How Do Indonesian Firms Use Their IPO Raised Funds?
596 company to go public is to rebalance its leverage in order to increase its
financial flexibility. On the other hand, enhancing liquidity is as important as reducing debt in a compan
y‘s capital structure. Companies may have less debt in their capital structure, but the ability to fulfill its financial obligation
when it dues depend on whether or not it has sufficient cash and cash equivalents. To ensure the avoidance of future financial distress, IPO
companies may use the raised fund to fortify their liquidity. Regardless the choice of debt reduction or liquidity enhancement, IPO companies can get
benefit of avoiding future financial distress, by increasing its financial flexibility. Table 3
Kolmogorov-Smirnov Tests on the Normality Assumptions on
the Changes in Leverage, PPE, NLB and WCR
Variables K-S statistics
p-value ΔWCR
2.938 0.000
Δ PPE 1.390
0.021 Δ NδB
2.882 0.000
Δ δeverage 2.468
0.000
The model is {V
T+t
– V
T-1
} total assets
T-1.
V = variable being measured. WCR is the Working Capital Requirement of the IPO company. PPE is the
Gross Property, Plant, and Equipment. NLB is the Net Liquid Balance. δeverage is the amount of δong Term Debt. Δ here represents the difference
of the aforementioned variables‘ figures between one year before and one year after the IPO. The changes in the variables are scaled with total assets
at the one year before each IPO.
Table 3 above presents the results of Kolmogorov-Smirnov K-S test. This goodness of fit test is performed to determine the normality assumptions of the variables used
in this study. As can be seen from the table 3 above, the test has consistently and significantly rejected the null hypothesis at least at the 5 level that the utilization of
IPO funds in WCR, PPE, NLB, and Long Term Debt are normally distributed. Accordingly, this result puts forward that one cannot put too much faith on the results
from parametric tests, the paired-sample t-test, in this study due to violation of the normality assumption on the data. To overcome that normality problem, a non
parametric test, the Binomial test, is employed to gauge the differences in the
597
variable prior and after IPO. For the shake of completeness, however, the results of the t-tests are also reported below.
Table 4
Univariate Tests on The Changes on WCR, PPE, NLB and Leverage
Variables Paired-Sample t-test
Binomial Test Mean
Difference t-statistics
p-value Observed Proportion
p-value Decrease
Increase Δ WCR
0.360 2.011
0.05 0.19
0.81 0.00
Δ PPE 0.354
4.218 0.00
0.25 0.75
0.00 Δ NδB
-0.145 -0.876
0.38 0.45
0.55 0.29
Δ δeverage 0.088
1.316 0.19
0.34 0.66
0.01
The model is {V
T+t
– V
T-1
} total assets
T-1.
V = variable being measured = WCR, PPE, NLB, Leverage. .WCR is the Working Capital Requirement of the IPO company. PPE is the Gross Property, Plant, and Equipment. NLB
is the Net Liquid Balance. Leverage is the amount of Long Term Debt. Δ here represents the difference of the
aforementioned variables‘ figures between one year before and one year after the IPO.
Table 4 above presents the results of the t-tests and the binomial tests on the
changes in the financial flexibility NLB and Leverage and in the Investment WCR and PPE one year after the firms conduct their IPOs. Since the
violation of the normality assumption on the data, the results of the binomial tests are the basis for the discussions in this section.
Looking at the observed proportion on Table 4 above, the utilization of IPO funds to finance future growth as reflected in the short term investment of
Working Capital Requirement WCR and in the Property, Plant, and Equipment PPE have higher probabilities to have positive values rather than
negative values. The p-values for those two variables are significant at less than the 1 level. This indicates strong evidence that IPO companies use
their IPO generated fund to finance future growth, both in the forms of WCR and PPE.
598 In contrast, the utilization of IPO funds to enhance financial flexibility as
reflected in the enhancement of Net Liquid Balance NLB is not significant at the conventional levels. This indicates that, on average, IPO companies do
not utilize their generated IPO fund to enhance their NLBs following the IPOs. When it comes to changes in Long Term Debt following IPOs, an unexpected
result emerges. Instead of reduction in Long Term Debt following IPOs, Indonesian firms experience an increase in the Leverage one year after their
IPOs . This increase is significant at the 1 levels. This indicates that IPO companies do not seem to use their generated IPO fund to enhance financial
flexibility in the form of reducing their long term debt for the purpose of rebalancing their leverage; instead, they rely heavier on the long term debt
financing for funding investing activities following their IPOs. The results of the univariate tests in this study suggest that Indonesian
IPO companies utilize their IPO generated fund to finance their future investment, not to enhance their financial flexibility. In addition, instead of
reducing their long term debt following the IPOs, these companies even use more long term debt in the post-IPO periods.
4 What are The Factors Affect New Public Listed Firms in Their Investing
Decision Following IPOs?
In running their firms on day to day operations, managers may see some potential threat and opportunities to the growth of their firms. Myers and Majluf
1984 argue that in the presence of asymmetric information and limited internally generated funds, a firm may pass up on profitable investment opportunities
because of the costs associated with raising external finance. Investigating the investing activities of firms following their IPOs, however, provides a unique
599
opportunity on the investment behaviour of firms when there is virtually no capital rationing and therefore, no need to raise external financing.
εyers 1977 implies that a firm‘s assets consists its existing assets and future assets. Although an asset has been existed, it may need a
replacement investment to maintain its production power. On the other hand, a future assets calls for either expansion investment if it is in the same
industry or a diversification investment if it is in other industries. To further study the investment behaviour of IPO firms following their IPOs, a multiple
regression model is employed as follows: Δ PPE
i
=
+
1
Lag PPE
i
+
2
Growth
i
+
3
Δ Leverage
i
+
4
IPO Fund
i
+
i
Where,
Δ PPE
i
is the difference in the Gross PPE of an IPO firm i between one year before and one year after the IPO and it is scaled by total asset at the one
year before the IPO year. Lag PPE
i
the Gross PPE of an IPO firm i at the one year before the IPO year it is scaled by total asset at the one year before the
IPO year. Growth
i
is the growth opportunity of an IPO firm i, this variable is proxied by the MBA Ratio of a company at the same industry and has the
closest amount of total assets with the IPO firm at the one year before the IPO year. IPO Fund
i
is total amount raised during an IPO of an IPO firm i.
i
is an error term. The results of the multiple regression analysis can be seen on
Table 6 below: It can be seen on Table 6 below that the magnitude of pre-IPO firm
‘s PPE has a positive and highly significant influence on a subsequent increase
600 in PPE following an IPO. The magnitude of the coefficient 0.612 is the
largest among the other coefficients. This finding signifies the sensitivity of the investment activities of the post-IPO era to the needs of the firms to
rejuvenate their existing PPEs.
601 Table 6
Relation among changes in PPE, Lag PPE, Growth, Changes in Leverage, IPO Fund
on 53 IPOs for the Periods of 2000-2005 Independent Variables
Coefficient s
t- statistics
p-value 1-tailed
VIF Constant
-0.194 -3.23
0.00 -
Lag PPE 0.612
4.74 0.00
1.25 Growth
0.001 5.45
0.00 1.00
Δ δeverage 0.282
2.24 0.02
1.71 IPO Fund
0.391 5.31
0.00 1.79
F-statistics p-value 40.64 0.00
Adj. R
2
0.75
Δ PPE
i
=
+
1
Lag PPE
i
+
2
Growth
i
+
3
Δ δeverage
i
+
4
IPO Fund
i
+
i
Where Δ PPE
i
is the difference in the Gross PPE of an IPO firm i between one year before and one year after the IPO and it is scaled by total asset at the
one year before the IPO year. Lag PPE
i
the Gross PPE of an IPO firm i at the one year before the IPO year and it is scaled by total asset at the one year before the IPO year. Growth
i
is the growth opportunity of an IPO firm i, this variable is proxied by the MBA Ratio of a company at the same industry and has the closest amount of total assets
with the IPO firm at the one year before the IPO year. IPO Fund
i
is total amount raised during an IPO of an IPO firm i.
i
is an error term.The t- statistics, in parentheses, use White‘s 1980 heteroscedasticity-consistent standard errors.
R
2
is the coefficient of determination, adjusted for degrees of freedom. VIF is equal to 11-R2, where R2 is estimated from the regression of an independent variable on all other independent variables. The threshold here is
that the data is hampered by Multi-collinearity problems if the VIF value is above 5.
Besides replacement of its existing PPEs, IPO Firms may also need the IPO
funds to invest in their growth opportunities of their respective industries. Table 6 above shows that the growth opportunities in each corresponding
industry also have a positive influence on IPO firms‘ decisions for subsequent investment following their IPOs. Although the magnitude of the coefficient
0.001 is the least among the other coefficients. this finding, nonetheless, signifies the decision of the IPO firms to invest in their growth opportunities
after going public. This findings also confirm the findings in Chemmanur and Fulgieri 1999, Stoughton, Wong and Zechner 2001, and Maksimovic and
Pichler 2001 that, based on the information asymmetric and costly information gathering arguments, firms from particular industries go public
because they discover new technology, which leads toward increases in productivity.
602 The other two variables in the model are the amount of new equity and
new debt funding and represent additional sources of financing besides internally generated funds. These funds are the additional funds which were
raised during and after the IPOs and, therefore, only available if the firms just went public before. Table 6 shows that the coefficient of the IPO Fund is
positive and highly significant at the 1 level. Similarly, the coefficient of the change in the Leverage is also positive and significant at the 5 level.
Judging from the magnitude of the both coefficients 0.391 and 0.282 respectively, The IPO firms in this study rely heavier on the equity financing
than debt financing to fund their investment in real assets. The results of the univariate and multivariate analyses on this study
then, supports the findings of Mikkelson et al. 1997 who documented that US IPOs are generally followed by a large growth in assets. While Mikkelson
et al. 1997 contains no explicit linkage between the companies‘ growth to the capital raising involved with the IPO, this finding is at least suggestive of
the view that companies go public so that they can raise public equity capital to finance growth. The results of this study also support Kim and Weisbach
2006 who examine that one motive for the IPO around the world is to raise capital for investment. The findings in this study, on the other hand, are in
contrast with the motivation of Italian IPO companies to rebalance their leverage after a period of high investment and growth as in Pagano, Panetta,
and Zingales 1998. 5. Why Does Leverage Increase after IPOs?
Based on the findings mentioned in the previous section, Indonesian IPO
firms, on average, increase their leverage following their IPOs. The leverage
603 in this study is measured relative to their total assets at the one year before
IPOs. Some new investments might need financing from equity, debt, or both. The funding composition of this new investment depends on each company‘s
capital structure. Nobel winner Merton Miller and Franco Modigliani 1958 demonstrate formally that in a perfect capital market the value of a firm
depends only on its investment policy and not on its financing policy. In the real world, however, there are some factors that influence a fim‘s capital
structure policy. Several theories have been advanced to propose those factors and their impacts on a firm‘s leverage have been tested empirically
both in the US and International settings Harris and Raviv, 1991; Rajan and Zingales, 1995; Shyam-Sunder and Myers, 1999; Graham and Harvey,
2001.
82
To investigate further on the relationship between changes in leverage and the amount of funding raised in IPOs, a multiple regression analysis is
employed as follows:
Δ δeverage
i
=
+
1
Size
i
+
2
Profitability
i
+
3
Growth
i
+
4
IPO Fund
i
+
i
;
Where, Δ δeverage
i
is the difference in Long Term Debt of an IPO firm i between one year before and one year after the IPO and it is scaled by total asset at the
one year before the IPO year. Size
i
is the natural log of total assets of an IPO
82 The most notable theories on factors affecting firm‘s target leverage are the ones based on the trade-off theory
and the pecking order theory.
604 firm i at the one year before the IPO year. Profitability
i
is the ROA of an IPO firm i for one year before the IPO year. Growth
i
is the growth opportunity of an IPO firm i, this variable is proxied by the Market to Book Asset ratio MBA of
a company at the same industry and has the closest amount of total assets with the IPO firm at the one year before the IPO year. MBA Ratio = Market
Assets Book Assets = {share price × shares outstanding + preferred stock + debt in current liabilities + long-term debt
– deferred taxes and investment
tax credit} Book value of assets. IPO Fund
i
is total amount raised during an IPO of an IPO firm i.
i
is an error term. The results of the multiple regression
analysis can be seen on Table 5 below.
Table 5 Relation among Changes in Leverage, Size, Profitability, Growth and IPO
Fund on 53 IPOs for the Periods of 2000-2005
Independent Variables Coefficient
s t-
statistics p-value
1-tailed VIF
Constant -4.231
-3.04 0.00
- Size
0.163 3.04
0.00 1.11
Profitability -0.148
-2.89 0.01
1.07 Growth
-0.001 -0.22
0.41 1.00
IPO Fund 0.342
1.77 0.04
1.04 F-statistics p-value
12.56 0.00 Adj. R
2
0.47
Δ δeverage
i
is the difference in Long Term Debt of the IPO firm between one year before and one year after the IPO scaled by total asset at the one year before the IPO year. Size
i
is the natural log of total assets of the IPO firm at the one year before the IPO year. Profitability
i
is the ROA of the IPO firm for one year before the IPO year. Growth
i
is the growth opportunity of an IPO firm, this variable is proxied by the Market to Book Asset Ratio MBA of a company at
the same industry and has the closest amount of total assets with the IPO firm at the one year before the IPO year. IPO Fund
i
is total amount raised during the IPO.
i
; is an error term. The t- statistics, in parentheses, use White‘s
1980 heteroscedasticity-consistent standard errors. R
2
is the coefficient of determination, adjusted for degrees of freedom. VIF is equal to 11-R
2
, where R
2
is estimated from the regression of an independent variable on all other independent variables. The threshold here is that the data is hampered by Multi-collinearity problems if the VIF value
is above 5.
It can be seen on Table 5 above that there is a positive relationship between the amount of funds raised in the IPOs and the decision to increase leverage
at the one year after the IPOs. This relationship is significant at the 5 level.
605 As mentioned earlier, some new investments might need financing from
equity, debt, or both. Assuming that, on average, the new investment calls for funding from the combination of equity and debt. After raising external equity
funding through IPO, these IPO companies also issue new debt afterwards. IPO raised funds might also be used as a safety cushion and this new
safety net attracts lenders to extend credits for the companies since they believe that by having more cash, these companies have increased their level
of credit worthiness in the debt engagement. This leads to these companies having a greater bargaining power with banks and other lenders in issuing
new debt. To elaborate more, a potential problem with bank loans is that banks
can extract rents from their privileged information about the credit worthiness of their customers. As highlighted by Rajan 1992, by gaining access to the
stock market and disseminating information to the generality of investors, a company elicits outside competition to its lender and ensures a lower cost of
credit, a larger supply of external finance, or both. Moreover, having a prestigious status as public companies might also help in obtaining new debt
from lenders. As stated by Pagano, Panetta, and Zingales 1998, the most cited
benefit of going public is probably the increased likelihood of those companies to gain more access to capital markets both equity and debt markets. In other
words, the status as public companies can be said to overcome the borrowing constraints of the companies. Hence, firms go public to raise equity financing
and, afterward, increase their debt financing following the IPO. These IPO
606 companies must be fund-hungry companies that seek for plenty of fresh funds
to fuel their future investment. Based on the trade-
off theory of the capital structure, a firm‘s target leverage is positively influenced by taxes and negatively influenced by costs
of financial distress and agency conflicts. Warner 1977 and Ang, Chua and McConnel 1982 find that costs of financial distress are higher for smaller
firms. With regard to probability of going into bankruptcy state, Titman and Wessels 1988 argue that larger firms tend to fail less often due to their
diversification nature of their operations. Diversification may also go hand in hand with more stable cash flows as implied by Jensen 1986 and
Easterbrook 1984. Accordingly, the theory predicts a positive relationship between size and leverage. Table 5 above shows that there is a direct
relationship between Leverage and Size. This positive relationship is significant at less than the 1 level.
The trade-off theory also prescribes a positive influence of taxes and a negative influence of financial distress costs and agency
conflicts on a firm‘s target leverage. Profitable firms have lower probability of going into
bankruptcy state. With tax deductibility feature of debt services, profitable firms also find themselves in the position to take fully advantage of that benefit.
In addition, higher debt may control the agency problems by forcing managers to pay out more of the firm‘s excess cash as suggested by Jensen and
Meckling 1976, Easterbrook 1984, and Jensen 1986. Therefore, the theory suggests a positive relationship between leverage and profitability.
With regard to the relationship between leverage and profitability of Indonesian IPO firms in this study, Table 5 shows a negative and significant
607 relationship between profitability at the one year before IPOs and the decision
to seek more debt financing at the one year after their IPOs .This result is in conflict with the one predicted by the trade-off theory. The pecking order
theory, on the other hand, argues that firms prefer raising capital, first from retained earnings, second from borrowing, and finally from issuing new equity.
This order of preferences is due to the direct and indirect costs of floating new shares in the presence of information asymmetries. Compare with less
profitable firms, highly profitable firms is more likely to have its investment needs less than its retained earnings. Consequently, the pecking order theory
prescribes a negative relationship between leverage and profitability. The finding in this study, therefore, more in line with the pecking order theory than
the trade-off theory of capital structure. In contrast with the other variables, Table 5 shows that there is no
significant relationship between growth opportunities and leverage. Myers 1977 demonstrate that the market value of a firm depend on the value of its
assets in place and present value of growth opportunities facing by the firm. A growth firm has its market value consists mainly from its present value of
growth opportunities. Accordingly, the theory predicts that firms with higher growth carry less debt in their capital structure because they face less
incentive to reduce conflicts between stockholder-bondholder due to underinvestment and asset substitution effects Galai and Masulis, 1976;
Jensen and Meckling, 1976. Moreover, Jensen 1986 argues that firms with higher growth opportunities have less need for the disciplining effect of fixed
payments to control their free cash flows. Consequently, it is predicted that there is a negative relationship between growth opportunity and leverage.
608 Previous empirical studies, however, find that results on the
relationship between growth opportunities and leverage are mixed, at best. A study conducted by Titman and Wessels 1988 find a negative relationship,
while by Rajan and Zingales 1995 find an opposite results that the relationship is positive report a positive relationship between leverage and
growth. The finding on this study that there is no relationship between growth opportunities and leverage may the results of the negative and positive effects
of growth opportunities on the leverage cancel each other out.