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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aggarwal RK, Krigman L, Womack KL (2002) Strategic IPO underpricing, information momentum, and lockup expiration selling. J Financ Econ 66:105–137
Bao Y-K, Liu ZT, Guo L, Wang W (2005) Forecasting stock composite index by fuzzy support vector machines regression. In: Proceedings of the international conference on machine learning and cybernetics, pp 3535–3540
Beatty RP, Ritter JR (1986) Investment banking, reputation, and the underpricing of initial public offerings. J Financ Econ 15:213–232
Benveniste LM, Spindt PA (1989) How investment bankers determine the offer price and allocation of new issues. J Financ Econ 24:343–361
Booth JR, Chua L (1996) Ownership dispersion, costly information, and IPO underpricing. J Financ Econ 41:291–310
Brailsford T, Heaney RA, Shi J (2004) ANU-digital collections: the cyclical behaviour of the IPO market in Australia. 2004-05-19T11:45:07Z
Busaba WY, Benveniste LM, Guo R-J (2001) The option to withdraw IPOs during the premarket: empirical analysis. J Financ Econ 60:73–102
Carter R, Manaster S (2012) Initial public offerings and underwriter reputation. J Financ 45:1045–1067
Chen H-C, Ho K-Y (2009) Do IPO index portfolios improve the investment opportunities for mean–variance investors? Financ Res Lett 6:159–170
Dunbar C (1998) The effect of withdrawals on contract choice in initial public offerings. J Financ Intermed 7:60–90
Esfahanipour A, Goodarzi M, Behzadi PS, Soltani GN (2013) Investigation of IPO underpricing considering probability of offering withdrawal: empirical analysis in tehran stock exchange (TSE). Eur J Sci Res 112:59–70
Gong N, Shekhar C (2001) Underpricing of privatised IPOs: the Australian experience. Aust J Manag 26:91–106
Grinblatt M, Hwang CY (2012) Signalling and the pricing of new issues. J Financ 44:393–420
Heiat A (2002) Comparison of artificial neural network and regression models for estimating software development effort. Inf Softw Technol 44:911–922
How J (2000) Initial and long-run performance of mining IPOs in Australia
James C, Wier P (1990) Borrowing relationships, intermediation, and the cost of issuing public securities. J Financ Econ 28:149–171
Kaminski W, Strumillo P (1997) Kernel Orthonormalization in radial basis function neural networks. IEEE Trans Neural Netw 8(5):1177–1183
Kanas A, Yannopoulos A (2001) Comparing linear and nonlinear forecasts for stock returns. Int Rev Econ Financ 10:383–398
Kim S-S (1998) Time-delay recurrent neural network for temporal correlations and prediction. Neurocomputing 20:253–263
Kodogiannis V, Lolis A (2002) Forecasting financial time series using neural network and fuzzy system-based techniques. Neural Comput Appl 11:90–102
Loughran T, Ritter JR (2002) Why don’t issuers get upset about leaving money on the table in IPOs? Rev Financ Stud 15:413–444
Maclaine D (2002) Australian IPO underpricing, offer mechanisms and the option to withdraw during the premarket: an empirical analysis
Moody JE, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1:282–294
Olson D, Mossman C (2003) Neural network forecasts of Canadian stock returns using accounting ratios. Int J Forecast 19:453–465
Park J, Sandberg IW (1991) Universal approximation using radial basis-functions networks. Neural Comput 3(2):246–257
Rock K (1986) Why new issues are underpriced. J Financ Econ 15:187–212
Scarselli F, Tsoi AC (1998) Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results. Neural Netw 11:15–37
Shapiro AF (2004) Fuzzy regression and the term structure of interest rates revisited. In: Proceedings of the 14th international AFIR colloquium, pp 29–45
Shen W, Guo X, Wu C, Wu D (2011) Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl-Based Syst 24:378–385
Titman S, Wessels R (2012) The determinants of capital structure choice. J Financ 43:1–19
Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49:1225–1231
Xiong Q, Yong S, Shi W, Chen J, Liang Y (2005) The research of forecasting model based on RBF neural network. In: International conference on neural networks and brain. ICNN&B’05, pp 1032–1035
Yan X-B, Wang Z, Yu S-H, Li Y-J (2005) Time series forecasting with RBF neural network. In: Proceedings of the international conference on machine learning and cybernetics, pp 4680–4683
Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Esfahanipour, A., Goodarzi, M. & Jahanbin, R. Analysis and forecasting of IPO underpricing. Neural Comput & Applic 27, 651–658 (2016). https://doi.org/10.1007/s00521-015-1884-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-015-1884-1