Are Private Investments in Public Equities and Alternative Source for Both Funding and Investment

THE LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE

ARE PRIVATE INVESTMENTS IN PUBLIC EQUITIES AN ALTERNATIVE SOURCE FOR BOTH FUNDING AND INVESTMENT?

MSc Finance and Private Equity

2015-2016

FM410- Private Equity

Supervisor- Dr. Juanita Gonzalez Uribe

Exam Candidate Number: 43158

Word Count: 6291 (Excluding Executive Summary)

The copyright of this dissertation rests with the author and no quotation from it or information derived from it may be published without prior written consent of

the author.

EXAM CANDIDATE NUMBER: 43158 1

TABLE OF CONTENTS: LIST OF TABLES ....................................................................................................... 2

LIST OF CHARTS ....................................................................................................... 2

EXECUTIVE SUMMARY ............................................................................................. 3

1. INTRODUCTION AND LITERATURE REVIEW ..................................................... 4 1.1- WHAT IS A PIPE? ....................................................................................................... 4 1.2- EVOLUTION OF PIPEs ............................................................................................... 5 1.3- WHY DO CORPORATIONS FUND THEMSELVES VIA PIPEs? ............................... 5 1.4- THE DARK SIDE OF PIPEs FOR ISSUERS ............................................................... 6 1.5- WHY DO PE FIRMS INVEST IN PIPEs? ..................................................................... 6 1.6- DRAWBACKS TO PE FIRMS INVESTING IN PIPEs ................................................. 9

2- DATA AND METHODOLOGY .............................................................................. 10 2.1- DATA ......................................................................................................................... 10 2.2- METHODOLOGY ....................................................................................................... 11

2.2.1- Testing for stationarity ......................................................................................... 11 2.2.2- Running the regression ....................................................................................... 15

3- ANALYSIS AND FINDINGS ................................................................................. 19 3.1- REGRESSION OUTPUT AND TEST RESULTS ....................................................... 19 3.2- PIPEs AS AN ALTERNATIVE SOURCE FOR BOTH FUNDING AND INVESTMENT ........................................................................................................................................... 22

4. CONCLUSION ...................................................................................................... 26

APPENDICES ........................................................................................................... 27 APPENDIX 1- PIPEs BY AGGREGATE DEAL VALUE ................................................... 27 APPENDIX 2- PIPEs BY AGGREGATE DEAL VALUE IN RECENT YEARS ................. 28 APPENDIX 3- PIPEs BY WORLD REGION (AGGREGATE DEAL VALUE) .................. 28 APPENDIX 4- PIPEs BY WORLD REGION (NUMBER OF DEALS COMPLETED) ....... 29 APPENDIX 5- PIPEs BY SECTOR (NUMBER OF DEALS COMPLETED) ..................... 30 APPENDIX 6- PIPEs BY SECTOR (AGGREGATE DEAL VALUE) ................................ 31 APPENDIX 7- PIPEs BY TARGET COUNTRY (NUMBER OF DEALS COMPLETED) .. 31 APPENDIX 8- PIPEs BY TARGET COUNTRY (AGGREGATE DEAL VALUE) .............. 35

REFERENCES .......................................................................................................... 36


LIST OF TABLES 2.1- DATA 10 2.2.1- PP TEST FOR AGGREGATE DEAL VALUE 12 2.2.2- PP TEST FOR PIPEs GROWTH 13 2.2.3- PP TEST FOR CPI INFLATION 14 2.2.4- PP TEST FOR DCRISES 15 2.2.5- CORRELOGRAM FOR EQUATION 2.2.3 16 3.1.1- REGRESSION OUTPUT 19 3.1.2- ENGLE'S ARCH TEST FOR HETEROSKEDASTICITY 20 3.1.3- JARQUE-BERA NORMALITY TEST 20 3.1.4- RAMSEY'S RESET TEST 21 3.1.5- CHOW TEST FOR STRUCTURAL BREAKS 22

LIST OF CHARTS 1.1- NUMBER OF PIPE DEALS BY PE FIRMS (WORLDWIDE) AND UNINVESTED PE CAPITAL 8 1.2- NUMBER OF PIPE DEALS BY PE FIRMS WORLDWIDE AND PE FUND RAISING PER QUARTER 8 2.2.1- AGGREGATE PIPEs DEAL VALUE (1998-2014) 12 2.2.2- PIPEs GROWTH (1998-2014) 13 2.2.3- CPI INFLATION (1998-2014) 14 2.2.4- DCRISES (1998-2014) 15 3.1.1- QUANTILE-QUANTILE PLOT 21 3.2.1- THE PECKING ORDER THEORY- TRADITIONAL VS EXTENDED VIEW 23 3.2.2- TOTAL % RETURNS ON OTHER MAJOR ASSET CLASSES (2015) 24


EXECUTIVE SUMMARY

This paper discusses two aspects. Firstly, Private Investment in Public Equity (PIPEs) as an alternative source of funding for companies either in need of funding that do not want to raise funds publicly due to the signalling hypothesis, or because they themselves are in distress, or since the entire economy or their industry is facing a crisis. Secondly, on the other side, its use as an alternative investment from the investors perspective is also analysed. It also looks at the benefits and costs of PIPEs to both issuers and investors.

The primary hypothesis tested in the paper is that PIPEs growth increases

during times of financial crises, as large investors have excess liquidity and PIPEs serve as a safe haven to park their money. In order to analyse this, the Russian crisis, dot.com bubble, and the subprime crisis were used as a dummy variable representative of crises, and a regression was run on the growth in PIPEs, on the aforementioned dummy for crises, with inflation, and an autoregressive component. It was found that the dummy on crises was significant, and the coefficient was positive, indicating that PIPEs grow in positive correlation to times of financial crises. This indicates diversification from the investors’ perspective.

The nature and motive behind a PIPE investment is then discussed with global trends and evolution over time, based on data from Zephyr. With GAFI, OECD, and G10 regions being the main users of PIPEs, the dramatic rise of such forms of investments in the last decade with a focus on 2015 is also looked into.

The paper establishes that PIPEs seem to fit into the traditional picture of the

Pecking Order Theory of Corporate Finance as a previously excluded asset class between Debt and Equity in the Pecking Order. With low global returns and excess liquidity, this paper concludes that PIPEs are without doubt an alternate source for both funding and investment.


1. INTRODUCTION AND LITERATURE REVIEW Private Investment in Public Equity (PIPEs) is a new type of investment, usually undertaken by large investors like Private Equity (PE) firms, Venture Capital (VC) funds, Hedge Funds (HF) and sometimes even by High Net Worth Individuals (HNWIs). Over the years, their importance has grown substantially, to levels above $400,000 million in just the last decade (Zephyr). In my dissertation I focus on PIPEs as an alternative investment, based on my hypothesis that firms with excess liquidity invest in PIPEs in times of crises in order to diversify their risks. Secondly, I set out to prove that it is also an alternative source of funding for corporations, both in times of crises, and in general; leading to an exponential growth of PIPEs in recent years. 1.1- WHAT IS A PIPE?

A Private Investment in Public Equity is a “privately negotiated equity or equity-linked investment in a public company” (Dresner and Ackerman, 2009).

There are 3 primary types of private placements under the US Securities Act 19331 - Rule 144A transactions, Non-Rule 144A transactions, and Registered Direct Transactions (RD).

Rule 144A transactions mainly structured either as convertible debt or convertible equity are NOT considered PIPEs, and involve the sale of equity or equity-linked securities to Qualified Institutional Buyers (QIBs), with the basic definition that these will be resold to other QIBs.

Non-Rule 144A transactions ARE considered as PIPEs. They involve the sale of equity or equity-linked securities, primarily common equity, convertible preferred equity, convertible debt, and straight debt with warrants; executed in compliance with certain exemptions from registration requirements provided for under the Act. These exemptions from registration requirements are available under Section 4(2) of the Act, if: a) The offer is made to a limited number of financially sophisticated investors, b) The offer does not involve any general advertising or general solicitation, c) The investors are given the information relevant to their potential investment. (Särve, 2013)

RDs are ALSO considered as PIPEs. They involve the issuance of equity and equity-linked securities, including equity lines of credit, preregistered common and preferred equity, preregistered convertible debt and convertible equity, and warrants. Here, the securities issued in such transactions are already registered for

1 Throughout the paper, PIPEs have been discussed with context to this Act, and therefore any mention of the ‘Act’ refers to Securities Act of 1933.


sale to investors, through a primary registration statement, filed with and declared effective by the SEC. 1.2- EVOLUTION OF PIPEs The PIPEs market started over 2 decades ago, when micro-cap companies were facing difficulties in funding due to tighter bank-lending policies. This compelled such companies to secure funds from PE firms, VC funds, HFs, and HNWIs. Subsequently, SEC’s Regulation S (REG S) in 1990, popularised PIPEs further as public companies were thereafter allowed to sell unregistered securities to non-U.S. entities, which could then be resold to the public markets after a 40 day holding period (to prevent arbitrages, due to short selling). These transactions were typically structured as preferred equity or convertible debt. From the mid-to-late 90s, the PIPEs market matured, as larger companies took advantage of the ease of financing it provided. This stimulated funding for biotechnology companies. According to PlacementTracker.com, in the first 6 months of 2005, in issuers with market caps under $25m, the average transaction size was approximately $20m, with common stock transactions representing the largest percentage of PIPEs. As is seen later in section 3.2, PIPEs growth explodes by 2015. Therefore, one needs to discuss the motivations which made PIPEs popular. 1.3- WHY DO CORPORATIONS FUND THEMSELVES VIA PIPEs?

According to Pidgeon and David (2006), PIPEs are not public offers, but private placements to few investors, and therefore not subject to any SEC review. Thus, capital can be raised within a short span of time (SEC reviews could increase the timeline for raising funds drastically).

Further, once the deal is negotiated, the issue price is fixed. Hence,

thereafter, investors find no need to short the stock, in order to hedge their position Pinedo and Tanenbaum (2016).

Moreover, issuers can issue a wide range of Securities through PIPEs such as convertible debt, preferred stock, common stock and warrants. A great advantage of convertible debt is that it offers backdoor equity as it is like a call option for the creditors, thus bringing equity into the capital structure indirectly. Moreover, it offers a lower yield on debt, making debt financing cheaper. However, according to Modigliani and Miller (1958), a lower yield on debt makes the equity riskier. It also mitigates shareholder and bondholder conflicts of interest by punishing the equity-holders by diluting their stake if they take on too much risk.

Lastly, transaction costs incurred in execution of PIPEs are far below those incurred for public offers, as there is no need for legal or advisory services.


1.4- THE DARK SIDE OF PIPEs FOR ISSUERS

The issue of a PIPE comes with many terms and conditions. As per Pidgeon and David op. cit. (2006), investors strive for: a. Reset provisions to insulate themselves against falls in prices. b. Veto rights in contingent events, like sale of the company. c. Pre-emptive or anti-dilution entitlement (when fresh equity is sold). d. Offers of participating convertible preferred equity to retain liquidity and veto bonus provisions.

Shares issued in a PIPE transaction cannot be sold immediately by the investors, due to a lock-in period. Therefore, investors demand discounts in the share sales price to compensate them for the lock-in period.

Further, PIPEs can be offered only to few investors thereby limiting the number of persons to whom the Securities can be offered, Pinedo and Tanenbaum op. cit. (2016). Moreover, only upto 20% of outstanding stock can be issued without prior shareholder consent, Pidgeon and David op. cit. (2006).

PIPEs transactions are vulnerable to insider trading and arbitrage. Hogboom (2004), states that associates of the investors could be privy to the ongoing PIPE deal negotiations and could resort to short selling to drive the company’s share price down, so that the deal can be executed at a lower price by the investors. On execution of deal, investors immediately short shares in the secondary market, effecting delivery once the lock-in period expires; thereby earning an arbitrage profit.

At the end of the day, the pros and cons of PIPEs from the issuers perspective, may be balanced, but the need for funding lures them. The company should therefore weigh the costs and benefits properly before issuing a PIPE. For instance, according to Chaplinsky (2003), eToys went bankrupt in 2001, by issuing PIPEs, as they underestimated the dilutive effect of the conversion of the convertible preferred stock. 1.5- WHY DO PE FIRMS INVEST IN PIPEs? The deals per se are complex, closed individually, and are a lot quicker than leveraged buyouts (LBOs). The companies are already listed and have the structural framework in place to issue new shares. The companies have to statutorily make their financials public. Therefore, it becomes simpler for the analysts to prepare reports, as there is no information asymmetry. Due diligence becomes simpler and faster. According to Gerhard (2008), the quick deal execution is an advantage of PIPEs over LBOs. Hence, for the PE firms, the advanced due diligence saves time and cost. Further, from the PE investor’s standpoint, not only entering cheap, but also selling well is important, and therefore a good exit is of significance. Usually PE funds find it hard to exit their investments as they are extremely illiquid, and have to rely on secondary buyouts, or trade sales (in both cases they would have to find a


buyer), or then a reverse LBO. However, when PE firms invest in a PIPE, the target’s shares are already publicly listed. So once the shares are registered with the SEC, the secondary market offers an easy and comfortable exit for the PE firm (i.e. it guarantees liquidity). This is advantageous as mentioned by Gerhard op. cit. (2008); Kuzneski and Landen (2006); Lerner (2003); and Poddar (2009).

Another significant factor is the possibility of taking over board seats, which is very similar to what happens in buyout deals. With board seats come governance rights, and influence on management enables implementation of strategic or operational measures in portfolio companies. This is also voiced by Thomas H. Lee, president of a PE fund of the same name and as mentioned in Kaplan and Strömberg op. cit. (2008), and also cited in an article by Sparks (2008), Therefore, they can get the board seats at a lower cost, and with lower or no leverage, i.e. lower risk of prepayment. According to Dai (2006), in 67% of the PE transactions in her sample, the investors obtained a board seat. According to Machera (2008), 16 of 22 PIPE investors in the US were granted 1-2 board seats and 21% of the transactions were granted additional veto rights. PIPEs offer PE funds access to certain markets which would otherwise be hard to enter. This is by: (1) Accessing certain industry sectors and/or geographies, and (2) Investing in technological companies, not accessible via a classical LBO. This may be due to an extremely diversified portfolio (therefore moving away from the reason the PE fund was raised), or due to the Limited Partner Agreement (LPA), prohibiting investments in start-ups. According to Smith (2002), the tech bubble stirred the interest of buyout firms. However, this area being already covered by Venture Capital (VC), the buyout firms resorted to PIPEs, to access this market. Sheng (2010) argues that buyout firms do not have enough targets in markets like China, and therefore they resort to PIPEs. Poddar op. cit. (2009) makes a similar argument for India, as Indian companies tend to get publicly listed early in their life cycle. Therefore, a full takeover of such companies would entail high premiums, making LBOs less preferable. PIPEs help PE firms in avoiding takeover premiums. In typical buyout deals, the full buyout is costly, time consuming, and risky. Acquisition of public companies to take them private, requires PE firms to pay huge premiums over the existing market value of the firm. These premia are 15%-50% according to Kaplan and Strömberg op. cit. (2008). Therefore, PE firms prefer PIPEs, as according to Dai op. cit. (2006), investors on average invest in 66% of the deals at a discount of 3% (median 6.2%) in PE lead PIPE deals. Lastly, the most popular and important hypothesis why PE firms engage in PIPEs is the that of surplus investment money. They have surplus investment money, and don’t know what to do with it. So they invest it in the next best available alternative investment strategy being PIPEs. Anson (2001), Davies (2007), Sheng op. cit. (2010), and Poddar op. cit. (2009) among other academics propose similar arguments. Charts 1.1 and 1.2 below give a graphical representation of the number of PIPE deals compared to uninvested capital, and the number of PE deals by PE firms worldwide compared with PE fundraising per quarter.


CHART 1.1- NUMBER OF PIPE DEALS BY PE FIRMS (WORLDWIDE) AND UNINVESTED PE CAPITAL:

Source: Thompson ONE Banker (data retrieved June 22, 2011) CHART 1.2- NUMBER OF PIPE DEALS BY PE FIRMS (WORLDWIDE) AND PE FUND RAISING PER QUARTER:

Source: Thompson ONE Banker (data retrieved September 7, 2010)

Both graphs above, show that there is a positive correlation between both the PIPE activity and unvested capital, and between PIPE activity and fundraising. Therefore, the surplus investment money hypothesis holds.


1.6- DRAWBACKS TO PE FIRMS INVESTING IN PIPEs

PIPEs come with trading liquidity risk. According to Kaplan and Strömberg op. cit. (2008), IPOs are a very popular exit route, and approximately 14% of PE exits are through IPOs. PIPE issuers have a small market cap and their shares are thinly traded. Therefore, selling shares in large blocks could drive prices down steeply and quickly. Hence, a strategic buyer for all the shares, would be a better alternative. Another problem is the 40-day lock-up period, during which the investor is not allowed to sell the shares. Therefore, there are restrictions associated with improved liquidity.

Also, Pinedo and Tanenbaum op. cit. (2016) suggest that issuers may suspend the resale registration statement for purpose of amendment or remedying omissions or misstatements. During this blackout period investors will not be able to fall back on the resale registration statement for selling the securities and their liquidity is impacted.

Therefore, from the investors’ viewpoint, it can be stated that the benefits of PIPEs clearly outweigh the costs.

It must however be noted that PIPEs are not the best for the Limited Partners (LPs), as they could simply take their money to an investment bank and invest directly in the firm, thus avoiding having to pay the 20% carry. This being in accordance with the proposition of Modigliani and Miller op. cit. (1958), according to which an investor will not pay an additional premium if he/she can replicate the same investment by himself/herself; i.e. homemade leverage.

Next, I will test my hypothesis that the excess liquidity of Private Equity firms,

among other investors is invested in PIPEs, as a source of diversification in the times of financial crises. I have used data from Zephyr and The World Bank to test whether there is an increase in PIPEs during times of crises, as what I believe are a flight to safety.


2- DATA AND METHODOLOGY 2.1- DATA

The purpose of this paper was to explore PIPEs as an alternative source for both funding and investment, and to test how the recent crises have affected their growth, i.e. companies selling unregistered shares privately, rather than through a public offering. Therefore, this was a test for the fundamental concept of the ‘Pecking Order Theory’ of Corporate Finance discussed in Section 3.2.

The data for this paper has been retrieved primarily from Zephyr and The World Bank. It adopts time series analysis to test PIPEs growth in relation to the various crises for the period ranging 1998-2014. It looks at data on PIPEs growth based on: (a) First differenced PIPEs data from Zephyr, (b) Inflation data from The World Bank and (c) A dummy variable that takes the value of 1 for every year during a global crisis, and 0 otherwise, between 1998 and 2014. The data is shown in table 2.1 below:

TABLE 2.1- DATA:

Source: Zephyr and The World Bank

DateAggregate deal value(mil USD)

PIPEs growth

Inflation, consumer prices (annual %)

Dcrises

1998 3 6.69 5.08 11999 2,410 -0.20 3.08 02000 1,967 -0.45 3.56 12001 1,256 1.04 3.98 02002 3,571 0.84 3.02 02003 8,247 0.31 3.22 02004 11,266 0.32 3.31 02005 15,556 0.58 4.04 02006 27,789 0.54 4.30 02007 47,920 0.06 5.00 12008 50,756 -0.62 8.96 12009 27,325 0.04 2.86 02010 28,568 -0.67 3.48 02011 14,608 0.33 4.91 02012 20,340 -0.10 3.71 02013 18,460 1.04 2.66 02014 52,142 0.57 2.51 0


2.2- METHODOLOGY2 2.2.1- Testing for stationarity

A series is said to be weakly stationary if at each point in time, the series has a constant mean, variance and covariance. However, a violation of even one of these conditions results in the series being non-stationary. Such a series would lead to a spurious regression output. Non-Stationarity can be of 2 forms- Unit root / Random walk and Trend Stationarity. A random walk series has a variance that tends to infinity, as it is heteroskedastic and increases linearly with time. Since time tends to infinity, so does the variance.

A trend stationary series will never have a constant mean. Once again, this makes it impossible to predict. Thus, in either case, the series must be 1st differenced to make it stationary. However, to make sure whether the series is a random walk or trend stationary, it must be tested. Therefore, this was the first thing done in the paper.

There are a number of tests for stationarity, however the one used was introduced by Phillips and Perron (1987). The PP test is better than that introduced by Dickey and Fuller (1979), as it is non-parametric. This means that it solves the problem of the ADF test, of choosing the correct number of lags, and employs the Heteroskedasticity and Autocorrelation Corrections (HAC). The ADF expression is as follows: EQUATION 2.2.1: ∆𝑦# = 𝜓𝑦#&' + 𝛽*∆𝑦#&* + 𝑢#

,*-'

However, this test is criticized on the grounds that it needs to find the accurate

value of p (the lags), to minimize loss of degrees of freedom. The only available selection methods are the Schwert (1989) rule of thumb, of choosing the frequency of the data + 1 and the information criteria. Therefore, the PP test for non-stationarity about an intercept is employed. It uses the following equation: EQUATION 2.2.2: ∆𝑦# = 𝛽.𝐷# + 𝜋𝑦#&' + 𝑢#

2 All Charts and Tables generated in Section 2 are my own, with the use of EViews.


Where Dt is a vector of deterministic terms such as the constant, trend, etc.; and H0 : 𝜋 = 0, as𝜋=𝛽-1, and therefore, indicating that yt~I(1). Thus, the test statistic was: 𝑡 = <

=>(<) ; and t*is derived using a Monte-Carlo simulation.

The test results would have- H0 : The series of Aggregate PIPEs Deal Value is non-stationary. H1 : The series of Aggregate PIPEs Deal Value is stationary about an intercept. CHART 2.2.1- AGGREGATE PIPEs DEAL VALUE (1998-2014):

The results are as follows: TABLE 2.2.1- PP TEST FOR AGGREGATE DEAL VALUE:

Therefore, the P-value suggests that the H0 cannot be rejected at the 5% significance level. Therefore, the series is non-stationary.

0

20,000

40,000

60,000

80,000

100,000

1998 2000 2002 2004 2006 2008 2010 2012 2014

Aggregate deal value (mil USD)


Hence, in order to correct for this, the first difference was taken, and so the PP test was run again, for PIPEs growth, where: 𝑃𝐼𝑃𝐸𝑠𝐺𝑅𝑂𝑊𝑇𝐻 = ln(𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒𝐷𝑒𝑎𝑙𝑉𝑎𝑙𝑢𝑒#/𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒𝐷𝑒𝑎𝑙𝑉𝑎𝑙𝑢𝑒#&') The test results would have: H0 : The series of PIPEs Growth is non-stationary. H1 : The series of PIPEs Growth is stationary about an intercept. CHART 2.2.2- PIPEs GROWTH (1998-2014):

TABLE 2.2.2- PP TEST FOR PIPES GROWTH:

Since the P-Value of the PP test is 0, the series is stationary about an

intercept and therefore H0 is rejected at the 5% significance level.

The next component of the regression output that needed to be tested for stationarity was inflation.

-1

0

1

2

3

4

5

6

7

1998 2000 2002 2004 2006 2008 2010 2012 2014

PIPEs growth


The test results would have: H0 : The series of Inflation is non-stationary. H1 : The series of Inflation is stationary about an intercept. CHART 2.2.3- CPI INFLATION (1998-2014):

TABLE 2.2.3- PP TEST FOR CPI INFLATION:

Since the P-value is less 5%, H0 is rejected at the 5% significance level and

so the series is stationary about an intercept. Finally, it was important to test the dummy variable for stationarity. The test results would have: H0 : The series of DCrises is non-stationary. H1 : The series of DCrises is stationary about an intercept.

2

3

4

5

6

7

8

9

10

1998 2000 2002 2004 2006 2008 2010 2012 2014

Inflation, consumer prices (annual %)


CHART 2.2.4- DCRISES (1998-2014):

TABLE 2.2.4- PP TEST FOR DCRISES:

Since the P-Value is less than 5%, H0 is rejected at the 5% significance level and so the series is stationary about an intercept.

Therefore, since all the components of the regression were now stationary, it was time to run an OLS regression. 2.2.2- Running the regression

The regression methodology applied in this case was the Classical Linear Regression Model (CLRM) often known as the Ordinary Least Squares (OLS) method, as it ensures a line of best fit for the data, in such a way that the sum of the square of the error terms is minimized.

0

1

2

1998 2000 2002 2004 2006 2008 2010 2012 2014

DCRISES


The model has the following assumptions:

1. E(ut) = 0; i.e. the residuals have a mean value of 0. 2. Var (ut) = σ2; i.e. the variance of the residuals is homoskedastic ~ remains

constant over time. 3. E(ui, uj) = 0; The residuals have 0 autocorrelation. 4. E(ut, xt) = 0; the residuals are not correlated to the regressors in any way. 5. ut ~ N(0,1).

In addition, the model has to be the best estimate, must be consistent, clear,

unbiased and linear. It must also be the most efficient estimator. Therefore, keeping these assumptions in mind, the following regression was run, and then tested to see if all assumptions were satisfied. Therefore, we have 2 regressions that were run: EQUATION 2.2.3- REGRESSION OF PIPES GROWTH ONLY ON DCRISES AND INFLATION: 𝑦# = 𝛼 + 𝛽'𝐷𝐶𝑅𝐼𝑆𝐸𝑆# +𝛽Y𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁# + 𝑢# EQUATION 2.2.4- REGRESSION OF PIPES GROWTH ON DCRISES, INFLATION, AND AR(2): 𝑦# = 𝛼 + 𝛽'𝐷𝐶𝑅𝐼𝑆𝐸𝑆# +𝛽Y𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁# +𝛽]𝐴𝑅 2 + 𝛽_𝜎#Y + 𝑢#

The lag was used as the correlogram shows a spike at the AR(2), indicating some autoregression in the model. This is observed by taking a closer look at the correlogram for Equation 2.2.3, which employs the Q-stat proposed by Ljung and Box (1978), as is shown below in Table 2.2.5. TABLE 2.2.5- CORRELOGRAM FOR EQUATION 2.2.3:


On running the regression, it was now important to test for any misspecifications in the model. Due to the lack of sufficient time series data, and the autoregressive nature of the model, the most proficient test for heteroskedasticity was the ARCH test proposed by Engle (1979). The following are the null and alternative hypotheses of the Engle’s test:

H0 : There is no autoregressive heteroskedasticity in the model. H1 : There is autoregressive heteroskedasticity in the model.

The next step was to test for the presence of serial correlation in each limited sample regression output, using the Breusche-Godfrey LM test for serial correlation developed by Godfrey (1978) and Breusche (1979). The following are the hypotheses of this test: H0 : There is no serial correlation in the model. H1 : There is serial correlation in the model.

However, since the data on which the model is based is only from 1998-2014, there are insufficient lags, to be able to determine whether or not there is serial correlation in the data.

Next it was important to test for normality in the data. For this, the Jarque-Bera test proposed by Bera and Jarque (1982) was used. The hypotheses were as follows: H0 : There model is normally distributed. H1 : There model is not normal.

In order to back this test up, the residuals of the equation were plotted using the Quantile-Quantile (QQ) plot. The plot charts the data against a 45° line which is supposed to represent another distribution. A normal distribution should fit this line perfectly. If the data points lie off the line, then it would represent outliers, and therefore skewed data. This in turn would represent non-normality. If the data on the other hand is more or less on the 45° line, then it can be said that it is normally distributed. The RESET test of Ramsey (1969), hypothesizes the following: H0 : The model is linear. H1 : The model in not linear. The above tests would conclude the tests for misspecifications.


The final test to be conducted would be the Chow breakpoint test proposed by Chow (1960) to test for structural stability. In this case, 2007 is chosen as a breakpoint. The hypotheses are as follows: H0: There are no significant breaks in the data.

H1: There is a significant break in the data.


3- ANALYSIS AND FINDINGS 3.1- REGRESSION OUTPUT AND TEST RESULTS

On applying equation 2.3 to the data, from 1998-2014, the following is the regression output: EQUATION 3.1.1- REGRESSION OF PIPEs GROWTH ON DCRISES, INFLATION AND AR(2): 𝑃𝐼𝑃𝐸𝑆𝐺𝑅𝑂𝑊𝑇𝐻# = 1.82 + 2.03𝐷𝐶𝑅𝐼𝑆𝐸𝑆# −0.45𝐼𝑁𝐹𝐿𝐴𝑇𝐼𝑂𝑁# − 0.76𝐴𝑅 2 +1.45𝜎#Y + 𝑢# The regression output from EViews is given below: TABLE 3.1.1- REGRESSION OUTPUT

The model shows a significance of both the dummy on crises as well as the

AR(2) at the 5% significance level (i.e. p-value < 0.05), i.e. the lagged values of PIPEs growth. Although inflation is insignificant, its presence in the model is important to account for the impact of economic variables on PIPEs growth. The fact that it is insignificant, proves that the model is firm specific. The crises on the other hand, make the model systematic, as they are positively related to the growth in PIPEs as can be seen by the coefficient’s sign. On the other hand, the PIPEs growth is negatively related to its lagged values. The model seems to be moderately good


as can be seen by the R2, however the information criteria indicate otherwise, as they are all positive. Some tests below will shed more light on the above model and few possible reasons for such a low goodness of fit, and possible alternatives. The first step was to look into whether or not the model satisfied the assumptions of the OLS. TABLE 3.1.2 ENGLE’S ARCH TEST FOR HETEROSKEDASTICITY:

Since the p value of the F-stat > 0.05, the H0 of the Engle op. cit. (1979) test cannot be rejected at the 5% significance level. Therefore, the model does not suffer from autoregressive heteroskedasticity. TABLE 3.1.3- JARQUE-BERA NORMALITY TEST:

The p value of the Bera and Jarque op. cit. (1982) test > 0.05. Therefore, once again, H0 is not rejected at the 5% significance level, and therefore, the model is normally distributed.

0

1

2

3

4

5

6

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Series: Residuals

Sample 1998 2014

Observations 17

Mean 0.109187

Median 0.310975

Maximum 3.332389

Minimum -1.651416

Std. Dev. 1.236053

Skewness 0.699787

Kurtosis 3.852757

Jarque-Bera 1.902586

Probability 0.386241


CHART 3.1.1- QUANTILE-QUANTILE PLOT:

Since most of the data lies on the 45° line in the QQ plot above, it is safe to say that the data is normally distributed, as discussed in Table 3.1.1 above. TABLE 3.1.4- RAMSEY’S RESET TEST:

Since the p value of the Ramsey op. cit. (1969) RESET test is always < 0.05, the H0 is always rejected at the 5% significance level, and therefore the model suffers from non-linearity. This is a misspecification in the model.

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

-1.2 -0.8 -0.4 0.0 0.4 0.8 1.2

Quantiles of UHAT

Quantile

s o

f N

orm

al


TABLE 3.1.5- CHOW TEST FOR STRUCTURAL BREAKS:

The Chow op. cit. (1960) test has a p value < 0.05 for the F-stat, indicating rejection of H0 at the 5% significance level. Therefore, the model has a significant structural break in 2007. This is probably due to the fact that the growth in PIPEs increased significantly after the beginning of 2007, due to the financial crisis.

Therefore, the model for the growth in PIPEs is homoskedastic, and normally distributed, with a moderate R2 (Goodness of Fit). However, at the same time, it suffers from shortage of time series data, and therefore inability to test for autocorrelation due to insufficient lags, and non-linearity. Moreover, the information criteria are positive, which indicates a bad model. Nonetheless, this may just stem from the non-linearity and autoregressive nature of the model, as it depends to a certain extent even on volatility, though it is insignificant. Alternative models that could be used include the error correction model- ECM (however, all series are stationary, and so this might not be needed), the Vector Autoregression Model- VAR, to employ panel data, and maybe some versions of the GARCH- Generalized Autoregressive Conditional Heteroskedastic model; as the model suffers from non-linearity. 3.2- PIPEs AS AN ALTERNATIVE SOURCE FOR BOTH FUNDING AND INVESTMENT

The above regression therefore proposes that the distribution of variables is i.i.d. normal, and that there is a growth in PIPE funding in times of crisis. This is in accordance with the ‘Pecking Order Theory’ of Corporate Finance, first proposed by Myers and Majluf (1984). The theory postulates that due to information asymmetry, when a firm issues new equity, the stock price will fall, ceteris paribus, as shareholders and managers do not share the same information, and therefore suffer from the adverse selection (market for lemons) problem. Therefore, shareholders will believe that the firm is only issuing equity because it is overvalued, and therefore is getting a good deal by issuing equity. Hence, the market will place a lower value to these newly issued equities. To avoid this problem, firms tend to prefer following a ‘Pecking Order’ in which to raise funds, i.e. first internally through retained earnings, then by issuing new debt, and finally by issuing new equity.

This now brings to focus another key topic arising from this, which relates

directly to the importance of private equity funding. The problem at hand is the debt overhang. As trivial as this may seem, PIPE funding has large implications on firms’ ability to take on positive NPV projects, as now, they will be able to raise liquidity


through the primary market quicker and cheaper than the secondary market, without anyone getting to know; implying that firms in distress or in need of additional funding can overcome this problem through PE funding, as the key issue with the debt overhang problem is that new project funding (even for positive NPV projects), in times of financial difficulties is hard. This is because the equity holders are not willing to finance a distressed firm. The primary reason for this is that the benefits of the new financing accrue primarily to the existing creditors who are the senior claimants on any cash flows that accrue to the firm. As a result, the benefits of the new project are much less than under status quo, if not 0.

However, when the funding is private, unregistered shares can be sold through the primary market to participants, such as PE firms, VC funds, HFs, HNWIs, and other big institutional players, and therefore, this helps to fund the projects quickly, and without many regulatory and compliance issues. The purchasers of these shares also like this because they get the shares at a discount to the market value, and therefore, once purchased, they can register them and trade these as normal equity in the secondary market, making a gain on the difference in values. The downside of this however, is that it dilutes the stake of the existing equity holders, but this is a much better option than having to liquidate or file for bankruptcy which is a costly and lengthy affair for a firm in distress, for which the new project is the best solution as part of the re-organization plan.

Moreover, in liquidation, there is an absolute priority rule (APR), whereby the creditors would get all the claims before the equity holders, and in formal bankruptcy, there is a lot of negotiating to be done with the creditors, about who gets what, and how much; and restructuring also involves a lot of time in terms of how the assets will be put to use, and can even involve an entire change of management. Thus, even the firms which are fundamentally stable, but are now in need of funding, simply due to a crisis either in the industry, or a small bump in the firm itself, lose out on the funding; as creditors won’t lend due to the poor financial position, and equity holders will underpay for a good company, simply due to the lack of information symmetry between the different stakeholders and the firm.

Therefore, PIPEs, seem to form a previously excluded asset class between Debt and Equity in the Pecking Order.

Here is a picture of my illustration of the Pecking Order Theory, accounting for PIPEs, as opposed to the traditional pecking order triangle. I have called this the extended view. CHART 3.2.1- THE PECKING ORDER THEORY- TRADITIONAL VS EXTENDED VIEW:

Retained Earnings Debt

Retained Earnings

Debt

Equity Equity

PIPEs

Traditional view

Extended view


This reiterates the regression run above. PIPE investment has always increased in times of crises. This is because it is believed empirically that firms find it easier to raise funds internally than externally, and likewise easier privately than publicly.

Across 7628 global PIPE deals downloaded from Zephyr, here findings which show where PIPE deals are concentrated the most, and some possible explanations for the same. These have been organized first by:

1. Aggregate deal value (Appendix 1 and 2) 2. World region- in aggregate value (Appendix 3) 3. World region- in number of deals completed (Appendix 4), 4. Sector- in number of deals completed (Appendix 5) 5. Sector- in aggregate deal value (Appendix 6) 6. Target country- in number of deals completed (Appendix 7) 7. Target country- in aggregate deal value (Appendix 8).

As can be seen in Appendix 1 and 2, there has been a massive increase in

PIPE funding in the last decade. Appendix 1 illustrates this, especially after the early 2000s (dot.com bubble), and then shows a clustering around 2008 and 2009 (the financial crisis). It also shows a massive increase in 2015. This might be due to one of the reasons discussed above in section 1.5, such as surplus investment money, or due to some unaccounted for present or potential crisis. The year 2015 is definitely significant, as Appendix 2 also illustrates this in the form of a pie chart that reveal that 19.27% of the aggregate deal value ($480,663m) comes from this year. A possible reason for the increase in investment in 2015 is due to the fact that the returns in other asset classes were very poor. This can be seen in Chart 3.2.2. Hence, Appendix 1 and 2 show a shift towards PIPEs as an alternative source for both funding and investment over the last decade.

CHART 3.2.2- TOTAL % RETURNS ON OTHER MAJOR ASSET CLASSES (2015):

Source: CapitalSpectator.com


Appendix 3 summarizes in aggregate deal value, the findings of Appendix 4 which looks at the total number of deals completed by region, and illustrates that the most significant regions that use PIPE funding are the United Nations, GAFI, OECD, and G10, amongst others. This is interesting as the GAFI region includes Egypt, which is an EME. Egypt has already seen major PE activity by funds such as Actis, and so could suggest that investments in PIPEs are increasing in EMEs as they are less prone to crises (i.e. being in the nature of a ‘flight to safety’ when there is a crisis in developed economies). However, the GAFI countries are high risk countries, and therefore, finding cheap finance to fund a new project is very difficult for companies in these countries, simply due to the sovereign risk level (once again, even though the idiosyncratic risk at the firm level may be low). Therefore, funding the firm via a PIPE is a viable option. A possible reason for the concentration in G10 countries is the fact that these countries are industrial, and as seen in Appendix 5, a lot of PIPE deals have been conducted by firms dealing in machinery, equipment, furniture, recycling; chemicals, plastics, rubber, and non metallic products. These companies generally find it hard to find debt funding, probably due to their scale and the risk involved (which pushes the cost of funding up). Moreover, their stock of retained earnings is generally low. Being fundamentally stable, but not wanting to send the wrong signal to the markets about their valuation, PIPEs generally appear to be the safest route.

By sector, the most significant users of PIPEs, by aggregate deal value are banks; and as discussed above- other services; machinery, equipment, furniture, recycling; chemicals, plastics, rubber, non metallic products; amongst others. Appendix 5 shows the number of deals completed by sector, and this is then summarized in Appendix 6.

Finally, by looking at the target country, Appendix 8, summarizes the data by aggregate deal value, on comparison with the data gathered from Appendix 7, which shows the number of deals completed. The most significant target countries by aggregate deal value are the USA, China, Canada, and India, amongst others. A possible cause for the targeting of deals in USA, Canada, China, and India, by aggregate deal value, could simply be because these are highly liquid markets, with low sovereign risk, and a well developed and highly integrated financial market. Another possible reason could be because there are not enough buyout targets for PE firms to target, as discussed by Sheng op, cit. (2010) with regards to China, and Poddar op. cit. (2009) with regards to India. Moreover, these countries have market participants that are rational. These are all features that make PIPE investments very tempting for institutional investors, specially PE firms and VC funds. The possible reason for the increase in PIPEs in 2015 is the lack of good returns globally, together with the potential investment opportunities for investors to use their excess liquidity.


4. CONCLUSION As discussed in Section 1, the issuers and investors have compelling reasons

to engage in PIPEs. My hypothesis that the excess liquidity of Private Equity firms, among other investors is invested in PIPEs, as a source of diversification is not rejected. This is fortified by my analysis, via the OLS regression output estimated in the paper which shows that PIPEs have significance as a source of funding for issuers in times of financial crises. However, the model risk in the paper is high as, there was a lack of data availability, as the sample size was only 1998-2014. Secondly, the model used was only as good as its assumptions. Also, some assumptions such as that of linearity have been violated. Therefore, there is scope for further research by employing more advanced econometric models.

Zephyr data indicates that PIPEs have become more important over time and the percentage of aggregate deal value in PIPEs has increased dramatically since 2000. They have increased significantly in 2015, probably, in my opinion, either due to poor returns on other asset classes, or due to veto rights, board seats or participating convertibility of shares, among other reasons. However, my primary belief is that PIPEs have developed into a new asset class for investors to park excess liquidity in critical times, as a safe haven. The database also establishes a focus of deals in the UN, GAFI, OECD, and G10 regions, and by sector, in banks; other services; machinery, equipment, furniture, recycling; chemicals, plastics, rubber, non metallic products; among others. The increase in PIPEs in EMEs, reinstates my reasoning on the excess liquidity hypothesis for diversification. Therefore, PIPEs are a good option when systemic (or sovereign) risk pushes the cost of debt up, although the idiosyncratic risk is very low, and the firm has a good credit rating.

Further analysis indicates that PIPEs have mainly been used to fund firms in USA, Canada, China, and India, probably due to the high liquidity, integrity and low sovereign risk in these financial markets, or due to the investor rationality in these markets that is coupled with the excess liquidity of the PE firms.

In conclusion, it is fair to say that PIPEs are not only an alternative source of funding for companies that need to raise additional funding, as they form a separate asset class between debt and equity in the standard Pecking Order Theory of Corporate Finance, but have also essentially become a new class of alternative investments by themselves for the sophisticated investors, such as PE firms, Venture Capitalists, Pension Funds, Hedge funds and so on due to the lack of good returns globally, and excess liquidity on their balance sheets.


APPENDICES APPENDIX 1- PIPEs BY AGGREGATE DEAL VALUE

Source: Zephyr


APPENDIX 2- PIPEs BY AGGREGATE DEAL VALUE IN RECENT YEARS

Source: Zephyr APPENDIX 3- PIPEs BY WORLD REGION (AGGREGATE DEAL VALUE)

Source: Zephyr


APPENDIX 4- PIPEs BY WORLD REGION (NUMBER OF DEALS COMPLETED)

Source: Zephyr


APPENDIX 5- PIPEs BY SECTOR (NUMBER OF DEALS COMPLETED)

Source Zephyr


APPENDIX 6- PIPEs BY SECTOR (AGGREGATE DEAL VALUE)

Source: Zephyr APPENDIX 7- PIPEs BY TARGET COUNTRY (NUMBER OF DEALS COMPLETED)


APPENDIX 7- CONTINUED





Source: Zephyr


APPENDIX 8- PIPEs BY TARGET COUNTRY (AGGREGATE DEAL VALUE)

Source: Zephyr


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Bera, A. and Jarque, C. (1982), ‘Model specification tests: A simultaneous approach’. Journal of Econometrics, 20, pp. 59–82.

Breusch, T. (1979), ‘Testing for Autocorrelation in Dynamic Linear Models’. Australian Economic Papers, 17, pp. 334–355. Chaplinsky, S. (2003), ‘PIPEs: Private Equity Investments in Distressed Firms’. Darden Business Publishing, University of Virginia. Chow, G. (1960), ‘Tests of Equality Between Sets of Coefficients in Two Linear Regressions’. Econometrica, 28 (3), pp. 591–605. Dai, N. (2006), ‘Does Investor Identity Matter? An Empirical Examination of Investments by Venture Capital Funds and Hedge Funds in PIPEs’. Journal of Corporate Finance, 13. Dickey, D. and Fuller, W. (1979), ‘Distribution of the Estimators of Autoregressive Time Series With a Unit Root’. Journal of the American Statistical Association, 74 (366), pp. 427-431. Dresner, S. and Ackerman, B. (2009), ‘PIPEs A Guide to Private Investments in Piublic Equity’. New York, USA: Bloomberg Press, pp.1-6. Engle, R.F (1979), ‘Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation’. Discussion Paper, London School of Economics, London, Econometrica, 50, pp. 987-1007, (1982). Gerhard, F. (2008), ‘Private Investments in Public Equity (PIPEs) – A Closer Look at PIPE Transactions in Switzerland’. European company and Financial Law Review, 5 (3), pp. 305-352. Godfrey, L. (1978), ‘Testing Against General Autoregressive and Moving Average Error Models when the Regressors Include Lagged Dependent Variables’. Econometrica, 46, pp. 1293–1302. Hogboom, J. (2004), ‘Private Investment in Public Equity: An Overview’. New Jersey Law Journal, 177 (7), 621, pp. 1-2. Kaplan, S. and Strömberg, P. (2008), ‘Leveraged Buyouts and Private Equity’. Journal of Economic Perspectives, 23(1), pp. 46-121.


Kuzneski, J. and Landen, R. (2006), ‘ Looking through the PIPE- opportunities for private equity investors’. Private Equity Alert, May 2006. Weil, Gotshal and Manges LLP. Lerner, L. (2003), ‘Discloseing Toxic PIPEs: Why the SEC Can and Should Expand the Reporting Requirements Surrounding Private Investments in Public Equities’. The Business Lawyer, 58, p. 655. Lewis, D. and Pidgeon, S. (2006), ‘Raising Capital Through a PIPE Transaction’. The Corporate Handbook Series, Snell & Wilmer LLP.

Ljung, G. M. and Box, G. E. P. (1978), ‘On a measure of lack of fit in time series models’. Biometrika, 65, pp. 297-303.

Machera, C. (2008), ‘In the PIPEline’. Private Equity Alert, May 2008, Weil, Gotshal and Manges LLP Modigliani, F. and Miller, M. (1958), ‘The Cost of Capital, Corporation Finance, and the Theory of Investment’. American Economic Review, 48, pp. 248- 297

Myers S.C. and Majluf N. (1984), ‘Corporate financing and investment decisions when firms have information that investors do not have’. Journal of Financial Economics, 13, pp. 187-221.

Phillips, P and Perron, P. (1987), ‘Testing for a Unit Root in Time Series Regression’. Cowles Foundation Discussion Paper. 795-R (1), pp. 335-346. Pinedo, A. and Tanenbaum, J. (2016), ‘Frequently Asked Questions About PIPEs’. Morrison & Foerster LLP. Poddar, S. (2009), ‘The PIPE paradox’. PEI Asia, 34.

Ramsey, J. B. (1969), ‘Test for Specification error in Classical Linear Least Squares Regression Analysis’. Journal of the Royal Statistical Society, Series B, 31, pp. 350-371.

Särve, B., (2013), ‘PIPE Investments of Private Equity Funds’. Diplomatic Verlag, p.13 Sheng, E. (2010), ‘Private equity firms increasingly buy listed companies in China’. Private Equity News, 04-08-2010.

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Smith, M. (2002), ‘PIPES: Better for VCs than LBOs?’. The Journal of Private Equity, Fall 2002, pp. 66-71.

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Are Private Investments in Public Equities and Alternative Source for Both Funding and Investment

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