Risk proxies and underpricing of ipo


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Risk proxies and underpricing of ipo

  1. 1. Risk Proxies and IPO Underpricing: An Empirical Investigation Seshadev Sahoo* and Prabina Rajib**This paper is motivated by the apparent belief that risk and uncertainty surrounding an Initial Public Offering (IPO)have significant impact on underpricing. IPOs are underpriced on initial day to compensate for the risk inherent in it.While a list of risk proxies are suggested in literature, it actually invokes several complications in selecting the suitablerisk for an IPO. Using the magnitude of underpricing as risk premium for initial investors, this paper seeks to shed lighton the controversy by taking a sample of 171 IPOs issued in India during 2002-2007. Suitability of risk surrogates isexamined to estimate underpricing. The findings revealed that high price deflated to low price (H/L) has a superiorestimation power for underprice, suggesting H/L as a better indicator for uncertainty surrounding IPOs rather thanother risk surrogates used in this study. Besides H/L, it is the investment bank prestige and inverse of offer proceedswhich proved to be statistically significant in explaining underprice. Across sectors, H/L, investment bank prestige,and age of issue firm are found to be suitable risk proxies for IPOs from the manufacturing sector, while risk for thenon-manufacturing sector IPOs is better explained by H/L, investment bank prestige, inverse of offer proceeds, and exante uncertainty.IntroductionInitial Public Offerings (IPOs) involve more pricing uncertainty than the existing listedequities. The non-availability of price history and published information makes it moredifficult to directly measure the magnitude of risks surrounding an IPO. Instead, the level ofunderpricing (initial day return) serves as a useful proxy for estimated market risk for theIPOs. The relationship between expected return and perceived risk level for IPOs is welldocumented in the literature (Welch, 1992; Ljungqvist and Wilhelm, 2002; and Loughranand Ritter, 2004). Ritter (1984) and Beatty and Ritter (1986) explained that there exists apositive relationship between underpricing and ex ante uncertainty. They argued that wheninvestors bid for IPOs, they implicitly invest in a call option. This option becomes morevaluable only when the post-issue price is more than the offer price. In other words, the valueof this option increases when uncertainty in the share price increases. Underpricing not only reflects the uncertainty surrounding the IPO at offer, but also indicatesthe after market variance (Gleason et al., 2008). In finance theory, standard deviation of aftermarket return is commonly used as a measure of risk. By convention, the standard deviation ofafter market returns for initial 20 trading is used as proxy for ex ante uncertainty. However, theuse of ex ante as risk surrogate has little explanation power (Johnson and Miller, 1988).More recent studies were found to use a different set of risk proxies while evaluating underpricing,* Doctoral Research Scholar, Vinod Gupta School of Management (VGSOM), IIT Kharagpur and Associate Professor (Finance), Institute of Management and Information Science (IMIS), Bhubaneswar, Orissa, India. E-mail: seshadev.sahoo12@gmail.com* * Associate Professor (Finance), Vinod Gupta School of Management (VGSOM), IIT Kharagpur, West Bengal 721302, India. E-mail: prabina.iitkgp@gmail.com38 2011 IUP All Rights Reserved.© . The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  2. 2. i.e., investment bank prestige, age of the firm, offer size, Parkinson’s extreme value measure1 anddebt equity ratio at IPO date. Besides the above risk, a surrogate, the IPO firm also publishes alist of risk factors in the issue prospectus. Since many factors have been used as proxies for riskmeasures, the following questions explain the focus of this paper: (a) Since risk has a positiveeffect on initial day return, is it that important and valid in our research? (b) Which risk proxyhas better explanation for underpricing? and (c) Assessing whether risk proxies are uniformlydistributed across industry sectors. The primary purpose of this paper is to identify the most suitable risk proxy for an IPO.We compare the significance of ex ante uncertainty with other risk surrogates, i.e., investmentbank prestige, age, offer proceeds, listing day high price scaled by low price (H/L) and debtratio at IPO date. Moreover, we attempt to evaluate the suitability of Parkinson’s extremevalue measure as proxy for risks and uncertainty contiguous to IPOs. This study also aims toprovide an insight into the variability of estimation efficiency of risk proxies across theindustry. Our analysis also intends to reveal the relationship between these risk proxies andshort-term reaction of the investors. We find that H/L explains a large portion of underpricing than other risk surrogates,suggesting that after market price range has superior explanatory power for uncertaintysurrounding an IPO. Besides H/L, investment bank prestige and inverse of offer proceedshave a statistically significant impact on underprice. However, the most surprising outcomeof the study indicates that both ex ante and age of the IPO firm are statistically insignificantin explaining underprice. Moreover, we studied the predictive behavior of the risk proxiesacross manufacturing and non-manufacturing sectors. H/L, investment bank prestige andage of the IPO firm were found suitable risk proxies for the manufacturing sector, while therisk for non-manufacturing sector was significantly represented by H/L, investment bankprestige, inverse of offer proceeds, and after market volatility. The study contributes to literature by investigating the suitable risk proxy for theuncertainty surrounding the IPOs. Alternatively, we attempt to find the relative predictivepower of various risk proxies for IPO underprice. We evaluate six most useful risk proxies, i.e.,after market high price scaled to low price, post-listing price volatility, investment bankprestige, age of the IPO firm, inverse of offer proceeds, and leverage ratio at the time of issuefor estimating underprice (risk premium). In such, we use the most recent dataset for assessmentof right proxies for IPO risk. Finally, our research confirms the adage, ‘higher the risk themore is the return’, even for IPO issue.Extant LiteratureEvidence on IPO UnderpricingAlmost in every country the IPOs issued have exhibited various degrees of underpricing.Reilly and Hatfield (1969) reported underprice of 11% for USA IPOs issued during 1963-65.1 Parkinson (1980) suggests that the use of extreme values (the high price scaled to low price) during the period serves as superior predictor for stock return standard deviation. Risk Proxies and IPO Underpricing: An Empirical Investigation 39
  3. 3. Subsequently, studies by Ibbotson (1975), Reilly (1977), Aggarwal and Rivoli (1989), Ritter(1991), Loughran and Ritter (1995), Ritter and Welch (2002) and Ljungqvist and Wilhelm(2003) found underpricing of IPOs in USA. More recently, Loughran and Ritter (2002)reported that IPO underpricing has doubled from 7% in 1980 to almost 15% in 1990 in USA.Even in dot com bubble, the underprice rate has shot up to 65%. In the Indian context, Shah (1995) documented underprice of 105.6% on analyzing adata base of 2056 IPOs listed in BSE during January 1991 to May 1995. Madhusoodanan andThiripalraju (1997) studied a sample of 1922 IPOs offered during the year 1992-1995 andfound that the Indian IPOs are underpriced at a higher rate than the international market.They evidenced listing delays (gap between date of closing the issue and initial trading day)and offer size is the key determinants in IPO pricing. Baral and Obaidullah (1998) documentedexcess initial returns for Indian IPOs. Krishnamurti and Kumar (2002) studied 386 IPOs andconcluded that the IPOs are listed at 72.3% higher than the offer price. Rajan and Madhusoodanan (2004) studied 92 IPOs issued during 1999 and 2003. Theystudied the impact of book-building mechanism on IPO pricing and found lesser amount ofunderpricing in book-built issues than fixed price issues. They also documented less underpricefor larger issues and more underprice for smaller issues. Ghosh (2005b) investigated 1842Indian IPOs issued during the year 1993 to 2001 and documented that volatility of aftermarket stock returns and possibilities of subsequent offerings have significant impact onunderprice. Kumar (2006) reported an underprice of 26.35% for Indian IPOs issued throughbook-built route during the period 1999-2006, which got listed on NSE. IPO market in India evidenced a large-scale underprice during the Controller of CapitalIssues (CCI) regime up to 1995. Alternatively, the Indian IPOs were found to be underpricedat a higher rate than their international counterparts during that period. Owing to theemerging need for a vibrant capital market, the CCI Act was repealed and an independentmarket regulator, Securities Exchange Board of India (hereinafter, SEBI) came into existencewith effect from 1992. Since its inception, SEBI has undertaken a series of initiatives, i.e.,book building, new listing guidelines, and Disclosure of Investors Protection Guidelines(DIP) to revive and restructure the IPO market. The resulting post-CCI period (after 1999)experienced a sharp reduction in the underprice rate due to the dominance of book-builtmechanism for pricing IPOs. The empirical literature is now fairly mature in claiming sufficient evidence for theunderpricing of IPOs. Broadly speaking, some sort of underpricing for IPOs is reported inevery capital market globally. The remarkable empirical regularity inspired large sections oftheoretical literature to explore the underlying rationality behind underprice. Studies onIPO underprice can be grouped under four different broad headings: (a) Informationasymmetry; (b) Institutional reasons; (c) Control considerations; and (d) Behavioralapproaches (Ljungqvist and Wilhelm, 2005).40 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  4. 4. • Information Asymmetry: This model argues that investors in the new issue market are vertically split into two categories, i.e., informed and uninformed (or less informed). Informed investors put in a large number of applications for underpriced issues, while uninformed investors apply indiscriminately. As a result, uninformed investors have lesser chance of getting an allotment in profitable IPOs because their demand is partly crowded out by informed investors. Thus, uninformed investors are cursed by getting optimum allocation in an unsuccessful IPO and hence will be losing their interest in the market (Rock, 1986). To attract the uninformed investors to the IPO market, the issues must be underpriced to some extent so that an assured return can be made. The more the magnitude of information friction, the more likely the issue will be underpriced because of uncertainty surrounding the IPO. This model is widely tested and empirically evidenced by Beatty and Ritter (1986), Habib and Ljungqvist (2001), Ljungqvist and Wilhelm (2003), and Yu and TSE (2006). • Institutional Explanations: Logue (1973) and Ibbotson (1975) suggest that issuers deliberately underprice the IPOs to avoid possible lawsuit threats from disappointed investors. Tinic (1988), Hughes and Thakor (1992), and Hensler (1995) empirically support the argument that underpricing is used to safeguard the possible lawsuit threat. Other studies supporting institutional approach for IPO underprice include, price stabilization theory (Ruud, 1993; and Chowdhry and Nanda, 1996), and tax arguments by Taranto (2003). • Ownership and Control Consideration: Issuing an equity through IPO route eventually leads to separation of ownership and control. Underpricing induced ownership dispersion as a large and diverse group of investors bid for the IPOs. Managers then strategically allocate the IPOs to protect their private benefits (Brennan and Franks, 1997). Other studies found evidence in support of the ownership and control consideration as motivation for underprice (Zingales, 1995 and Booth and Chua, 1996). Another argument put forward by Stoughton and Zechner (1998) suggests that allocating shares to large outside investors is value enhancing, because these investors can effect strict supervision and managerial control on the IPO firm. • Behavioral Explanations: Behavioral theories find that investors bid IPOs irrationally. Overenthusiastic investors bet the price of IPO shares beyond their true fundamental value. Welch (1992) documents that latter investors subscribe to the IPOs by sequentially learning from the response of the earlier investors. Alternatively, latter investors base their investment decision on the initial investors’ information and discount their private information. As a result, an informational cascade is developed for the IPOs, where latter investors take a free ride on it. The possibility of cascades gives more power for the initial investors to bargain more in terms of underpricing. Existing studies (Loughran and Ritter, 2002; and Ljungqvist and Wilhelm, 2005) found sufficient evidence in support of the behavioral imperfection theory.Risk Proxies and IPO Underpricing: An Empirical Investigation 41
  5. 5. Risk Proxies and UnderpricingUncertainties surrounding the IPOs have played an important role in pricing the IPOs.At the time of the IPO, the investors need to be compensated with higher rate of discounts(underprice), so that they can be motivated to participate in the risk IPOs. Several studiesfound a positive relationship between risk and initial day return (underprice). Beatty andRitter (1986) suggest that the level of underprice reflects the magnitude of ex ante uncertaintycoupled with the IPO at the time of issue. Other studies (Knopf and Teall, 1999; Ljungqvistand Wilhelm, 2002; Loughran and Ritter, 2004; and Gleason et al., 2008) found usingunderprice as an indicator of IPO risk. There are several risk proxies suggested in literature toexplain underprice. Our study organizes the literature review to characterize the IPOunderprice through risk surrogates. Parkinson (1980) demonstrates that natural log value of initial day high price deflatedwith initial day low price (H/L) serves as an excellent predictor of the variance of the stockreturn on the listing day. He also documents that H/L and IPO underprice report high degreeof positive correlation. Knopf and Teall (1999) also found support for H/L serving as a surrogatefor IPO risk on studying a sample of 822 IPOs issued during 1986 to 1989. They provideevidence that log of H/L is a better proxy for ex ante risk than any other. BesidesH/L, their study also uses investment bank prestige and inverse of offer proceeds as other riskproxies to explain underprice. Barry and Jennings (1991) also use the Parkinson’s extremevalue measure as risk proxy for after market performance of IPOs. They classify the entiresample as overpriced and underpriced IPOs, and estimate the difference in risk values forboth categories by using H/L as proxy for risk. They document that the differences in riskvalues are statistically insignificant. Certification value hypothesis propounded by Beatty and Ritter (1986), Carter et al. (1998),and Carter and Manaster (1990) report less underprice for IPOs underwritten by prestigiousunderwriters. They argued that IPO firms which are taken public by reputed investmentbanks have benefitted from superior certification from third party. Because superior attestationis attached to the capital, investors do not demand larger discount from these offers. Slovinet al. (1990) find that investment banks are used as external monitoring agencies and have asignificant impact on investors’ perception about new issues. They conclude that high rankedinvestment banks provide more value addition during the process of due diligence certification,and hence minimize the degree of underprice. In contrast to the above studies, Loughran andRitter (2004) find that investment banks underprice IPOs strategically in an effort to enrichthemselves or their investment clients. Top banks have lowered their criteria for selectingIPOs to underwrite, resulting in a higher average risk profile for their IPOs, i.e., high prestigerank investment banks underprice more. Other empirical evidence (Nanda and Yun, 1997;Dunbar, 2000; and Gleason et al., 2008) supports the argument that prestigious underwritersunderprice more. One of the most popular proxies on company characteristics in terms of maturity oroperational history is the age of the IPO firm. Ritter (1991) finds age has strong but negativerelationship with underprice, which is consistent with the notion that mature firms underprice42 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  6. 6. less than the younger firms. Loughran and Ritter (2004) form a study on a sample of 6,391IPOs issued during the period 1980-2003, and evidence that younger firms are moreunderpriced than older firms. The operational maturity of the IPO firm and initial returnmove in the opposite direction, and also take log value of sales and total assets as other riskproxies for IPOs. Offer size as a signaling variable has been explored by Mok and Hui (1998), Rajan andMadhoosudanan (2004), and Yu and Tse (2006). These studies suggest that offer size andunderprice have opposite relationship, i.e., IPO firms with smaller offer size underprice moreand vice versa. Small size issues are more speculative than large size issues and hence commandmore underprice (Beatty and Ritter, 1986). Among offering characteristics, a popular proxyfor IPO valuation uncertainty is offer proceeds because the larger the offer value, the lesser isthe probability of failure of issue. Pre-IPO debt-equity ratio also serves as an indicator of riskproxies for the IPO valuation because it signals the financial strength of the firm. Commonly,it is perceived that the higher the pre-IPO leverage, the more the ex ante uncertainty in themarket. Ritter (1984) and Lougrhan and Ritter (1995) use leverage along with age, size, andindustry classification, etc. as proxies for risk. Debt is positively related to IPO valuation andis directly related to divestment of insiders (Ross, 1977). Jayati and Subrata (2005) argue thatwhile disciplinary effect of debt was absent in the pre-liberalization period in India (before1995), the post-reform debt had positive impact on firm value and profitability. The higherthe pre-IPO leverage, the higher the underprice. Underpricing found an increase as the ex ante uncertainty about the firm valueincreased (Rock, 1986). Sohail and Nasr (2007) undertake ex ante uncertainty as proxy formarket risk of IPOs. As historical market price for IPO issues does not exist, they estimatestandard deviation of the first 20 trading days return of IPO as a measure of ex ante uncertaintyand document significant relationship between ex ante uncertainty and underprice. Beattyand Ritter (1986) and Brennan and Franks (1997) also report that investors seek higherreturns to compensate for their anxiety about future performance of IPOs and use standarddeviation of stock return (post-listing) as proxy for uncertainty. In the Indian context, Ghosh(2005a and 2005b) uses volatility of stock return just after the listing as proxy for uncertainty.This study uses ex post standard deviation of the market price for initial 20 trading days as aproxy for ex ante uncertainty to measure the variability of the IPO stock return just after thelisting. In our empirical model, we use ex ante as proxy for measuring post-listing IPO pricesvolatility. Other risk proxies used in previous literature for explaining underprice are listed inTable 1. In summary, it can be observed from Table 1 that various authors have used different typesof risk proxies for estimating underprice. The literature survey also evidenced that IPOunderprice is explained by several risk surrogates. From time to time, various authors haveused different kinds of risk proxies for a better understanding of the perceived risk profile ofthe issue. This paper adds to the literature by proposing H/L as the best alternative for risksurrogate in IPO valuation. Further, we seek an explanation for direct relationship betweenrisk proxies and IPO initial day return (underprice).Risk Proxies and IPO Underpricing: An Empirical Investigation 43
  7. 7. Table 1: Risk Proxies Used for Explanation of IPO Underpricing Author Year Source Risk Proxies Chang et al. 2008 Accounting and Finance Auditor quality Su Dongwei 2004 International Financial Insider ownership Markets & Money Benveniste et al. 2003 Journal of Finance Industry affiliation Ljungqvist and Wilhelm 2003 Journal of Finance Pre-IPO ownership structure and insider selling behavior Habib and Ljungqvist 1998 Economic Letters Gross proceeds Beatty and Welch 1996 Journal of Law and Number of uses of IPO Economics proceeds Miller and Reilly 1987 Financial Management Trading volume Ritter 1984 Journal of Business VolatilityData and Sample SelectionOur sample data stems from three different sources: (1) The IPO sample is derived fromPrime database annual reports for the period 2002 to 2007. Prime database maintains on linedatabase of all IPOs issued in India. The database publishes the fundamental characteristicsof the issues, i.e., offer size, offer price, syndicate structure, market intermediaries, etc.;(2) Multiple online databases are used (http://www.nseindia.com, http://www.bseindia.com,http://www.sebi.gov.in, and http://www.capitaline.com) for listing and post-listing priceinformation; and (3) The firm-specific characteristics along with primary market informationare collected from prime database and cross-referred with online database. Finally, wecomplement our sample data with Red Herring Prospectus (RHP), available at SEBI website(http://www.sebi.gov.in). The initial sample was 206 IPOs (equity offerings only) issued during the period 2002-03to 2006-07 and listed in the National Stock Exchange (NSE), Mumbai, India. As this studyfocuses on IPOs of unlisted companies only, we exclude Seasoned Equity Offerings (SEOs) aswell as debt issues. We exclude 06 IPOs due to missing offer documents. We also excludeanother 16 IPOs, i.e., banking and financial firms’ IPOs because these firms have altogetherdifferent characteristics. Table 2 explains the sample size and its selection methodology. Thefinal sample eligible for analysis consists of 171 IPOs, which represents 83.01% of the totalIPOs issued during the period. The other data collected are: listing day high price, low price and closing price for eachIPO. Firms going public in India can list themselves on multiple exchanges. Practically, allIPO firms in India get listed either on Bombay Stock Exchange (BSE) or the National StockExchange (NSE), which are the major exchanges in India today. In recent times most of theissues are listed in both of the exchanges. Secondary market price data for all sample IPOs aretaken from http://www.nseindia.com.44 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  8. 8. Table 2: Sample Selection Criterion for the Sample IPOs During 2002-2007 Number of IPOs offered during the period 206 (–) IPO prospectus unavailable 6 (–) Valid day one closing price unavailable 4 (–) IPOs with incomplete financial and issue specific characteristics 5 (–) Number of financial firms that issued their IPOs 16 (–) Number of outliers2 4 = Final sample 171 % of sample IPOs for empirical study 83.01Measurement of Underpricing and Description of Risk ProxiesDetailed methodologies for calculation of underprice and measurement of risk proxies isgiven here.Calculation of UnderpricingUnderpricing (initial day return) for the IPOs is estimated by two calculated formulae whichare widely used in international empirical studies—raw return, and excess or Market AdjustedReturns (hence after, MAAR). The raw return for IPOs on the listing day is the initial dayprice differential deflated with offer price. Difference between listing day close price (LPi)and offer price (IPi) is the initial day price differential. Raw return (SRi) on first day for eachIPO is defined as follows: LPi  IPi SR i   100 ...(1) IPi MAAR for individual IPO on listing day is estimated as the difference between raw returnfor the IPO security and the return on general market index on listing day. We estimatemarket return of S&P CNX Nifty (henceforth ‘Nifty’) as a benchmark reference rate forexcess return. This kind of evaluation reveals whether the IPO outperforms the marketbenchmark on the listing day. Benchmark return is estimated as the difference of closingNifty value on listing date, and issue closing date is deflated with the values at offer close date.The return earned on the market portfolio over the same period is defined as follows: MI c  MI o MR   100 ...(2) MI owhere MR is the initial trading day’s equivalent market return. MIc is the market index valueat close of the first trading day, MIo is the closing market index value at the date of closure ofthe issue. The excess return or market adjusted abnormal return (UPi) for any IPO (i) in thefirst trading day is calculated as follows:2 Outliers are evaluated by using Z-statistics. Even the presence of outliers does not affect the result substantially. Risk Proxies and IPO Underpricing: An Empirical Investigation 45
  9. 9.  (1  SRi )  UPi    1  100 ...(3) (1  MR)  The above formula confirms Miller and Reilly (1987) for calculating the initial return.MAAR is calculated by taking Nifty data for the period of study 2002-2007. The benchmarkNifty is a well-diversified 50-stock index accounting for 22 sectors of the economy. It is usedin tandem with the Bombay Stock Exchange Sensitive Index (BSE Sensex) for a variety ofpurposes, including benchmarking fund portfolios, index-based derivatives and index funds.For matter of simplicity, we use underprice (UP) as synonym to market adjusted abnormalreturn for IPOs.Description and Measurement of Risk ProxiesWe have used six proxies for measuring the risk in an IPO: (1) Parkinson’s extreme valuemeasure, i.e., ratio of H/L; (2) Operational history of the IPO firm, measured by age of thefirm (AGE) (Age is defined as the year of an IPO minus the year of incorporation of the firm);(3) Offer proceeds (OP); (4) Investment bank prestige (PREST); (5) Leverage ratio (LEV) ofthe IPO firm at the time of issue; and (6) After market price volatility (Ex ante), which iscommonly referred to as ex ante uncertainty of the IPO stock return. Table 3 provides adetailed explanation for each of the risk proxies used in the study. Table 3: Description and Measurement of Risk Proxies and Expected Impact on Underpricing Expected Risk Proxies Description Impact on UP High Price to Low The H/L ratio for each sample IPO is calculated for initial Price (H/L) one month of trading, including listing day. We therefore use mean value of H/L ratio of trading price for initial one + month of trading days. Operational history Age is estimated as calendar year of IPO minus calendar of firm (AGE) year of incorporation. For example, age for NTPC Ltd. is – estimated at 29 years. The company was incorporated in the year 1975, and it went public on October 7, 2004. Inverse of Offer Offer proceeds are the amount of capital the company Proceeds (INVOP) wants to raise through IPO. Estimated as the number of + offering shares multiplied by offer price. For OLS regression model, we take inverse value of offer proceeds. Leverage at time of Leverage is defined as book value of long-term debt to the IPO (LEV) paid up equity capital of the firm at the IPO date. + Ex ante uncertainty We use ex post standard deviation of the market price for for IPO returns initial 20 trading days as a proxy for ex ante uncertainty in (Ex ante) our regression analysis. + Investment Bank Detailed methodology given under the heading Prestige (PREST) Measurement of Investment Bank Prestige (PREST). –46 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  10. 10. All the six risk proxies used in this study can be categorized into two different groups. Thefirst category comprises fundamental firm characteristics, and the issue attributes are: offerproceeds, leverage, age of the firm, and investment bank prestige. The second category of riskaddresses post-IPO market price performance, and the risk surrogates include H/L and standarddeviation of post-listing return for IPO stocks (ex ante).Measurement of Investment Bank Prestige (PREST)Prestige of the investment banks is quantified in three steps: (1) Ratings of investmentbanks from the Prime database annual reports for the period 2002-2007 were collected.Prime database assigns ratings to investment banks based on their annual market share.Investment banks having highest market share are assigned a rating of 1, while the next oneis given the rating of 2, and so on and so forth. This methodology is consistent withMegginson-Weiss (1991) model. This approach is superior, as it does not assume the reputationof an investment bank to be constant over time. Additionally, the market share provides acardinal value of reputation than ordinal value. In the second step, Rank Value Points (RVP) are assigned to each investment bank on thebasis of Prime Database Rankings (PDR). For example, a bank with PDR of 1 got an RVP of10, while PDR of 2 got RVP of 9. The RVP declines with the decreased ratings of the bank. Asthere are more than 10 investment banks, a bank having a PDR range of 11-15 was given anRVP of 0.50. Similarly, PDR from 16-20 were given RVP of 0.25 and ratings with >20 weregiven RVP of 0.125. Table 4 lists the PDR and its corresponding RVP . Table 4: Rank Value Points for Investment BanksPDR 1 2 3 4 5 6 7 8 9 10 11-15 16-20 > 20RVP 10 9 8 7 6 5 4 3 2 1 0.5 0.25 0.125 Note: PDR – Prime Data Base Ratings; and RVP – Rank Value Points assigned. Finally, we estimate the prestige value of the top two investment banks which appearedon the red herring prospectus. For example, a syndicate having three investment banks withPDR of 2, 3, and 6 has a cumulative RVP of 17 (= 9 + 8), while ignoring the value for theinvestment bank having PDR = 6. Similarly a syndicate having four members with PDR of 8,15, 18 and 27 has a cumulative RVP of 3.5 (= 3 + 0.5).Descriptive StatisticsA brief analysis of summary statistics for entire sample of 171 IPOs for the period 2002-2007is presented here. The statistics are reported in Table 5. The methodology of calculation ofunderprice (UP) is given under the heading ‘empirical methodology’ further in the paper. Wereport mean, median, standard deviation, maximum value, minimum value, skewness andkurtosis for the total sample. While evaluating the key characteristics for IPOs as given in Table 5, we find that theunderprice persists in the Indian IPO market during the period 2002-07. On an average, theIndian IPOs are undervalued at 35.61% at the initial day closing price. The high percentageRisk Proxies and IPO Underpricing: An Empirical Investigation 47
  11. 11. of underpricing implies that issuers leave too much on the table. The median value ofunderprice reports at 24.92% along with a standard deviation of 53.01%, indicating large-scale variations of initial day return among the sample IPOs. Further, higher spread betweenmaximum and minimum value of underprice confirms the widespread fluctuations. Table 5: Descriptive Statistics of the Variables Used in the Study Std.Particulars Mean Median Max.Value Min.Value Skewness Kurtosis Deviation UP 35.61 24.92 53.01 258.31 –37.7 1.96 4.96 H/L 1.09 1.08 0.03 1.16 1.02 0.70 0.64 PREST 8.75 7.75 7.42 18.00 0.13 1.03 0.57 AGE 15.08 13.00 9.97 57.00 1.00 1.60 3.31 OP 297.17 91.94 825.10 5428.90 7.50 5.44 31.47 LEV 1.02 0.50 1.63 10.84 0 3.43 14.26 Ex ante 16.32 10.35 17.24 100.81 0.44 2.11 5.52 The extreme value measure (H/L) ranged from 1.02 to 1.16, with mean and median values of1.09 and 1.08 respectively. The insignificant difference between mean and median values,coupled with low standard deviation of 0.03, indicates perfect normal distribution for thevariable. Least values of skewness and kurtosis further confirm the normal distribution of H/L.Investment bank prestige value for the sample IPOs varies from 0.13 points to as large as 18points. The median value for prestige is 7.75 with a standard deviation of 7.42. A significantlylow value of skewness and kurtosis for the prestige variable indicates the uniform distributionof the variable PREST. The average age for Indian IPO firms is 15.08 years at the time of going public. There arenine firms with an age of more than 30 years, and if we exclude these firms from the sample,then the average age for the firms would be 12.855 years, which is virtually similar to themedian age of 13 years for the sample IPOs. Low standard deviation for the age of IPO firmsindicates that the new issue firms are of identical operational history. The amount of moneyraised (OP) through IPOs varies from 7.5 cr to 5428.9 cr. The average issue size for thesample during the period is 297.17 cr, with a standard deviation of 825.10 cr. Large-scalestandard deviation for the offer size documents wide variations in offer value. Table 5 indicates that mean value of leverage is 1.02 times. Median value for leveragefor IPO firms at the time of issue is 0.50, with a standard deviation of 1.63. There arethree firms with a debt-equity ratio of more than 5; if we drop these firms, the averageleverage ratio becomes 0.72, with standard deviation of 1.02. Low value for leverage ratioevidenced that average IPO firms during the period of study do not bear a risky capitalstructure. The data for volatility of IPO returns as a measure of ex ante uncertainty(ex ante) in Table 5 reports a mean value of 16.32, with standard deviation of 17.24. Post-listing variation in IPO returns ranges from 0.44% to 100.81%, indicating larger variabilityin the post-listing return.48 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  12. 12. Empirical MethodologyMultivariate regression (OLS) is used to test the influence of the explanatory variables, i.e.,risk proxies on market adjusted underprice (UP). The whole set of explanatory variablesincludes investment banker prestige (PREST), operational history of the firm (AGE), inverseof offer proceeds (INVOP), Parkinson’s extreme value measure (H/L), and ex ante uncertainty(ex ante) and Leverage (LEV). To start with, we include all the six risk proxies in our model. To find out the impact of H/L, PREST, AGE, INVOP LEV and ex ante on UP, ,Equation (4) has been used. UP   0  1 H  L    PREST    AGE    Ex ante  2 3 4 ...(4)  5  INVOP   6  LEV   where 0 – intercept,  1 – regression coefficient between UP and H/L,  2 – regressioncoefficient between UP and PREST,  3 – regression coefficient between UP and AGE,4 – regression coefficient between UP and Ex ante, 5 – regression coefficient between UPand OP, 6 – regression coefficient between UP and LEV, and  – error term. Furthermore, because cross-sectional data and regression equations are used, there areheteroscedasticity problems in the residuals. Therefore, the adjusted White’s heteroscedasticityconsistent estimates are employed for regressions. Dependent variable (UP) is also tested fornormal distribution.Results and InterpretationsThe results of the OLS regressions on market adjusted abnormal rate of return (UP) of IPOs,which have been corrected for heteroskedasticity, are reported in Table 6. Empirical tableshows the estimation results for risk measures and underprice for IPOs issued during theperiod 2002-2007. We document that the risk surrogates, i.e., H/L, PREST, and INVOP are ,statistically significant in explaining underprice for IPOs. Moreover, not all risk factors areregarded important, and some of them are not significant at all. The risk proxies, i.e., AGE,Ex ante, and LEV, fail to predict the initial day return substantially. Hence, our modeldisapproves the validity of the risk factors including age of the firm (AGE), leverage (LEV),and aftermarket volatility (Ex ante) as measures for IPO risk. In addition, our result reveals asignificant positive regression coefficient between H/L and UP indicating that the higher ,the H/L ratio, the more the underprice. Also, H/L reports maximum standardized betacoefficients of 0.719 (measures relative importance of risk factors) relative to other riskfactors, suggesting superior explanation for risks in IPO valuation consistent with Knopf andTeall (1999). Next to H/L, the other significant risk proxies, i.e., investment bank prestige (PREST)and inverse of offer proceeds (INVOP), reflect larger standardized beta coefficients of 0.380and 0.225 respectively. We propose suitable risk proxies for IPOs in order of merit as H/L,followed by PREST, and INVOP. The most striking observation is the positive regressioncoefficient between investment bank prestige (PREST) and underprice, indicating that theRisk Proxies and IPO Underpricing: An Empirical Investigation 49
  13. 13. Table 6: OLS Regression – Risk Proxies and IPO Underprice Unstandardized Standardized Coefficients Coefficients Model Variables Standard t-Value Interpretation  (Beta)  (Beta) Error 1 Constant –1405.056 196.818 –7.582 Significant* H/L 1408.031 180.672 0.719 7.713 Significant* PREST 3.265 0.545 0.380 4.326 Significant* AGE 0.402 0.356 0.079 1.063 Insignificant Ex ante 0.338 0.282 0.084 1.153 Insignificant INVOP 416.244 184.254 0.225 2.490 Significant* LEV –2.391 2.310 –0.078 –1.036 Insignificant Model Summary: R = 0.567, Adjusted R = 0.527, and F-value = 17.854* 2 2 Note: * indicates significance at 1% level and 99% confidence.IPOs managed by superior (prestigious) investment banks generate more underprice, whichis consistent with the studies of Loughran and Ritter (2004), and Gleason et al. (2008).Surprisingly, the age of IPO firm reports positive regression coefficient with underprice,which is inconsistent with international evidence (Loughran and Ritter, 2004). Volatility ofafter market price as a measure of ex ante uncertainty reports positive regression coefficientwith underprice, consistent with international evidence (Rock, 1986; and Sohail and Nasr,2007). In contrast to international empirical leverage ratio of IPO firm on issue date, LEVbears inverse relationship with underprice, though it is statistically insignificant. On the basis of the empirical result, we suggest that H/L is a better measure for risks in anIPO than any other risk proxies. Standardized  coefficient values of 0.719 for H/L is highestamong other risk surrogates, followed by investment bank prestige and inverse of offerproceeds. The empirical evidence in this paper suggests that the perceived range of variationsin the post-listing prices for IPOs results in more underprice. The higher the relative factorof high price to low price for initial one month of trading days, the higher would be the ex anterisk, hence more is the underprice. Positive regression coefficient between H/L and underpriceis consistent with risk and uncertainty (Ritter, 1984; and Beatty and Ritter, 1986). Empirical results given in Table 5 document that underprice is adequately explained by allof the risk proxies, including H/L, PREST, AGE, Ex ante, INVOP and LEV. Collectively, these ,variables explain 56.7% (value of R2) of the variation in underprice. The F-statistic value forthe empirical model stands at 17.854 higher than the critical values at 0.01% level ofsignificance, which reflects that the model is highly significant.Risk Proxies Across SectorsThe explanatory power of the risk proxies, i.e., H/L, PREST, AGE, INVOP, LEV, and Ex ante,across two major categories of industries, i.e., manufacturing and non-manufacturing sectors,50 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
  14. 14. is highlighted here. Thus, the predictive powers of these risk factors along with their relativecontribution to the risk profile for IPOs are documented in Table 7. Table 7: OLS Regression – Risk Proxies and IPO Underpricing Across Sectors Particulars Manufacturing Non Manufacturing Number of IPOs 95.000 76.000 R2 62.200% 46.800% Adjusted R 2 58.200% 44.200% F-value 12.801* 7.643* Standardized Beta Coefficients H/L 0.847 (7.548*) 0.628 (3.972*) PREST 0.468 (4.343*) 0.365 (2.331**) AGE 0.156 (1.624**) –0.019 (–0.251) Ex ante –0.064 (–0.613) 0.166 (1.696)** INVOP 0.045 (–0.469) 0.448 (2.645)* LEV –0.037 (–0.377) –0.002 (0.021) Note: t-values are given in parentheses; * and ** indicate significance level at 1% and 5%, respectively along with a confidence level of 99% and 95% in order. First during this period, the explanatory power of these risk proxies is more formanufacturing sector than the non-manufacturing sector. Reportedly, the variance inunderprice for manufacturing sector is explained with all risk proxies by 62.2%, while fornon-manufacturing sector the value is 46.8%. Amongst all risk factors, H/L has superiorexplanatory power for risk,3 followed by PREST. We also find that AGE played a significantrole in estimating the underprice for manufacturing sector, hence a suitable proxy for risk issuggested. Therefore, after market H/L, investment bank prestige and age of IPO firm foundsuitable risk proxies for the manufacturing sector. While reporting risk proxies for the non-manufacturing sector, we document that H/L,PREST, INVOP, and Ex ante are statistically significant in explaining the variation in underprice.The most surprising observation is age of the firm as risk surrogate. Unlike manufacturingfirm, we report a negative regression coefficient between age of the IPO firm and underprice,indicating mature service firms underprice less than the younger firms. However, the result isfound statistically not compelling. Though there is a difference in the standardized beta coefficient values of H/L for manufacturingand non-manufacturing sectors, H/L reports superior risk proxies for IPOs than other measures ofrisk in both the sectors. Put differently, this research suggests that H/L might itself serve as astrong surrogate for IPO risk estimation even across sectors. We also found that for manufacturing3 H/L measures the variability of the IPO stock price. Though standard deviation of after market volatility of IPO stock price is a more popular risk proxy among the studies, this research suggests H/L as a relatively more effective measure of uncertainty surrounding the IPO. Risk Proxies and IPO Underpricing: An Empirical Investigation 51
  15. 15. sectors, H/L has superior estimation power (R2 = 62.2%) than the non-manufacturing sector(R2 = 46.8%). Alternatively, H/L proves as a more effective risk proxy for the manufacturingsector than the non-manufacturing sector (Table 7).ConclusionThe paper contributes to the literature by investigating risk in an IPO and initial day return.We use six different risk surrogates for explaining underprice. These risk factors include:operational history or age of the firm, investment bank prestige, after market price volatilityfor initial 20 trading days price, inverse of offer proceeds, debt equity ratio at IPO date, andhigh price to low price ratio for initial one month of trading days. We used multivariate OLSregression model for finding out the most suitable measure of risk in an IPO. We provide evidence that H/L is a better proxy for ex ante risk than others. The H/L ratioestimated for initial one month of trading has superior predictive ability for underprice.Following H/L, the other risk proxies that prove statistically significant include investmentbank prestige and inverse of offer proceeds. Hence investment bank prestige ratings andinverse of offer proceeds can be used as rational risk surrogate for IPOs. Collectively, all riskproxies, including H/L ratio, are accountable for 56.7% of the variation in initial day return.Consequently, the previous studies (Barry and Jennings, 1991) which have rejected theexplanatory power of H/L to explain initial IPO returns are questionable. The other riskproxie including age of the IPO firm, leverage at IPO date, and after market price volatility ofthe IPO stock proves statistically insignificant in predicting underprice, hence is not suggestedas risk surrogate for IPOs. Thus, it is predicted that it is the after market price performancerisk that drives the IPO valuation. Moreover, we studied the predictive behavior of the risk proxies across manufacturingand non-manufacturing sectors. We found no significant difference between manufacturingfirm H/L and that of non-manufacturing firms. We also document that after market H/L,investment bank prestige and age of the IPO firm were found to be suitable risk proxies forthe manufacturing sector, while the risk for non-manufacturing sector is represented by H/L,investment bank prestige, inverse of offer proceeds, and after market volatility.References 1. Aggarwal R and Rivoli P (1989), “Fads in the Initial Public Offering Market”, Journal of Financial Management, Vol. 19, No. 4, pp. 45-57. 2. Baral S K and Obaidullah M (1998) “Short-Run Price Behavior of IPOs in India: Some Empirical Findings”, in T P Madhusoodanan (Ed.) Indian Capital Markets: Theories and Empirical Evidence, pp. 15-30, Quest Publishers, India. 3. Barry C and Jennings R (1991), “The Opening Price Performance of Initial Public Offerings of Common Stock”, presented at the 1991 Financial Management Association Meetings in Chicago, 1991 and published in 1993, Financial Management, Vol. 22, No. 1, pp. 54-63.52 The IUP Journal of Applied Finance, Vol. 17, No. 4, 2011
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