Financial regulation and IPOs: Evidence from the history of
the Italian stock market
Abstract
In different conditions, financial regulation has different impacts on the development of
public equity markets. By covering the population of 879 IPOs from the unification of
Italy (1861) to today, this is the first paper that examine the effects of different
regulatory regimes on the survival of IPOs in a long run perspective. We find that when
the demand for investor protection is high, the stricter listing requirements increase IPO
survival. In presence of a high demand for easier capital formation on the financial
markets, survival of newly listed firms is shorter, but the number of firms going public
does not grow.
Key words:
IPOs; Regulation; Survival; Investor protection; Italy
1
1. Introduction
The number of firms going public has declined over the last decade. This has generated
an intense debate, involving academics, as well as policy-makers and stock exchange
officials. The drop in number of IPOs has been attributed to a regulatory overreach, in
which regulatory changes, such as the Sarbanes-Oxley Act of 2002 (SOX) and SOXlike provisions in Europe, are blamed responsible. The increased compliance costs that
these tighter regulations imposed on publicly traded firms, meant to prevent the repeat
of corporate scandals (e.g. Enron, WorldCom, Parmalat), reduced the attractiveness of
being public1. Gao, Ritter and Zhu (2013) propose a different explanation. In recent
years, the number of IPOs declined as a private firm is much more likely to be acquired
than to go public, due to the higher importance of fast time-to-market and to the higher
economies of scope that can be achieved through a merger with a strategic, established
acquirer. However, they also agree that regulatory changes could help in restoring a
higher number of IPOs. On the other side, the financial crisis comes with an increased
demand for regulation, meant to counteract the destruction of trust which stops trades
and investments (Sapienza and Zingales 2012). In financial critical conditions, indeed,
regulatory interventions could be a way to curb widespread distrust among investors
and foster the functioning of the financial markets (Glaeser and Shleifer 2003).
The effectiveness of regulation is controversial (Shleifer 2005; Zingales 2009). Recent
IPO studies find that the level of uncertainty is lower for IPOs issued after tightening
regulatory changes (Johnston and Madura 2009; Ekkayokkaya and Pengniti 2012; Shi et
al. 2012). Akyol at al. (2012) show that the effects of SOX-like provisions adopted by
EU Member States have been effective in reducing the uncertainty surrounding
valuations in regulated markets. In contrast, the average level of underpricing of IPOs
on the exchange-regulated markets, primarily the London Stock Exchange’s Alternative
Investment Market (AIM), which were not affected by the new rules, did not decline
after the adoption of the corporate governance codes. From an historical perspective,
Chambers and Dimson (2009) highlight that regulations has been less effective than
market forces at reducing information asymmetry in UK IPOs. Two studies investigate
See , for instance, the article “The Demise of the IPO Market--and Ideas on How to Revive It” by J.
Zweig in the Wall Street Journal, June 25, 2010.
1
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the relationship between of regulation and IPO survival. Simon (1989) finds that the US
Securities Act of 1933 did not the failure rate of firms listed NYSE. Burhop et al.
(2012) find that the IPO failure rate was similar on the Berlin and the London financial
markets at the beginning of the twentieth century, even if they were characterized by
two different levels of regulation. However, these studies refer to concise periods of
time, up to 15 years). Our paper is the first to evaluate the impact on IPO survival of the
evolution of the regulatory framework on financial markets over time.
We contribute to the debate drawing upon the political economy of finance to study the
consequences of regulatory interventions occurring under dissimilar market conditions
on financial development. This theory offers a clue to identify when and why one can
expect financial regulation to change over time, evaluating financial reforms and their
feasibility (Pagano and Volpin 2001). Specifically, through the guidance of the public
and private interest theories, we entail explaining the effects of regulatory changes
taking place in different conditions. Examining the extent to which public and private
interests may influence the design of new regulatory interventions, we analyze the
implications of regulation for financial development.
Rules typically need complementary enforcement to be effective (Leuz and Wysocki
2008) and their effects should be evaluated as a whole. A long-run perspective is
therefore required to consistently evaluate the effects of regulation on the capital
markets (Levine 2011). With reference to the entire population of IPOs in Italy since its
unification in 1861, we investigate the impact of major changes in regulation on the
number of firms going public per year and on their survival profile. IPO-firms’ survival
is often considered a measure of capital market development (Fama and French 2004;
Burhop et al. 2012) and laws have been intended to ensure its growth (La Porta et al.
2002). As suggesting by the “law and finance literature”, better regulated capital market
develops corrective mechanisms and remedies to address information asymmetries
increasing the survival of firms going public (La Porta et al. 1997a, 1998).
The population 879 IPOs in the period 1861-2011 offers the opportunity to examine the
evolution of the Italian regulatory framework along the political and economic changes
occurred over a long time period. The Italian context here represents an ideal framework
to investigate the evolution of regulation, because Italy has been characterized for a long
time by a weak protection for both shareholders and creditors (La Porta et al. 1998), and
3
recently evolved into one of the developed countries in terms of shareholder protection
(Enriques 2003). We investigate the effects of the long-run evolution of regulation
occurred in Italy, focusing on the impact of four different regulatory regimes (18611935, 1936-1973, 1974-1997, and 1998-2011) that we identified adapting the
classification of the Italian legal framework provided by Aganin and Volpin (2005).
We find a significant increase in the IPO survival when regulatory interventions occur
in periods of higher demand for investor protection, while a decrease is registered when
a demand for regulation aimed to enhance capital formation. The aim of capital
formation are not reached, if measured in terms of number of firms going public.
Controlling for macroeconomic variables, the shorter survival profile of firms going
public after restricting regulatory changes is not accompanied by an increase in the
number of IPOs per year, scaled by GDP.
This paper is organized as follow. Section 2 develops the testable hypotheses. Section 3
describes the evolution of the Italian regulatory framework identifying different
regulatory regimes over time. Section 4 presents the data, the sample and the
methodology. Section 5 reports the results and Section 6 concludes.
2. Literature review and testable hypotheses
Regulators face indeed difficulties to strike the right balance between investors
protection and an effective capital raising strategy (Ritter 2013). IPO markets offer an
interesting setting in which to calibrate the effectiveness of financial regulation, testing
whether new regulatory interventions are able to explain IPO market development.
Regulation is an inevitably dynamic issue and is driven by external conditions. In
practice, how regulation evolves in time and its effects on the financial markets is still
an unexplored issue. We aim to address this issue considering the impact of regulatory
regimes that develop when different conditions and interests are in place. According to
two competing views, the public and the private interest motives for regulation, we
elaborate two sets of hypotheses explaining how the demands for regulation arises.
The need to correct market failures and protect investors from harm is one of the basic
fundamentals of government regulatory interventions (Joskow and Noll 1981). The
literature on political economy of finance suggests that in these cases regulation calls in
4
“public interest”, law interventions are meant to provide more stability to the financial
system as a whole (Pagano and Volpin 2001). However, interventions cannot occur
without costs, such as the amount of resources to comply with new laws, and the burden
imposed to firms that should not have been regulated, but nevertheless, are subject to it
(Zingales 2004). This is why regulation is expected to come when its benefits outweigh
the costs of implementation.
In periods of corporate scandals or financial crises, regulatory intervention could
represent a way to curb and prevent distrust, coherent with a higher demand for
regulation to protect investors. An increase in the demand for regulation is indeed
expected when people perceive unfairness of existing social order (Glaeser and Shleifer
2003, Djankov et al. 2003). Distrust, defined as a poor propensity of players to
cooperate for socially optimal solutions (La Porta et al. 1997b), represents the real
source of such disorder (Aghion et al. 2010). In critical economic conditions, the more
stringent interventions of regulators aim to reduce problems of distrust that grips
potential investors and undermines the efficient functioning of the capital markets.
Regulation might be a successful screening device to select firms entering the markets,
although it does not always represents the optimal solution.
In the IPO setting, regulation can be effective in limiting agency problems. Information
asymmetry would ideally disappear if regulation required insiders to fully disclose their
private information. In practice, by introducing more stringent listing requirements a
tighter regulation should discourage and limit the access to the capital markets to
“lemon” firms. We therefore expect that firms going public in compliance with stronger
regulations aimed to restore investor protection might be of higher quality, increasing
their survivability on the financial markets.
At the same time, tighter regulations might limit the number of firms going public.
Recently, there has been an increase in the number of foreign firms delistings from US
markets to list elsewhere. Doidge et al. (2010) argue that this happens also because of
the passage of the SOX, with firms escaping the newly imposed obligations. Firms can
indeed avoid the constraints of their home country’s rules and regulations. This has
generated a debate in the US (Gao et al. 2013, Doidge et al. 2013), which led to a new
law in 2012, the Jumpstart Our Business Startups (JOBS) Act, designed to stimulate
economic growth by improving access to the public capital markets for emerging
5
growth companies. The undesired consequence of restricting laws aimed to restore
investor protection is indeed a reduction in the attractiveness of listing avenues, and
therefore a drop in IPO volumes.
Hypothesis 1a. Regulatory interventions occurring in periods of higher demand for
investors protection lead to an increase in the survival of firms going public
Hypothesis 1b. Regulatory interventions occurring in periods of higher demand for
investors protection lead to a decrease of the IPO activity.
Regulatory interventions have different aims when taking place in favorable conditions.
In these periods, regulatory changes are typically aimed to increase the possibilities of
capital formation, making it easier to go public and, in general, lowering the restrictions
in raising funds. For instance, the “financial accelerator” effect (Bernanke et al. 1999),
that arises when collateral values increase and firms are able to gain easier access to
external financial sources, can amplify and propagate economic and financial cycles.
However, it might also lead to a socially suboptimal outcome, that of financial
instability. Indeed, while risk is overestimated in recessions, leading to a prudential
behavior, it is often underestimated in economic booms (Borio et al. 2001). Among the
numerous side effects, this contributes to excessive rapid credit growth, inflated
collateral values, artificially low lending spreads, and to financial institutions holding
relatively low capital and provisions. In the meanwhile, few investors care about the
transparency of firms when capital markets grow (Wagenhofer 2011).
In expansion periods, regulators are affected by a reduced demand for regulation, as a
diffuse pressure exerted by financial markets’ agents to implement less restrictive
interventions and increase as much as possible capital formation. The private interest
theory of regulation (Stigler 1971), also called the economic theory of regulation,
predicts that regulators cannot work independently from the existing forces operating on
the financial markets. While pressure can take different forms, from the coercive power
of the state used by groups to achieve their own interests (Becker 1983; Peltzman 1989;
Rajan and Zingales 2003), to the widespread sentiment of agents demanding more
financial openness to promote markets’ growth by reducing the cost of capital and
increasing its availability for the borrowers (Chinn and Ito 2006), governments are
6
inevitably influenced when designing law interventions. Decreasing the compliance
costs firms have to bear, the regulators’ aloof behavior may increase the probability that
lemon firms enter the capital markets. In this case, the development of financial markets
is negatively influenced by the expected lower survivability of firms joining it.
Hypothesis 2a. Regulatory interventions occurring in periods of higher demand of
reduced requirements for capital formation lead to an decrease in the survival of firms
going public.
Hypothesis 2b. Regulatory interventions occurring in periods of higher demand of
reduced requirements for capital formation lead to an increase of the IPO activity.
3. The regulatory framework and the evolution of the IPO market
in Italy
The first IPO on the Milan Stock Exchange, the railway company Società delle Strade
Ferrate Lombardo Veneto, took place in the year of the unification of Italy, 1861.
Examining the evolution of the IPO market from its foundation makes possible to
uncover long-term patters in the effectiveness of regulatory interventions in fostering
the development of the financial markets. We identify specific regulatory regimes, each
of them characterized by several law interventions, but with a common design in terms
of principles, shareholders’ rights, and requirement for access to the market. The set of
regulations provide the structure, while the period in which there are implemented
establishes the character of what can be labeled a regulatory regime (Hood et al. 2001).
We adopt the classification of the Italian legal framework provided by Aganin and
Volpin (2005), identifying four sub-periods, or regulatory regimes, in the evolution of
the Italian stock market: 1861-1935, 1936-1973, 1974-1997, and 1998-20112.
1861-1935: Lack of financial market regulation
2
We modified the first sub-period identified by Aganin and Volpin (2005) from 1861-1941 to 1861-1935.
This choice is motivated by our focus on the evolution of the capital market, rather than on the
development of the corporate law. In 1936, the Bank Law dramatically reshaped the banking sector. Since
banks were directly responsible in defining the listing requirements for issuers, we argue that such date,
rather than 1942, represents the starting point for the second regulatory regime.
7
At the birth of the Reign of Italy, the Italian stock market was almost totally selfregulated. In those years, firms could issue shares with multiple votes using crossshareholdings with no limits, banks were often not concerned about the quality of
listings firms (Vivante 2003). Such loose existing legal framework did not formalize
any requirement in terms of information disclosure for listed firms.
1936-1973: Post-29 financial market reforms
As a response to the crisis of the 1929, a dramatic change in the Italian legal framework
occurred in 1936, when the Law 375/36 (Bank Law) was approved with the aim to
overcome the disorder and to restore trust among investors (Aganin and Volpin 2005).
Laws implemented primarily aimed to satisfy the existing demand for investor
protection imposing important innovations in disciplinating markets’ agents. The stock
market regulation improved dramatically, with the Bank Law establishing a clear
separation between short term and medium-to-long term financing and with the Civil
Code (Royal Decree 262/42) imposing that all shareholders acquired the right to vote at
the annual shareholder meetings.
1974-1997: Alignment to the international markets
The important legal improvements that occurred since 1974 allow to identify this year
as the beginning of a new regulatory regime. Specific disclosure requirements were set
to ensure the quality of issuers and, more importantly, the government delegated part of
the supervision of the financial markets to a commissions equivalent of the US SEC.
Notwithstanding the establishment of CONSOB (Commissione Nazionale per le Società
e la Borsa), the regulatory interventions in this period contained loopholes (Aganin and
Volpin 2005), and shareholders were more likely to be expropriated by controlling
blockholders (La Porta et al. 1998).
1998-today: Investor protection reforms
The latest major regulatory change took place in 1998. The Legislative Decree 58/98
(Draghi Reform) represented a “cornerstone” (Mallin 2011), because it explicitly
focused on strengthening minority shareholders’ protection. This law changed the
process of capital market offerings and takeovers, the functioning of audit firms, as well
as minority shareholders’ rights, increasing minority shareholders’ protection through a
wider application of the right to withdrawal from the company. Later, the EU Takeover
Directive (2004/25/EC), containing requirements for the bidders in M&As, and the
8
Savings Law (Law 262/05) signed a further progress in favor of a greater investor
protection in avoiding corporate frauds. The regulatory regime starting in 1998 was
characterized by a conspicuous demand for investor protection.
4. Methodology and data
4.1 Data and sources
In this paper, we analyse the population of 879 Italian IPOs that went public over the
full history of Italy since its foundation, in 1861, until December 2011, on the Milan
Stock Exchange. Different datasets were used to obtain the information. The listing and
delisting dates, the establishment and failure years, the industry identification, and the
delisting reasons were collected referring to three sources: a publication by Mediobanca
(2012), the historical collection of De Luca (2002), which integrates the primary source
of Mediobanca by punctually describing the history of each listed firm, and the
EURIPO database 3 . Macroeconomic and financial variables are taken from the
publications of three institutions. We refer to Siciliano et al. (2011) for the data on the
market return index, to Mediobanca (2012) for the number of firms listed per year, and
to the national statistical agency (ISTAT) for the value of GDP, population and the
number of insolvency per year. Since our primary source of data contained the full
population of listings in the Milan stock exchange, our first task consisted in identifying
IPOs. Similarly to Chambers and Dimson (2009) we excluded introductions, new
listings by firms with a dispersed and broad prior stockholder base, transfers of listings
from other markets, the cross-listings of companies quoted on another exchange, IPOs
of closed-end-funds commonly known as investment trust. For each IPO firm we
assessed the survival time after the listing, defined as the time elapsed from the IPO up
to the suspension, liquidation or any other event leading to the delisting from a stock
exchange, with the exception of transfers to another market.
4.2. Sample
Figure 1 reports the number of IPOs, the number of delisting and the number of listed
companies from 1861 to 2011. The y-axis reports IPOs above zero, and the number of
3
See Vismara et al. (2012) for a description of the database.
9
delisted firms below zero, highlighting voluntary delistings. The classification of
regulatory regimes is also represented.
[INSERT SOMEWHERE HERE FIGURE 1]
Table 1 provides a description of the population of IPOs and delistings on the Milan
Stock Exchange, classified by regulatory regime and by industry. This disaggregation
shows how the different IPO waves were characterized by different industries over time.
For instance, in the first sub-period there was a large percentage of IPOs in the
Consumer Goods industry (31%, 119 IPOs out of 385). The Financial industry was
leading in the period following the Second World War (26%, 30 IPOs out of 114), while
Industrial IPOs were the most frequent in the last two sub-periods. In Italy, the
Technological industry increased its weight over time (from 0% between 1861-1935 up
to 14% in 1998-2011). With respect to firms exiting the financial markets, the total
number of delistings decreased in time (from 43% between 1861-1935 to 19% in the
period 1998-2011), with a contemporaneous decrease in the percentage of voluntary
delistings. Data show that almost half of firms going public in the first regulatory
regime were delisted in the same period, with the exception of the Utilities that
registered only a 14% (6 delistings on 43 IPOs) of failures. During the following
periods, delistings rates decreased to large an extent especially for Industrials (from
45% to 18%), Consumer Services (from 46% to 17%) and Financials (from 58% to
17%).
[INSERT SOMEWHERE HERE TABLE 1]
4.3 Methodology
This section describes the methodologies employed for the analysis of how the long-run
evolution of financial regulation impacts on the survival of IPO firms and the IPO
activity in time.
The first test of hypotheses (1a – 2a) investigates the effects of regulation on the
survival of IPO firms. M&A delistings are considered as not negative delistings and,
10
specifically, as right censored cases, since, often, one of the reasons to go public is to
take advantage of the market for facilitating valuation and divestment (Högholm and
Rydqvist 1995)4. By using a Cox proportional hazards models we firstly regress the
survival time of our sample of IPOs considering all types of delistings; second, we
exclude voluntary delistings, namely cases where a firm is in compliance with an
exchange’s listing standards and voluntarily takes steps to delist its shares. In fact,
voluntary delistings are not necessarily negative cases. In all regressions, observations
that survive at the end of the analysis are treated as right censored. In particular, IPOs
are considered as censored observations if they were not delisted on 31 December 2011.
The impact of the financial regulation on IPO survival is investigated including a set of
three dummy variables, Post 1935, Post 1973 and Post 1997, which identify the
changes of regulatory regimes according to the classification we presented in section 3.
Each dummy equals 1 after the change in regulation. For instance, the dummy variable
Post 1935 equals 0 for IPOs in the period 1861-1935 and value 1 in the period 19362011. The first sub-period, 1861-1942, is the reference case in our analysis. Hypothesis
1a is validated if dummies Post 1935 and Post 1997 assume a negative estimated
coefficient which means that regulation occurred in period of high demand for investor
protection decrease the hazard and thus resulting in a higher IPO survival. On the
contrary, hypothesis 2a is verified when the coefficient of variable Post 1973 is
negative.
The second set of hypotheses (1b – 2b) examine whether the development of regulation
in time is a determinant in IPO activity changes. We investigate the time series of IPO
volume in Italy, and we test whether the changes in regulation had a positive (or
negative) impact on the number of companies going public. A yearly time series
regression is estimated with a first-order autoregressive error term, employing the
number of IPOs in each year divided by yearly real GDP (measured in € billions) as the
dependent variable.
4.4 Control Variables
4
In previous literature on IPO survival there is no shared consensus on this issue: companies delisted for
M&As are considered as non-survivors (Carpentier and Suret 2011, they are completely excluded from
the analysis (Hensler et al. 1997) or treated as censored survivors (Jain and Kini 2000) as in our case.
11
IPO survival and IPO activity are however not only determined by the regulatory
regime, as they are likely to be affected by several other determinants that we consider
through a comprehensive set of explanatory variables.
4.4.1 IPO survival controls
Previous contributions generally control for different firm-level characteristics, such as
firm’s age, size and ownership (e.g. Demers and Joos 2007). However, in a very long
run analysis, the state of the financial markets and that of the economy take on even
more importance since they determine the conditions reducing (increasing) the
probability that lemon firms have access to the market. In addition to the main firmlevel characteristics at the IPO, considered in former literature we add different controls
for contextual conditions to distinguish the impact of the financial development from
the overall economic development (Tsoukas 2011) and capture the impact of sociopolitical exogenous events occurred over the past 150 years in Italy.
Firm-level controls
Firm age: According to prior studies dealing with IPO failure risk (e.g. Demers and
Joos 2007), younger firms may suffer from greater uncertainty because of higher
managerial inexperience and a lack of record of past performances (Jain and Kini 1999).
Age at IPO is defined as the number of years from the firm’s incorporation date to the
IPO date.
Firm size: Information asymmetry tends to be negatively correlated with firm size.
Larger firms have greater visibility, and information that prospective investors can use
to evaluate issuers is more readily available for larger firms. On the contrary, smaller
firms are likely to face more severe problems of asymmetric information. We therefore
expect a negative relation between firm size and IPO survival. Because of lack of
punctual data on the size of Italian IPOs (e.g. total sales) when relying on a very longrun perspective, we proxy the firm size classifying all IPOs in three classes: large,
medium and small-size firms with respect to the ICB (Industry Classification
Benchmark) classification. Large firms refer to the Oil and Gas, Basic Materials,
Utilities and Telecommunications sectors. Medium firms belong to the Industrials,
Consumer and Goods, Consumer and Services and Financials sectors. Firms in Health
12
Care and Technology are considered as small. We report in our regressions the value for
the top (large firms) and the bottom (small firms) groups considering the medium-firm
class as the reference case.
State control: A crucial determinant in IPO survival concerns the type of ownership. In
terms of long-run performance, state ownership can be more detrimental than private
ownership because of the “grabbing hand” of the government, which tries to perceive its
own interests (Shleifer and Vishny 1998). However, when considering the access to the
capital markets, state-controlled IPOs may be viewed as “too big to fail”. This may be
for both an economic and a political reason. First, these firms are generally peculiar
cases, so well-interconnected that their failure may lead to the failure of others. Second,
politicians cannot resist in protecting their interest avoiding the collapse of a company
in the short term. Therefore, we include a dummy variable equals to 1 for statecontrolled IPOs (State-controlled).
Contextual level controls
GDP per capita: Firms that are able to go public in periods of recessions (i.e. decline in
real GDP (Gross Domestic Product) per capita) may be of higher quality and stronger
since there are lesser opportunities to raise public capital in such periods. Investors
indeed perceive a greater risk of financial distress. We measure the real GDP scaled by
the Italian population at the time of each IPO.
Number of insolvencies: Firms attempting to go public might face a higher risk of
bankruptcy when the rate of firms’ insolvency is high. This might be due to an increase
in risk aversion of investors, reducing the availability of public capital. In these periods,
IPOs should be more financially sound to raise public capital in order to convince
investors that they are not selling junk stocks. We therefore include the rate of firms’
insolvency in our regressions, measured as the number of fallimenti5 per year at the time
of each IPO collected in Di Martino and Vasta (2010).
Market capitalization to GDP: The more developed is the capital market structure of a
country, the more are the services and facilities for listed firms and the tools for
investors to evaluate the quality of firms going public. Coherently, we measure the
5
The Aglo-Saxon legal jargon generally use different names for insolvency. It refers to bankruptcy to
indicate personal cases and insolvency for companies. According to Di Martino and Vasta (2010) we
adopt the Continental European jargon and use insolvency and bankruptcy as synonyms. In Italy the word
fallimento applies to both cases.
13
importance of the equity market relative to the economy, as the total market
capitalization to GDP (Beck et al. 2002). This indicator is measure of the weight of the
equity market, allowing the comparisons across time periods because of its relative
stability (Rajan and Zingales 2003).
Number of UK IPOs: Additionally, we also include the extent to which European IPO
markets have been developed over time. As a proxy we consider the number of UK
IPOs per year aggregating data from different sources: Chambers and Dimson (2009),
Burhop et al. (2012) and Ritter’s IPO database6.
Market return index: Consistent with the window-of-opportunity theory (Ritter 1991),
investors can become over-optimistic in poor-quality firms and less discriminating
during hot markets periods. Specifically, the high initial returns firms may acquire in
hot periods lead lower quality issuers to take advantage of investors sentiment. Our
measure relies on the total return of the Italian index including reinvested dividends, in
the one year before each IPO.
Historical trend: By means of a trend variable, running from 0 (for IPOs in 1861) to 150
(for IPOs in 2011), we also control whether the change in IPO survival has simply
followed a constant trend over time. Specifically, we compare regressions including
dummies for different regulatory regimes with a specification including only a time
trend variable.
Furthermore, dummy variables to capture the effects of the dramatic political and social
conditions occurred during the First and the Second World Wars (World Wars I & II)
and for firms going public on the Italian New Market (Nuovo Mercato) as a
distinguishing case of hot market period in the Italian markets history are included.
4.4.2 IPO activity controls
The effects of the different regulatory regimes in increasing or decreasing IPO volume
over time is assessed after controlling for both economic and financial conditions, and
other exogenous events.
First, we capture the effects of economic conditions including both the percentage
growth in real GDP adjusted for inflation using 2011 purchasing power from year (t-1)
6
http://bear.warrington.ufl.edu/ritter/ipodata.htm
14
to year (t) (Real GDP growth [t-1, t]) and the number of IPOs in the UK to control for
the development of financial markets in Europe. On the other side, financial conditions
are included relying on the percentage value of the total return of the Italian index
including reinvested dividends adjusted for inflation using 2011 purchasing power in
year (t-1) to (t) (Market return index [t-1, t]). A dummy for the First and the Second
World Wars contributes to control for the negative effects of the dramatic political and
social conditions occurred during those periods on markets development. A dummy for
firms that went public on the Nuovo Mercato captures the euphoria of hot periods on
IPO activity.
In the Appendix we report the correlation matrix including all variables.
5. Results
We first provide preliminary evidence on the investigated phenomena considering the
survival profile of IPOs by reporting their cumulative survival rates with respect to
different regulatory regimes. Table 2 reports the survival rates of the population of
Italian IPO firms, grouped by regulatory regime at the time of the listing (Panel A), and
by industry (Panel B), respectively one, three, five and ten years after the IPO. The
survival rates are calculated using the non-parametric Kaplan-Meier method7.
[INSERT SOMEWHERE HERE TABLE 2]
The values reported in Panel A show that IPO survival largely varies in the different
sub-periods, ranging from 66% to 84% for delisting within 10 years. The IPOs issued in
1935-1973 and 1998-2011 show a higher survival rate with respect to 1974-1998. The
differences of the survival curves across these different periods are statistically
significant according to the log rank test (chi-square: 18.03, p-value: 0.000). The
7
The IPO survival rates are estimated non-parametrically using the Kaplan-Meier method:
𝑛𝑗 − 𝑑𝑗
𝑆(𝑡𝑗 ) = (
) 𝑆(𝑡𝑗−1 )
𝑛𝑗
where S(tj) is the probability of being listed at year tj, S(t j−1 ) is the probability of being listed at year tj-1,
ni is the number of IPOs listed just before the year tj (the risk set at tj), dj is the number of IPOs delisted
during the year tj. Firms that went public during the last decade and may consequently not have a compete
survival profile at one, three, five and ten years, and are still listed on the financial markets are treated as
right-censored cases.
15
survival rates in Panel B, on the contrary, show that differences among industries are
not statistically significant (chi-square: 22.56; p-value: 0.550). Nevertheless, we control
for industry fixed effects across all our econometric analyses that validate in a
multivariate setting these preliminary observations.
Table 3 reports the estimates for a Cox proportional hazard model. First, we regress
survival time considering all delistings (Models 1 – 3); second, we exclude voluntary
delistings as they may not be necessarily failures (Models 2 – 4). In both cases we
compare results obtained including dummy variables for different regimes (Post 1935,
Post 1973, Post 1997) with a specification including only a time trend variable. All
regressions treat M&As as right censored cases.
Firstly considering all delistings, Model (1) presents the time-trend specification
showing that the historical trend is significantly associated with a lower incidence of
delistings (at a 5% significant level). This is an evidence of an improvement in the IPO
survival over the full history of the Italian capital market. Concerning the control
variables, we find that being large or state-controlled firms decreases the hazard rate to
be delisted for negative reasons. This confirms that larger firms are seen as “too big to
fail”, and this causes a smaller probability to be delisted after going public. Results are
confirmed also when excluding voluntary delisting (Model 2).
In Model (3) we test our first set of hypotheses (1a and 2a) measuring the impact of
regulation by means of the three dummies corresponding to the changes in regulatory
regime after controlling for the historical trend. We predict that when the demand for
investor protection is high, IPO survival increases (Post 1935 and Post 1997), while in
presence of a high demand for easier capital formation on the financial markets, survival
of newly listed firms is shorter (Post 1973). Coherently to our expectations, the first
dummy shows a strongly significant negative coefficient (at a 1% significant level). The
second change is positive and significant (at a 5% significant level), indicating a
decrease in the survival time stating from 1974, while the Post 1998 dummy is an
evidence of a neat significant increase in the survival time after 1998 (at a 5%
significant level). Accordingly, when excluding voluntary delistings reasons results are
16
confirmed, and the dummy Post 1935 becomes more significant (at a 1% significant
level) (Model 4)8.
As a whole, the analysis shows that the improvement in the IPO survival found in
models (1 – 2) did not follow a monotonic trend in time. As expected, from 1974 to
1997, the regulation had a negative and significant effect on the survival profile of
IPOs. Our results confirm that, once acknowledged the need to implement new rules to
fuel the IPO market after the stagnation following the Second World War, the Italian
government tried to encourage firms’ capital raising and enlarge the capital markets, but
did not dedicate enough attention to the quality of the firms joining the market. This
result is coherent with the finding of Pagano et al. (1998), showing that firms going
public from 1982 to 1992 did not finance their growth and investments but, on the
contrary, used the proceeds to repay their debts.
Our results are also in accordance to La Porta et al. 1998, describing the level of
minority protection in Italy before 1994 to be very poor and unattractive to new
investors (La Porta et al. 1998). It is only with the following Draghi Reform, in 1998,
that minority protection becomes the ultimate aim of financial regulation in Italy, and
the positive outcome in terms of IPO quality is well documented by the increase in IPO
survival to delisting. Further, Dyck and Zingales (2004) show that after such regulatory
interventions the private benefits for the dominant blockholders decreased at the
expenses of minority shareholders interest.
[INSERT SOMEWHERE HERE TABLE 3]
Table 4 reports the results of the validation of the second sets of hypotheses (1b and 2b)
on the effects of different regulatory regimes on IPO volume. Consistent to our
hypothesis 1b, the Post 1935 dummy shows a significant (at a 5% significant level)
increase in the IPO activity after controlling for the effects of market conditions, which
are found to significantly influence IPO volumes. However, the coefficient related to the
last regulatory regime, although negatively associated to the propensity of firms in
8
In order to check for potential multicollinearity problems in our estimations, we calculate for each
regression the mean Variance Inflator Index (VIF) of included variables. All regressions reported a mean
VIF lower than 4 indicating that multicollinearity does not seem to be severe.
17
accessing financial markets, is not statistically significant. Our hypothesis 1b is
therefore partially supported. By contrast, in presence of a high demand for easier
capital formation on the financial markets, although the survival of newly listed firms is
shorter, the number of firms going public has not grown. We therefore do not find
statistical evidence supporting our hypothesis 2b.
[INSERT SOMEWHERE HERE TABLE 4]
5.1 Robustness checks
Following Christensen et al. (2011), we perform an additional test to check the
robustness of tour results, both in terms of IPO survival (Table 3) and IPO activity
(Table 4).
In practice, since it is possible that the average level of IPO survival and IPO activity
declined (increased) prior or after the entry-into-force of the regulatory changes we
identified. To therefore investigate this possibility, and examine the IPO survival and
IPO activity pattern around the entry-into-force dates of the changes in regulatory
regimes by using dummy variables Change of the regulatory regime (t + N). This
variable equals one if a change occurs after t + N, where t is the entry-into-force date of
the regulatory change and N is number of years relative to year t; the variable equals
zero otherwise. The value of N is an integer whose value ranges from -3 to +3. We
perform seven regressions. The independent variables are the same as those in the Table
3 and Table 4 regressions, except for the differences noted above. To save space, we
only report the coefficient estimate for the Regulatory regime (t + N) from each of the
different regressions.
Panel A confirms our previous finding showing the estimations for the effects of
different regulatory regimes on IPO survival. The coefficients become significant after
the breaks we identified across the history of the Italian regulatory framework. In
regressions t+1, t+2 and t+3, coefficients of regulatory interventions are all statistically
significant with respect to those associated to previous years, especially for the change
occurred in 1936 (at 1% significant level). Panel B presents the same analysis when
considering the yearly time series regression on IPO activity. We find that only the first
18
regulatory regime started in 1936 shows statistically significant coefficients in the
different regressions presented, as suggested by the results of Table 5.
[INSERT SOMEWHERE HERE TABLE 5]
6. Conclusions
In this paper, we contribute to the literature examining the effects of financial regulation
by investigating the impact of different regulatory regimes on the survival and the
activity of IPOs, from the birth of the Reign of Italy, in 1861. The two major regulatory
changes that arose from a higher demand for investor protection, the introduction of the
Bank Law in 1936 and Draghi Law in 1998, determine a significant improvement in
IPO survival. By contrast, the major change characterized by an important demand of
capital formation, the establishment of CONSOB in 1974, leads to a lower IPO survival,
but the number of firms going public did not grow.
19
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23
Table. 1 Population of IPOs and delistings on the Milan Stock Exchange, classified by
regulatory regime and by industry
The table reports the number of IPOs and the delistings across different regulatory regimes and
industries. Data are relative to the number of delistings occurred in each period The industry
classification is the ICB (Industry Classification Benchmark), officially adopted by the European
Stock Exchanges. We rely on the one digit-level to classify firms.
Regulatory Regime
Oil & Gas
Basic Materials
Industrials
Consumer Goods
Health care
Consumer Services
Telecommunications
Utilities
Financials
Technology
Tot.
Regulatory Regime
Oil & Gas
Basic Materials
Industrials
Consumer Goods
Health care
Consumer Services
Telecommunications
Utilities
Financials
Technology
Tot.
1861-1935
1936-1973
IPOs
Delistings
Excluding Voluntary
IPOs
Delistings
Excluding Voluntary
2
49
75
119
2
26
4
43
64
1
385
0
20
34
53
2
12
0
6
37
1
165
0
12
20
27
2
7
0
3
15
1
87
6
10
20
18
3
5
1
18
30
3
114
1
3
9
19
0
3
0
1
5
0
41
1
1
4
11
0
1
0
0
1
0
19
1974-1997
1998-2011
IPOs
Delistings
Excluding Voluntary
IPOs
Delistings
Excluding Voluntary
4
14
55
31
3
12
1
4
42
1
167
3
7
17
16
0
2
0
1
7
0
53
2
2
11
10
0
2
0
1
5
0
33
2
4
50
34
10
36
1
18
29
29
213
0
4
9
9
1
6
0
1
5
5
40
0
4
8
8
1
5
0
1
5
5
37
24
Table. 2 Cumulative survival rates
The table reports the cumulative survival rates for the population of 879 IPOs listed during 1861-2011,
classified by regulatory regime (Panel A) and by industry (Panel B), calculated with the Kaplan-Meier
method (see footnote 13 for details), for one, three, five and ten years after the IPO. Firms that went public
during the last decade and do not have a complete survival profile at one, three, five and ten years by being
still listed on the financial markets are treated as right-censored cases. A log rank test is performed to assess
the statistical significance of the differences among the survival curves. We show the chi-square value and
the p-value in brackets.
Panel A: by period
1861-1935
1936-1973
1974-1997
1998-2011
Full sample (1861-2011)
Log Rank
Panel B: by industry
Oil & Gas
Basic Materials
Industrials
Consumer Goods
Health care
Consumer Services
Telecomunications
Utilities
Financials
Technology
Log Rank
Obs.
1 YR
385
114
167
213
879
18.03
(0.000)
100
100
99
100
100
92
98
96
98
95
82
96
91
92
87
66
79
70
84
72
14
77
200
202
18
79
7
83
165
34
7.85
(0.550)
100
99
100
100
100
100
100
100
99
100
100
92
96
95
94
92
100
96
95
97
100
86
89
86
83
90
86
90
83
94
93
74
68
72
67
77
86
78
64
82
25
3 YR 5 YR 10 YR
Table. 3 Survival profile of IPOs
Estimated coefficients for the cox proportional hazard models wherein all delistings are firstly treated as
failures and then the voluntary ones are excluded. Models (1 - 2) include the baseline specification and an
historical trend. Model (3 - 4) show the effects of the changes in the regulatory regime. Robust standard
errors are in parentheses. All regressions include control for World War I and World War II, and for IPOs
listed on the Nuovo Mercato. Stars identify significance level at less than 1% (***), 5% (**), and 10% (*).
(1)
(2)
Delistings Excluding voluntary
Age at IPO
0.000
0.002
(0.003)
(0.002)
Large size
-0.304**
-0.281**
(0.122)
(0.136)
Small size
0.085
0.143
(0.353)
(0.360)
State-controlled
-0.827**
-0.991**
(0.356)
(0.444)
No. of insolvencies
-0.001
0.012
(0.025)
(0.027)
GDP per capita
-2.742
-2.333
(2.722)
(1.738)
Mkt cap to GDP
-1.250
-1.256
(1.088)
(1.173)
Number of IPOs in UK
0.002
0.002
(0.002)
(0.002)
Market return index
-0.000
0.001
(0.002)
(0.003)
Historical trend
-0.499**
-0.661**
(0.253)
(0.263)
Post 1935
Post 1973
Post 1997
No of observations
Pseudo R2
Log pseudolikelihood
879
0.062
-1,735
879
0.042
-1,413
26
(3)
(4)
Delistings Excluding voluntary
0.003
0.003
(0.002)
(0.002)
-0.284**
-0.310**
(0.137)
(0.123)
0.119
0.074
(0.375)
(0.363)
-1.090**
-0.908**
(0.458)
(0.362)
-0.035
-0.029
(0.029)
(0.027)
-1.726
-2.291
(2.427)
(2.346)
-0.036
0.569
(1.205)
(1.127)
0.003
0.003
(0.002)
(0.002)
-0.001
-0.004
(0.003)
(0.003)
-0.468*
-0.288
(0.275)
(0.264)
-0.705**
-0.983***
(0.296)
(0.280)
0.634**
0.616**
(0.322)
(0.309)
-1.493**
-1.460**
(0.628)
(0.622)
879
879
0.055
0.085
-1,405
-1,725
Table. 4 The number of IPOs per year
Estimated coefficients for time-series regressions with residuals following an AR(1) process.
The dependent variable is the number of IPOs in each year, all scaled by yearly real GDP,
measured in € billions of 2011 purchasing power. Real GDP growth is the percentage growth in
real GDP from year (t-1) to year (t). Market return index growth is the percentage value of the
total return of the Italian index including reinvested dividends adjusted for inflation using 2011
purchasing power and in year (t-1) to (t). Stars identify significance level at less than 1% (***),
5% (**), and 10% (*).
Real GDP growth [t-1, t]
36.575
(41.521)
Market return index [t-1, t] 19.130**
(7.489)
New markets
1.527
(6.195)
World War I & II
-3.312
(12.875)
Number of IPOs in UK
0.088
(0.102)
Historical trend
0.041
(0.437)
Post 1935
-52.607**
(25.988)
Post 1973
-8.843
(14.954)
Post 1997
-1.099
(10.060)
AR(1) coefficient
0.560***
(0.174)
Constant
56.943**
(25.773)
Observations
149
R-squared
0.66
27
Table. 5 Analysis of varying entry-into-force dates
This table reports results from COX proportional hazard regressions and yearly time-series with
residuals following an AR(1) process that are variations of the second regression model in Table
3 and the regression in Table 4. In Panel A, we report the coefficient estimates of the interaction
term Change of the regulatory regime (t + N). This variable equals one if a change occurs after t
+ N, where t is the entry-into-force date of the regulatory change and N is number of years
relative to year t; the variable equals zero otherwise. The value of N is an integer whose value
ranges from -3 to +3. Each coefficient estimate reported is from a separate regression. Panel B
reports the same analysis re considering the yearly time series regression on IPO activity. Stars
identify significance level at less than 1% (***), 5% (**), and 10% (*).
Panel A: COX proportional hazard regressions
t-3
t-2
t-1
t
t+1
t+2
t+3
Number of observations
Panel B: Time series regression on IPO activity
Post 1935 Post 1973 Post1997
-0.528
0.202
-0.631
(0.327)
(0.293)
(0.471)
-0.488
0.276
-0.631
(0.328)
(0.299)
(0.471)
-0.457
0.315
-0.958
(0.328)
(0.293)
(0.638)
-0.983*** 0.616** -1.460**
(0.280)
(0.309)
(0.622)
-0.966*** 0.634** -1.444**
(0.283)
(0.321)
(0.619)
-0.896*** 0.671** -1.439**
(0.282)
(0.319)
(0.732)
-0.848***
0.568*
-1.417*
(0.279)
(0.323)
(0.734)
879
879
879
Post 1935 Post 1973 Post1997
-47.528
-6.540
1.831
(35.262)
(15.734) (10.101)
-54.921
-5.422
-3.020
(36.291)
(17.262)
(9.055)
-60.064*
-13.062
-2.720
(30.823)
(13.954)
(8.571)
-51.840**
-9.379
-1.099
(26.063)
(14.791) (10.060)
-49.243** -10.119
-6.860
(24.927)
(13.578)
(8.308)
-48.020*
-11.114
-1.143
(26.041)
(13.559)
(9.915)
-41.777*
-9.300
-5.057
(24.312)
(14.207)
(8.603)
149
149
149
t-3
t-2
t-1
t
t+1
t+2
t+3
Number of observations
28
A. 1 Correlation Matrix
Correlation coefficients. Significant correlations at less than 1% are identified with *.
1
2
3
4
5
6
7
8
9
Variables
Age at IPO
Large Size
Small Size
State-controlled
No. of Insolvencies
GDP per capita
Market cap to GDP
Market return index
Number of IPOs in UK
1
1.000
0.089*
-0.051
0.222*
0.229*
0.279*
0.227*
0.005
0.129*
2
3
4
5
6
7
8
9
1.000
-0.232*
0.280*
-0.049
-0.011
-0.055
-0.108*
-0.004
1.000
-0.065
0.243*
0.176*
0.317*
0.280*
0.226*
1.000
0.018
0.077
0.06
-0.003
0.018
1.000
0.806*
0.807*
0.583*
0.621*
1.000
0.731*
0.466*
0.592*
1.000
0.781*
0.608*
1.000
0.570*
1.000
29
II reg. change
No. of listed firms
I reg. change
1961-1935
1936-1973
30
III reg. change
1974-1997
1998-2011
No. of IPOs - delistings
Figure. 1. Number of IPOs and delistings in Italy during 1861-2011
X-axis represents years. Vertical bars below the X-axis identify the number of delisted companies per year (voluntary delistings are in grey), while bars
above the X-axis describe IPOs per year (IPOs listed on the Nuovo Mercato are in black). The curve above the X-axis represents the number of listed
firms per year.