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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2016

01.04.2016 | Original Article

Analysis and forecasting of IPO underpricing

verfasst von: Akbar Esfahanipour, Milad Goodarzi, Reza Jahanbin

Erschienen in: Neural Computing and Applications | Ausgabe 3/2016

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Abstract

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.
Vorheriger Artikel Global dissipativity of memristor-based complex-valued neural networks with time-varying delays
Nächster Artikel Feature-level fusion of mental task’s brain signal for an efficient identification system

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Metadaten
Titel
Analysis and forecasting of IPO underpricing
verfasst von
Akbar Esfahanipour
Milad Goodarzi
Reza Jahanbin
Publikationsdatum
01.04.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1884-1

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