Explaining financial difficulties based on previous payment behavior, management background variables and financial ratios

Abstract

This paper shows evidence that it is possible to explain financial difficulties in small and medium sized firms based on non-financial variables. The results indicate that the estimated model based on non-financial variables classified firms even better than the financial ratio model, especially when classifying bankrupt firms and firms with payment delays. The best overall classification was achieved using the model combining financial ratios and non-financial variables. The non-financial variables measuring the number of payment delays were statistically the most important. The main implication of the results is that non-financial variables embrace important information in attempts to explain financial difficulties, and this should be of interest given that payment behavior variables (payment delays and payment disturbances) may occur more frequently than the publication of intermittent financial statements.

Notes

¹ The grouping was made under the assumption that bankrupt firms are more financially distressed than reorganized firms, which on the other hand are more distressed than firms with recorded payment disturbances and which are more distressed than firms with payment delays. An example: firms that had filed for bankruptcy were classified as (1) ‘bankrupt firms’, even if they had reorganization remarks, payment disturbances or payment delays. Firms with a reorganization code were classified as (2) ‘reorganized firms’, even if they had payment disturbances or payment delays. Further, firms with payment disturbances were classified as (3) ‘firms with recorded payment disturbances’, even if they had payment delays. Finally firms with only payment delays were classified as (4) ‘firms with payment delays’. Firms with no remarks were classified as (5) ‘healthy firms’.

² A possible problem with this variable is the fact that the financier may have put its own ‘man’ on the board. If this would be the fact then the variable would indicate something else than the assumed phenomena. In order to try to make this clear, five chiefs of finance at five different credit institutes were interviewed. According to them none of the credit institutes have the practice of putting their own man on the board in small and medium sized firms with financial difficulties.

³ The original list of variables in Laitinen's (1999) study included this variable, but it was excluded in the final model. The selection of variables was made using the stepwise procedure and if several variables from the same class had significance levels close to each other, the variable was selected with the highest reliability or suitability (Laitinen, 1999).

⁴ Three dummies were started with, one for firms with one payment delay, one for firms with two payment delays and one for firms with three or more payment delays. The result was that the first two of these dummies had approximately the same coefficient value. Therefore, these two dummies were combined into one, PAYMENT DELAYS (1–2).

⁵ Credit analysts at Finska (Suomen Asiakastieto Oy) interviewed the main debtors of the firms. Since most of the firms can be classified as small or medium sized the number of debtors (banks and suppliers) was rather small. Even though some debtors (not main debtors) were not interviewed, the material should, according to Finska, give an overall picture of the payment situation of the firm, since all the main debtors were interviewed anyway.

⁶ The age of the firms is calculated from the date of registration, which might in some cases lead to an error in measurement, due to the fact that the age does not always correspond to the start of a firm. For example, if a firm merges then the date of registration will alter, so there may exist old firms that are ‘young’ because of a merger. On the other hand, an old firm can be bought and a new type of business started just in order to have the date of registration of the old firm. Both these types may occur in the data, but it is believed here that this problem does not necessarily affect the results of the analysis.

⁷ The definition of age varied in the different studies. Berger and Udell (1995) defined age as ‘the number of years current owners have owned the firm’, while Shumway (2001) defined it as ‘the number of calendar years it [the firm] has been traded on the NYSE or AMEX’.

⁸ This ratio had good bankruptcy prediction ability in a study by Back (2001), where it classified 76.2% of the firms correctly one year before bankruptcy.

⁹ ‘If the cost of a type-I error were five times that of a type-II error, classification costs would be minimized by using a cutoff of 0.167, such that a firm with a probability of bankruptcy greater than 0.167 would be presumed bankrupt. At this point the relative expected cost of a type-I error (0.167*5 = 0.833) would be equal to the expected cost of a type-II error (0.833*1 = 0.833)’ (Jones, 1987).

¹⁰ That is, salaries, costs of sending out reminders and telephone costs, amongst others.

¹¹ That is, fees to debt collecting firms.

¹² That is, the CEO, the board of directors and the substitutes to the board of directors.

¹³ Public limited companies and private limited companies differ in terms of the minimum required share capital, amongst others. Public limited companies must have a minimum share capital of 500,000 FIM and private limited companies 50,000 FIM (Companies Act). In a partnership company each partner has the right to manage the company affairs, while the silent partners in a limited partnership company who have invested capital in the company do not have the right to manage the company affairs (Partnerships Act).

¹⁴ Period 1 is from February 1995 to January 1996; period 2 from February 1996 to May 1997; and period 3 from June 1997 to March 1998. The lengths of the periods are not equal. However, they are continuous over time for each of the firms.

¹⁵ This was tested using a logistic regression with two categories of firms: (1) bankrupt firms (71), and (2) firms with payment disturbances during period 1 other than the bankrupt firms (92). A dummy variable was used, which takes the value of one if the firms had payment disturbances during both periods 2 and 3, otherwise zero. Given the fact that a firm had payment disturbances during period 1, the results from the regression indicated that the probability for bankruptcy increases if a firm also had payment disturbances during the following two periods. The dummy variable was significant at the 5% level (not reported).

¹⁶ It is assumed that large firms might use their own payment periods, and when this period is longer than allowed then there will be a payment delay. Since the data consists of small and medium sized firms this should not be a problem.

¹⁷ The liquidity measure current ratio was, due to insignificancy, excluded in an earlier version of this paper.