "U.K. statistical distributions of financial ratios, 1975", Journal of Business Finance and Accounting 7/1, 39-47.6. Frecka, T.J., and Hopwood, W.S. (1983), "The effects of outliers on the cross-sectional distributional properties of financial ratios", Accounting Review 58/1, 115-128.7. Deakin, E.B. (1976), "Distributions of financial accounting ratios: some empirical evidence", Accounting Review, January 1976, 90-96.8. Ezzamel, M., Mar-Molinero, C., and Beecher, A. (1987), "On the distributional properties of financial ratios", Journal of Business Finance and Accounting 14/4, 463-481.9. Ezzamel, M., Brodie, J., and Mar-Molinero, C. (1990), "The distributional properties of financial ratios in UK manufacturing companies", Journal of Business Finance and Accounting 17/1, 1-29.10. Kolari, J., McInish, T.H., and Saniga, E.M. (1989), "A note on the distribution types of financial ratios in the commercial banking industry", Journal of Banking and Finance 13/3, 463-471.11. Buckmaster, D., and Saniga E. (1990), "Distributional forms of financial accounting ratios: Pearsons's and Johnson's taxonomies", Journal of Economic and Social Measurement 16, 149-166.12. Foster, G. (1978), Financial Statement Analysis. Prentice-Hall, first ed.13. Foster, G. (1986), Financial Statement Analysis. Prentice-Hall, 2nd ed.14. McLeay, S. (1986a), "Studentst and the distribution of financial ratios", Journal of Business Finance and Accounting 13/2, 209-222.15. McLeay, S. (1986b), "The ratio of means, the means of ratios and other benchmarks: an examination of characteristics financial ratios in the French corporate sector", Finance, The Journal of the French Finance Association 7/1, 75-93.16. Martikainen, T. (1991), "A note on the cross-sectional properties of financial ratio distributions", Omega 19/5, 498-501.17. Martikainen, T. (1992), "Time-series distributional properties of financial ratios: empirical evidence from Finnish listed firms", European Journal of Operational Research 58/3, 344-355.18. Buijink, W., and Jegers, M. (1986), "Cross-sectional distributional properties of financial ratios in Belgian manufacturing industries: aggregation effects and persistence over time", Journal of Business Finance and Accounting 13/3, 337-363.19. Virtanen, I., and Yli-Olli, P. (1989), "Cross-sectional and time-series persistence of financial ratio distributions; Empirical evidence with Finnish Data", European Institute for Advanced Studies in Management, Working paper 89-04, Brussels.20. Barnes, P. (1982), "Methodological implications of non-normally distributed financial ratios", Journal of Business Finance and Accounting 9/1, 51-62.21. Tippet, M. (1990), "An induced theory of financial ratios", Accounting and Business Research 21/81, 77-85.22. Ledford, M.H., and Sugrue, P.K. (1983), "Ratio analysis: application to U.S. motor common carriers", Business Economics, September 1983, 46-54.23. McDonald, B., and Morris, M.H. (1984), "The statistical validity of the ratio method in financial analysis: an empirical examination", Journal of Business Finance and Accounting 11/1, 89-97.24. Karels, G.V., and Prakash, A.J. (1987), "Multivariate normality and forecasting of business bankruptcy", Journal of Business Finance and Accounting 14/4, 573-593.25. Watson, C.J. (1990), "Multivariate distributional properties, outliers, and transformation of financial ratios", Accounting Review 65/3, 682-695.26. Susan Ward (2008), Article Financial Ratio Analysis for Performance Check.27. Jonas Elmerraji (2005), Article Analyze Investments Quickly With Ratios