International Journal of Commerce and Finance, Vol. 2, Issue 1, 2016, 91-101

WHY GO PUBLIC?

An EMPIRICAL ANALYSIS of IPO’s COMPETITIVE EFFECT

on TURKISH FIRMS

Yakup Ergincan, (Ph.D.)

Central Securities Depository of Turkey, Turkey

Fatih Kiraz, (Ph.D.)

Central Securities Depository of Turkey, Turkey

Özgür Uysal, (Ph.D.)

Central Securities Depository of Turkey, Turkey

Abstract:
The motivation of this study is to approach the IPO issue from a different perspective. Most of the studies in the existing l iterature deal
with three broad issues which are operating performance, stock performance, and the reasons to go public. However, there aren’t many
studies which tackle the IPO issue from a pure competitive perspective which enables limited but clear results. This study contributes to the
literature, not by answering a broad and old question but by providing new and partial evidence which seem to contradict the whole at first
glance. Most of the 60 BIST (Borsa Istanbul) listed large industrial firms in this study have improved their relative ranks after their
IPOs, when compared to their own large competitors most of which are not listed in BIST. These ranks are available in Turkey’s TOP
1.000 Industrial Enterprises annual lists and they are officially assigned by ICI (Istanbul Chamber of Industry) according to firms’ sales
revenue figures. Thus, they provide us with the single and clear window to observe. Keeping in mind that this window is limited, this study
comes up with some non-negligible findings and then elaborates on their significance for the IPO literature, raising more questions than
answers for the sake of a more solid theory.

Keywords: Initial public offerings (IPO), Borsa Istanbul (BIST), Competitive Effect, Finance and Product Market Competition,
Large Industrial Firms

1. Introduction

There is a vast amount of studies on IPO subject which can be divided into at least three broad categories. Naturally,
first of them is the basic question that why do firms go public. More specifically, why the motivation to do an IPO
does ever exist and why it is stronger in some situations or times. There are a few theories in the literature, including
entrepreneurs’ chance to sell their companies/shares to a higher price (Zingales, 1995), control regain opportunity
for entrepreneurs in favorable conditions (Black and Gilson, 1998), raising funds for further growth (Pagano et al.,
1998), allowing more dispersion of ownership (Chemmanur and Fulghieri, 1999), inspiring more faith in the firm
(Maksimovic and Pichler, 2001), first-mover advantage (Schultz and Zaman, 2001), IPO timing models based on
asymmetric information (Lucas and McDonald, 1990), and window of opportunity due to investor sentiment (Baker
and Wurgler, 2000). These are just some examples for the first category. The second category, short/long term stock
performance after IPO, has very much to do with the concept of ‘underpricing’. The main theories trying to explain
it can be grouped as the ones which focus; asymmetric information between issuers and investors, legal liabilities of
the issuers, share allocation concerns, and valuation methods. A very good review of these issues is available in Ritter
and Welch (2002). However, neither stock performance nor the reasons and timings of IPOs is interesting for us for
the time being, because the core subject of this study falls to the third and the last broad category which is operating
performance before and after an IPO. Our interest in this latter category was aroused by a naive question that we

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want to answer and also by the availability of a very good dataset to help us come up with that answer. That basic
question is ‘whether going public is a really bad signal, in general, for the future of the firm itself or not’. The
important thing here is that we focus our attention on the firm itself and not on issuers, investors, or other thir d
parties such as government bodies. Sure, we are aware that even this narrowed version of the issue has many
dimensions to consider, but if we were forced to make a sweeping generalization, the quick and dirty result, from the
literature, would be poor overall operating performance after IPOs. This is something that the authors of this study
and some other researchers find hard to believe and to say out loud. Main reason for this ignorance is that one can,
within seconds, think of many possible theoretical/practical reasons for things to go well for IPO firms. Secondary
reasons are supplied by our own market experience and the results from a few related studies from the literature.
Thus, we hypothesized that firms, at least relatively large firms if not all, would become more competitive after their
IPOs. In this situation of a possible conflict, we needed to provide new, clear, and reliable evidence either for or
against the seemingly prevailing claim. Istanbul Chamber of Industry’s (ICI) publicly available yearly dataset came in
to rescue, providing us with the actual ranks of industrial firms over the years.
The next part of this paper is a brief summary of the related literature on the effects of going public on operating
performances and competitiveness of firms. Part three, first by describing the data, presents the methodology and
results. The last part concludes by highlighting the main findings and possible future directions.

2. Literature Review
There are many studies on operating performance after IPO. However, only a few of them approach the issue from
a competition perspective or use Turkish data. There is no study focusing on the competitiveness of Turkish large
firms which have gone public in any year since 1993. Below is a brief review of the general IPO performance
literature.
Jain and Kini (1994), by analyzing 682 IPOs performed during 1976-1988, find a significant decline in performance
(market-to-book ratio, price/earnings ratio, and earnings per share) subsequent to the initial public offering. They
also claim that there is a significant positive relation between post-IPO operating performance and equity retention
by the original entrepreneurs. On the other hand, Cai and Wei (1997), relying on their regression results for 180
Japanese firms, state that managerial ownership structure is not a significant determinant of performance. Thus, for
the new issue puzzle, they do favor ‘window of opportunity’ explanation against ‘ownership’. Spiess and Pettway
(1997) approach the problem from a different perspective and they claim that the only thing matters is the way the
firms define an IPO. More specifically, the firms which have low corporate governance scores and see IPO as a
single financing event not as a process to be planned, are more likely to be the underperformers. These papers are
just some examples of studies which do not directly deal with sales growth or market share. Like the ones above,
there are many studies which support underperformance hypothesis but differ in the reason(s) outlined. However,
there are also some studies which do not find convincing evidence of underperformance. Brav and Gompers (1997)
is one good example for this. They argue that not IPO but size matters because crises, such as the one in mid -1980s,
do affect smaller firms more. When controlled for size, they do not find a significant difference between the
performances of IPO firms and other firms.
There is another group of papers which find some mixed evidence on underperformance in some financial ratios but
improvement in sales growth performance. Thus, this is the most interesting group for the authors of this study.
One of the earliest members of this group is Kim et al. (2004). They analyze, mainly, three different operating
performance measures of 133 firms quoted at Thailand stock exchange during 1987-1993. Their key measures are;
sales growth, profitability of assets, and turnover of assets. For the period beginning with IPO date (t) and ending at
three years later (t+3), they do find declines in all measures but sales growth. However, instead of focusing on this
questionable contradiction, they focus on explaining the causes of lower profits. Main cause they find is the level of
managerial ownership. When the level is intermediate, the relationship with change in profitability is negative
whereas the level is high or low enough, that relationship becomes positive. Thus, naturally, they discuss these
findings under the light of ‘entrenchment’ and ‘alignment-of-interest’ hypotheses. Although the variable at the center
of discussion is ‘profitability’ here and not ‘competitiveness’ or more specifically ‘ranks based on sales’, this study is
still a good example since it differentiates between sales growth and other operating measures in terms of IPO.
There are two very similar studies analyzing the IPOs at BIST some of which are the observations of our study as
well. First of these two studies is Kurtaran and Er (2008) in which the sample consists of all firms which have gone

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public at BIST between 1992 and 2000. The same sample of 205 firms is used in Bulut et al. (2009) but this latter
study is written in Turkish. All of the three articles mentioned in this paragraph have something in common. The
authors want to analyze the effects of managerial and ownership structure changes, not the IPO concept as a whole.
Thus, they consider IPO event just as an opportunity since every IPO changes the structure to a degree. As another
common point, they find that sales growth behavior is not like other operating measures’ after IPO. This finding was
promising at the beginning. However, it surely is not a direct proof of improved competitive power since none of
these studies take the rivals, including also the firms which have not gone public, into account. In fact, the lack of
this proof is one of the big motivations for our study. A more recent paper, Spiegel and Tookes (2015), is another
study which aims to see ‘competition’ in the big IPO picture. Their main question is ‘how one firm’s decision to
switch from being private to public impacts its rivals’. To answer this, they develop a continuous time model in
which heterogeneous firms producing heterogeneous goods compete for consumers. Their general results imply that
IPOs forecast future industry changes but do not cause them. This may seem to be in conflict with our findings.
However, there are important differences in methodologies of these two studies. Firstly, their paper is more
theoretical than empirical. Secondly, they focus on rivals’ average profitability and market value, not the new IPO
firm’s rank based on sales. Finally and the most importantly, their sample includes mostly small firms with already
small market shares whereas our sample consists of relatively large firms. On the other hand, Hsu et al. (2010)
investigate the returns and operating performance of publicly traded competitors around the time of 134 large IPOs
in their industries. They find that industry competitors experience negative stock price reactions around IPOs and a
significant deterioration in their operating performance after these IPOs. They also claim that these large IPOs are
responsible for this underperformance, since they see that publicly traded competitors respond positively to the
withdrawal of an IPO in their industry. This finding is in line with our main finding. They say competitors become
worse and we say newly public firms become better, relatively. However, there is one thing that might be important;
they do not include non-public firms in the competitors group. Thus, the private rivals are missing in this picture.
Chemmanur and He (2011) provide us with the missing part, since they develop a model to compare newly public
firm against a non-public competitor in a set of different external conditions such as existence/inexistence of
productivity shocks and IPO waves. Then they test this model empirically as well. This study has a lot to say on IPO
waves but what is more interesting for us is one of their general findings; “Going public, though costly, not only
allows a firm to raise external capital cheaply, but also enables it to grab market share from its private competitors”.
Tests of our data provide partial support for the general claim that new IPO firms grab market share from both
private and public rivals. Support is partial, because our dataset includes only relatively large firms, whether they are
new IPOs or private/public rivals.
To summarize this section, there is a very long literature on IPOs but the literature seems to have only recently
turned its attention to the issue of competition effects of IPOs. Our study falls under this young category. Although
our main question, methods, and datasets are somewhat different from these recent studies’, as explained throughout
the study, our main finding is not that different.

3. Data, Methodology, and Findings

We used the publicly available dataset of ICI which include, as the key variable, sales revenue figures of top 1,000
industrial firms in Turkey. ICI has been providing this yearly data since 1993 so that it was possible to create rank
histories for all firms in those datasets, whether or not they are selected to be in the final sample of this study. Final
sample consists of 60 IPO firms and the selection process is as follows: (1) Any of 111 industrial firms which
performed an IPO at BIST (Formerly Istanbul Stock Exchange) after year 1993 is a candidate. (2) Each of these 111
firms has a “t” value representing its own IPO year. (3) The ones which have valid data for all of their own “t-1”,
“t”, “t+1” periods are included in the final sample of 60 firms. During this process, 51 firms were eliminated because
we do not have data about their performance in pre and/or post IPO periods. Absence of this data has two reasons.
First, some firms do not let ICI to publish their names in some years, so we know their ranks but we do not know to
which firms those ranks belong. For example, 55 of the top 1,000 firms in 2014 fall into this category. Including
them was an option but we would need some assumptions in that case and we rejected to base our analyses on some
questionable assumptions. Second and more important reason for the missing data is simply that some firms could
not find a place for themselves in top 1,000 lists for some related periods. The reader may rightfully think that

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eliminating these firms from the final sample may result in a survivorship bias and thus may provide an artificial and
unjust support to the findings of this study. In fact, the opposite seems to be true, since there are only 4 firms which
were in top 1,000 before their IPOs but were not in top 1,000 during or right after the IPO. On the other hand,
there are 32 different examples for the reverse case, which makes one think that firms get better ranks after IPO
event. As a consequence, by omitting all these firms and refusing to rely on any assumptions, we are actually
purposefully reducing the probability of rejecting equal or less post-IPO performance when compared to pre-IPO
performance.
Once the final sample is decided, the key variable of this study is calculated for the years around IPO by the
following formula:

(1)
i,t+c si,t+c i,t+c

Comp =MeanRank -Rank

where (i) represents the firm, (t) represents the IPO year of that firm, (c) is an integer between -3 and +3, and (si)
represents the sector of that firm which includes itself and its competitors. ‘Rank’ variable is already available in ICI
dataset. The dataset also provides sector codes for all firms assigned by ICI according to ‘International Standard
Industrial Classification of All Economic Activities, Rev.2’ (ISIC Rev2).
‘MeanRank’ variable gives the sector average rank for the related year. From the top 1,000 list, all firms in the related
sector, whether they are quoted at BIST or not, are included in the following formula.




 ,

,
(2)

j t c

si t c

t c

Rank
MeanRank

where (j) stands for all firms in the related sector and (n) is the total number of these firms. As in formula (1), (t+c)
represents the years around the IPO of firm (i). There may be at least three important issues to discuss here. Firstly,
non-quoted firms’ inclusion is necessary because we are interested in all rivals and ‘not going public’ may well be a
wise decision at some circumstances. Secondly, ‘median’ could be utilized instead of ‘mean’ but the overall results do
not change in our case. Finally, sector average/median may suddenly change from year to year, at least theoretically,
since we have data for at most 1,000 firms for each year. To make things more clear, let’s consider the following
example. Suppose that there are only three firms in a sector in (t) moment. First firm’s rank is 1, second firm’s is 500,
and the last firm’s is 999 in the top 1,000 list. Let the second firm be an IPO firm and be in our final sample. In this
case, the sector average rank is 500 and our IPO firm’s performance is neither better nor worse when compared to
its sector. One year later (t+1), suppose that both first and our IPO firm protected their positions at 1 and 500
respectively. However, the third firm is no longer at top 1,000 list since its rank is now more than 1,000 and we have
no chance to know exactly what the new figure is. Now, the sector average is 250.5, much better than our IPO firm’s
rank. Applying formula 1, we should deduce that the IPO firm performed poorly and lost some of its
competitiveness just after its going public. In fact, there is no such thing and our IPO firm is at least as competitive
as it was one year ago. Thus, one can claim that our key variable calculation/interpretation process is clearly biased
by design. Nevertheless, the important thing is that it is biased towards only one direction and this is intentional.
What this design actually performs for us is to decrease the probability of failing to reject a false improvement signal
for IPO firms. Fortunately, the opposite is not true since, in the actual dataset, there is no firm which was better than
its sector average at a year and then was suddenly out of the list in the following year.
The idea experiment above is about only two or three firms within a sector, just to clarify something. In our real
dataset, number of firms within any sector at any year is generally much more than three. Table 1 below may give a
hint about this issue. As expected, the distribution is not uniform, some sectors are overrepresented, but since the
motivation of this study is not comparing the sectors, this does not pose a significant problem for the time being and
this issue is revisited at the findings part. However, another feature, the total number of different firms in the final
dataset, is somewhat striking. 2,067 is a low figure when you compare it with a potential maximum value of 20,000
(think about a completely different firm list in each year since 1993). This means that turnover is low for top 1,000
lists, making our calculated figures more reliable for testing our hypotheses.
In fact, the total number of firms which appeared at least once in one of the top 1,000 lists is 2,371. The difference, a
group of 304 firms, is missing because their sectors do not have any representatives in the final sample of 60 IPO

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firms for this period. Trying to explain why these sectors seem to refrain from going public is beyond the scope of
this study and it might be an interesting future work, especially for the policy makers.

Table 1. Sectoral Frequency Distribution of All Firms in ICI Top 1,000 lists

Sector Code Number of Firms %

321 396 19.2

311 295 14.3

322 200 9.7

384 167 8.1

352 137 6.6

383 135 6.5

371 125 6

369 116 5.6

382 93 4.5

381 75 3.6

356 74 3.6

341 53 2.6

351 37 1.8

342 36 1.7

400 30 1.5

313 27 1.3

324 20 1

332 19 0.9

361 18 0.9

390 14 0.7

Total 2067 100

Returning back to our original path, we are now ready to discuss the most important variable which is ‘Comp’.
‘Comp’ is a very good proxy of a firm’s competitive power, since it shows the relative rank against rivals. Calculating
it for five different moments, from (t-1) to (t+3), for each IPO firm, enables the direct comparison of pre and post
IPO periods. Results for a broader version, from (t-3) to (t+3), are also available but in that case number of firms
without missing data falls from 60 to 26. Nevertheless, as Figure 1&2 illustrates, results and interpretations below are
quite similar for these two cases. To compare pre and post periods, nonparametric tests were preferred because
Shapiro-Wilk test results rejected normality for ‘Comp t+3’ (p = .002). Besides this fact, the same quantitative
variable is measured at different times from the same sample in this study, thus Friedman test is appropriate to check
whether the distributions are the same or not. Figure 2 below shows the distributions of ‘Comp’ variable in each of
the five important periods. Friedman test statistic (30.413, p=.000) clearly points out a significant change in
distribution through the years. Furthermore, the change seems to be in only one direction.

Figure 1. Changes in Distributions of COMP, for 26 firms from t-3 to t+3

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Figure 2. Changes in Distributions of COMP, for 60 firms from t-1 to t+3

Looking at these figures, especially Figure 1, readers may think that IPO firms do better than their competitors also
before their IPO years. Besides, they may think that this is in line with Pagano et al. (1998) claims about growth and
IPO decision and/or timing. That paper, analyzing IPOs in Italy, implies that firms are more likely to go public after
large investments and abnormal growth and they might be seeing an IPO event just as a tool to raise the needed
funds for additional growth. However, this is not a valid explanation in our case, for two reasons. Firstly, please
remember the discussion on 36 IPO firms purposefully discarded from this study. 32 of them were not in the top
1000 list before their own IPOs, but they are successful enough to be in top 1000 list only after their IPOs. So,
including these 36 firms would certainly lower the mean rank figures observed in pre-IPO periods. Secondly, IPO is
not a single point event, but it is a process. The important thing is deciding to do IPO and getting ready for it and
finally timing it. This process and its possible positive effects might have begun even before t-3. In other words,
‘making the decision to do an IPO at a future time’ might be preceding the growth as well. As Pagano et al. (1998)
suggests and as the firms know, the probability of successfully going public increases according to firm size. Thus,
IPO may be one of the major aims for a firm’s life, not just an ordinary tool to raise fund. However, whether we see
it as an important goal/aim or as a simple tool, our main finding is not affected. In the special case of large industrial
Turkish firms, the competition-based results of complete IPO process seem to be favorable for IPO firms and not
very good news for their (private or not) competitors.
Friedman post-hoc analysis results are presented in Table 2, for a better understanding of the aforementioned
general difference in distributions. Pre-IPO (Compt-1) and post-IPO (Compt+3) values seem to be mostly responsible
for that difference.

Table 2. Pairwise Comparisons of COMP, for 60 firms from t-1 to t+3

Pair
Test

Statistic
Std.

Error
Std. Test
Statistic Significance

Adj.
Significance

Compt-1 - Compt -0.333 0.289 -1.155 0.248 1.000

Compt-1 - Compt+1 -0.433 0.289 -1.501 0.133 1.000

Compt-1 - Compt+2 -0.983 0.289 -3.406 0.001 0.007**

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Compt-1 - Compt+3 -1.417 0.289 -4.907 0.000 0.000**

Compt - Compt+1 -0.100 0.289 -0.346 0.729 1.000

Compt - Compt+2 -0.650 0.289 -2.252 0.024 0.243

Compt - Compt+3 -1.083 0.289 -3.753 0.000 0.002**

Compt+1 - Compt+2 -0.550 0.289 -1.905 0.057 0.567

Compt+1 - Compt+3 -0.983 0.289 -3.406 0.001 0.007**

Compt+2 - Compt+3 -0.433 0.289 -1.501 0.133 1.000

**: Significant at 99% confidence level

In general, IPO firms seem to have improved their positions (ranks based on sales) within their sectors after their
IPOs. However, we need to calculate the median of the differences to test this hypothesis. Hence, Wilcoxon signed
rank test for related samples is proper here and Table 3 provides the detailed results for each meaningful term-pair.

Table 3. Wilcoxon Signed Rank Test Results for COMP, for 60 firms from t-1 to t+3

year1 year2
no of positive

differences
no of negative

differences
Wilcoxon test

score
p

t-1 t 39 21 3.188 0.010*

t-1 t+1 33 27 2.282 0.022*

t-1 t+2 40 20 3.754 0.000**

t-1 t+3 46 14 4.262 0.000**

t t+1 35 25 0.942 0.346

t t+2 39 21 2.356 0.018*

t t+3 43 17 3.379 0.001**

t+1 t+2 36 24 1.855 0.064

t+1 t+3 44 16 3.364 0.001**

t+2 t+3 34 26 1.980 0.048*

*: Significant at 95% confidence level

**: Significant at 99% confidence level

As seen above, the difference of ‘year2’ and ‘year1’ figures is significantly higher than zero in most of the cases.
Exceptional pairs are (t) and (t+1), (t+1) and (t+2). For those periods, the difference is still positive but not
significant. On the other hand, the most obvious improvement is observed at pair (t-1) and (t+3), which are the
beginning and the end of this study’s time period. 46 out of 60 firms seem to be in better positions against their
rivals three years after their IPOs when compared to their pre-IPO ranks within their sectors. Although the number
of firms, which have improved their relative positions, changes in both ways from year to year, there are at least 20
firms which showed continuous improvement till the end, beginning with their IPOs. There are 7 examples for

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exactly the opposite case, but 4 of those firms are actually in better positions when compared to their pre-IPO years.
33 firms have experienced both up and downs in their post-IPO years, yet most of them enjoyed higher ranks than
their pre-IPO ranks at least once. Given these facts, only 3 firms’ market positions seem to have become totally
worse after their IPOs. To present a general and more clear picture of these findings, we derive another variable
which we call general success score (GSS). It is, as stated in formula 3, average post-IPO scores minus just before
IPO scores.


(3)

i,t+p

i i,t-1

Comp
GSS = -Comp

where (i) represents the firm, (t) represents the IPO year of that firm, (p) is an integer between +1 and +3. Tables 4,
5, and 6, together, present detailed info on the GSS’s distribution.

Table 4. Descriptive Statistics for GSS

Statistic Std. Error

Mean

95.43 20.73

%95 Confidence
Interval for Mean

Lower Bound 53.95

Upper Bound 136.91

%5 Trimmed

Mean

98.13

Median

116.99

Variance

25779.45

Std. Deviation

160.56

Minimum

-272.64

Maximum

447.38

Range

720.02

Interquartile

Range

241.60

Skewness

-0.36 0.31

Kurtosis

-0.38 0.61

Table 5. One-Sample Wilcoxon Signed Rank Test Results for GSS

Paira
Total

N
Test

Statistic
Std.

Error
Std. Test
Statistic

Asymptotic
Significance

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Observed vs Hypothetical 60 1459.000 135.840 4.005 0.000**

a: Observed median= 116.99 and Hypothetical median= 0
**: Significant at 99% confidence level

Table 6. One-Sample t-test Results for GSS

Statistic

Total N

Mean Difference

95.43

%95 Confidence Interval of

the Difference

Lower Bound 53.95

Upper Bound 136.91

Table 6 (Cont’d)

Statistic

4.60

Significance

0.000**

**: Significant at 99% confidence level

As seen above, mean and median are both significantly higher than zero (p=.000) and thus the distribution is left
skewed. This is the macro-level assessment. If we look at it at micro level, 44 firms out of 60 have got a positive
score. In other words, more than 73% of IPO firms seem to have enjoyed some benefits of going public and as a
result increased their market shares against the closest rivals. These figures may be somewhat surprising to some
readers, especially when our aforementioned strict ‘final sample selection process’, which obviously favors the null
hypothesis of equal performance, is considered. With a more flexible missing data handling procedure, the
percentage figure above would easily be more than 79% (76/96). All findings up to now lead us to a one single
direction which is simply ‘going public is a good option if an industrial firm cares about improving its near future
market share levels’. However, before jumping into that conclusion, we should elaborate on the issue a bit more.
Whether IPO decision is the most important reason for the up and downs or not, as expected, does not have a
straightforward answer. A crystal clear answer requires a thorough elimination or importance ranking process of all
other possible reasons. To make things even worse, this should also be done case by case for each firm, since firm-
specific attributes may always alter the process. However, fortunately, the way we derive our key variable ‘Comp’
inherently prevents our results from being significantly biased by time-specific and sector-specific attributes/shocks
at least. If ‘Comp’ were an absolute measure not a relative one and/or if t moments had represented the same
calendar years for all firms, we should have utilized some extra control variables to eliminate the possible biases as
much as we could. But, again, we still need to assume that the effects of these external shocks are perfectly or at least
almost uniform across the firms within the same sectors. Firm-specific attributes mostly come into play at this stage.
In a few circumstances, they may increase the chance of a violation of the uniformity assumption above but proving,
if possible at all, that they are not a significant part of the equation requires extensive case studies with a much richer
dataset. However, such an effort is needless for the time being, since this study’s aim is neither a factor
decomposition of relative success/failure nor identifying the ideal time and conditions for a firm to go public. The
aim is providing some missing evidence from an emerging market, which should never be overlooked by any
comprehensive approach to IPO field.
The summary of what we have learnt from the related IPO literature and the findings of this study is that an IPO,
especially a relatively large one, is bad news for the rivals. Their performance ratios, profitability being at the top of
them, and their market shares as well are very likely to be adversely affected. On the other hand, this is not totally
true for that newly public firm since its market share is very likely to improve. There have been a few recent
direct/indirect promising attempts to explain some parts of this situation. Hsu et al. (2010) discuss the issue through

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loosening of financial constraints, financial intermediary certification, and the presence of knowledge capital.
Chemmanur and He (2011) deal with IPO waves and relate performance directly to timing. Going public; off the
wave, during a wave, earlier in a wave, or later in a wave, does matter according to them. Each of these possibilities
affects performance in a different way. Timing seems to be important also from a different perspective. Ruan and
Qian (2014) results suggest that industry rivals' earnings news, during the book-building period of a first-time issuer
firm, exert a competitive effect on that issuer. On the other hand, Spiegel and Tookes (2014) stress the profitability
issue within the industry. They find that post IPO industry profits per unit of market share decline and customers
become easier to steal. However, they see an IPO as a canary in the coal mine. In other words, IPOs do not cause
danger but do just inform that something bad will happen. These attempts described in this paragraph are just some
examples worth to explore further. These and alike should be tested, jointly whenever possible, in different settings.
This would lead us through a sort of unified competition based theory which is required to fill a very important gap
in broad IPO literature.

4. Conclusion
IPO literature is voluminous, however there are not many studies approaching the issue from a pure competition
perspective. Furthermore, most of these already few studies deal with only rivals or the general changes in the
competitive environments within sectors after IPOs. Thus, there is an important gap here. By directly focusing on
the relative competitive powers of big IPO firms in an emerging country for twenty years, this study tries to fill this
gap.
Main finding of the study is that going public seems to be a good option if an industrial firm cares much about
improving its relative market share ranks. Thus, it also provides support to the idea that an IPO is generally bad news
for the rivals. As discussed in the methodology part, any comprehensive IPO study should take this evidence
seriously. However, before accepting it as a given and general fact, we all should see some similar results for other
stock markets and different time periods. Trying a similar methodology in some different settings would be fruitful
and the authors of this study sincerely believe that those new evidence would support this study’s findings. After
then, a natural continuation might be trying to form a sort of unified competition based theory, from the promising
but not yet conclusive attempts aforementioned and may be a few new ones as well. Connecting it successfully to the
broad IPO literature would finalize the issue. Without this solid connection, something will always be missing in this
field.

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http://dx.doi.org/10.2139/ssrn.2437027