Fuzzy techniques for IPO underpricing prediction

Initial public offerings often show abnormal fist-day returns. These, usually referred to as underpricing, are difficult to predict. Among the main obstacles, we could mention challenges like the fact that not all relevant variables have been identified yet; the mix of weak and strong indicators or the prevalence of outliers. In this context, we suggest that adaptive neuro-fuzzy inference systems and fuzzy rule-based system with genetic optimization have a lot to bring to the table. We test the predictive performance of these on a sample of 866 US IPOs and we benchmark them against six fuzzy algorithms and a set of eight classic machine learning alternatives. We conclude that both fuzzy systems, especially the former should be seriously considered in this domain.

[1]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[2]  Jalil Heidary Dahooie,et al.  Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick , 2015, Expert Syst. Appl..

[3]  J. Ritter,et al.  Where Have All the IPOs Gone , 2013 .

[4]  William L. Megginson,et al.  Venture Capitalist Certification in Initial Public Offerings , 1991 .

[5]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[6]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[7]  Bao Rong Chang,et al.  A study of hybrid genetic-fuzzy models for IPO stock selection , 2012, 2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012).

[8]  Geoff Holmes,et al.  Generating Rule Sets from Model Trees , 1999, Australian Joint Conference on Artificial Intelligence.

[9]  Dan Meng,et al.  Credit Rating Method with Heterogeneous Information , 2008, RSKT.

[10]  Wing Lon Ng,et al.  Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems , 2014, Neurocomputing.

[11]  Xi Yang,et al.  3-Dimension Evaluation Method for Stock Investment Based on 2-Tuple Linguistic , 2014, HCC.

[12]  Jing-Wei Liu,et al.  OWA Rough Set to Forecast the Industrial Growth Rate , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[13]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[14]  Nikola K. Kasabov,et al.  HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.

[15]  Isao Hayashi,et al.  A learning method of fuzzy inference rules by descent method , 1992 .

[16]  You-Shyang Chen,et al.  An Alternate Method Of Examining Ipo Returns , 2010, Intell. Autom. Soft Comput..

[17]  Dalibor Petković,et al.  Adaptive neuro-fuzzy approach for estimation of wind speed distribution , 2015 .

[18]  Colin Davis,et al.  Underwriter reputation and underpricing: Evidence from the Japanese IPO market , 2005 .

[19]  John W. Peavy,et al.  Initial Public Offerings: Daily Returns, Offering Types and the Price Effect , 1987 .

[20]  Witold Pedrycz,et al.  Associations and rules in data mining: A link analysis , 2004, Int. J. Intell. Syst..

[21]  Ruth V. Aguilera,et al.  Corporate Governance and Investors' Perceptions of Foreign IPO Value: An Institutional Perspective , 2014 .

[22]  P. Spindt,et al.  How investment bankers determine the offer price and allocation of new issues , 1989 .

[23]  Alexander Ljungqvist,et al.  IPO Pricing in the Dot-Com Bubble: Complacency or Incentives , 2001 .

[24]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[25]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[26]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[27]  F. Herrera,et al.  A Hybrid Learning Process for the Knowledge Base of a Fuzzy Rule-Based System , 2004 .

[28]  Mohammad Saniee Abadeh,et al.  Mining fuzzy association rules with 2-tuple linguistic terms in stock market data by using genetic algorithm , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[29]  G. Schwert,et al.  The Variability of IPO Initial Returns , 2006 .

[30]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[31]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[32]  Wen-June Wang,et al.  Constructing a user-friendly GA-based fuzzy system directly from numerical data , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[33]  Worawat Choensawat,et al.  Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis , 2013, J. Adv. Comput. Intell. Intell. Informatics.

[34]  Mirko Dohnal,et al.  Decision-making on Implementation of IPO Under Topological Uncertainty , 2015 .

[35]  Saumen Maiti,et al.  A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction , 2014, Environmental Earth Sciences.

[36]  K. Hanley,et al.  The underpricing of initial public offerings and the partial adjustment phenomenon , 1993 .

[37]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[38]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[39]  Jacek M. Zurada,et al.  Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction, with a Real Estate Illustration , 2014, J. Organ. Comput. Electron. Commer..

[40]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[41]  Jing-Yi Yang,et al.  Approaches to dynamic multiple attribute decision making with 2-tuple linguistic information , 2014, J. Intell. Fuzzy Syst..

[42]  Akbar Esfahanipour,et al.  Analysis and forecasting of IPO underpricing , 2015, Neural Computing and Applications.

[43]  Ivo Welch,et al.  A Review of IPO Activity, Pricing and Allocations , 2002 .

[44]  S. Chiu.  Method and software for extracting fuzzy classification rules by subtractive clustering , 1996, Proceedings of North American Fuzzy Information Processing.

[45]  Jay R. Ritter,et al.  Equilibrium in the Initial Public Offerings Market , 2011 .

[46]  Liang-Ying Wei,et al.  A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting , 2013, Appl. Soft Comput..

[47]  Kevin J. Murphy,et al.  Executive Stock Options and IPO Underpricing , 2006 .

[48]  Jian Zhou,et al.  IPO Underpricing and Audit Quality Differentiation Within Non-Big 5 Firms , 2007 .

[49]  You-Shyang Chen,et al.  Classifying Initial Returns of Electronic Firm_s IPOs Using Entropy Based Rough Sets in Taiwan Trading Systems , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[50]  H. Ishibuchi,et al.  Empirical study on learning in fuzzy systems by rice taste analysis , 1994 .

[51]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[52]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[53]  Mohsen Akbari,et al.  Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization , 2014, Expert Syst. Appl..

[54]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[55]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[56]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[57]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[58]  Francisco Herrera,et al.  Genetic tuning on fuzzy systems based on the linguistic 2-tuples representation , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[59]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[60]  Francisco Herrera,et al.  A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base , 2001, Inf. Sci..

[61]  Hayne E. Leland,et al.  INFORMATIONAL ASYMMETRIES, FINANCIAL STRUCTURE, AND FINANCIAL INTERMEDIATION , 1977 .

[62]  Scott Smart,et al.  Control as a Motivation for Underpricing: A Comparison of Dual- and Single-Class Ipos , 2000 .