Evolutionary rule-based system for IPO underpricing prediction

Genetic And Evolutionary Computation Conference. Washington DC, USA, 25-29 June 2005Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs).Most of the empirical analysis that has been carried out to date to explain underpricing through the offering structure is based on multiple linear regression. The alternative that we suggest is a rule-based system defined by a genetic algorithm using a Michigan approach. The system offers significant advantages in two areas, 1) a higher predictive performance, and 2) robustness to outlier patterns. The importance of the latter should be emphasized since the non-trivial task of selecting the patterns to be excluded from the training sample severely affects the results.We compare the predictions provided by the algorithm to those obtained from linear models frequently used in the IPO literature. The predictions are based on seven classic variables. The results suggest that there is a clear correlation between the selected variables and the initial return, therefore making possible to predict, to a certain extent, the closing price.This article has been financed by the Spanish founded research MCyT project TRACER, Ref: TIC2002-04498-C05-04M

Predicting IPO underpricing with genetic algorithms

This paper introduces a rule system to predict first-day returns of initial public offerings based on the structure of the offerings. The solution is based on a genetic algorithm using a Michigan approach. The performance of the system is assessed comparing it to a set of widely used machine learning algorithms. The results suggest that this approach offers significant advantages on two fronts: predictive performance and robustness to outlier patterns. The importance of the latter should be emphasized as the results in this domain are very sensitive to their presence.We acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10-UC3M/TIC-5029)

Evolutionary rule-based system for IPO underpricing prediction

Genetic And Evolutionary Computation Conference. Washington DC, USA, 25-29 June 2005Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs).Most of the empirical analysis that has been carried out to date to explain underpricing through the offering structure is based on multiple linear regression. The alternative that we suggest is a rule-based system defined by a genetic algorithm using a Michigan approach. The system offers significant advantages in two areas, 1) a higher predictive performance, and 2) robustness to outlier patterns. The importance of the latter should be emphasized since the non-trivial task of selecting the patterns to be excluded from the training sample severely affects the results.We compare the predictions provided by the algorithm to those obtained from linear models frequently used in the IPO literature. The predictions are based on seven classic variables. The results suggest that there is a clear correlation between the selected variables and the initial return, therefore making possible to predict, to a certain extent, the closing price.This article has been financed by the Spanish founded research MCyT project TRACER, Ref: TIC2002-04498-C05-04M

Political connections of new business ventures

The perceived capability of corporate organizations to influence politics, although fueling an ongoing public debate, features in literature as a source of probable benefits. According to the majority of the pertinent studies, these benefits, more often than not, materialize with important value-adding implications. In the U.S. context, whereby political money contributions constitute the prevalent way of establishing connections, this can result in a hefty return on a firm’s political investment. Our research posits that if political connections formed via monetary donations elevate the donor to a higher status, this should reflect in circumstances whereby a firm needs to assert its quality to other economic agents. This is the case for firms that are plagued by the market newness liability. Whether as a form of insurance from tail risk or entitlement to economic rents, proximity to politics offers legitimacy and a compelling way of introducing a new venture to the marketplace. To prove this conjecture, we mainly draw from IPOs for representing a setting of acute uncertainty. Our findings confirm that both lobbying and PAC (Political Action Committee) expenditure pays off on listing day as donors incur less underpricing; an effect which can be amplified with contribution size and strategic targeting of recipients. Donor IPOs also experience negative offer price revisions and lower aftermarket volatility. Collectively, these results offer new empirical grounding to uncertainty and signaling theories. Subsequently, we frame IPO pricing as an efficiency problem for prospective issuers and develop an approach of general application in finance, where relationships of influence are suspected. Rather than imposing a regression-based framework, we allow relationships to manifest themselves in a data-driven manner. Our analysis reveals nonlinearities between IPO pricing efficiency and the two contribution avenues (justifying the fully nonparametric treatment). We are able to uncover relationships separately according to business sector, which we interpret in terms of varied competitive environments. Broadening up our scope prior to and after the IPO event, we document that connected firms are associated with a longer time to venture or other equity capital financing, attesting to a greater financial autonomy. Additionally, they attain larger market shares and have a superior likelihood of survival in the public domain