Bharat A. Jain

Does the Presence of Venture Capitalists Improve the Survival Profile of IPO Firms

The role of venture capital in the creation of the public corporation is now widely recognized. This study investigates whether venture capitalists add value to the going public process by improving the survival profile of IPO issuers. The survival of IPO issuers is not only likely to depend on managerial actions but also on the effectiveness of key market participants such as investment bankers and analysts. The form and function of the venture capital industry allows venture capitalists to influence the actions of managers, investment bankers, and analysts, and attract institutional interest. Conducting survival analyses using the Cox hazard methodology, we find that the involvement of venture capitalists improves the survival profile of IPO firms. Several other variables that are potentially influenced by VC involvement like R&D allocations, analyst following, investment banker prestige, and success on road shows are also positively related to the survival time of IPO issuers. Copyright Blackwell Publishers Ltd 2000.

The Life Cycle of Initial Public Offering Firms

This study uses an integrated and comprehensive approach to study the evolution of IPO issuing firms to the three basic post-IPO states: survive as an independent firm, get acquired, or fail. We develop multinomial logit models that utilize information available at or prior to the IPO to predict the probability of subsequent transition to the three post-IPO states. We find that lower risk, larger firm size, higher investment banker prestige, higher pre-IPO operating performance, and higher industry R&D intensity increase the probability of survival relative to failure. We also find that higher firm size, higher industry R&D intensity, and industry concentration increase the probability of survival relative to being acquired. Finally, lower risk and higher investment banker prestige increase the probability of being acquired relative to failure. Overall, we identify several factors that influence the probability of subsequent transition to one of the three basic post-IPO states. Copyright Blackwell Publishers Ltd 1999.

On investment banker monitoring in the new issues market

This article primarily addresses two largely unanswered questions in the financial economics literature: (1) Is there a demand for lead bank monitoring in the IPO market? and (2) Does monitoring by lead investment banker lead to better post-issue performance? We find evidence consistent with the demand for underwriter monitoring in the IPO market. We examine variables which proxy for the incentives of lead underwriters to supply monitoring post-issue. These variables include lead investment bank reputation and whether warrants are issued to the underwriter by the issuing firm. We find that lead bank reputation is positively associated with the post-issue performance of IPO firms. We also examine whether additional valude added monitoring is provided by unaffiliated analysts. The number of unaffiliated analysts following is positively correlated with post-issue performance. Our results are consistent with thid party monitoring in the new issues market.

Performance evaluation of neural network decision models

Recently, promising results with neural networks have been reported for two-group classification problems such as bankruptcy prediction and thrift failures. Such applications are usually characterized by unequal frequencies of the two states of interest. This creates a major obstacle to effective performance evaluation of various decision models. Critical issues affecting the comparison include training sample design and the use of an appropriate performance metric. This paper addresses these two issues by comparing the performance of neural networks with that of statistical models for the decision problem of identifying successful new ventures.

Predictors of performance of venture capitalist-backed organizations

Abstract Venture capitalists seek investment opportunities in young, privately-held companies with promising growth prospects. In addition to supplying capital, venture capitalists are decision-making agents who participate in the governance of their ventures and attempt to influence objectives, strategy, structure, and control processes. This study develops and evaluates models examining the impact and relative importance of venture-capitalist factors, managerial strategy, industry structure variables, and their interactions on performance of venture capital-backed organizations. The models are evaluated based on their ability to predict whether the VC-backed venture will provide superior long-term performance.

The underpricing of ‘unit’ initial public offerings

Abstract Unit issues consist of a bundle of common stock and warrants sold together as a package. These issues are often associated with initial public offerings (IPOs). While numerous studies have examined the underpricing of non-unit IPOs, relatively little is known about unit IPOs. This study examines the underpricing of unit IPOs and compares it to that of non-unit IPOs. The unit IPOs are smaller in offer size, riskier, and marketed by less prestigious underwriters in comparison to non-unit IPOs. However, the initial returns of unit issues are not significantly different from non-unit IPOs. After controlling for size, risk, and underwriter reputation, unit IPOs have significantly lower initial returns in comparison to non-unit IPOs. Further, unit IPOs display a lower propensity to issue subsequent seasoned offerings in comparison to non-unit IPOs. Several possible motivations for issuers to go public with units are examined.

The effect of task complexity and conflict handling styles on computer-supported negotiations

Prior research has indicated that groups using Negotiation Support Systems (NSS) achieve better outcomes than face-to-face groups. However, these studies indicate that the main source of value added is provided by the Decision Support System (DSS) component, with very little additional value provided by the electronic communication component. This study examines the value added by the electronic communication component, taking into consideration task complexity and conflict handling style of the participants. Both these are likely to impact the extent to which computer-support enhances negotiation outcomes.

A decision-support model for investment decisions in new ventures

Abstract The decision to invest in new ventures is characterized by incomplete information, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Qualitative attributes are defined in a relative sense. We develop a decision support model for identifying successful new ventures. The model integrates quantitative and qualitative variables through the use of the Analytic Hierarchy Process (AHP). The decision model gains in predictive accuracy through the use of qualitative attributes, and AHP imparts robustness to the qualitative measures.

Family Involvement and Post-IPO Investment Policy

Drawing from agency theory and socioemotional wealth considerations, we evaluate the extent post-IPO investment policy choices and their economic consequences differ for family firms relative to nonfamily firms. Our results suggest that family firms underinvest in post-IPO liquidity, total investment spending, and R&D expenditures, relative to similar non-family firms. On the other hand, family firms overinvest in capital spending and underinvest in acquisition spending relative to nonfamily firms with dispersed but not concentrated ownership structures. Furthermore, while increases in R&D spending decrease shareholder value in family firms, the reverse is the case with acquisition spending.

A neural network model to predict long-run operating performance of new ventures

The prediction of long-run operating performance of new ventures, known as Initial Public Offerings (IPOs), represents a challenging decision problem. Factors adding to the complexity of the problem include asymmetrically informed agents, incentive problems, and inability to specify functional relationships between variables. Research literature identifying determinants of long-run performance of new issues is limited. This study uses a data driven, nonparametric, neural network based approach to predict the long-run operating performance of new ventures. The classification accuracy of the neural network model is compared with that of a logit model. Methodological issues such as sample design and estimation of optimal cutoff probabilities for classification are addressed. The results suggest that the neural networks generally outperform logit models.