of observation *127 5192 5319*

*Financial fragility scores for the two samples before and after the bond issuance date. Standard deviations are reported in italics. T0 is the event year of bond issuance for both samples. Values are average scores of the financial fragility indicator that spans from a score of 1 (lowest financial fragility) to a score of 5 (highest financial fragility). Stars denote the standard level of p-value significance: \*=10%, \*\*=5%, \*\*\*=1%. Our elaboration on proprietary dataset.*

*0.64 0.98 0.98*

*0.62 0.93 0.93*

*0.63 0.58 0.58*

sector and time fixed effects*.*

weights).

group.

**Table 7.**

**151**

*Differences in the financial fragility average score.*

**4.2 Dependent and explanatory variables**

*DOI: http://dx.doi.org/10.5772/intechopen.93701*

*Difference between the issuers' sample and the control group. Standard deviations are reported in italics. Size is measured by the natural logarithm of sales. D/E Ratio is the issuer's debt to equity ratio; Bank debt exposure is the ratio between the bank debt to total debt. The interest coverage ratio is the ratio between the issuer's EBITDA and its interest expenses. The short-term bank debt ratio is the ratio between bank short term debt and total debt. The current ratio is the ratio between issuer's current assets and current liabilities. Tangible ratio is the ratio between tangible fixed assets and total assets.*

#### **Table 6.**

*Differences between the two samples (issuers and nonissuers).*

(2013–2017). This methodology has often been employed in the prior going public literature, starting from the Pagano et al. study [20], on IPOs equity markets.

We choose as the dependent variable a measure of financial fragility (or vulnerability) using an equally weighted basket of financial ratios that aims to capture the exposure of the firm to the negative consequences of potential real and financial shocks.

In the OLS regressions, we estimate beta coefficients using a proxy of financial fragility as the dependent variable and combinations of the explanatory variables for different specifications, as depicted in the next section. More in detail, we compute the variation in the score of our financial fragility indicator for each firm between 2 years after the event (the corporate bond issuance) and the year before the same event. When the difference is positive, it means that our proposed financial fragility metric has worsened (becoming higher), the opposite if the difference is negative.

The basic structure of our regressions is as follows:

$$
\Delta \text{FinFragility} = a + \beta\_1 (\text{Minibond})\_{i,t} + \sum\_k \gamma\_k \text{FirmControls}\_{i,t-1} + \epsilon,\tag{1}
$$

where *Minibondi,t* is a dummy variable equal to 1 in case of mini-bond funding of firm *i* at time *t* and zero otherwise, and *FirmControlsi,t*�*<sup>1</sup>* is a vector of firm-specific

control variables about the issuers and nonissuers characteristics using the last available accounting information at the date of the bond offering. We control for sector and time fixed effects*.*

#### **4.2 Dependent and explanatory variables**

As indicated previously, our dependent variable is the change in firms' financial fragility, and it portrays the exposure of the firms to the negative consequences to potential financial shocks. We build a measure of financial fragility (or vulnerability) using an equally weighted scoring indicator of five financial ratios that capture the most significant dimensions of firms' financial health. They are the following: interest coverage financial ratio; current ratio; short-term bank debt over total debt; financial leverage (i.e. debt to equity ratio), bank debt exposure (bank debt over total debt). The procedure is the ensuing: for each year and for each five financial ratio we create a ranking system starting from a score of 1 (lowest financial fragility) up to 5 (highest financial fragility) based on a quintile classification of the financial ratio (we used also different ranking criteria, but our empirical results remain robust and are not affected significantly). For example, for year 2016 we have a starting sample of 28 mini-bond issuers and 1200 firms in the control group. Then, for each financial ratio we compute the score for all firms. Then, we compute the financial fragility indicator for all firms by computing the average of all 5 scores (with equal weights).

Next, we calculate the difference of the score of the financial fragility indicator between t + 2 and t 1, relative to the reference year. We think that a two-year time span after the event is a good compromise in order to assess the effect of the firms' financial policy choices on the desired outcomes in terms of better financial resilience. Longer event windows (up to 3 year after the event or more) have undesired features such as: the loss of a significant number of observations in our issuers sample since for mini-bond issued during 2017 we do not have a 3 year ex-post track record of financial reports; and the longer the time horizon the more the effects on our financial fragility indicator can be influenced by other factors than merely the financial policy choice under scrutiny. **Table 7** shows the differences in the average financial fragility indicator score for the two sub-samples. As a matter of fact, minibond issuers have a higher average score because they are more leveraged, more indebted to banks and have a lower interest coverage with respect to the control group.


*Financial fragility scores for the two samples before and after the bond issuance date. Standard deviations are reported in italics. T0 is the event year of bond issuance for both samples. Values are average scores of the financial fragility indicator that spans from a score of 1 (lowest financial fragility) to a score of 5 (highest financial fragility). Stars denote the standard level of p-value significance: \*=10%, \*\*=5%, \*\*\*=1%. Our elaboration on proprietary dataset.*

#### **Table 7.**

*Differences in the financial fragility average score.*

(2013–2017). This methodology has often been employed in the prior going public literature, starting from the Pagano et al. study [20], on IPOs equity markets.

shocks.

**Table 6.**

*assets and total assets.*

is negative.

**150**

We choose as the dependent variable a measure of financial fragility (or vulnerability) using an equally weighted basket of financial ratios that aims to capture the exposure of the firm to the negative consequences of potential real and financial

**Variable Issuers Control sample**

*1.68 1.06*

*2.58 3.53*

*19.47% 23.81%*

*14.08 46.68*

*15.06% 19.42%*

*0.44 0.75*

*7.64 7.81*

*13.90*% *8.45*%

*22.11% 17.79%*

Sales (Natural logarithm) 17.69 18.05

*Entrepreneurship - Contemporary Issues*

D/E Ratio 2.14 1.46

Bank debt exposure 45.62% 29.72%

Interest coverage 7.71 24.77

Short-term bank debt ratio 25,17% 20.31%

Current ratio 1.17 1.42

ROI *8.55* 8.65

EBITDA/Sales 14.30% 7.37%

Tangible ratio 25.02% 19.22%
