Amsterdam Business School
Determinants of IPO waves in the United States from 1995 to 2014.
Name Anna Schot
Student number 10010513
Program MSc Business Economics Specialization Finance
Number of ECTS 15
Supervisor I. Naaborg Completion Final version Number of pages 51
Abstract
Periods of high IPO volume followed by periods of low IPO volume create IPO waves in the economy. This paper explains fluctuations in IPO volume through capital demand of firms,
information asymmetry costs, investor sentiment levels and alternative financing through debt. Sets of time-series regressions are run on an aggregate and industrial level providing insight into the behavior of firms, thus explaining IPO waves. Results provide strong evidence for the relevance of capital demand of firms both at an aggregate and industrial level and investor sentiment particularly at an industry level. Some ambiguity remains surrounding the debt hypothesis, but evidence is provided in support of the trade-off theory, however under certain conditions IPO volume and debt are
complementary at the industry level. Information asymmetry is not statistically or economically relevant in predicting IPO volume.
Keywords:
Statement of Originality
This document is written by Anna Schot who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in
creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
1 Table of Contents
1. Introduction 2
2. Literature Review 4
3. Methodology and Data 11
3.1. Methodology and Hypotheses 11
3.1.1. Capital demand hypothesis and methodology 11 3.1.2. Asymmetric information hypothesis and methodology 13 3.1.3. Investor sentiment hypothesis and methodology 14 3.1.4. Debt hypothesis and methodology 16
3.1.5. Combined regression 17
3.1.6. Industry analysis methodology 18
3.2. Data and descriptive statistics 20
3.2.1. Capital demand proxies 24
3.2.2. Asymmetric information proxies 24
3.2.3. Investor sentiment proxies 25
3.2.4. Debt proxies 27
3.2.5. Industry-level analysis proxies 28
4. Results 31
4.1. Aggregate time-series results 31
4.1.1. Capital demand results 33
4.1.2. Information asymmetry results 33
4.1.3. Investor sentiment results 34
4.1.4. Debt results 35
4.1.5. Combined regressions results 37
4.2. Industry panel time-series analysis 40
4.3. Robustness check 44
5. Conclusion and discussion 47
1. Introduction
In the world’s economy today there are many possibilities open to firms to raise funds, since the introduction of the stock market centuries ago firms have sold parts of their company as equity shares to the public on stock exchanges. The initial public offering (IPO) market is a dynamic market rapidly moving from highly active to extremely inactive periods creating strong waves in the volume of IPOs. In the past different theories have been developed regarding the drivers of changes in IPO volume, attempting to find patterns and factors that predict IPO frequency in the market. However, over the past two decades a series of
economic crises have taken place vastly changing the market climate and responses of investors and firms to different circumstances. To the stock market two mayor events are of particular interest: the dotcom bubble in 2000 and the financial crisis in 2008; preceding the dotcom bubble IPO volume rose to an incredible record of $65 billion in the United States in 1999-2000, the effect following the bubble was of equal remarkable nature as the IPO dropped in volume drastically (Ritter & Welch, 2002). The financial crisis in 2008 was not preceded by an equally high increase in IPO volume, but the market for IPOs dried up drastically following the initiation of the crisis. With the IPO market increasing rapidly to a remarkable level of $ 85.2 billion in 2014 (Renaissance Capital Database), it is offer utter interest to study the drivers of the dotcom bubble in last two highly turbulent economic decades. The behavior of firms in based on a wide variety of factors, creating ambiguity as to which factors are the decision making drivers causing IPO waves.
The aim of this research is to identify and explain the determinants of aggregate IPO volume in the period 1995-2014; This study focuses on the United States market and concentrates in an economically turbulent time period, creating a new testing environment in relation to previous research on IPO waves. Ultimately the outcome of the sets of regressions will answer the central question this research: What are the determinants of IPO volume in the period 1995-2014?
The behavior of firms in terms of volume on the market based on several aggregate as well as industrial variables is tested and analyzed using two sets of time-series regressions. In these analyses three existing historically tested hypotheses are modified and retested over a new time period, a time period that is economically turbulent potentially changing the importance of different theories. The three hypotheses that were previously tested are the capital demand hypothesis, the asymmetric information hypothesis and the investor sentiment hypothesis, however the hypotheses have different proxies as they have been adapted to the
change in information availability and interpretation of relevance on this particular time period. Additionally a new hypothesis is introduced covering the relationship between equity and debt; research has been done on the choice between equity and debt and the driving factor behind this choice, however the debt-equity choice of firms has not been related to aggregate IPO volume to my knowledge. Ambiguity of the effect of the debt-equity ratios significance on the choice of firms to go public is existent (Hovakimian, Opler & Titman, 2001), making it particularly relevant to study the choice between debt and an IPO; a firm is only at liberty to do an IPO once, thus there is a difference in regular debt-equity choices and taking on additional debt and going public for the first time. The debt hypothesis test the effect of debt proxies on the volume of IPO’s in the market, a new approach in testing the effect of the costs and volume characteristics of debt on the IPO market.
In section 2 an overview of existing theories is analyzed, background information is given on the assumed effects of debt and the importance of the industry to IPO volume is presented. Section 3 describes the two time series analyses used to test hypotheses, their underlying hypotheses and the data used to perform these regressions. Section 4 presents the results for the sets of regressions including a thorough analysis studying the correlation between variables and a robustness check. Finally, section 5 will provide an overview of the main conclusions and provide some suggestions for further investigation.
2. Literature review
An initial public offering is a major form of financing a firm through public equity in the United States, however this is a highly turbulent market. The concept of “hot” and “cold” markets was introduced into academics by Ritter, a professor specialized in initial public offerings, in 1984. A market is defined as “hot” when there is an unusual amount of public offerings an underpricing is extremely high, contradicting the “hot” market is the “cold” market where there are almost or no IPOs and there is low underpricing. The market moves from “hot” to “cold” markets and vice versa, thus creating IPO waves.
A widely studied theory is based on capital demand of individual firms, it states that when there is increased opportunity for investment the demand for funding goes up. As an IPO is a primary source of funding private companies by private investors this increases the number of IPOs engaged in in the market. One of the main reasons for a firm to go public is to acquire capital (Ritter & Welch, 2002).The capital demand or ‘market’ theory focuses on the effect of the demand of capital that is assumed higher in periods of economic prosperity; changes in firms economic environment can lead to new investment opportunities which increase capital demand (Buttimer, Hyland and Sanders, 2005).
Early research (Ibbotson and Jaffe, 1975 and Ritter, 1984) and more recent research (Baker and Wurgler, 2002) both provide evidence from hot issues periods, supporting the hypothesis that more firms go public in periods of high market returns versus periods of low returns. Choe, Masulis and Nanda (1993), study a sample of public offerings of equity and debt on the NYSE, AMEX and NASDAQ from 1971 to 1991 and find that demand for capital is higher is periods of favorable market condition and that this leads to more public offerings as a source of capital. They propose that the increased investment opportunities and asset prices lead to more prospective profits, therefore firms are willing to bear adverse selection costs associated with issuing equity. These adverse selection costs that the firm has to bear are greatly reduced as the public offering creates the opportunity for them to engage in the
currently highly profitable investment opportunities.
Pastor and Veronesi (2005), use an annual sample of initial public offerings in the United States between 1960 and 2002 and find that IPO waves are ‘rational’ implicating that varying market conditions are the reason for fluctuations in IPO volume. To measure this they use expected market returns, expected profitability and uncertainty about the profitability of IPOs in excess of market profitability; they conclude that IPO volume is highly determined by the market conditions and thus is caused by changes in these conditions. Additionally, they conclude that IPO waves are caused by clustering of the initial investors IPO timing
decisions; when market conditions are favorable more firms reach their optimal IPO timing point an thus go public. A plausible reason for reaching this optimal IPO timing point is that many profitable investment opportunities are present in the economy now and postponing the IPO results in forgoing these profitable investment opportunities.
Buttimer, Hyland and Sanders (2005) perform a study on real estate investment trust (REIT) IPOs and find strong supportive evidence that the capital demand hypothesis is the most relevant theory to explain IPO waves. The nature of REIT IPOs and the regulations involved lead to more transparency and therefore eliminate the price uncertainty eliminating the noise caused by mispricing making it possible to study the effects of capital demand in an isolated climate. An important note by these researchers is that when a particular industry is experiencing economic prosperity this results in growth of the entire industry and not just IPO volume increase. Therefore the seasoned equity offering market follows the same pattern as the IPO volume market and both increase when large market capitalization increases occur in the industry.
Another widely employed theory in the cause of fluctuating IPO volume is that there is a form of mispricing in the market and firms are seeking to take advantage of this. This theory is most widely known as the ‘market timing’ theory, this is one of the two main theories regarding the motivation of an individual firm to go public. The market timing theory states that firms wait for the ‘perfect’ moment to go public, they want to maximize their profits from an IPO. Lucas and McDonald(1990) find that firms put off going public if they know that their equity is currently undervalued. The opposing theory to the market timing theory is the life-cycle theory first introduced by Rajan and Zingales in 1995; they claim that firms operate through a life-cycle, they are founded by an entrepreneur, then a time comes when the firm want to be acquired by another firm or party. The life-cycle theory claims that the market circumstances are irrelevant in the decision to go public which is entirely based on the history and current state of the firm. In order to de identified by a potential buyer and to receive a fair price for the firm it is in the firms best interest to go public before being acquired; through entry into the market a firm is valued and a price is determined.
Baker and Wurgler (2007) conclude that the number of IPOs is extremely sensitive to investor sentiment and find that investment bankers often refer to investor optimism as
‘windows of opportunity’ for IPOs. Especially with high fluctuation in the stock market in the past decades market peaks are reached frequently, intuitively this means that it is in the firms best interest to try to profit from these market peaks and thus time their IPO accordingly. One
of the most studied source of this mispricing lies in the perception that investors have of the market, known as investor sentiment. The level of mispricing is much higher, stocks are overvalued, when the level of investors sentiment is high (Baker & Wurgler, 2007). Investor sentiment is the confidence that investors, particularly smaller parties with less expertise, have in the economy; these small investors are the least informed investors and act mostly on perception rather than inside or other superior information.
The investor sentiment hypothesis is based on attitude and beliefs of investors concerning the economy, the assumption is made that when investors are optimistic this will drive the number of IPOs up due to higher gains available through a public offering(Lowry, 2003). During a period of high sentiment investors subscribe to IPOs more and are willing to buy stocks at a higher price, simultaneously the increase in trading activity directly following the IPO drives up the price immediately creating the phenomenon of underpricing (Ibottson & Jaffe, 1975). Contrary, when investor sentiment is low firms may put off going public as they would receive less than the fair value for their shares.
Hot issues refer to stock issues that have exceeded their initial offering price by higher than average premiums after the issuance (Ibbotson and Jaffe, 1975). The number of IPOs skyrocketed at the end of the nineties, creating the hottest IPO market up to that time
(Derrien, 2005). Derrien finds that investor sentiment does not only determine the pricing of IPOs it also contributes significantly to determining the number of IPOs in the market. Helwege and Liang (2004) find that investor optimism is the primary driver of hot IPO markets and conclude that adverse selection costs, managerial opportunism and technological innovations are not primary determinants of hot IPO markets.
Closely related to this mispricing is the information asymmetry problem, related to investors as well whom employ a low tolerance for high levels of differences in information between them and the managers of companies (Myers & Majluf, 1984). If analysts are unable to predict earnings accurately due to high levels of information asymmetry the risk-averse investors are less inclined to invest on the stock market leading to lower subscriptions for IPOs and generally less interest in them reducing the offer prices dramatically(Myers & Maljuf, 1984). Firms now have to offer large discounts, which increases the costs of going public for firms. Through this pattern going public becomes less attractive for the company and therefore less IPOs will be offered in these market circumstances, firms will either seek alternative forms of financing that are less costly for them or try to reduce information asymmetry.
The effect of difference in the information available to the firm’s management and to its investors influences the decision to go public vastly. Assuming that managers go public when the company is overvalued influences investors attitude towards an initial public offering, they respond in the market by lowering their estimated firm value leading to correct average pricing of firms in the market (Dierkens, 1991). When costs of going public are high due to high asymmetry it is very expensive for a firm to go public, they will have to offer high discounts to compensate the uncertainty for investors and firms will try to seek alternative financing. The discount that they offer will have to be in the form of underpricing, meaning that the firm offers shares at a lower price than investors believe it to be worth. Lowry (2003) states adverse-selection costs are caused by the variation in investor’s uncertainty regarding the true value of a firm at its IPO. Lowry finds that the adverse-selection costs are statistically significantly negatively related to the number of IPOs; however, she concludes that the adverse selection does not have a significant economic effect.
Korajczyk, Lucas and McDonald (1992) find that firms with positive NPV values on projects will avoid raising capital through equity offering because of adverse selection costs in place. When the combined costs of the offering and the adverse-selection costs exceed the benefits of the equity offering the firm will seek financing elsewhere. Similarly, Choe, Masulis and Nanda (1993) find that more announcements on the returns on stock offerings is positively related to the amount of public equity offerings, more availability of information related to equity offerings creates less problems concerning information surrounding IPOs. Choe’s , Masulis’ and Nanda’s study on US IPO data links the capital demand theory to a reduction in information asymmetry, they argue that more firms going public when there are more investment opportunities implies that investors have less concerns about the motives of the firm going public. Intuitively, investors require less of an effort of firms to compensate them for the risk of investing in the firm; the risk of investing in a company is reduced as there are more opportunities in the economy for a firm to make profits.
In IPO pricing there is not only information asymmetry between the investors and management of a firm, but there is also a difference between the information known to
different investors. The winner’s curse theory is applied to IPOs frequently, when uninformed investors are informed beforehand they do not have to compete with informed investors this results in the elimination of underpricing (Michealy and Shaw, 1994). Therefore, when high underpricing is present on the IPO market this indicates that there is high information asymmetry amongst investors. However, there are other factors that play a role in the degree
of underpricing, Michealy and Shaw(1994) also find that better underwriter reputation decreases underpricing significantly.
A firm has many options when it comes to financing investment opportunities, there is a choice between public and private financing and in these categories there are many different options as well. Hickman (1950), one of the first to investigate the offering of equity relative to offering of debt finds that the ratio of equity to debt increases in expansionary phases of the U.S. business cycles and that is decreases during contractions. This theory implies that equity is the favorable vehicle during times of economic prosperity and that debt is the preferred form of financing when market circumstances are less favorable. However, the economic climate has progressed highly since the 1950s adding additional vehicles for financing and creating alternative motives for choosing particular forms of financing. Many researches in the past find that lower levels of debt follow levels of high IPOs, firms are assumed to have raised funds by going public and therefore demand less new funds in the year after the IPO. However, as firms are profit-maximizing organizations they will always seek the least costly form of financing. A study on the costs of different forms of financing employed by firms in the United States between 1990 and 1994 find that IPOs are the most costly form of
financing; the study calculates average costs for IPOs at 11%, seasoned equity offerings (SEOs)at 7,1%, convertible bonds at 3,8% and straight bonds at 2,2% (Lee et al., 1996).
In 2008 the market went through the financial crisis, which caused an immediate decrease in the availability of debt for financing. The cost of debt increased, providing less incentive for firms to make efforts to obtain new debt and encouraging them to seek other options to finance their business (Hovakimian, Opler &Titman, 2001). There are two main theories on the choice between equity and debt. The first theory is the pecking order theory by Myers, it states that internal financing is preferred to external financing and that when
external financing is needed debt is preferred to equity; in this context that means that debt is preferred to equity under most circumstances and that firms only issue equity when the circumstances surrounding debt are extremely negative. Shyam-Sunder and Myers (1999) support this theory through a test on 157 American firms that are trade continuously between 1971 and 1989, they regress debt issuance on the financing deficit of firms and find a slope coefficient of one. This results implies that the financing deficit is fully determined by the debt a firm issues and completely rules out equity financing. However a more recent test of this pecking order theory by Frank and Goyal (2003) finds contradictive results and claim that equity is a significant component of external finance; when testing the pecking order theory
empirically on a larger set of firms they find that it has lost significance over time. Small firms do not support the pecking order theory and as more small firms become publicly traded over the course of the 1990s and 1990s than in the 1970s the pecking order theory becomes less relevant for publicly traded firms. Even when testing the top quantile of largest firms traded publicly the evidence does not support the pecking or theory (Frank and Goyal, 2003).
The second existing theory in debt versus equity financing is the trade-off theory stating that the optimal debt level is chosen based on the trade-off between benefits and costs. The benefits of debt are evident through tax benefits reducing costs of financing for firms and the cost are the bankruptcy costs that a firm faces as it defaults on debt(Frank & Goyal, 2003). Baker and Wurgler (2002) argue that it is not the trade-off theory that determines debt versus equity financing, but that market circumstances determine whether firms go public; periods in which many firms go public are followed by a decade of lower leverage meaning less debt is issued. Through this they argue that debt is a result of firms trying to achieve the maximum profit from timing the market and going public at its peak.
Therefore, it is highly relevant to measure the effect of the availability of debt, the cost of debt and the ease of obtaining debt for firms. Therefore a debt hypothesis is introduced to test whether the terms of debt in the market effect the volume of IPO’s. The expectation is that there is a negative relationship between the characteristics contributing to higher or easier debt and the number of IPO’s. When making the decision to go public firms often consider the costs of engaging in the process. If the alternative form of financing debt is harder and more costly to obtain raising financing through an equity offering becomes a more attractive option.
Another important aspect of the dynamics of the public offering market is the industry in which a firm operates. Hot markets are defined as markets in which unusually high volumes of IPOs, severe underpricing and frequent oversubscriptions of offerings occur (Helwege and Liang, 2004). Helwege and Liang (2004) find that IPOs are concentrated in particular
industries but that this is irrelevant for hot or cold markets, they find that in particular
industries like the personal computer market in the 1980s and internet firms in the 1990s tend to go public despite the state of the market. New products can spark hot IPO markets, many firms tend to go public in this time, but the market generally cools down before all the firms raise funds through their IPOs leaving them to go public in a cold market (Helwege & Liang, 2004). Thus the main finding is that IPOs are generally clustered in specific industries and that the timing is irrelevant. This finding disputes the theory that IPO waves are in one
specific industry only, rather when an wave reaches a peak more firms go public in all industries in relative proportions.
Ritter (1991) argues that firms take advantage of market peaks in particular industries, investors are overly optimistic about stocks in a particular industry driving prices up. Through this increase in ‘mispricing’ firms in a particular industry seize the opportunity for large profits and go public. During hot markets the common characteristics become relatively more important to firms than their individual characteristics, which means that market variables but also industry variables should become more important in predicting IPO volume (Alti, 2005). Pagano, Panetta and Zingales (1998) perform a study on the drivers of IPO waves in Italy, they find that going public is more attractive when industry market to book ratios are higher. Firms base their decision on going public directly on the moves of their closest peers in the industry, as many firms in the industry go public the average initial offer price goes up allowing a form of free-riding. The circumstances in the market are very beneficial for a particular firm without the firms effort into creating these circumstances, it profits from the high valuation of the industry without carrying the burden of costs.
3. Methodology and Data
The first analysis is a quarterly time series analysis from 1995 to 2014. Secondly a panel study is performed over the same quarterly time period, but dividing companies into 16 different industries. Four hypotheses relating to capital demand, asymmetric information, investor sentiment and debt are tested. These hypotheses are tested using proxies for the specific hypothesis and a lag of the number of IPOs making it an autoregressive model of degree 1. Section 3.1 describes IPO volume per period and industry, in section 3.2 the proxies and assumptions are discussed and section 3.3 describes the industry-level analysis and proxies.
3.1. Methodology and hypotheses
The multiple regression time series analysis consists of seven regressions; four regressions are based on hypotheses: three studying the existing capital demand, information asymmetry and investor sentiment theory and one regression is applied for the first time to my knowledge is the debt hypothesis. The fifth regression is a controlling regression to capture the dynamic of the market, it is ambiguous what the effect of the market is on the different hypotheses and therefore the combined regression is run twice, once with the control for the market and once without. The sixth is the combined regression excluding the control variable; in the seventh regression the control variable is included. The regressions per hypothesis and are discussed in section 3.1.1 to 3.1.4, then the control regression is presented in section 3.1.5 and the combined regressions a discussed is 3.1.6.
3.1.1 Capital demand hypothesis and methodology
In prosperous economic times firms are seeking funds to invest to grow and increase sales. There are many ways in which a company can fund this growth, the capital demands hypothesis assumes that firms use an IPO to acquire funds to grow. As previous research (Baker & Wurgler, 2002) shows the varying market conditions are assumed significant in determining IPO volume. Increases opportunities for investment to grow the firm will lead to a higher capital demand; capital demand will increase the demand for funds, raising IPO volume and this will result in higher future growth. Thus, I assume that future economic growth is a result of a high volume of IPOs that were initiated by firms increasing their capital demand. Therefore a capital demand hypothesis is tested:
H1: Higher capital demand by firms leads to higher IPO volume, funds raised through IPOs are used to invest and results in higher future growth.
To test this hypothesis three proxies are used to measure the future growth in the economy, if future growth follows after the current quarter this shows that firms have high capital demand currently to achieve this growth; to raise these funds firms will engage in IPOs to be able to seize investment opportunities available. The effect of this future growth is captured in three proxies that together capture future economic growth: future GDP growth, future investment growth and future sales growth. Future GDP growth reflects the overall level of wealth growth within the US economy; strictly speaking gross domestic product (GDP) is the sum of all market value of goods and services produced by labour and property located in the United States (Bureau of Economic Analysis definition).
GDP captures overall economic growth, but for the purpose of investigating whether the IPOs are driven by investment opportunities the future investment is an interesting proxy to include. If future investment increases after a period with many IPOs this indicates that proceeds raised from IPOs were used to invest, meaning that the higher capital demand of firms was indeed driven by investment opportunities. Investment is defined as real private, non-residential, fixed investment which is a summary of all investment done in the economy, excluding residential wealth means that private investment of people in the United States is excluded. Thus an increase in the investment of firms has to be funded, an IPO is a wat to raise funds to meet new funding requirements; therefore firms go public now to invest in the future.
The third proxy is the change in future sales, high sales follow an IPO when funds are used to expand and grow the company. Data on sales is more specific than the other two proxies, instead of using market wide sales the sales of US firms that are available on exchanges are used, creating a more specific variable for this study. The data of all of the public firms in the United States is combined and an average is taken versus taking full economy data in the other two proxies. Higher sales are assumed a result of an investment made by the company; they have to invest in new or existing opportunities more than before to increase sales.
The regression is a linear multiple variable regression. In addition to the three future growth variables a lagged variable of the dependent variable is included. The model is autoregressive of degree one, as IPO volume depends on the IPO volume of the preceding
period a lag of the dependent variable is included to capture this effect. The regression to test the capital demand hypothesis is as follows:
IPO volume = α + β1future GDP growth + β2future investment growth + β3future sales growth
+ IPO volumet−1
3.1.2 Asymmetric information hypothesis and methodology
Increased asymmetric information specifically between managers and investors increases the cost of public offering (Dierkens, 1991). Adverse-selection costs are driven up by uncertainty amongst investors about the true value of the firm. Investors pay less for the shares when there are high levels of asymmetry; they demand a higher discount for more risk. Therefore, I assume that increases in levels of information asymmetry reduce IPO volume.
H2: Increases in information asymmetry increase the adverse selection costs of IPOs reducing the volume of IPOs in the following period.
Information asymmetry between investors and firms’ management is measured by two proxies: dispersion around earnings announcements and analyst dispersion around
announcement. Dispersion is the degree of scatter around a value, a higher degree of scatter means that different levels of information are available to different investors and analysts. Dispersion around earnings announcement shows the reaction of the market to the firms earnings announcement, if a reaction to an announcement is very high this indicates that there is a high level of asymmetric information (Dierkens, 1991); managers have important
information that is released through the earnings announcement. If the average dispersion around earnings announcement in the economy goes up this implies that the adverse-selection costs of going public increase making and IPO a less attractive form of financing. Therefore, the assumption here is that a positive change in the level of the dispersion around earnings results is a decrease in IPO volume.
Analyst dispersion is the standard deviation of the forecast made by analyst
concerning the returns of stocks in a period. Information known to analysts, whom act in the benefit if investors, can be seen as the information available to investors. If the standard deviation of analysts in a quarter increases the overall spread of forecasts made by these analyst is larger implying more uncertainty about firms in the economy. If there is an increase
in the standard deviation of analysts’ forecasts this is an indication for an increase in information asymmetry and assumingly this leads to a decrease in IPO volume.
Again, a lag is included in the regression to create an AR(1) model: IPO volume in the previous period, yielding the regression to investigate the effect of information asymmetry:
IPO volume = α + β1∆ dispersion around earnings announcements
+ β2∆standard deviation of analysts′forecasts + IPO volumet−1
3.1.3 Investor sentiment hypothesis and methodology
Investor sentiment, the attitude of investors in the market, strongly effects the decision of a firm to go public. When investors have positive expectations of the economy an IPO is more likely to be successful, because investors are willing to buy stocks and offer higher prices (Baker & Wurgler, 2007). These circumstances offer a beneficial opportunity for firms to go public, the likelihood of selling all shares offered at the IPO increases and the price at which these shares are sold increases as well. ‘Mispricing’ is a frequently used term for the pricing during periods of high sentiment through which a ‘window of opportunity’ exists for firms to go public, firms can maximize their value from an IPO when they go public in a high
sentiment period (Ritter, 1991). A high sentiment period is partially driven by current high returns in the market, this will influence the attitude of investors positively. Therefore, the assumption here is that firms go public during periods of high investor sentiment when investors are overpaying for stocks. Investor sentiment is believed to be a driver of economic bubbles, thus period preceding the burst of a bubble are particularly high sentiment periods. The assumptions with respect to investor sentiment are reflected in the third hypothesis:
H3: In periods of high sentiment it is value-maximizing for a firm to go public increasing aggregate IPO volume.
Three characteristics of investor sentiment are translates into proxies: investor beliefs, their trading behaviour and returns following periods of high sentiment. First the beliefs of investors are measured as the change in the consumer confidence index following the period of high IPO volume. Investors in this research are non-institutional small investors, they are often the most uniformed investors acting based purely on their beliefs in the economy. The consumer confidence index is closely correlated to the sentiment of investors as small investors are consumers as well. Investors are greatly influenced by high returns and high
equity values, they perceive this as a sign of economic welfare, therefore their confidence increases following a period of high returns and high equity values. Firms go public following high return and at the peak of equity levels, thus an increases in future consumer confidence follows after a period of high IPO volume, thereby a positive relationship is assumed between future consumer confidence growth and IPO volume.
The second proxy is the change is trade volume, trade volume is number of shares that is traded. A high trading volume is equivalent to high liquidity in the stock market, as trading is more frequent it is easier to trade stocks than when there is low market activity (Baker & Stein, 2004). The high liquidity makes investing in the stock market in attractive option to investors, increasing the demand for stocks and thus driving up the average prices of shares. High demand and high prices in the stock market make it attractive for firm to go public, they will receive high values per share often exceeding the underlying asset value per share of the firm. Firms seize this opportunity and go public when they perceive the market to be at its highest sentiment value; the assumption in this model is that an increase in trading volume increases the volume of IPOs.
During the periods in which investor sentiment is rapidly increasing the market returns are high. Firms time the market and try to go public right at the peak of high sentiment which means that the stock market is at a high level. If future return is high following the previous period is assumingly a period of low returns. Baker and Wurgler (2000) find that IPO volume is higher during bull markets, soon after the peak of these bull markets low market returns follow, thus a negative correlation between future market returns and IPO volume is expected. Optimistic investors drive market activity and prices up with their eager trading behavior causing a peak in the market which translates in to a peak in returns, firms time the market to maximize value through going public at a peak. After a peak returns return to lower levels, similarly the IPO volume decreases again as a period of low returns is not value-maximizing, the price the firm can receive per share is not at its best. Therefore a negative relationship is expected between future returns and current IPO volume, if IPO volume is at a peak right now the returns are expected to decrease in the year following the period of high IPO activity. Vice versa, when future returns are low this implies that currently there is a high-return period creating a value-maximizing climate for firms to go public increasing IPO volume.
The regression to measure the effect of investor sentiment and accompanying reaction by investors contain the three investor sentiment proxies, a constant and a one-quarter lag of IPO volume:
IPO volume = α + β1future consumer confidence index + β2∆trade volume + β3future market return
+ IPO volumet−1
3.1.4 Debt hypothesis and methodology
Equity through public offering is a possibility to finance investment and development of a company, however there are other ways to gather funds(Lee et al., 1996). The major other source of funding is debt and is available in different forms and sizes. In this research the relationship between the characteristics of debt and IPO volume is testes in addition to existing theories. Debt in the form of loans is an important alternative to going public, loans allow firms to stay private and may in certain scenario’s be the more beneficial form of financing. Issuance of bonds is another form of financing through debt, a vehicle in which debt is offered to the public instead of being taken on by banks. It is hard to scientifically prove a causal effect of debt on equity offering, however the assumption that I make here is that there is a preferred form of financing in the market at particular times; this is equivalent to assuming the trade-off theory holds. I assume that the characteristics of both debt and equity are compared by firms and that the most favourable type of funding is chosen. If circumstances for acquiring debt through a loan are bad firms may be more inclined to go public than they would be if the characteristics of loans at that point are favourable. The same assumption holds for bonds, as offering bond becomes more attractive this decreases the relative attractiveness of equity. An increase in the relative attractiveness of equity, increases the attractiveness of an IPO and results in an increase of aggregate IPO volume; combined this give the fourth hypothesis:
H4: If the attractiveness of debt decreases the relative attractiveness of equity increases resulting in an increase in IPO volume.
Three proxies are used to capture the attractiveness of debt in the economy. The cost of debt is hard to grasp, however the costs of an average loan to a large firm is available through the prime rate. The prime rate is the rate that banks charge their best customers on a loan, even if not all the firms in the economy are the banks’ ‘best’ clients this is still a good benchmark for measuring the interest costs a firm incurs when entering into a new debt contract. When the prime rate goes up, the best loans become more expensive and less favourable loans increases in interest costs as well, firms will try to avoid high interest costs because they are profit-maximizing entities. If debt is expensive this raises the attractiveness of equity, therefore I
assume that a high prime rate preceding a period increases the appeal of equity and leads to an increase in IPO volume.
The amount of loans that are issued varies over time as well, I assume that loans issued is determined by the demand and supply there is of loans in the economy. However, in assuming this economic circumstances in the economy have to be taken into account before making conclusion on the impact this has on IPO volume. There are two possible reasons why the amount of loans decreases, the first is that equity is more attractive than loans and the demand of loans decreases, providing strong support of the applicability of the debt-equity trade-off theory on the IPO market. On the other hand, loan volume could decrease because supply decreases as banks become more risk averse in issuing new loans due to economic turbulence. When the economy is in a contraction banks are less willing to lend money as the risk of default increases investors are also less willing to invest in possible equity issues due to increased risks. Therefore the effect that the volume of loans has on equity is ambiguous and both reasons are taken into account in interpreting the results.
Another way to engage in debt is to issue bonds, which is equivalent to publicly financing debt. An increase in the amount of bonds implies that investors find bonds an attractive security to invest in, in this context two motives could apply for this increase. Either the investors find bonds the most attractive security to invest in or it is the most
value-maximizing form of financing the firm has; a combination of the two is also possible, but more importantly both motives lead to an increase in bond volume. If firms are raising more capital through bonds and investors are investing more in bonds this results in less funds of investors available for other securities including the shares of IPOs.
These three proxies even if somewhat ambiguous in assumption are combined into a regression to measure the effect that the characteristics of debt have on initial equity issuance. An important aspect of this regression is that the prime rate and the changes are measured in the period preceding the period of high IPO volume, only then can conclusions be made about what a change in debt means for current IPO volume. Again a lag is included in the regression to create an AR(1) regression:
IPO volume = α + β1prime rate𝑡𝑡−1+ β2∆amount of loans + β3∆amount of bonds + IPO volumet−1
3.1.5 Combined regression
As IPOs take place on primary exchanges the market fluctuations are used as a control for market circumstances in general. The assumption made here is that more IPOs take place
when the market is up, the change in the value of the stock market is measured through the change in the book ratio preceding the current period. However, the market-to-book ratio and previous market returns are potentially related to the other hypotheses. First, market to book ratio may reflect capital structure of firms relating it to the choice between equity and debt potentially relating to the debt hypothesis. Second, higher market-to-book ratios follow after a period of high market returns, thus reflecting changes is profitability; a highly profitable stock market will undoubtedly have an effect on investor sentiment.
Investors are assumed to have a more positive view on the stock market when market-to-book ratio increased. As the effects that the controls have on the four hypotheses are not clear the regression is run with and without these control variables. The isolated effect that the change in the MB ratio has on IPO volume is tested to examine the relevance of market effects on IPO volume, the regression for this is:
IPO volume = α + β1∆market to book ratio + β2IPO volumet−1
The market effects are assumed to be relevant, thus a combined regression controlling for market effects is included in the set of regressions. As in all other regressions a lag is included for IPO volume in the previous period to control for the cyclicality in the IPO market; the combined regression takes the following form when it includes the control for the market:
IPO volume = α + β1future GDP growth + β2future investment growth + β3future sales growth
+β4future consumer confidence index + β5∆trade volume + β6future market return + β7prime rate𝑡𝑡−1+
β8∆amount of loans + β9∆amount of bonds + 𝛽𝛽10∆market to book ratio + 𝛽𝛽11IPO volumet−1
The combined regression without the control for the market takes the exact same form minus the tenth variable: the change in the market to book ratio. As stated before excluding the market control is important as the effects of the market control on other hypotheses is unclear.
3.1.6 Industry analysis methodology
The effect that industry characteristics have on IPO volume in that specific industry are of interest in studying the origin of the start of an increase in IPO wave and it is interesting to study what the characteristics are that firms base their decision to go public on. Industry variables and aggregate variables are tested simultaneously to study if firms base their choice to go public on industry characteristics or on aggregate characteristics or a combination. The results in the aggregate analysis are taken into account in selecting the proxies for this
analysis. The information asymmetry hypothesis is not tested as it is irrelevant for
determining IPO volume in economic terms, thus there is no reason to retest it here, section 4.1.2 provides reasoning for the economic insignificance of the information asymmetry hypothesis.
The capital demand hypothesis is tested at an industry level using the future sales growth and compared to the aggregate future sales growth; GDP and investment are not measurable at an industry level, however due to the strong correlation between GDP, investment and sales, table 5 in results section shows correlation between proxies, sales is a good proxy for measuring future growth. Future sales growth represent the current investment opportunities in the industry and are expectedly relevant for firms decision making.
Investment opportunities arise because there is an advancement in technology or another area concerning the industry specifically. However, firms are also expected to base their decision on the sales growth of the overall market, because they can go public to generate funds for overall general expansion of the firm that is not based on new advancements in the industry but rather on economic growth economy wide. Therefore, both the industrial and the aggregate proxy for measuring future growth are expected to be significant in determining IPO volume.
The investor sentiment hypothesis is tested at the industry level using the future market returns and the trading volume change in the industry; the CCI is not measurable per industry. The future market return in the industry is expected to decrease if firms are going public in the industry now, this is similar to the assumption made in the aggregate analysis that high aggregate IPO volume results in lower future market returns. Comparison between this proxy at industry level and aggregate level is assumed to give one of the two proxies more relevant than the other thus revealing if firms base their decision on industrial or aggregate characteristics. The same comparative method is applied to the second proxy to measure investor sentiment, the change in trading volume, it is expected to be relevant at either the industrial or aggregate level. Based on conclusions made in past research that the industrial characteristics are relevant for the decision to go public made by firms I expect to find that the industry proxies are relevant and that the aggregate proxies are less significant in this hypothesis.
The debt hypothesis is difficult to translate into industry variables as the existing proxies cannot be measured at an industry level with available data. Therefore, a new proxy is introduced to measure the change in debt in the industry, long term debt for each firm is available the change in the average long-term debt in the industry is a reflective variable for
an overall change in debt financing in an industry. The change is viewed as the ease at which firms can acquire debt in the economy is and is assumed relevant in the choice between equity or debt, if debt is expensive for a specific industry due to uncertainty specifically in the
industry equity is a more attractive choice. However, other factors than industrial
characteristics are assumed to play a role in the cost of debt to firms as well, therefore the aggregate ratio of the prime rate is included in the industrial analysis to control for these differences. A positive relationship that is highly significant in the aggregate analysis between the prime rate ratio and IPO volume is expected here as well, in addition a negative
relationship is expected between the change in the average long-term debt in the industry and IPO volume in the specific industry. However, the assumption on long-term debt is not certain as debt levels may increase simultaneously with an IPO as firms want to seize the opportunity to take advantage of positive attitudes of both bankers and investors, thus the expected result with respect to long term debt changes is potentially ambiguous.
3.2. Data and descriptive statistics
In this section the chosen time frame is presented, following this is a complete description of the dependent variable: IPO volume. Then separate subsections discuss the proxies used in the regressions used to test the hypotheses that were presented in section 3.1. Finally, a short description is given on the altered and additional proxies used in the industrial analysis.
The timeframe of this research is selected based on two major economic events highly
affecting the volume of public offering. The first event is the widely known ‘dotcom’ bubble. The second event is the financial crisis in 2008. These two events have led to a turbulent IPO market, with high volumes preceding the burst and extremely low volume in the period preceding the bubbles. Research in earlier years found investor sentiment and capital demand to be the most important drivers of IPO volume (Lowry, 2003). This study employs a
timeframe ranging from 1995 to 2014, thus capturing the full effect around these two economic events, the years preceding economics crises as well as the years following crises are included. The sample further consists of all IPOs based in the United States, to eliminate potential cross-country effects such as differences in culture or investor attitude one country is selected.
The Thomson One database provides data on initial public offerings, the dependent variable in all regressions is IPO volume in the United States. From 1995 to 2014 a total of
4589 firms went public 1. As the aim of this research is to study IPO volume in the market, it is relevant to deflate the number of firms that go public by the number of public firms in the economy in a quarter to control for the non-stationary feature of IPO volume.
Figure 1: Quarterly time series IPO volume 1995-2014. The total number of IPO's is deflated by the number of public firms (in thousands)
at the end of the previous quarter. Number of IPOs is the total number of firm-commitment IPOs excluding secondary IPOs(more than 75% of offering is secondary), penny stocks(price less than $5), ADRs, REITs, units, close-end funds, and mutual-to-stock conversions. Number of IPOs is obtained from Thomson Reuters: Thomson One database and number of public firms is obtained from CRSP database. The CRSP database provides information on firms listed in the United States (NYSE, NYSE MKT, NASDAQ and Arca exchanges).
Over the past decades the market has changed vastly, amongst the changes is the is the rise of internet and digital companies whom have significantly influenced the market. Table 1 shows the division of the IPOs in the past two decades per industry dividing the IPOs in two sixteen different industry types determined by SIC code; the Communication, Computer and
Electronics industry is the clear leader in the IPO market. An important driver of this is the high uncertainty in the industry, which decreases the alternative financing opportunities. The industry analysis performed after the aggregate analysis will provide further insight into the important contributors to IPO volume that are relevant only for an industry. Table 1 also shows the statistics for the dependent variable, the deflated IPO volume per quarter; the average is 6.98 and with a mean of 5.34 the dependent variables distribution is skewed to the left, indicating that IPO volume is more often below the average than it is above. This also shows that some periods are marked by high IPO volume periods, supporting the existence of
1 Sample includes firms that went public in 1995-2014 in the United States, excluding ADRs, close-end funds, units, REITs, mutual-to-stock conversions, offerings in which more than 75% consisted of secondary shares and penny stock offerings (prices lower than 5).
0 5 10 15 20 25 IP O 's (#) /P ubl ic f ir m s (# in 1000s ) 1995 2000 2005 2010 2014
peaks in the market, followed by longer periods of decreased IPO volume. The standard deviation of 5.52 supports this finding as it shows that large fluctuations take place in the market.
Table 1: IPO statistics 1995-2014
Panel A: Statistics on total aggregate IPO volume
Total number IPOs 4589
(1995-2014)
Mean Median Standard deviation
Absolute IPOs 57,36 41 51,69
Number IPOs/number of public firms 6,98 5,34 5,52
Panel B: IPOs per industry
Industry SIC codes No. Of IPOs Percentage total IPOs
Agriculture, Mining 100–1299, 1400–1499 23 0,50%
Apparel 2200–2399, 3100–3199 35 0,76%
Communication, Computer and 3570–3579, 3600–3699 1611 35,11% Electronics 4800–4899, 7370–7379 Construction 1500–1799 43 0,94% Finance 6000–6499, 6700–6799 437 9,52% Food 2000–2099, 2100-2111 57 1,24% Healthcare 2830–2839, 8000–8099 499 10,87% Manufacturing 2400–2499,2500–2699 251 5,47% 2800–2829, 2840–2899, 3000–3099,3200–3569, 3580–3599, 3900–3999 Oil, Gas 1300–1399, 2900–2999, 198 4,31% 4600–4699, 4920–4929 Printing, Publishing 2700–2799 25 0,54% Recreation 7000–7099, 7800–7999 97 2,11%
Scientific Instruments and 3800–3899, 8710–8719 351 7,65%
Research 8730–8739, 9661 Services 6500–6599, 7200–7369, 355 7,74% 7380–7399, 7600–7699, 8100–8399, 8720–8729, 8740–8749 Trade 5000–5999 395 8,61% Transportation 3700–3799, 4000–4299, 177 3,86% 4400–4599, 4700–4799, 7510–7549 Utilities 4910–4919, 4930-4979 35 0,76%
Panel A shows summary statistics on IPO volume. IPO data is gathered from Thomson One, number of public
firm is obtained from CRSP. Total number of IPOs is the total amount of firms that went public between 1995 and 2014. The absolute IPO statistics are based on the total IPOs as a number. Number IPOs/number of public firms is the number of IPOs in a quarter divided by the number of firms in the economy at the end of that quarter. Panel B divides the IPOs into industries, data on IPOs is obtained from Thomson One, each IPO is grouped into a category based on the SIC codes displayed in column 2. SIC code groups have are adapted from Lowry(2003) and have been slightly adapted to include IPOs in the cigarette business and additional SIC codes in the manufacturing industry. The percentage of total IPOs reflects how many IPOs in the period 1994-2015 take place in that particular industry as a percentage of total IPOs.
Table 2 summarizes the descriptive statistics of the proxies and the following subsections describe the proxies in detail.
Table 2 : Descriptive statistics proxies
Quarterly interval: 1995 – 2014
Mean Median Standard deviation
Capital demand proxies
GDP growth t to t+3 0,0243 0,0260 0,0187
Investment growth t to t+3 0,0421 0,0715 0,0742
Sales growth t-1 to t+3 0,0293 0,0367 0,0337
Information Asymmetry proxies
Δ Earning AR dispersion 0,0082 -0,0014 0,1034
Δ Analyst forecast dispersion 0,0006 0,0015 0,0054
Investor sentiment proxies
Future market return t+1 to t+4 0,1305 0,1620 0,2173
Δ Consumer Confidence Index t+1 to t+4 0,0226 0,0540 0,2334
Δ trade volume t-1 to t 0,0196 0,0337 0,0763
Debt proxies
Prime rate ratio t-1 0,4364 0,4000 0,3703
Δ Amount of loans t-1 to t 0,0250 0,0429 0,1670
Δ Amount of bonds t-1 to t 0,0300 0,0544 0,1763
Proxies designed for use in regressions. Future GDP growth is measures using GDP growth per quarter as calculated by the Bureau of Economic Analysis(BEA) and is obtained through Datastream, quarterly percentages from t to t+3 are added up to find the future growth over a year following the current period. Future investment growth is defined as private, non-residential, fixed investment and is also calculated by the BEA and obtained through Datastream, the quarterly percentages are added from t to t+3 to find future investment growth over a year following the current period. Future sales growth is calculated using the sales/turnover data available for all public firm through the CRSP database, the log of t+3 is taken and the log of t-1 is subtracted from that to create a proxy that includes the change in sales following the current quarter. The change in dispersion around the earnings announcement is measured obtaining the earnings announcements and stock prices one day before, on the day of announcement and one day after the announcement through the I/B/E/S database. The dispersion over one day before the announcement to one day after the announcement is computed per firm, the average
dispersion is taken for each quarter and the change between the previous quarter and the current quarter is the final value of the proxy change in dispersion around earnings announcement. The second information asymmetry proxy is the change in the dispersion of analysts announcements, I/B/E/S provides the standard deviation of analyst forecasts per quarter; the average of the analyst standard deviation of all forecasts is taken each quarter and then compared to the previous period to find that change. The future market returns are obtained from CRSP, the equally weighted market return from t+1 to t+4 is added up to provide the future market return proxy. The future change in consumer confidence index is computed by obtaining the value of the CCI by the Conference Board through Datastream, the change from one quarter to the next is computed and then added up over interval t+1 to t+4 to generate the change in consumer confidence following the current period. The trade volume per quarter is provided by COMPUSTAT in the form of the number of shares traded per firm each quarter, the number of shares traded per quarter are added up; the change in the number of shares traded between t-1 and t provides the percent change in trade volume. Data on the prime rate is retrieved from Thomson One, the minimum is 3.25% and the maximum is 9,5% over the selected time of this research; the relative height of the prime rate in the context of the research is used, thus 3.25 is subtracted and the remaining value is divided by the difference between the maximum and the minimum which is 6.25 giving the prime rate ratio in context of this research. The change in the amount of loans and the change in the amount of bonds are both obtained from Thomson One, and the change between the t-1 an d t is computed to give the change in the amount of loans and bonds.
3.2.1 Capital Demand Proxies
Three proxies are used to determine the private capital demand of firms, as stated earlier the assumption is made that higher capital demand results in higher IPO volume. These three proxies are future measures of GDP, investment and sales. The proxies are measure over future intervals because current capital demand is determined by the capital needed today to keep up with increased growth in the future. IPO volume is expected to be highly correlated with the future capital demand proxies. When the economic climate is more favorable the demand for capital is assumed to be higher. Therefore a favorable economic climate implies higher IPO volume. Gross domestic product percentage change from t to t+3, which is equivalent to the yearly percentage change starting from the current quarter, is used to measure the effect of overall economy growth on IPO volume. Data on GDP is widely available; data is obtained from Thomson’s Datastream which gives quarterly percentage change; the data source of Datastream on this subject is the Bureau of Economic
Analysis(BEA) of the United States.
Investment is defined as the real private nonresidential fixed investment; quarterly data on this is available through Thomson’s Datastream, the source that this database uses is the BEA. This percentage increases when investment opportunities increase, before being able to engage in these investment opportunities funding is needed and thus future investment is expected to be positively related to current capital demand. The time interval used is the same as used for GDP, yearly measure from the current quarter.
Before sales can increase costs are incurred to allow growth of sales, thus funding is needed preceding sales growth, again capital demand will be needed before growth. Lowry (2003) finds that sales data are more sensitive to cyclicality, thus the measure for sales is the log of sales in t+3 minus the log of sales in t-1 (last year). Sales data are obtained from CRSP by averaging the sales of all firms in the US economy per quarter; sales is defined as the sales divided by the turnover and is available per firm. These three proxies are all in essence measures of economic welfare and therefore there is a high correlation amongst the variables, further to be discussed in the results section table 5.
3.2.2 Asymmetric Information proxies
Asymmetric information is defined as the difference between information available to investors and managers. The difference is measured using two proxies, both using dispersion around earnings announcements and average standard deviation of analysts. The dispersion
around earnings announcements is the scatters of the stock price one day before and after earnings announcements. Through I/B/E/S the daily stock prices one day, the day of and the day after an earnings announcement are retrieved, the change in the price of shares measures the dispersion. The average dispersion is taken of all stocks that are available through I/B/E/S, the change is then computed from the previous quarter to the next. High dispersion levels mean that earnings announcements reveal information from the managers to investors, when as a result of an earnings announcement stock price increase or decrease heavily dispersion increases. Higher dispersion around these announcements of earnings means that there is a heavier reaction to information made public by managers through these announcements and this is a sign that the overall information asymmetry in the economy is higher.
The second proxy focuses on analysts’ standard deviation in their forecasts of earnings announcements. Information provided by analysts is used as the main source for information about stocks by small investors, they strongly base their beliefs and adjust their behavior in the market to this. If the average standard deviation of the forecasts by analysts increases this means that analysts’ estimates are worse, they are spread more which means more analysts’ forecasts are further from the actual value. An increase in the deviation from this actual value is due to the lack of information available to analysts which implies that the level of
information asymmetry in the market is high. Data on the standard deviation of analysts on specific stocks is available through the I/B/E/S database; the mean value of all forecasts analyst standard deviation each quarter is taken to measure the change in the dispersion of analysts’ forecasts in that given quarter. Similarly to the first proxy the change is measured from the previous quarter to the current quarter.
3.2.3 Investor sentiment proxies
Investors act according to their beliefs about the current state and the future state of the economy, the three proxies used to measure this are: the change in the consumer confidence index, the change in trading volume and future returns.
The consumer confidence index is an indicator designed by The Conference Board, an independent membership an research association working in the interest of the public. The definition of investors in this research is small, private investors whom trade using personal funds, through this definition the similarity between investors and consumers is evident. Every month the Conference Board surveys 5000 diverse households on their feelings toward the current business conditions, the business conditions over the next 6 months, current employment conditions, the employment conditions over the next 6 months and their total
family income over the next 6 months. Based on these answers that are positive, negative or neutral the Conference Board constructs the CCI. The current benchmark also referred to as base year for the index is 1985 is which the value of the index was set at 100. Consumer confidence increases following a period of high equity values, during these period IPO
volume peaks as firms are taking advantage of these high values, thus an increase in consumer confidence follows after a period with many IPOs. Data on the CCI are collected through Thomson’s Datasteam, the value of the index per quarter is given and with that the change from one period to the next is computed.
The positive attitude of investors is translated into increased market activity which in turn increases trade volume, investors are more actively seeking investment opportunities driving the amount of transactions up. Trading volume is measured as the number of shares traded on the primary exchanges in the United States each quarter. COMPUSTAT provides data on the amount of shares traded in each quarter for all firms available on public
exchanges, adding the shares traded volume of all firms each quarter gives the trade volume. Changes in trade volume are then computed through calculating the change from the previous quarter to the current quarter. Increases in trading volume imply that equity is increasing in popularity, investors are willing to participate actively in the market; when firms notice increases in trading volume and their accompanying price increases they gravitate towards going public.
The third proxy is not assumed to affect the IPO volume instead it is the result of a period of high IPO volume, it has a controlling nature. The proxy is the average future returns in the year following the quarter of IPO volume, as markets move from high to low returns the future return also implicates the level of returns preceding the IPO volume. The future market returns are equally weighted market returns including dividend stock market indexes available through CRSP. The CRSP database covers four large stock indexes and combines their returns into the equally-weighted return: the New York Stock Exchange(NYSE), the American Stock Exchange(AMEX), the National Association of Securities Dealers
Automated Quotations (NASDAQ) and NYSE Arca (ARCA). Future returns are defined as the returns over the next year following the current quarter, thus the returns ranging from t+1 to t+4.
3.2.4 Debt proxies
There are multiple securities that a firms can issue and many forms of debt a bank can take on, three proxies capture the main alternative forms of financing: the prime rate in the previous quarter, the change in the amount of loans and the change in the amount of bonds.
The prime rate, the interest rate that commercial banks charge their most trustworthy credit customers, is a reflection of the overall price of debt in the economy. The prime rate between 1995 and 2014 ranges from 3.25% to 9.5%; using this a variable is created: the ratio of the prime rate. In this research the level of the prime rate is of interest, as the goal is to compare times of low prime rates to times with high prime rates a new variable is created. The relative height of the prime rate is of particular interest, therefore the ‘primary rate ratio’ takes on the value 0 is the prime rate is 3,25% and the value 1 if the prime rate is at 9,5%; this ‘primary rate ratio’ defines at wat level on a scale of 0 to 1 the prime rate is, multiplied by 100 this gives the height of the prime rate as a percentage in context of the time frame. The relative level at which the prime rate is in the period preceding the quarter of interest is assumingly positively related to IPO volume; if the prime rate is high debt is expensive resulting in equity as a more attractive option to fund projects.
The amount of loans is an indicator for the level of activity in the direct loan market, the market in which banks issue a loan to a firm. Thomson One provides data on the amount of loans, that is the value in US dollars, newly issued per month; totaling this each quarter gives the value issued in a quarter. The variable is based on all loans activity available as public information meaning that private loans are excluded from this amount as data on these loans is unavailable. The change in the amount of loans is computed from the previous period to the current and indicates the change in the activity level of the loan market just preceding the current quarter of interest.
The change in the amount of bonds issued is relatively similar to the change in the amount of loans in terms of computation of the change from one period to the next. Again Thomson One provides data about the value of all bonds issued per month from which the quarterly value is computed. A large difference with the loans is that bonds are financed by the public rather than a bank, making this proxy a variable of the preferences of investors. If activity in bonds increases bonds have become relatively more attractive than equity to investors, leading to an expected decrease in IPO volume. Another feature of this proxy versus the loans proxy is that bonds are traded through exchanges creating central places in which the trade in this security is registered, therefore it is safe to say that all bond activity for public firms is included in the database used.
