scholarly journals A Study of IPO Underpricing Using Regression Model Based on Information Asymmetry, Media, and Institution

2021 ◽  
Author(s):  
Liangda Liu ◽  
Zixuan Zhang ◽  
Kexin Lyu
Keyword(s):  
Regression Model ◽  
Information Asymmetry ◽  
Ipo Underpricing ◽  
Model Based

scholarly journals Does ownership structure effect IPO underpricing: Evidence from Thai IPOs

2006 ◽  
Vol 3 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Sunder Venkatesh ◽  
Suman Neupane
Keyword(s):  
Regression Model ◽  
Information Asymmetry ◽  
Ownership Structure ◽  
Financial Crises ◽  
Negative Relationship ◽  
Rank Correlation ◽  
Ownership Concentration ◽  
Ipo Underpricing ◽  
Signaling Hypothesis ◽  
The Relationship

The study utilizes a unique set of IPOs data in Thailand post Asian Financial crises to identify the relationship between initial market adjusted underpricing and the ownership concentration. We find that a weak but a negative relationship exists between the two and therefore to certain extent refuting the signaling hypothesis of high ownership and high underpricing. We employ a rank correlation to identify the association between the two variables. A regression model using the widely used proxies of information asymmetry model fails to up hold the information asymmetry model in the context of Thai IPOs.

Investor awareness or information asymmetry? Wikipedia and IPO underpricing

2021 ◽  
Author(s):  
Thomas Boulton ◽  
Bill B. Francis ◽  
Thomas Shohfi ◽  
Daqi Xin
Keyword(s):  
Information Asymmetry ◽  
Ipo Underpricing ◽  
Investor Awareness

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

scholarly journals An empirical study of IPO underpricing: Evidence from Chinese stock market

2014 ◽  
Vol 12 (1) ◽  
pp. 139-152 ◽  
Author(s):  
Tianxiang Xu ◽  
Yujie Zhao
Keyword(s):  
Empirical Study ◽  
Information Asymmetry ◽  
Initial Public Offerings ◽  
Initial Public Offering ◽  
Chinese Stock Market ◽  
Short Term ◽  
Ipo Underpricing ◽  
Public Offerings ◽  
Public Offering ◽  
Different Characteristics

Initial public offerings, as one of the most important activities for firms, have raising massive amount of researches. Regarding China, the stock markets are experiencing a massive level of IPO underpricing, which leads to trillions of dollars leaved on the table. This study is conducted for the question why Chinese IPO are so heavily underpriced and the determinants of IPO underpricing, also the possibility of IPO be underpriced in China. We confirm again that Chinese IPOs are heavily underpriced and the average underpricing level is about 110%. Further, Chinese IPO will experience a negative short term return starting from 10 days after listing, and there are significantly different characteristics for state owned IPOs and private IPOs. This study finds that information asymmetry, proportion of state owned share and risk are the mainly determinants of IPO underpricing in China. Additionally, one of the biggest reason that Chinese initial public offering is underpriced so much is because of government participation, since we find that firms with larger proportion of government state owned shares will be more underpriced.

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