210 M. Marozzi and L. Santamaria
out financial information following standard rules. First
Mi=∑^4
k= 1Xik−MED(Xk)
MAD(Xk), i= 1 ,..., 338 (14)is computed. This is a composite indicator of liquidity for companyiwhich takes
into account simultaneously the partial liquidity indicatorsX 1 ,X 2 ,X 3 ,X 4. Then,
the corresponding rank vectorR 4 (X 1 ,X 2 ,X 3 ,X 4 )=R 4 is computed. This vector
has been compared to the vectors corresponding to the consideration of three partial
indicators: 4 R 3 (X 1 ,X 2 ,X 3 )= 4 R 3 , 3 R 3 (X 1 ,X 2 ,X 4 )= 3 R 3 , 2 R 3 (X 1 ,X 3 ,X 4 )=
2 R 3 and 1 R 3 (X 2 ,X 3 ,X 4 )= 1 R 3 , through the Spearman correlation coefficient. In
the first step of the procedure the quick ratioX 2 left the analysis since we have
s(R 4 , 4 R 3 )= 0. 9664 , s(R 4 , 3 R 3 )= 0. 9107 ,s(R 4 , 2 R 3 )= 0. 9667 , s(R 4 , 1 R 3 )= 0. 9600.In the second step, we compare 2 R 3 (X 1 ,X 3 ,X 4 )= 2 R 3 with 4 , 2 R 2 (X 1 ,X 3 )=
4 , 2 R 2 , 3 , 2 R 2 (X 1 ,X 4 )= 3 , 2 R 2 and 1 , 2 R 2 (X 3 ,X 4 )= 1 , 2 R 2. The cash flow to interest
expense ratioX 4 left the analysis since we have
s( 2 R 3 , 4 , 2 R 2 )=^0.^956 , s( 2 R 3 , 3 , 2 R 2 )=^0.^909 , s( 2 R 3 , 1 , 2 R 2 )=^0.^905.In the last step, the current ratioX 1 left the analysis since it iss( 4 , 2 R 2 , 3 , 4 , 2 R 1 )=
0 .672 ands( 4 , 2 R 2 , 1 , 4 , 2 R 1 )= 0 .822.
We conclude that the ranking obtained by considering togetherX 1 ,X 2 ,X 3 ,X 4
is similar to that based on the interest coverage ratioX 3 , and then the analyst is sug-
gested to focus onX 3 in addressing the liquidity issue of the companies. Our method
reduces the information included in the original data by dropping the relatively unim-
portant financial data. These dropped financial data, however, might have important
information in comparing a certain set of companies. For example, the quick ratioX 2
has been excluded in the first step of the procedure, and then the inventory has not
become an aspect for the analyst to decide whether to invest in a company or not. But
depending on Rees [9, p. 195], the market reaction to earnings disclosure of small
firms is great. If the inventory becomes large, the smaller firms might go bankrupt
because they cannot stand its cost, whereas the larger firms endure it. Moreover there
may be a lot of seasonality effect on sales because monthly sales may differ greatly.
This affects small firms deeply; in fact many studies have suggested that the bulk of
the small firm effect is concentrated in certain months of the year [9, p. 180]. There-
fore it might not be possible to apply the results of this paper to smaller firms without
taking into account the inventory issue. Moreover, the importance of the financial data
available differs among industries. For example, the importance of inventory might
be different between the manufacturing industry and the financial industry. In general,
the importance of financial data may vary between the comparison among the whole
set of companies and the selected set of certain companies. For financial analysts the
comparison should be done to selected sets of companies. The analysis of variance
can help in evaluating the bias generated from this method.