ارزیابی عرضه اولیه عمومی(IPO) و ارائه سهام فصلی شرکت ها(SEO)

We examine the pricing of initial public offering (IPO) and seasoned equity offering (SEO) firms using a stochastic frontier methodology. The stochastic frontier framework models the difference between the maximum possible value of the firm and its actual market capitalization at the time of the offering as a function of observable firm characteristics. Using a new data set, we find that commonly used pricing factors do indeed influence valuation. Ceteris paribus, firms in industries with great earnings potential are more highly valued, and IPO firms are underpriced. Theories regarding underwriter reputation or windows of opportunity for equity issuance are not supported in our empirical results.

Valuation plays a central role in corporate finance for several reasons. First, corporate control transactions such as hostile takeovers and management buyouts require the valuation of equity. Second, privately held corporations that need to set a price for their initial public offerings, or public firms that require further equity financing, must first establish the value of their equity. Finally, the estimated equity value is important in setting the capital structure of these issuing firms. Standard finance models imply that the value which the market places on a firm's equity should reflect the firm's expected future profitability. In the absence of data on the latter, it is common to use variables that might proxy for future profitability (e.g. net income, revenue, earnings per share, total assets, debt, industry affiliation, etc.) in an effort to value equity. One purpose of the present paper is to investigate the roles of various potential explanatory variables in valuing equity using a new, extensive data set involving many firms and many explanatory variables. However, it is often the case that firms, which are similar in terms of these observable characteristics will be valued quite differently by the market. We refer to this difference as “misvaluation”. Accordingly, a second purpose of this paper is to investigate this misvaluation using stochastic frontier methods. The questions of particular interest are whether initial public offering (IPO) and seasoned equity offering (SEO) firms are valued in a different manner and whether they exhibit different patterns of misvaluation (e.g. are IPOs underpriced relative to SEOs?). Using a sample of 2969 IPO and 3771 SEO firms between 1985 and 1998, we find that IPO firms are misvalued (e.g. underpriced), while SEO firms are almost efficiently priced. Furthermore, the market capitalization of an offering firm is positively related to net income, revenue, total assets, and underwriter fees, and negatively related to its debt level. Ceteris paribus, firms in industries with great earnings potential such as chemical products, computer, electronic equipment, scientific instruments, and communications are more highly valued, whereas firms in more traditional industries such as oil and gas, manufacturing, transportation and financial services are valued less. Finally, we find no evidence that underwriter reputation or macroeconomic factors are related to misvaluation. Hunt-McCool et al. (1996) is the paper most closely related to our own. Their paper examines the IPO underpricing phenomenon using a stochastic frontier methodology. The authors stress that the advantage of stochastic frontier models is that they can be used to measure the extent of underpricing without using aftermarket information. This property could be very useful to corporate executives involved in IPOs when they select underwriters and determine the offer price. Hunt-McCool et al. (1996) conclude that the measure of premarket underpricing cannot explain away most anomalies in aftermarket returns and that the measure of IPO underpricing is sensitive to the issue period (e.g. hot versus nonhot IPO periods). The contributions of our work can be illustrated in contrast to their methodology. A first difference is that we apply the stochastic frontier modeling approach to both IPO and SEO firms. By construction, the stochastic frontier methodology uses firms that are efficiently priced (e.g. not misvalued) to estimate the frontier, and then misvalued firms are measured relative to this frontier. This of course, assumes that some of the firms are efficient. Seen in this way, it is interesting to see what happens if we include data both on firms that we expect to be undervalued (e.g. most IPO firms) and on those that we expect to be efficiently priced (e.g. many SEO firms). This is an important distinction between our paper and the work of Hunt-McCool et al. (1996). The latter only uses data on IPOs and cannot answer general questions such as, “Are IPOs underpriced?”. They can only answer questions such as, “Are some IPOs underpriced relative to other IPOs?”. However, if all IPO firms are massively and equally mispriced, their econometric methodology will misleadingly indicate full efficiency (e.g. with no efficient firms to define the pricing frontier, the frontier will be fit through misvalued IPO firms). In sum, it is important to include SEO firms to help define the efficient pricing frontier. Of course, if SEOs are consistently overpriced, then IPOs may appear underpriced using our approach even if they are efficiently priced. Furthermore, apparent undervaluation may simply reflect the influence of omitted explanatory variables. Such qualifications must be kept in mind when interpreting our results. Nevertheless, we feel that the stochastic frontier methodology, using both IPOs and SEOs, provides a new and interesting way of looking at the data and even if our findings are not definitive, they are suggestive. A second contrast with the work of Hunt-McCool et al. (1996) is our use of the market value of common equity as the dependent variable. Hunt-McCool et al. (1996) use the offer price as a dependent variable. Since the market value of common shares is more comparable across firms than the stock price, we would argue that our approach is more sensible and our results have more general implications. Third, by explicitly modeling misvaluation at the time of the offering as a function of observable firm characteristics, we categorize firm-specific characteristics into pricing factors and factors that are associated with misvaluation. Hence, our paper offers further evidence on the determinants of time-varying adverse selection costs in equity issues. Finally, the Bayesian approach adopted in this paper overcomes some statistical problems which plague stochastic frontier models (see e.g. Koop et al., 1995, Koop et al., 1997 and Koop et al., 2000). For instance using classical econometric methods, it is impossible to get consistent estimates and confidence intervals for measures of firm-specific underpricing. Since the latter is a crucial quantity, the fact that our Bayesian approach provides exact finite sample results is quite important. In summary, our work combines two distinct areas of research—the valuation literature and the stochastic frontier literature—to shed light on the determination of market capitalization in the equity issuing process. The rest of the paper proceeds as follows. In the next section, we describe the data before introducing the stochastic frontier model in Section 3. Our choice of explanatory variables are discussed in Section 4. We report the empirical results in Section 5 and conclude in Section 6.

In this paper, we have examined the pricing of IPOs and SEOs using a stochastic frontier methodology. The model introduces a systematic one-sided error term that captures misvaluation defined as the difference between the maximum value of the firm and its actual market capitalization at the time of the offering. To uncover the sources of mispricing, we further model the misvaluation distribution in the pricing equation as a function of observable firm- and issue period-specific characteristics. Data for the analysis are comprised of 2969 IPO and 3771 SEO firms between the period of 1985 and 1998. Our estimated valuation frontier is reasonable. Measures of profitability, level of operations, risk and underwriter fees are found to have significant explanatory power. Ceteris paribus, firms in industries with great earnings potential such as chemical products, computers, electronic equipment, scientific instruments, and communications are more highly valued, whereas firms in more traditional industries such as oil and gas, manufacturing, transportation and financial services are valued less. The variables included to explain misvaluation are mostly insignificant. For instance, variables reflecting underwriter reputation or windows of opportunity are not significant. However, the dummy variable for whether the issue is a SEO or an IPO is highly significant, indicating that IPOs are underpriced relative to SEOs. The advantage of stochastic frontier models is that they can be used to measure the level of mispricing in the premarket without resorting to aftermarket information. This property is important to management of the offering firm in selecting underwriters and determining if the suggested offer price is appropriate. We believe the stochastic frontier approach has many more practical applications in finance.