The Aftermarket in High-Tech IPOs
|
|
- Cameron Norton
- 5 years ago
- Views:
Transcription
1 The Aftermarket in High-Tech IPOs By Sanjiv Jaggia Satish Thosar* Department of Economics School of Finance and Economics Suffolk University University of Technology, Sydney 8 Ashburton Place P.O. Box 123 Boston, MA Broadway, NSW 2007 U.S.A. Australia * Corresponding author satish.thosar@uts.edu.au Phone: Fax:
2 The Aftermarket in High-Tech IPOs Abstract We examine the medium-term (six-month) aftermarket in high-tech IPOs launched during the late 1990s. We assume the perspective of an investor who has no preferential allotment and access only to virtually costless information in the public domain. Using an ordered logistic regression approach, we demonstrate the potential to earn marketadjusted returns in excess of 100 percent with an optimal exit strategy. Our model indicates that momentum variables are important while fundamental variables have either no, or at best weak, explanatory power. We discuss our results in light of the minimally rational standard for market rationality articulated by Rubinstein (2000). 1
3 The Aftermarket in High-Tech IPOs Introduction Most empirical studies relating to IPOs focus on two persistent so-called anomalies: the initial underpricing and the long-run underperformance of IPO firms. These patterns have been documented in various markets and sample periods. 1 The theoretical work has mainly attempted to explain the initial underpricing phenomenon. 2 Our paper sidesteps these issues and examines the medium-term (six-month) aftermarket in high-tech IPOs launched in the late 1990s, arguably a significant hot issue period. We assume the perspective of an investor who has no preferential allotment and access only to easily available and virtually costless information in the public domain. Our objective is to study whether such an investor can earn significant market-adjusted excess returns in an environment characterized by high uncertainty. Our sample is deliberately narrowly drawn. Our high-tech IPO firms fall primarily in the following sectors: computer hardware/software, e-commerce, telecommunications and biotechnology. Clearly, these sectors have huge potential for future growth and profitability but individual firms and their investors face considerable uncertainty about the viability of their technology and/or business models. At one level therefore, our study is about devising and implementing an optimal investment strategy to exploit the turbulent IPO aftermarket. Indeed, using an ordered logistic regression approach, we show that it is possible to earn market-adjusted returns in excess of 100 percent with an appropriate exit strategy. Our model indicates that a long 1 See Loughran et al (1994) for a survey of the international evidence. Also see Jain and Kini (1994), Lee et al (1996) and Loughran and Ritter (1995). 2
4 position in a high-tech IPO stock entered into at the close of day 1 after the IPO should be cashed out 14 weeks after the IPO date. It appears that momentum variables are important while fundamental variables have either no, or at best weak, explanatory power. In a broader context, we believe that the IPO aftermarket may well be a laboratory for the examination of some of the issues raised in the ongoing debate about whether markets are rational. Rubinstein (2000) in arguing the affirmative case for rational markets concedes that the weight of paper in academic journals supporting anomalies is now much heavier than evidence to the contrary. He points out that while anomalies may exist, there may still not be abnormal profit opportunities in which case markets are at least minimally rational. We discuss our findings in light of Rubinstein s arguments. The next section outlines our data and methodology. The third section describes and discusses our results. The final section contains concluding comments. Data and Methodology In carrying out this study, we assume the perspective of an investor with no preferential allotment in the IPO, access only to freely available information but with some expertise in statistical modeling. Our self-imposed constraint is that the investor should be able to implement her strategy without access to large research departments or subscriptions to expensive databases. 3 2 See Rock (1986), Welch (1989), Grinblatt and Hwang (1989), Benveniste and Spindt (1989) and Loughran and Ritter (2000). 3 It is worth noting that most academic studies ignore potentially large research and information gathering costs in generating and reporting excess returns. 3
5 Accordingly, our primary sample of high-tech firms was drawn from ipo.com, which lists the universe of U.S. IPOs with dates; offer prices etc. broken down in a number of categories. We chose all IPOs from January 1, 1998 through October 30, 1999 in the following sectors: biotechnology, computer hardware, computer software, electronics, Internet services, Internet software and telecommunications. This resulted in a sample of 316 high-tech IPO firms. Daily closing prices for each firm in the sample and the corresponding NASDAQ index level are downloaded for 125 trading days (approximately six months) beyond the IPO date from yahoo finance. 4 We execute an ordered logistic regression in which the dependent variable, the market adjusted excess return, belongs to one of five categories. The market adjusted return for firm i, where i = 1,2,, N is defined as: R it Pit P i1 PMt P M 1 = 2. Pi 1 PM1 For each firm, P it represents the price t days after the IPO and P i1 is the initial price at the close of day 1 after the IPO. Similarly P Mt and P M1 are the corresponding levels for the market index (Nasdaq). The sample is created so as to make each firm fall into one of the five categories defined in the following table. The dependent variable is captured in terms of the dummy variables Y 1, Y 2, Y 3, Y 4, Y 5 where Yj = 1 if R it exceeds some threshold value; 0 otherwise. For instance, Y 5 = 1 if R it > 1.0 and Y 4 = 1 if Y5 1 and R it > 0.5. Categories Dummy Variables Description Category 1 Y 1 =1, Y 2 =0, Y 3 =0, Market adjusted returns are in the < 0 range Y 4 =0, Y 5 =0 Category 2 Y 1 =0, Y 2 =1, Y 3 =0, Market adjusted returns are in the (0, 0.25] range 4 Price data were spot-checked for validity from alternate sources. 4
6 Y 4 =0, Y 5 =0 Category 3 Y 1 =0, Y 2 =0, Y 3 =1, Y 4 =0, Y 5 =0 Category 4 Y 1 =0, Y 2 =0, Y 3 =0, Y 4 =1, Y 5 =0 Category 5 Y 1 =0, Y 2 =0, Y 3 =0, Y 4 =0, Y 5 =1 Market adjusted returns are in the (0.25, 0.50] range Market adjusted returns are in the (0.50, 1] range Market adjusted returns are in the >1 range The above categories are defined when the threshold is first reached. At this point, the time variable defined as the number of weeks (one week is represented by five trading days) after the IPO date is also recorded. For instance if the firm's market adjusted return becomes 100 percent in 4 weeks, Y 5 equals 1 and the time variable takes on value 4. For the worst category (Y 1 =1), the time variable equals zero. In many economic applications the dependent variable is discrete and represents an outcome of a decision between a finite set of alternatives. Sometimes there are multinomial choice variables that are naturally ordered (Greene (2000)). Examples include opinion surveys (strongly agree, agree, disagree and strongly disagree), insurance coverage (full, partial, none), bond ratings, etc. In this application, a firm's aftermarket IPO performance falls into one of the five ordered categories defined above. We need a model that explains the influence of variables on the probability of the firm falling into these categories. In the estimation process, although the underlying performance variable (Z) is continuous, only the discrete responses are observed. Consider the following grid that puts firms in the various categories: Y 1 =1 Y 2 =1 Y 3 =1 Y 4 =1 Y 5 =1 γ 0 γ 1 γ 2 γ 3 γ 4 Z P(Y 1 =1) = P(Z < γ 0 ), P(Y 2 =1) = P(γ 0 Z<γ 1 ), etc. For an ordered logit model, 5
7 ( 1 ) 1 PZ ( < γ j ) = 1 + exp( β' X γ ), j where ( 2 ) β X β0 β1x1 β2x2 β k X k = The coefficient β j measures the influence of the explanatory variable X j on the probability of falling into a particular category. The γ j s are the unknown parameters to be estimated along with the βs. These probabilities are used to specify the following loglikelihood function that is maximized to obtain the parameter estimates: ( 3 ) = N 5 i= 1 j= 1 ( Yj = 1)ln P( Yj = 1) Further, given a constant term in X, γ 0 is set equal to zero without any loss of generality in the estimation. 5 Potential explanatory variables, X, are selected with guidance from previous literature and researcher intuition, the latter coming into play mostly in constructing the so-called momentum variables. These variables and their sources are as follows: P1i Oi! Underpricing at t=1: O i where P 1i is the closing price at the close of the first day s trading and O i is the offer price. Offer price data were obtained from ipo.com. This represents the extent of initial underpricing (or overpricing). Pi2 P i1 PM2 P M1! Momentum at t=2: 2 Pi 1 PM1 5 Maximum likelihood estimates are obtained using the MAXLIK module of the GAUSS programming language. 6
8 This represents the market adjusted return on day 2. This is a momentum variable in the sense that it is a purely technical indicator to indicate price direction net of market movement after the first day s trading.! Net Income/Revenue in the pre-ipo year. (Source: ipo.com)! UW Reputation. The lead underwriter s identity was obtained from ipo.com and the reputation proxy is the Carter-Manaster measure reported in Carter et al (1998).! Offer Size. This represents the Offer price * Number of shares sold in the IPO (Source: ipo.com). In the regression, we use the log value of the offer size.! Age. This represents the number of years from the date the firm was incorporated to the IPO date. (Source: FISOnline)! Green Shoe provision. This dummy variable takes value 1 if there is a green shoe provision in the IPO contract, 0 otherwise (Source: ipo.com). Briefly, a green shoe provision gives the underwriter the option to purchase additional shares at the offer price to cover overallotments.! Ind23. This dummy variable takes value 1 if the firm belongs either to the computer hardware or software sectors, 0 otherwise.! Ind56. This dummy variable takes value 1 if the firm belongs either to the Internet services or Internet software sectors, 0 otherwise.! Ind7. This dummy variable takes value 1 if the firm belongs to the telecommunications sector, 0 otherwise. 6 6 No industry dummy variables were set up for the biotechnology or electronic sectors because there were relatively few firms in these sectors in our sample. 7
9 Table 1 provides a comparison between the distribution of firms across the five categories if the investor always cashes out at the optimal point defined as when the threshold is reached versus the distribution that results if a simple buy and hold strategy is adopted. It should be clear that the optimal distribution is based on perfect hindsight and the buy and hold is a naïve strategy that involves buying every IPO stock at the Day 1 closing price and selling it after six months. Nonetheless, the contrast is striking. With the optimal strategy, 106 (33.5 percent of the total) firms end up in category 5 (market adjusted return exceeding 100 percent) whereas only 47 firms (14.8 percent of the total) achieve the same result under buy and hold. Also, the numbers for category 1 (negative market adjusted return) are 35 (11 percent of total) for the optimal versus 215 (68 percent of total) for the buy and hold. It s obvious that timing the sell decision correctly is of paramount importance and also that time has a non-linear influence. Accordingly, we construct two additional momentum variables before executing the regression. As indicated above, the first variable (Time) represents the time in weeks from the IPO date to the optimal sell date (defined as the date the threshold is reached, not necessarily the peak stock price) and the second variable (Time-Sqd) is simply Time squared to capture the implicit non-linearity. We fully realize that these two variables involve ex-post look back and cannot be used in a pure predictive model. However, as we discuss in greater detail in the results section, we will show that it is possible to simulate the effect of the time variable to predict the distribution of firms across the five categories by evaluating the other non-look back variables. It may appear to the reader that there is a degree of arbitrariness in the way that we construct our market adjusted excess returns or the thresholds that define the ordered 8
10 categories or even the number of categories. This is quite true but far from being a shortcoming, this arbitrariness is actually an advantage in the context of our application. It allows the individual investor to set her own bar in terms of excess returns while the model itself is flexible enough to accommodate (within reason) any number of categories. Since, there is no a-priori beta type risk measure available for high-tech IPO firms, our measure of excess return (actual return minus twice the Nasdaq return over the comparable period) is in our opinion a reasonably challenging hurdle. Also, the cut points to define the categories are chosen to reflect ambitious but still reasonable thresholds for a speculative investment strategy. Results The results of the ordered logistic regression are reported in Table 2. An interesting but not necessarily surprising finding is the unimportance or comparative weakness in the explanatory power of fundamental variables. For instance, the profitability (or lack thereof) of the firm in the pre-ipo year plays no role in the mediumterm IPO aftermarket. Similarly, the number of years that the firm has been in business prior to the IPO does not seem to matter. These are classic old-economy variables that supposedly enable formation of expectations about future cash flows and/or risk. Our sample is designed to reflect high-tech so-called new-economy IPO firms in an arguably hot-issue period. Therefore, the lack of significance associated with these fundamental variables validates the observation by market watchers that technology sector valuations in the late 1990s did not conform to traditional pricing models. Other variables related to the IPO contract such as the offer size, the presence of a green-shoe 9
11 provision and even the offer price do not impact the aftermarket. In our sample, the mean of the underpricing variable is 47.3 percent, which seems broadly in line with other recent studies. 7 We were particularly surprised that the extent of initial underpricing (or overpricing) had no carry through effect. However, in a sense this too is a fundamental variable that presumably captures information about the IPO firm implicit in the first day s trading. The only fundamental variables that do appear to have some influence in aftermarket price behavior are underwriter reputation and the industry dummy (Ind56) relating to specifically Internet firms. Both these variables have positive and significant coefficients though the t-statistics are not dramatic in magnitude. 8 It should be noted that the interpretation of the coefficients in an ordered logistic regression is not straightforward. However, in our application, a significantly positive coefficient implies that the variable positively influences the probability of a good outcome, i.e., the probability that the market adjusted excess return will end up in category 4 (50 to 100 percent range) or 5 (>100 percent range). It turns out that momentum variables are basically driving most of the aftermarket action. For instance, the market adjusted return on day 2 (Momentum) is highly significant. According to our analysis, if this variable is positive it strongly favors the probability of the preferred outcomes. However, it is one thing to identify variables that influence the outcome and quite another to devise and implement an appropriate investment strategy. It was intuitively clear to us based on the analysis reported in Table 1 that timing the sell decision was crucial. In other words, in the largely momentum 7 See Loughran and Ritter (2000) and Arosio et al (2000). 8 The original analysis was done using the Carter-Manaster reputation rankings. We found that almost all the lead underwriters in our sample were at the high end (>7). We ultimately substituted the Carter- 10
12 driven IPO aftermarket, time itself is a key non-linear variable. The results bear this out. Time has a positive impact while Time-Sqd has a negative one and both are highly significant variables with t-statistics of and 8.81 respectively. This means that in the initial period after the IPO, it pays to hold the stock for a while because the probability of landing in the higher categories is improving but as more time passes, this probability wanes. In Table 3, we report the actual proportions across categories versus those predicted by our ordered logistic regression model. While recognizing that this is an insample analysis, the predicted proportions are virtually identical to the actual ones; this gives us confidence that the overall model is reasonably well specified. Obviously, an investor cannot employ our model in a traditional predictive sense because the time variable is constructed using ex-post look back. However, it is possible to simulate the probabilities of various outcomes for different values of time. In Table 4, we report the results of the simulation analysis that generated predicted proportions across the five categories with respect to time with other variables at their actual ex-ante values for each firm. The simulated proportions (or probabilities) are averaged across our sample of 301 high-tech IPO firms. For instance, we can see that the probability of ending up in category 5 (market adjusted return > 100 percent) is maximized at 14 weeks after the IPO date. In fact the probability of a category 4 or 5 outcome at 14 weeks is over 90 percent. 9 The same probability at 28 weeks is considerably lower. Manaster number with a dummy variable which takes value 1 if the underwriter ranking was greater than 7, 0 otherwise. 9 It is important to note that the probabilities reported in Table 3 are conditional on the time variable taking on a positive value. As indicated in the methodology section, the value of the time variable is determined when the ordered threshold is crossed but takes on a value of zero if the firm never does cross any positive market adjusted return threshold, i.e., ends up being a category 1 firm. Therefore, the probabilities in Table 11
13 It appears that an investor with access to the kind of virtually costless and easily accessible information we use in this study can buy a high-tech IPO stock at the close of the first day s trading and earn market adjusted returns in excess of 100 percent with high probability if she employs the appropriate exit strategy for our sample a cash-out point of approximately 14 weeks after the IPO date. So the broad conclusion appears to be that a purely technical trading strategy is viable and potentially extremely profitable in the medium term IPO aftermarket. Does this mean that this particularly segment of the market is not even minimally rational as defined by Rubinstein (2000)? Do our results imply abnormal profit opportunities? While suggestive of such a conclusion, there are many caveats. First, our unorthodox (and somewhat arbitrary) approach to generating market adjusted excess returns may be inadequate, though no obvious alternative suggests itself. Second, and perhaps more importantly, our study could be classified as an exercise in data mining, even though we have relied on virtually costless data sources and certainly a rather narrow sample size and period. We have no evidence that our findings can be replicated outside our sample period or in other hot issue markets. Therefore, we remain agnostic as to the implications of our findings for the rational markets debate. We do believe, however, that the market setting examined by us could serve as a laboratory for the investigation of a possible source of market uncertainty. Specifically, Rubinstein (2000) proposes that there may be a rational explanation for the excess volatility anomaly: much of the volatility in prices derives from changes in beliefs about the demand curves of other investors, a form of endogenous uncertainty this 3 are intended to be illustrative and not definitive. However, an independent mean-reversion analysis also indicated that the mean reversion effect is strongest at around 14 weeks. 12
14 may also explain that while stock prices typically react to news about fundamentals, they also seem to change when there is no news. An IPO aftermarket may be the ideal arena to generate and test hypotheses in this emerging strand of literature. Finally, we would like to propose that the ordered logistic regression approach adopted by us is a potentially powerful methodological tool in the study or practice of financial economics. As alluded to above, a number of responses or outcomes that constitute dependent variables are either already naturally ordered or can be modeled in that manner. In particular, this approach could be utilized in the formulation of trading strategies involving technical indicators. Conclusion In this study, we examine the medium-term (six-month) aftermarket in high-tech IPOs launched during the late 1990s. We assume the perspective of an investor who has no preferential allotment and access only to virtually costless information in the public domain. Using an ordered logistic regression approach, we demonstrate the potential to earn market-adjusted returns in excess of 100 percent with an optimal exit strategy, which essentially involves cashing out 14 weeks after the IPO date. Our model indicates that momentum variables are important while fundamental variables have virtually no explanatory power. We discuss our results in light of the minimally rational standard for market rationality articulated by Rubinstein (2000) and suggest that the IPO aftermarket is an ideal setting for studying a possible source of excess volatility: endogenous uncertainty arising from changes in investor beliefs about other investors demand curves. 13
15 Table 1 Distribution of IPO firms across categories based on Naïve versus Optimal Timing Strategies. The Naïve strategy involves buying at the end of first day s trading (t=1) price and selling exactly six months later. The Optimal strategy assumes perfect hindsight and requires buying at the end of first day s trading price and selling at the point the threshold (not necessarily price peak) is reached. Categories Number of Firms (Naïve Strategy) Number of Firms (Optimal Strategy) Category Category Category Category Category Definition of Categories Categories Category 1 Category 2 Category 3 Category 4 Category 5 Description Market adjusted returns in the < 0 range Market adjusted returns in the (0, 0.25] range Market adjusted returns in the (0.25, 0.50] range Market adjusted returns in the (0.50, 1] range Market adjusted returns in the >1 range 14
16 Table 2 Results of Ordered Logistic Regression; Sample Size 301 (original sample is 316; 15 firms are dropped due to incomplete information on one or more explanatory variable) The dependent variable is captured in terms of the dummy variables Y 1, Y 2, Y 3, Y 4, Y 5 where Yj = 1 if the market adjusted return exceeds some threshold value; 0 otherwise. Categories Dummy Variables Description Category 1 Y 1 =1, Y 2 =0, Y 3 =0, Market adjusted returns are in the < 0 range Y 4 =0, Y 5 =0 Category 2 Y 1 =0, Y 2 =1, Y 3 =0, Market adjusted returns are in the (0, 0.25] range Y 4 =0, Y 5 =0 Category 3 Y 1 =0, Y 2 =0, Y 3 =1, Market adjusted returns are in the (0.25, 0.50] range Y 4 =0, Y 5 =0 Category 4 Y 1 =0, Y 2 =0, Y 3 =0, Market adjusted returns are in the (0.50, 1] range Y 4 =1, Y 5 =0 Category 5 Y 1 =0, Y 2 =0, Y 3 =0, Y 4 =0, Y 5 =1 Market adjusted returns are in the >1 range Model Estimates Parameters Mean of the Variable Estimates t-statistics Constant Time * Time-Sqd * Underpricing Momentum * Net Income/Revenue UW Reputation * Offer Size Green Shoe Dummy Ind23 Dummy Ind56 Dummy * Ind7 Dummy Age * Indicates significance at 5 percent or lower level in a two-tailed test. 15
17 Table 3 Actual versus Estimated Average Proportions Categories Actual Proportions Predicted Proportions C1 (<0) C2 (0,0.25] C3 (0.25,0.5] C4 (0.5,1] C5 (>1)
18 Table 4 Results of a simulation exercise with respect to the time variable where all other variables are evaluated based on their ex-ante actual values. Proportions for each category simulated for each firm and then averaged across the sample of 301 firms. Time in Weeks C1 C2 C3 C4 C Definition of Categories Categories Category 1 Category 2 Category 3 Category 4 Category 5 Description Market adjusted returns in the < 0 range Market adjusted returns in the (0, 0.25] range Market adjusted returns in the (0.25, 0.50] range Market adjusted returns in the (0.50, 1] range Market adjusted returns in the >1 range 17
19 References Arosio, R., Giudici, G., and Paleari, S., Why Do (or Did?) Internet-Stock IPOs Leave So much Money on the Table?, Working Paper, December Benveniste, L. M. and Spindt, P. A., How Investment Bankers Determine the Offer Price and Allocation of New Issues, Journal of Financial Economics, 24(2), 1989, Carter, R. B., Dark, F. H., and Singh, A. K., Underwriter Reputation, Initial Returns and the Long-Run Performance of IPO Stocks, Journal of Finance, 53(1), 1998, Greene, W. H., Econometric Analysis, 4th Edition, 2000, Prentice-Hall Inc. Grinblatt, M. and Hwang, C. Y., Signaling and the Pricing of New Issues, Journal of Finance, 44(2), 1989, Jain, B. A. and Kini, O., The Post-Issue Operating Performance of IPO Firms, Journal of Finance, 1994, Lee, P. J., Taylor, S. L., and Walter, S.W., Australian IPO pricing in the short and long run, Journal of Banking and Finance, 20, 1996, Loughran, T., Ritter, J.R., and Rydqvist, K., Initial Public Offerings: International Insights, Pacific-Basin Finance Journal, 2, 1994, Loughran, T., and Ritter, J.R., The new issues puzzle, Journal of Finance, 50, 1995, Loughran, T., and Ritter, J.R., Why Don t Issuers Get Upset About Leaving Money on the Table in IPOs?, Working Paper, August Rock, K., Why New Issues Are Underpriced, Journal of Financial Economics, 15, 1986, Rubinstein, M., Rational Markets: Yes or No? The Affirmative Case, forthcoming in Financial Analysts Journal. Welch, I., Seasoned Offerings, Imitation Costs, and the Underpricing of Initial Public Offerings, Journal of Finance, 44, 1989,
Momentum Investing: The Case of High-Tech IPOs
Momentum Investing: The Case of High-Tech IPOs Sanjiv Jaggia Satish Thosar Momentum Investing: The Case of High-Tech IPOs Abstract: We document significant momentum effects in the high-tech IPO aftermarket
More informationThe Role of Demand-Side Uncertainty in IPO Underpricing
The Role of Demand-Side Uncertainty in IPO Underpricing Philip Drake Thunderbird, The American Graduate School of International Management 15249 N 59 th Avenue Glendale, AZ 85306 USA drakep@t-bird.edu
More informationRESEARCH ARTICLE. Change in Capital Gains Tax Rates and IPO Underpricing
RESEARCH ARTICLE Business and Economics Journal, Vol. 2013: BEJ-72 Change in Capital Gains Tax Rates and IPO Underpricing 1 Change in Capital Gains Tax Rates and IPO Underpricing Chien-Chih Peng Department
More informationUnder pricing in initial public offering
AMERICAN JOURNAL OF SOCIAL AND MANAGEMENT SCIENCES ISSN Print: 2156-1540, ISSN Online: 2151-1559, doi:10.5251/ajsms.2011.2.3.316.324 2011, ScienceHuβ, http://www.scihub.org/ajsms Under pricing in initial
More informationOwnership Concentration and Initial Public Offering Performance: Evidence from Thailand
Ownership Concentration and Initial Public Offering Performance: Evidence from Thailand Abstract This study examines the relation between ownership concentration and performance of initial public offerings
More informationThe Changing Influence of Underwriter Prestige on Initial Public Offerings
Journal of Finance and Economics Volume 3, Issue 3 (2015), 26-37 ISSN 2291-4951 E-ISSN 2291-496X Published by Science and Education Centre of North America The Changing Influence of Underwriter Prestige
More informationBiases in the IPO Pricing Process
University of Rochester William E. Simon Graduate School of Business Administration The Bradley Policy Research Center Financial Research and Policy Working Paper No. FR 01-02 February, 2001 Biases in
More informationDo VCs Provide More Than Money? Venture Capital Backing & Future Access to Capital
LV11066 Do VCs Provide More Than Money? Venture Capital Backing & Future Access to Capital Donald Flagg University of Tampa John H. Sykes College of Business Speros Margetis University of Tampa John H.
More informationIPO s Long-Run Performance: Hot Market vs. Earnings Management
IPO s Long-Run Performance: Hot Market vs. Earnings Management Tsai-Yin Lin Department of Financial Management National Kaohsiung First University of Science and Technology Jerry Yu * Department of Finance
More informationDemand uncertainty, Bayesian update, and IPO pricing. The 2011 China International Conference in Finance, Wuhan, China, 4-7 July 2011.
Title Demand uncertainty, Bayesian update, and IPO pricing Author(s) Qi, R; Zhou, X Citation The 211 China International Conference in Finance, Wuhan, China, 4-7 July 211. Issued Date 211 URL http://hdl.handle.net/1722/141188
More informationGrandstanding and Venture Capital Firms in Newly Established IPO Markets
The Journal of Entrepreneurial Finance Volume 9 Issue 3 Fall 2004 Article 7 December 2004 Grandstanding and Venture Capital Firms in Newly Established IPO Markets Nobuhiko Hibara University of Saskatchewan
More informationPerformance of Initial Public Offerings in Public and Private Owned Firms of Pakistan. Henna and Attiya Yasmin Javid
Performance of Initial Public Offerings in Public and Private Owned Firms of Pakistan Henna and Attiya Yasmin Javid Introduction When any private company first time sells his stock to general public is
More informationA Comparison of the Characteristics Affecting the Pricing of Equity Carve-Outs and Initial Public Offerings
A Comparison of the Characteristics Affecting the Pricing of Equity Carve-Outs and Initial Public Offerings Abstract Karen M. Hogan and Gerard T. Olson * * Saint Joseph s University and Villanova University,
More informationSyndicate Size In Global IPO Underwriting Demissew Diro Ejara, ( University of New Haven
Syndicate Size In Global IPO Underwriting Demissew Diro Ejara, (E-mail: dejara@newhaven.edu), University of New Haven ABSTRACT This study analyzes factors that determine syndicate size in ADR IPO underwriting.
More informationAnalyzing the Determinants of Project Success: A Probit Regression Approach
2016 Annual Evaluation Review, Linked Document D 1 Analyzing the Determinants of Project Success: A Probit Regression Approach 1. This regression analysis aims to ascertain the factors that determine development
More informationThe Influence of Underpricing to IPO Aftermarket Performance: Comparison between Fixed Price and Book Building System on the Indonesia Stock Exchange
International Journal of Economics and Financial Issues ISSN: 2146-4138 available at http: www.econjournals.com International Journal of Economics and Financial Issues, 2017, 7(4), 157-161. The Influence
More informationBANK REPUTATION AND IPO UNDERPRICING: EVIDENCE FROM THE ISTANBUL STOCK EXCHANGE
BANK REPUTATION AND IPO UNDERPRICING: EVIDENCE FROM THE ISTANBUL STOCK EXCHANGE Abstract This study examines the effect of underwriter reputation on the initial-day and long-term IPO returns in an emerging
More informationThe Geography of Institutional Investors, Information. Production, and Initial Public Offerings. December 7, 2016
The Geography of Institutional Investors, Information Production, and Initial Public Offerings December 7, 2016 The Geography of Institutional Investors, Information Production, and Initial Public Offerings
More informationDIVIDEND POLICY AND THE LIFE CYCLE HYPOTHESIS: EVIDENCE FROM TAIWAN
The International Journal of Business and Finance Research Volume 5 Number 1 2011 DIVIDEND POLICY AND THE LIFE CYCLE HYPOTHESIS: EVIDENCE FROM TAIWAN Ming-Hui Wang, Taiwan University of Science and Technology
More informationThe Role of Industry Affiliation in the Underpricing of U.S. IPOs
The Role of Industry Affiliation in the Underpricing of U.S. IPOs Bryan Henrick ABSTRACT: Haverford College Department of Economics Spring 2012 This paper examines the significance of a firm s industry
More informationAnother Look at Market Responses to Tangible and Intangible Information
Critical Finance Review, 2016, 5: 165 175 Another Look at Market Responses to Tangible and Intangible Information Kent Daniel Sheridan Titman 1 Columbia Business School, Columbia University, New York,
More informationPROSIDING PERKEM IV, JILID 1 (2009) ISSN: X
PROSIDING PERKEM IV, JILID 1 (2009) 395-412 ISSN: 2231-962X SIGNIFICANCE OF INVESTOR DEMAND, FIRM SIZE, OFFER TYPE AND OFFER SIZE ON THE INITIAL PREMIUM, FIRST-DAY PRICE SPREAD AND FLIPPING ACTIVITY OF
More informationThe Short-Run and Long-Run Returns of Initial Public Offerings in Taiwan
»{ The Short-Run and Long-Run Returns of Initial Public Offerings in Taiwan ƒf6,'&!# % 1 '% ' '& & " pv v o { k k ku g²š{ { { k j g² ui k¼v {»» k { : k k Abstract Researches related to the study of initial
More informationShould IPOs be Auctioned? The Impacts of Japanese Auction-Priced IPOs
Should IPOs be Auctioned? The Impacts of Japanese Auction-Priced IPOs By Richard H. Pettway College of Business and Public Administration 239 Middlebush Hall University of Missouri-Columbia Columbia, MO
More informationNBER WORKING PAPER SERIES INSTITUTIONAL ALLOCATION IN INITIAL PUBLIC OFFERINGS: EMPIRICAL EVIDENCE. Reena Aggarwal Nagpurnanand R. Prabhala Manju Puri
NBER WORKING PAPER SERIES INSTITUTIONAL ALLOCATION IN INITIAL PUBLIC OFFERINGS: EMPIRICAL EVIDENCE Reena Aggarwal Nagpurnanand R. Prabhala Manju Puri Working Paper 9070 http://www.nber.org/papers/w9070
More informationKey words: Incentive fees; Underwriter compensation; Hong Kong; Underwriter reputation; Initial Public offerings.
Incentive Fees: Do they bond underwriters and IPO issuers? Abdulkadir Mohamed Cranfield University Brahim Saadouni The University of Manchester This paper examines the impact of incentive fees in mitigating
More informationDOES IPO GRADING POSITIVELY INFLUENCE RETAIL INVESTORS? A QUANTITATIVE STUDY IN INDIAN CAPITAL MARKET
DOES IPO GRADING POSITIVELY INFLUENCE RETAIL INVESTORS? A QUANTITATIVE STUDY IN INDIAN CAPITAL MARKET Abstract S.Saravanan, Research Scholar, Sathyabama University, Chennai Dr.R.Satish, Associate Professor,
More informationUnderwriter reputation and the underwriter investor relationship in IPO markets
Underwriter reputation and the underwriter investor relationship in IPO markets Author Neupane, Suman, Thapa, Chandra Published 2013 Journal Title Journal of International Financial Markets, Institutions
More informationLitigation Risk and IPO Underpricing
Litigation Risk and IPO Underpricing Presentation by Gennaro Bernile Michelle Lowry Penn State University Susan Shu Boston College Problem in hand and related literature Model proposed and problems with
More informationInstitutional Allocation in Initial Public Offerings: Empirical Evidence
Institutional Allocation in Initial Public Offerings: Empirical Evidence Reena Aggarwal McDonough School of Business Georgetown University Washington, D.C., 20057 Tel: (202) 687-3784 Fax: (202) 687-4031
More informationGiraffes, Institutions and Neglected Firms
Cornell University School of Hotel Administration The Scholarly Commons Articles and Chapters School of Hotel Administration Collection 1983 Giraffes, Institutions and Neglected Firms Avner Arbel Cornell
More informationAdvanced Corporate Finance. 8. Raising Equity Capital
Advanced Corporate Finance 8. Raising Equity Capital Objectives of the session 1. Explain the mechanism related to Equity Financing 2. Understand how IPOs and SEOs work 3. See the stylized facts related
More informationKeywords: Seasoned equity offerings, Underwriting, Price stabilization, Transaction data JEL classification: G24, G32
ACADEMIA ECONOMIC PAPERS 32 : 1 (March 2004), 53 81 Underwriter Price Stabilization of Seasoned Equity Offerings: The Evidence from Transactions Data James F. Cotter Wake Forest University Wayne Calloway
More informationUnderwriter Switching in the Japanese Corporate Bond Market
Underwriter Switching in the Japanese Corporate Bond Market 1 McKenzie, C.R. and 2 Sumiko Takaoka 1 Faculty of Economics, Keio University, E-Mail: mckenzie@econ.keio.ac.jp 2 Faculty of Economics, Seikei
More informationEquity, Vacancy, and Time to Sale in Real Estate.
Title: Author: Address: E-Mail: Equity, Vacancy, and Time to Sale in Real Estate. Thomas W. Zuehlke Department of Economics Florida State University Tallahassee, Florida 32306 U.S.A. tzuehlke@mailer.fsu.edu
More informationThe performance of initial public offerings in the biotechnology industry
Gonzaga University From the SelectedWorks of Todd A Finkle 1998 The performance of initial public offerings in the biotechnology industry Todd A Finkle, Gonzaga University Dan French, University of Missouri
More informationHow Markets React to Different Types of Mergers
How Markets React to Different Types of Mergers By Pranit Chowhan Bachelor of Business Administration, University of Mumbai, 2014 And Vishal Bane Bachelor of Commerce, University of Mumbai, 2006 PROJECT
More informationThe Variability of IPO Initial Returns
The Variability of IPO Initial Returns Michelle Lowry Penn State University, University Park, PA 16082, Micah S. Officer University of Southern California, Los Angeles, CA 90089, G. William Schwert University
More informationUnderwriter s Discretion and Pricing of Initial Public Offerings
International Journal of Business Management and Economics Research. ISSN 2349-2333 Volume 2, Number 2 (2015), pp. 107-122 International Research Publication House http://www.irphouse.com Underwriter s
More informationInvestor Preferences, Mutual Fund Flows, and the Timing of IPOs
Investor Preferences, Mutual Fund Flows, and the Timing of IPOs by Hsin-Hui Chiu 1 EFM Classification Code: 230, 330 1 Chapman University, Argyros School of Business, One University Drive, Orange, CA 92866,
More informationRetirement. Optimal Asset Allocation in Retirement: A Downside Risk Perspective. JUne W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT
Putnam Institute JUne 2011 Optimal Asset Allocation in : A Downside Perspective W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT Once an individual has retired, asset allocation becomes a critical
More informationHow Important Are Relationships for IPO Underwriters and Institutional Investors? *
How Important Are Relationships for IPO Underwriters and Institutional Investors? * Murat M. Binay Peter F. Drucker and Masatoshi Ito Graduate School of Management Claremont Graduate University 1021 North
More informationLitigation Risk and IPO Underpricing
Litigation Risk and IPO Underpricing Michelle Lowry Penn State University Email: mlowry@psu.edu Phone: (814) 863-6372 Fax: (814) 865-3362 Susan Shu Boston College Email: shus@bc.edu Phone: (617) 552-1759
More informationOnline Appendix Results using Quarterly Earnings and Long-Term Growth Forecasts
Online Appendix Results using Quarterly Earnings and Long-Term Growth Forecasts We replicate Tables 1-4 of the paper relating quarterly earnings forecasts (QEFs) and long-term growth forecasts (LTGFs)
More informationDeclining IPO volume: Cold issue market or structural change in the capital markets?
Declining IPO volume: Cold issue market or structural change in the capital markets? Preliminary thesis Hanne Levardsen, Iselin Dybing Vaarlund BI Norwegian Business School Supervisor: Janis Berzins 16.01.2016
More informationWinner s Curse in Initial Public Offering Subscriptions with Investors Withdrawal Options
Asia-Pacific Journal of Financial Studies (2010) 39, 3 27 doi:10.1111/j.2041-6156.2009.00001.x Winner s Curse in Initial Public Offering Subscriptions with Investors Withdrawal Options Dennis K. J. Lin
More informationLong Run Stock Returns after Corporate Events Revisited. Hendrik Bessembinder. W.P. Carey School of Business. Arizona State University.
Long Run Stock Returns after Corporate Events Revisited Hendrik Bessembinder W.P. Carey School of Business Arizona State University Feng Zhang David Eccles School of Business University of Utah May 2017
More informationThe Macrotheme Review A multidisciplinary journal of global macro trends
The Macrotheme Review A multidisciplinary journal of global macro trends Signal models and the initial undervaluation of the French IPOs Afef AYADI*, Hatem MANSALI**, and Mohamed Tahar RAJHI*** * Faculté
More informationFACTORS INFLUENCING THE UNDERPRICING OF INITIAL PUBLIC OFFERINGS IN AN EMERGING MARKET: MALAYSIAN EVIDENCE
IIUM Journal of Economics and Management 12, no.2 (2004): 2004 by The International Islamic University Malaysia FACTORS INFLUENCING THE UNDERPRICING OF INITIAL PUBLIC OFFERINGS IN AN EMERGING MARKET: MALAYSIAN
More informationWhy Are Stock Exchange IPOs So Underpriced and Yet Outperform in The Long Run? A Test of the Signaling Hypothesis
Why Are Stock Exchange IPOs So Underpriced and Yet Outperform in The Long Run? A Test of the Signaling Hypothesis Abstract: Isaac Otchere Sprott School of Business Carleton University Ottawa, Canada [This
More informationThe relevance and the limits of the Arrow-Lind Theorem. Luc Baumstark University of Lyon. Christian Gollier Toulouse School of Economics.
The relevance and the limits of the Arrow-Lind Theorem Luc Baumstark University of Lyon Christian Gollier Toulouse School of Economics July 2013 1. Introduction When an investment project yields socio-economic
More informationWho Receives IPO Allocations? An Analysis of Regular Investors
Who Receives IPO Allocations? An Analysis of Regular Investors Ekkehart Boehmer New York Stock Exchange eboehmer@nyse.com 212-656-5486 Raymond P. H. Fishe University of Miami pfishe@miami.edu 305-284-4397
More informationDo Venture Capitalists Certify New Issues in the IPO Market? Yan Gao
Do Venture Capitalists Certify New Issues in the IPO Market? Yan Gao Northwestern University Baruch College, City University of New York, New York, NY 10010 Current version: 6 Novermber 2002 Abstract In
More informationInternet Appendix for: Does Going Public Affect Innovation?
Internet Appendix for: Does Going Public Affect Innovation? July 3, 2014 I Variable Definitions Innovation Measures 1. Citations - Number of citations a patent receives in its grant year and the following
More informationCHAPTER 12: MARKET EFFICIENCY AND BEHAVIORAL FINANCE
CHAPTER 12: MARKET EFFICIENCY AND BEHAVIORAL FINANCE 1. The correlation coefficient between stock returns for two non-overlapping periods should be zero. If not, one could use returns from one period to
More informationFresh Momentum. Engin Kose. Washington University in St. Louis. First version: October 2009
Long Chen Washington University in St. Louis Fresh Momentum Engin Kose Washington University in St. Louis First version: October 2009 Ohad Kadan Washington University in St. Louis Abstract We demonstrate
More informationPremium Timing with Valuation Ratios
RESEARCH Premium Timing with Valuation Ratios March 2016 Wei Dai, PhD Research The predictability of expected stock returns is an old topic and an important one. While investors may increase expected returns
More informationIPO Allocations to Affiliated Mutual Funds and Underwriter Proximity: International Evidence
IPO Allocations to Affiliated Mutual Funds and Underwriter Proximity: International Evidence Tim Mooney Pacific Lutheran University Tacoma, WA 98447 (253) 535-8129 mooneytk@plu.edu January 2014 Abstract:
More informationUnraveling versus Unraveling: A Memo on Competitive Equilibriums and Trade in Insurance Markets
Unraveling versus Unraveling: A Memo on Competitive Equilibriums and Trade in Insurance Markets Nathaniel Hendren October, 2013 Abstract Both Akerlof (1970) and Rothschild and Stiglitz (1976) show that
More informationInitial public offering in ports: The determinants of the long-term aftermarket performance
Initial public offering in ports: The determinants of the long-term aftermarket performance Author(s): G. Satta, T. Notteboom, F. Parola, L. Persico This paper had been presented at: IAME Conference 2016,
More informationIt is well known that equity returns are
DING LIU is an SVP and senior quantitative analyst at AllianceBernstein in New York, NY. ding.liu@bernstein.com Pure Quintile Portfolios DING LIU It is well known that equity returns are driven to a large
More informationArticle from: Product Matters. June 2015 Issue 92
Article from: Product Matters June 2015 Issue 92 Gordon Gillespie is an actuarial consultant based in Berlin, Germany. He has been offering quantitative risk management expertise to insurers, banks and
More informationInvestor Demand in Bookbuilding IPOs: The US Evidence
Investor Demand in Bookbuilding IPOs: The US Evidence Yiming Qian University of Iowa Jay Ritter University of Florida An Yan Fordham University August, 2014 Abstract Existing studies of auctioned IPOs
More informationCompletely predictable and fully anticipated? Step ups in warrant exercise prices
Applied Economics Letters, 2005, 12, 561 565 Completely predictable and fully anticipated? Step ups in warrant exercise prices Luis Garcia-Feijo o a, *, John S. Howe b and Tie Su c a Department of Finance,
More informationJOURNAL OF INVESTMENT MANAGEMENT, Vol. 1, No. 1, (2003), pp. 1 26
JOURNAL OF INVESTMENT MANAGEMENT, Vol. 1, No. 1, (2003), pp. 1 26 JOIM JOIM 2003 www.joim.com PRIVATE EQUITY RETURNS: AN EMPIRICAL EXAMINATION OF THE EXIT OF VENTURE-BACKED COMPANIES Sanjiv R. Das a, Murali
More informationRevisions to the national accounts: nominal, real and price effects 1
Revisions to the national accounts: nominal, real and price effects 1 Corné van Walbeek and Evelyne Nyokangi ABSTRACT Growth rates in the national accounts are published by the South African Reserve Bank
More informationNCER Working Paper Series
NCER Working Paper Series Momentum in Australian Stock Returns: An Update A. S. Hurn and V. Pavlov Working Paper #23 February 2008 Momentum in Australian Stock Returns: An Update A. S. Hurn and V. Pavlov
More informationThe Impact of Institutional Investors on the Monday Seasonal*
Su Han Chan Department of Finance, California State University-Fullerton Wai-Kin Leung Faculty of Business Administration, Chinese University of Hong Kong Ko Wang Department of Finance, California State
More informationSHORT RUN PERFORMANCE OF INITIAL PUBLIC OFFERINGS IN INDIA
CHAPTER 5 SHORT RUN PERFORMANCE OF INITIAL PUBLIC OFFERINGS IN INDIA It is a pervasive feature of markets, the world over, those investors who subscribed to initial public offerings, on the offer day,
More informationEstimating the Market Risk Premium: The Difficulty with Historical Evidence and an Alternative Approach
Estimating the Market Risk Premium: The Difficulty with Historical Evidence and an Alternative Approach (published in JASSA, issue 3, Spring 2001, pp 10-13) Professor Robert G. Bowman Department of Accounting
More informationIPO Underpricing in Hong Kong GEM
IPO Underpricing in Hong Kong GEM by Xisheng Wang A research project submitted in partial fulfillment of the requirements for the degree of Master of Finance Saint Mary s University Copyright Xisheng Wang
More informationInt. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS001) p approach
Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS001) p.5901 What drives short rate dynamics? approach A functional gradient descent Audrino, Francesco University
More informationEssays on Herd Behavior Theory and Criticisms
19 Essays on Herd Behavior Theory and Criticisms Vol I Essays on Herd Behavior Theory and Criticisms Annika Westphäling * Four eyes see more than two that information gets more precise being aggregated
More informationStock Price Reaction to Brokers Recommendation Updates and Their Quality Joon Young Song
Stock Price Reaction to Brokers Recommendation Updates and Their Quality Joon Young Song Abstract This study presents that stock price reaction to the recommendation updates really matters with the recommendation
More informationManagerial confidence and initial public offerings
Managerial confidence and initial public offerings Thomas J. Boulton a, T. Colin Campbell b,* May, 2014 Abstract Initial public offering (IPO) underpricing is positively correlated with managerial confidence.
More informationAn analysis of momentum and contrarian strategies using an optimal orthogonal portfolio approach
An analysis of momentum and contrarian strategies using an optimal orthogonal portfolio approach Hossein Asgharian and Björn Hansson Department of Economics, Lund University Box 7082 S-22007 Lund, Sweden
More informationMarketability, Control, and the Pricing of Block Shares
Marketability, Control, and the Pricing of Block Shares Zhangkai Huang * and Xingzhong Xu Guanghua School of Management Peking University Abstract Unlike in other countries, negotiated block shares have
More informationReal Estate Ownership by Non-Real Estate Firms: The Impact on Firm Returns
Real Estate Ownership by Non-Real Estate Firms: The Impact on Firm Returns Yongheng Deng and Joseph Gyourko 1 Zell/Lurie Real Estate Center at Wharton University of Pennsylvania Prepared for the Corporate
More informationAn analysis of the relative performance of Japanese and foreign money management
An analysis of the relative performance of Japanese and foreign money management Stephen J. Brown, NYU Stern School of Business William N. Goetzmann, Yale School of Management Takato Hiraki, International
More informationMarket Timing Does Work: Evidence from the NYSE 1
Market Timing Does Work: Evidence from the NYSE 1 Devraj Basu Alexander Stremme Warwick Business School, University of Warwick November 2005 address for correspondence: Alexander Stremme Warwick Business
More informationGlobal Finance Journal
Global Finance Journal 21 (2010) 253 261 Contents lists available at ScienceDirect Global Finance Journal journal homepage: www.elsevier.com/locate/gfj The short-run price performance of initial public
More informationThe Role of Venture Capital Backing. in Initial Public Offerings: Certification, Screening, or Market Power?
The Role of Venture Capital Backing in Initial Public Offerings: Certification, Screening, or Market Power? Thomas J. Chemmanur * and Elena Loutskina ** First Version: November, 2003 Current Version: February,
More informationInvestment performance of "environmentallyfriendly" firms and their initial public offers and seasoned equity offers
University of Wollongong Research Online Faculty of Business - Papers Faculty of Business 2014 Investment performance of "environmentallyfriendly" firms and their initial public offers and seasoned equity
More informationInternet Appendix to Quid Pro Quo? What Factors Influence IPO Allocations to Investors?
Internet Appendix to Quid Pro Quo? What Factors Influence IPO Allocations to Investors? TIM JENKINSON, HOWARD JONES, and FELIX SUNTHEIM* This internet appendix contains additional information, robustness
More informationINITIAL PUBLIC OFFERINGS:
INITIAL PUBLIC OFFERINGS: THE MALAYSIAN EXPERIENCE 1990-1994 Othman Yong ABSTRACT The existence of underpricing for initial public offerings (IPOs) of stocks in the advanced markets in the West is well
More informationTHE POLICY RULE MIX: A MACROECONOMIC POLICY EVALUATION. John B. Taylor Stanford University
THE POLICY RULE MIX: A MACROECONOMIC POLICY EVALUATION by John B. Taylor Stanford University October 1997 This draft was prepared for the Robert A. Mundell Festschrift Conference, organized by Guillermo
More informationCross Border Carve-out Initial Returns and Long-term Performance
Financial Decisions, Winter 2012, Article 3 Abstract Cross Border Carve-out Initial Returns and Long-term Performance Thomas H. Thompson Lamar University This study examines initial period and three-year
More informationThe Variability of IPO Initial Returns
The Variability of IPO Initial Returns Journal of Finance 65 (April 2010) 425-465 Michelle Lowry, Micah Officer, and G. William Schwert Interesting blend of time series and cross sectional modeling issues
More informationEXECUTIVE COMPENSATION AND FIRM PERFORMANCE: BIG CARROT, SMALL STICK
EXECUTIVE COMPENSATION AND FIRM PERFORMANCE: BIG CARROT, SMALL STICK Scott J. Wallsten * Stanford Institute for Economic Policy Research 579 Serra Mall at Galvez St. Stanford, CA 94305 650-724-4371 wallsten@stanford.edu
More informationChanges in Analysts' Recommendations and Abnormal Returns. Qiming Sun. Bachelor of Commerce, University of Calgary, 2011.
Changes in Analysts' Recommendations and Abnormal Returns By Qiming Sun Bachelor of Commerce, University of Calgary, 2011 Yuhang Zhang Bachelor of Economics, Capital Unv of Econ and Bus, 2011 RESEARCH
More informationMr. Kedar Mukund Phadke 1, Dr. Manoj S. Kamat 2 ABSTRACT
IMPACT OF IPO GRADING ON LISTING RETURNS AT THE NATIONAL STOCK EXCHANGE (NSE) IN INDIA Mr. Kedar Mukund Phadke 1, Research Scholar Assistant Professor National Institute of Construction Management and
More informationForeign Direct Investment and Economic Growth in Some MENA Countries: Theory and Evidence
Loyola University Chicago Loyola ecommons Topics in Middle Eastern and orth African Economies Quinlan School of Business 1999 Foreign Direct Investment and Economic Growth in Some MEA Countries: Theory
More informationPrivate Equity and IPO Performance. A Case Study of the US Energy & Consumer Sectors
Private Equity and IPO Performance A Case Study of the US Energy & Consumer Sectors Jamie Kerester and Josh Kim Economics 190 Professor Smith April 30, 2017 2 1 Introduction An initial public offering
More informationThe use of real-time data is critical, for the Federal Reserve
Capacity Utilization As a Real-Time Predictor of Manufacturing Output Evan F. Koenig Research Officer Federal Reserve Bank of Dallas The use of real-time data is critical, for the Federal Reserve indices
More informationParent Firm Characteristics and the Abnormal Return of Equity Carve-outs
Parent Firm Characteristics and the Abnormal Return of Equity Carve-outs Feng Huang ANR: 313834 MSc. Finance Supervisor: Fabio Braggion Second reader: Lieven Baele - 2014 - Parent firm characteristics
More informationDemand Estimation in the Mutual Fund Industry before and after the Financial Crisis: A Case Study of S&P 500 Index Funds
Demand Estimation in the Mutual Fund Industry before and after the Financial Crisis: A Case Study of S&P 500 Index Funds Frederik Weber * Introduction The 2008 financial crisis was caused by a huge bubble
More informationInvestor Sentiment and IPO Pricing during Pre-Market and Aftermarket Periods: Evidence from Hong Kong
Investor Sentiment and IPO Pricing during Pre-Market and Aftermarket Periods: Evidence from Hong Kong Li Jiang a, Gao Li a a School of Accounting and Finance, Hong Kong Polytechnic University, Hong Kong,
More informationPitching IPOs. Exaggeration and the Marketing of Financial Securities
Pitching IPOs Exaggeration and the Marketing of Financial Securities Introduction This is a study of the marketing of financial securities in general, and IPOs in particular, looking at the initial wave
More informationStabilization Activities by Underwriters after Initial Public Offerings
THE JOURNAL OF FINANCE VOL. LV, NO. 3 JUNE 2000 Stabilization Activities by Underwriters after Initial Public Offerings REENA AGGARWAL* ABSTRACT Prior research has assumed that underwriters post a stabilizing
More informationThe Journal of Applied Business Research January/February 2013 Volume 29, Number 1
Stock Price Reactions To Debt Initial Public Offering Announcements Kelly Cai, University of Michigan Dearborn, USA Heiwai Lee, University of Michigan Dearborn, USA ABSTRACT We examine the valuation effect
More information