Unlisted businesses are not financial clones of listed businesses.
Barnes's (1987) review article on the analysis and use of financial ratios cites 98 references. Of these, 43 are in the area of liquidity, prediction, and bankruptcy; 28 deal with the problems of probability distributions and outliers; 4 are general texts; and, 23 are other citations dealing with topics such as financial analysis within an industry using financial ratios. No attention has yet been given to the question of "sameness" between the listed and unlisted companies.
Several questions regarding the listed versus non-listed companies are of interest. Can the Capital Asset Pricing Model (CAPM) be used as a risk/return guide in the non-listed companies by using betas calculated in the listed sector? Are GAAP applied as carefully vis-a-vis smoothing of results in the non-listed sector as in the listed sector? Are non-listed companies substantially different by their very nature?
This paper compares the financial statements of listed and unlisted businesses. This comparison is made by using all of the publicly listed companies in New Zealand to compare to two samples of unlisted companies, one from New Zealand and one from Australia. These comparisons include the raw statistical data on several common financial ratios as well as Spearman Rank correlations of these ratios.
The New Zealand data were collected for two groups: listed and unlisted companies. Financial statements of all New Zealand companies listed on the New Zealand Stock Exchange at the end of 1988 were collected from Datex (a statistical service company) in New Zealand. New Zealand companies are only required to publicly report annually, so each annual report was taken as an observation. From the group of 229 listed companies, there were 778 observations. Many companies were newly listed during the 1984-1987 time period, others provided accounting statements of other than a 12-month basis, and several companies merged during the period. For these reasons, the 778 observations are not exactly equal to 4 years times 229 companies.
The unlisted company data were privately collected in the Waikato/Bay of Plenty region of New Zealand from four accounting firms and two government agencies. The data were provided on condition that the identity of the firms be kept confidential. Therefore, no additional demographic information on the non-financial dimensions of the firms were available. There were over 500 financial statements collected, 209 of which were for 97 companies that had not failed as of the beginning of 1988. These 209 statements formed the data sample used for the non-listed businesses in this study.
The second unlisted data set utilized was that used by McNamara, Cocks, and Hamilton. This data set was used as the control sample with which to compare results. Their data were reorganized for use in this study: namely only surviving firms were used, consistent with the method of choosing the New Zealand sample. Their data resulted in 176 observations from Queensland, Australia, for the period 1980-1983.
"Small" businesses have specific definitions in various countries (less than 500 employees is one given by Solomon (1986) for the USA). The unlisted sample in this study includes "small" businesses according to the definition used by the New Zealand Small Business Agency Act: "Less than 50 employees in the manufacturing sector, 25 or fewer in wholesale and retail, or fewer than 10 in the service sector." This defines the term "small" in this study. It should be noted, however, that many "small" business studies can be conducted solely by using the data set of all listed companies and discriminating by some size measure. Where differing use of the term "small" may be important, the implications will be noted.
Both the listed and unlisted companies' samples are across several industries. Table 1 shows the industry breakdown of both samples. Industry classification of the listed sample is done by Datex of New Zealand. The unlisted sample is classified into one of Datex's industry headings. The unlisted sample does not include observations from each of the Datex industry headings. However the unlisted sample fits the New Zealand definition of "small," providing barriers to entry into industries such as mining, insurance, or finance and banking.
Evans and Archer (1968), Tole (1982) and others have shown that by holding as few as 18 assets, 90% of the unique risk is diversified leaving only the systematic risk. The difference in industry distribution between the two samples has little effect on the results of this study since both samples are diversified across different businesses as well as different industries.
Each data set is diversified across industries as well as across different observations within an industry. While the data are too sparse for a chi-square test to be performed, the central limit theorem holds that if the number of observations is sufficiently large, the sample will approximate the population.
The data were organized so that financial statements for each of the years 1984 through 1987 could be analyzed separately or totally. The accounting practices used in creating the financial statements were not studied. The statements as presented to owners were assumed to carry information that was "true and fair" and not irrelevant to the owners. (See Ball & Brown, 1968 and 1969 and others to confirm that financial statements are not irrelevant.)
Table 1 Distribution of Firms By Industry Type Industry Type Number of Observations Unlisted Companies Metals and Machinery 18 Rubber, Plastics & Other 7 Mainly Wholesale 2 Mainly Retail 52 Building & Construction 52 Investment 17 Pastoral 53 Printing & Packaging 2 Misc. Services 4 Meat Freezing & Preserving 2 Listed Companies Pastoral 10 Building & Construction 39 Finance & Banking 8 Rubber, Plastics & Other 5 Property 38 Transport and Tourism 21 Investment 32 Automotive 12 Retail 10 Misc. Services 12 Apparel Textile 12 Food 32 Liquor & Tobacco 4 Medical Supplies 8 Forestry 13 Engineering 16 Fertilizer & Chemicals 8 Electronics & Appliances 8 Media & Communications 12 Mining 91 Frozen Meat & Byproducts 31 Insurance 7 Printing & Packaging 4
From the raw data, 11 financial ratios are calculated. They are:
CR = current ratio QR = quick ratio DE = debt to equity LTDE = long term debt to equity TIE = times interest earned ES = earnings to sales ROA = return on assets ROE = return on equity TAT = total asset turnover IT = inventory turnover ART = accounts receivable turnover
The use of these ratios allows a direct comparison with the 1975 correlation results presented by Foster (1978). Foster's results provide for comparisons with other listed companies. Thus both the unlisted and listed data sets in New Zealand can be compared to other unlisted (Hamilton, 1986) and listed (Foster, 1978) data sets to see if the differences between the two groups are unique to this particular sample set or are the result of differences between listed and unlisted companies.
Comparisons between the listed and non-listed companies are then drawn. The raw data are compared, other statistical comparisons are made, and intra-financial statement correlations are compared.
The most striking difference in the raw data is that the data ranges for unlisted firms are generally much wider than those of listed businesses. The listed businesses' data are audited and the unlisted data are generally not audited. Auditing of data naturally reduces errors, so the unlisted data may not be as error-free as the listed data. Yet, the differences in the range of results are striking, as can be seen in Table 2.
The total ranges of the observations are given for all of the financial ratios under consideration for both the listed (L) and unlisted (U) firms, in total as well as for each of the years studied. Of the 55 comparisons of ranges (4 individual years plus the total times 11 ratios), the range of observations for the unlisted firms is larger than the listed firms in all but 8 cases.
The notion of the unlisted firms being more variable is confirmed by the marked difference between the standard deviations of the unlisted business data and the listed business data. In 50 of 55 cases, the unlisted businesses' standard deviations are larger--often by a considerable amount, as shown in Table 3.
TABULAR DATA OMITTED
Table 3 Difference in Standard Deviation: Amount Unlisted |is greater than~ Listed Raw Data Total Sample 1987 1986 1985 1984 CR -2.423 1.981 -2.367 19.837 -2.336 QR 2.448 8.367 0.441 2.315 0.972 DE 109.032 10.833 8.326 257.030 6.474 LTDE 930.032 11.359 10.340 219.209 3.300 TIE 5394.155 6095.749 10313.190 1234.491 244.817 ES 0.433 0.660 0.334 0.308 0.341 ROA 2.999 0.428 0.301 0.371 6.440 ROE 19.487 9.350 4.164 45.977 0.925 TAT 3.102 1.024 1.046 0.338 7.539 IT 226.758 815.311 80.916 -345.780 5.979 ART 746.126 -50.786 51.706 2265.336 122.819
Generally, the financial ratios of unlisted companies in this sample are much more variable than those of the listed company sample. Perhaps this difference between unlisted and listed is not due to the basic nature of the businesses themselves, but rather due to the accounting data conveying different information about the underlying businesses. To check the behavior of the accounting statements of the two groups, it was decided to examine the Spearman Rank correlation coefficients of the ratios concerned.
Comparison of all 11 financial ratios between the listed and unlisted samples proves difficult. As Ezzamel (1987), So (1987), and many others have found, the distributions of these financial ratios are not normal. Attempts to transform the distributions with log transformations only resulted in a few of the resulting distribution of ratios being normal. Analysis of variance comparisons, therefore, between listed and unlisted financial ratios is not appropriate, since normal distributions are assumed. Thus the non-parametric Wilcoxon Sign Ranked tests, which permit comparisons between non-normally distributed distributions, were performed. Table 4 shows the results which prevent, in 7 of the 11 ratios, rejection of a null hypothesis that the two samples have identical probability distributions. (That null hypothesis could be rejected if the absolute value of the Wilcoxon Z score is greater than 1.95 for alpha = .05.)
The Hotelling |T.sup.2~ statistic is one statistic that could be used to compare multivariate data sets to each other. However, one underlying assumption of all such multivariate comparison tests is that the covariance matrices are similar. Therefore this assumption must be checked. This can be done by comparing the correlation tables, which are, in effect, standardized forms of the covariance matrix. Further, since the data are not normally distributed, another assumption of multivariate tests is violated. It therefore becomes necessary and interesting to compare the correlation tables first in a non-parametric fashion using Spearman Ranked correlations, and then by examining the relationship between the correlations for the two data sets.
Table 4 Wilcoxon Z Scores of Financial Ratios: Comparisons of Listed to Unlisted for Same Ratios Ratio Z Score Ratio Z Score CR -0.253 ROA -0.794 QR -0.856 ROE -0.544 DE 1.550 TAT -2.431 LTDE 1.016 IT -2.052 TIE -2.136 ART -2.214 ES 0.694
Brealey and Myers (1988) and Foster (1978) are but two who caution against the use of too many financial ratios to gain additional insight into the company. Foster showed that many of the financial ratios were indeed highly correlated with each other due to the way financial statements and then financial ratios are calculated. He showed, for example, that for his data set 80% of the information contained in the ROA is present in the ROE. It would be unwise to think that using both ratios adds greatly to one's insight into the performance of the firm. But are the correlations found in listed businesses similar to those found in unlisted businesses? If not, it could be necessary to examine financial ratios of unlisted businesses differently from listed businesses. Examining the correlations of the financial ratios provides insights into the question of "sameness," both statistically and practically.
In considering this question, comparisons to the same correlations used by Foster (1975, as reported in 1978) could reveal some insights. If the inter-relationships between the accounting ratios found in the New Zealand data for all companies were similar to those of Foster, then it may be possible to suggest that the reasons for the differences between the financial statements found above are not due to accounting practices. Otherwise, if the correlations are not seen to be similar, the basic underlying comparability of the unlisted and listed data sets must be questioned.
The Spearman Rank correlation tables of Foster and this study are included in Appendix 1. While Foster's data had a minimum number of observations of 1,978, the New Zealand data were much less at 65 to 201 observations, depending on the individual ratio. Yet more than 30 observations implies that a 95% level of confidence can be assumed if the statistic is above .305 (McClave & Benson, 1982).
Using a statistic of .350 as a benchmark, Table 5 summarizes the comparison of the correlations in Appendix 1.
There are 55 correlations in the tables. The comparison between New Zealand listed companies and Foster resulted in basic similarity. Where either table had a correlation of greater than .305, 31 of 32 were of the same sign and 13 of 32 were within .15 (an arbitrary standard) of each other. This comparison supports the notion that accounting statements in New Zealand and those in the US (in 1975) tend to correlate in the same way.
In comparing the unlisted businesses to Foster, and again looking only at those TABULAR DATA OMITTED correlations where either table was greater than .305, only 23 of 34 were of the same sign, and only 12 of 34 were within .15 of each other. Comparing the same unlisted businesses to the New Zealand listed companies found 24 of 29 to be of the same sign, but only 10 of 29 to be within .15 (where at least one correlation is above the significant .305). These results suggest that New Zealand accounting practices result in similar signed correlations, but that the magnitude of difference between New Zealand unlisted and either New Zealand listed or US (Foster) listed is the same. Unlisted companies do not appear to have the same correlations of their financial ratios as do either New Zealand listed businesses or US listed businesses.
It is interesting that the New Zealand unlisted companies are as similar to the Australian unlisted companies (in terms of sign and closeness) as the New Zealand listed companies are similar to Foster's. This suggests a dichotomy between listed and unlisted companies at least in Australia and New Zealand.
To examine the relationship between correlations of listed and unlisted firms, a simple regression using paired correlations was estimated with the correlation for listed firms as the dependent variable. The plot of the data is shown in Figure 1. As expected, the intercept was not significantly different from zero, so the constant term was dropped from the relationship. Using all data points, including the insignificant correlations of less than an absolute value of .305, results in a coefficient of determination (|r.sup.2~) of 0.61. As apparent from Figure 1, this confirms a significant positive association between the two sets of correlations. However the slope coefficient of 0.783 was significantly different from unity (using a two-tailed test) at a significance level of 98% or less (standard error of 0.085, t-value 9.188). Accordingly, the null hypothesis of "cloneness" between the two types of firms can only be accepted at a very high significance level. In summary, these results confirm that while financial ratios of the two sets of firms convey similar information as to sign and magnitude, there is a significant probability that they are not perfect substitutes for each other.
CONCLUSIONS AND IMPLICATIONS
By comparing publicly listed to unlisted companies it is possible to draw some distinction between these classes of firms. If the two groups were clones of each other, then it would be unnecessary to study small unlisted businesses as distinct from larger listed businesses. Close examination of the two data sets shows that while financial ratios may be correlated to each other, differences across several dimensions are clear. The basic essence of "small" is captured in the unlisted data set used here. The most glaring difference between listed and unlisted (small) companies is that in unlisted businesses, extreme ranges of performance in terms of accounting ratios are more likely to be reported. Further non-parametric statistical comparisons fail to confirm that the two data sets of listed and unlisted companies are "similar." This could be due to several factors.
First, accounting practices in the listed firms are subject to audit, while those of the unlisted firms are not. This could imply that more "true and fair" information is conveyed in the financial statements of the listed firms than in those of the unlisted firms. But listed firms employ GAAP carefully to their reported results while keeping an eye on their share price. Avoidance of large ranges of reported results would help minimize share price volatility, which is seen as desirable. Since unlisted businesses are not hostage to share price movements, their results can be, and indeed are, more variable. This increased variability is reflected not only in the larger ranges of results, but in larger standard deviation of results even when the range is restricted by excluding outliers.
Next, the motivation for preparing financial statements is very different for the unlisted firm. Listed companies are concerned about their stakeholders: share holders, debt holders, creditors, customers, and government. Unlisted businesses, on the other hand, have many of these stakeholders represented by the same owner/manager. Therefore financial statements are often prepared with a close eye on the government, for example to reduce the tax bill. While adjusting the "profit" figure on the unlisted businesses to include directors' and shareholders' salaries is possible, this does not change the results of this study. This difference in motivation could explain the differences found in this study.
Ball and Brown (1968, 1969) and others have been able to make a connection between the information conveyed in accounting statements and the market's determination of value. If financial statements of unlisted firms are not comparable to those of listed firms, it is not clear that the financial information conveyed by unlisted companies' financial statements will necessarily be related to the value of those firms.
There could also be fundamental differences in the very nature of listed and unlisted businesses. The motivation for being in a small business is often very different from the driving motive of a listed business. (See Hamilton (1986), for example.) Also, single-product small businesses are not similar to multi-product multi-regional larger firms.
Of course, some combination of these possibilities or others not discussed here may be the cause of the differences found. The financial statements of unlisted businesses seem to show significant differences from those of the listed companies.
At least two implications of this paper are clear. The first is that using publicly listed companies as benchmarks for performance for unlisted companies is unwise. For example, lenders to and investors in unlisted businesses could calculate accounting betas (Hill & Stone, 1980) for listed companies and reasonably expect that they relate to actual market betas. Using unlisted companies' financial statements to calculate accounting betas may not be the proper way to capture the risk profile of unlisted firms. Similar care should be taken when expecting unlisted companies' financial reports to meet "average" performance standards when those averages are established using listed companies' results.
The second implication is that the admonition to beware of using intercorrelated financial ratios to gain additional insight into the company may not apply to the world of unlisted businesses. Yes, there are still intercorrelations, but they are different. These differences are not due to New Zealand accounting practice versus US practice, but due to the very essence of being unlisted (small). For example, Foster (1978) showed that 80% of the information of ROA is included in ROE, for listed companies, whereas the New Zealand data on unlisted business financial statements suggest that this percentage drops to 38%. Adjusting the profit figure to include directors' and shareholders' salaries does not change these results either (see the correlation table in Appendix 1 which is calculated on this basis).
Future research in this area should examine the ways in which financial performance of unlisted businesses is best captured. Financial statements may indeed be capturing relevant market information for unlisted businesses as they have been shown to do for listed businesses, but this is not yet clear. Differences in accounting practices which dampen extreme results being reported could be investigated to see if they are contributing significantly to the results shown here. Motivational differences for being in business between listed and unlisted firms could result in different financial results; this could be investigated. While this study has shed some light on the differences between listed and unlisted firms, it remains unclear whether a small unlisted business is truly a small version of a listed firm--even if their financial statements show them to be different.
Foster 1975, Unlisted Companies, Not Failed Matrix of Spearman Correlation Coefficients CR QR DE LTDE TIE CR 1 QR 0.74 1 DE -0.58 -0.52 1 LTDE -0.39 -0.29 0.80 1 TIE 0.30 0.33 -0.59 -0.56 ES 0.02 0.32 -0.33 -0.17 0.56 ROA 0.22 0.26 -0.40 -0.33 0.85 ROE 0.01 0.110 -0.12 -0.13 0.64 TAT 0.25 -0.04 0.09 -0.28 0.33 IT -0.42 0.01 0.05 0.09 0.16 ART -0.10 -0.30 -0.05 0.00 0.15 ES ROA ROE TAT IT ES 1 ROA 0.63 1 ROE 0.60 0.80 1 TAT -0.37 0.31 0.20 1 IT 0.10 0.18 0.26 0.23 1 ART -0.08 0.17 0.49 0.49 0.37 Unlisted, Not Failed, Uncleaned Data Matrix of Spearman Correlation Coefficients CR QR DE LTDE TI CR 1.000 QR 0.769 1.000 DE 0.043 -0.431 -1.000 LTDE 0.056 -0.325 0.923 1.000 TIE 0.435 0.324 0.082 0.078 1.00 ES 0.522 0.414 0.011 0.079 0.75 ROA 0.459 0.235 0.069 0.073 0.85 ROE 0.224 0.051 0.044 -0.026 0.52 TAT 0.140 -0.392 0.141 0.007 0.44 IT -0.488 -0.090 -0.352 -0.346 -0.02 ART -0.169 -0.390 0.058 0.139 -0.03 ES ROA ROE TAT I ES 1.000 ROA 0.857 1.000 ROE 0.403 0.552 1.000 TAT 0.214 0.561 0.369 1.000 IT -0.091 0.001 -0.045 0.289 1.00 ART 0.062 0.090 0.074 0.239 -0.00 Listed Companies, Uncleaned Data, All Years Matrix of Spearman Correlation Coefficients CR QR DE LTDE TIE CR 1.000 QR 0.685 1.000 DE -0.377 -0.300 1.00 LTDE -0.229 -0.135 0.692 1.000 TIE 0.339 0.318 -0.263 -0.303 1.000 ES 0.318 0.091 -0.219 -0.285 0.621 ROA 0.346 0.175 -0.153 -0.383 0.624 ROE 0.206 0.168 0.010 -0.275 0.555 TAT 0.135 -0.116 0.205 -0.252 0.289 IT -0.246 0.319 0.106 0.136 -0.031 ART 0.154 -0.255 -0.139 -0.053 0.208 ES ROA ROE TAT IT ES 1.000 ROA 0.795 1.000 ROE 0.717 0.962 1.000 TAT 0.087 0.581 0.619 1.000 IT -0.205 -0.027 0.078 0.126 1.000 ART 0.185 0.346 0.308 0.463 0.261 Australian, Unlisted Companies, Uncleaned Data Matrix of Spearman Correlation Coefficients CR QR DE LTDE TI CR 1.000 QR 0.345 1.000 DE 0.257 0.106 1.000 LTDE 0.181 0.269 0.938 1.000 TIE 0.192 0.089 0.196 0.337 1.00 ES 0.386 0.069 0.115 0.070 0.65 ROA 0.314 0.155 0.268 0.320 0.85 ROE -0.184 -0.105 -0.402 -0.406 -0.15 TAT -0.002 0.219 0.163 0.289 0.10 IT -0.415 0.581 -0.183 -0.056 -0.13 ART -0.210 -0.476 0.178 0.278 0.14 ES ROA ROE TAT I ES 1.000 ROA 0.499 1.000 ROE -0.035 -0.226 1.000 TAT 0.058 0.270 0.088 1.000 IT -0.184 0.001 0.336 0.423 1.00 ART -0.105 0.052 -0.124 0.615 -0.24 Unlisted, Not Failed Companies Clean Data--Adjusted Profit (all years) Matrix of Spearman Correlation Coefficients CR QR DE LTDE TIE CR 1.000 QR 0.703 1.000 DE 0.142 -0.228 1.000 LTDE 0.120 -0.190 0.941 1.000 TIE 0.376 0.248 0.134 0.130 1.000 ES 0.493 0.273 0.093 0.095 0.697 ROA 0.424 0.134 0.165 0.145 0.794 ROE 0.173 -0.015 0.079 0.006 0.410 TAT 0.251 -0.089 0.158 0.046 0.491 IT -0.382 0.052 -0.489 -0.353 0.139 ART -0.420 -0.606 -0.066 -0.003 -0.005 ES ROA ROE TAT IT ES 1.000 ROA 0.883 1.000 ROE 0.325 0.429 1.000 TAT 0.423 0.644 0.380 1.000 IT 0.055 0.136 -0.073 0.285 1.000 ART 0.003 0.159 0.064 0.414 0.109
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The author wishes to thank the editor and the referees for their helpful comments.
Ed Vos is Senior Lecturer in Finance at the University of Waikato in New Zealand.
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|Title Annotation:||includes appendix|
|Publication:||Entrepreneurship: Theory and Practice|
|Date:||Jun 22, 1992|
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