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Appl. Sci. 2017, 7(6), 636;

Random Forest Prediction of IPO Underpricing

Department of Computer Science and Engineering, Universidad Carlos III de Madrid, 28903 Madrid, Spain
Author to whom correspondence should be addressed.
Academic Editor: Andrea Prati
Received: 2 May 2017 / Revised: 12 June 2017 / Accepted: 15 June 2017 / Published: 20 June 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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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. View Full-Text
Keywords: random forest; initial public offering; prediction; underpricing random forest; initial public offering; prediction; underpricing

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Quintana, D.; Sáez, Y.; Isasi, P. Random Forest Prediction of IPO Underpricing. Appl. Sci. 2017, 7, 636.

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