Predicting mispricing of initial public offerings

Reber, Beat; Berry, Bob; Toms, Steve

doi:10.1002/isaf.253

Cited by 18 publications

(14 citation statements)

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“…Following [3], we complement the analysis based on predictive performance illustrating the potential economic benefits of using these techniques to price IPOs. In Table 5 we compare the actual observed underpricing with the initial returns resulting should we use forecasts of the models as the offering price.…”

Section: Ibkmentioning

confidence: 99%

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Random Forest Prediction of IPO Underpricing

2017

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Abstract:The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that random forests are a potentially powerful tool. In this paper, we benchmark this algorithm against a set of eight classic machine learning algorithms. The results of this comparison show that random forests outperform the alternatives in terms of mean and median predictive accuracy. The technique also provided the second smallest error variance among the stochastic algorithms. The experimental work also supports the potential of random forests for two practical applications: IPO pricing and IPO trading.

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Section: Ibkmentioning

confidence: 99%

“…These authors try to predict the post-issue market price using artificial neural networks. More recently, Reber et al [3], Meng [4], Chen [5], and Esfahanipour et al [6] followed suit with the same technique. This line was…”

Section: Introductionmentioning

confidence: 99%

Random Forest Prediction of IPO Underpricing

2017

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“…Support vector machine, unlike linear models, reflects nonlinear effects and complex interactions between signals of company's value (Reber, 2005).…”

Section: Business and Economic Researchmentioning

confidence: 99%

“…Support Vector Machine is able to establish meaningful information from the data and so provide better forecasts compared to economic models. Unlike linear models, reflect nonlinear effects and complex interactions between signals of company's value (Reber, 2005).…”

Section: The Importance Of This Researchmentioning

confidence: 99%

“…Application of support vector machine is mostly in the scope of issues that involve forecasts based on pattern matching or classification. One of the distinctive characteristics of the support vector machine is its ability to provide a reasonable response, even assuming the data is incomplete or abnormal (Reber, 2005). Since financial data are often incomplete or abnormal, support vector machine technology becomes more important.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Initial Public Offering Pricing Using Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) In Iran

Gandoman

Kiamehr

Hemetfar

2017

BER

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The present study compares the ability of neural networks, support vector machine, and model derived from combining particles swarm optimization (PSO) algorithm and support vector machine (SVM) to forecast the initial public offering pricing. The purpose of this research is to design a model that helps investors recognize the validity of the initial public offering pricing and hunt profitable opportunities. The variables used in this study are selected among those variables which are in the disposal of investors who have limited access to information before the offering. On the other hand, these results can be useful for publishing companies, admissions consultant, underwriting and legislators of the stock exchange. We have considered the ninth day offering prices, since volatilities are gone and prices seem to be more realistic. The results show that the combination of particle swarm optimization (PSO) algorithm and support vector machine (SVM) markedly increases the forecasting power. As a result, support vector machine models can increase the accuracy of initial public offering pricing and provide significant economic benefits as reducing less than real pricing costs.

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