An Empirical Study on Identification of False Accounting Information of Listed Companies -Based on the Comparison between Logistic Regression Model and Multiple Eigenvalues Detection Model

2011 ◽  
Vol 268-270 ◽  
pp. 753-758
Author(s):  
Ai Hao Guan
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
Regression Model ◽  
Empirical Analysis ◽  
Logistic Regression Model ◽  
Accounting Information ◽  
Listed Companies ◽  
Detection Model ◽  
Multiple Eigenvalues

In this study, multiple eigenvalues detection model has been put forward and empirical analysis and comparison between logistic regression model and multiple eigenvalues detection model has been conducted in the same sample on the basis of overall data. The results have showed the characteristics of accuracy, stability or extensive adaptability of multiple eigenvalues detection model in detecting false accounting information.

scholarly journals Dose Underwriter Credit and its Guiding Function Have Impact on IPO Break in China?

2017 ◽  
Vol 9 (5) ◽  
pp. 36
Author(s):  
Chang Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Opposite Effect ◽  
Negative Relationship ◽  
Investor Sentiment ◽  
Listed Companies ◽  
Pricing Behavior ◽  
Guiding Function ◽  
A Share

Underwriters’ pricing behavior is partially unreasonable in China, and the overreaction of investors can easily lead to IPO break. The paper explores datum of listed companies between 2009 and 2016 in A share market and establishes Logistic Regression Model, aimed to pro how Underwriter Credit and Investor Sentiment affecting IPO break. Conclusions are as follows: overpricing of new shares will make IPO break easier; Underwriters Credit have opposite effect on IPO break; Investor Sentiment has negative relationship with IPO break significantly; underwriters can guide Investor Sentiment by credit weekly, in order to lower the probability of IPO break.

scholarly journals Religious vs Secular volunteering motivations: A study on European elders

2018 ◽  
Vol 6 (1) ◽  
pp. 82 ◽  
Author(s):  
Antonio Ariza-Montes ◽  
Pilar Tirado-Valencia ◽  
Vicente Fernández-Rodríguez ◽  
Mark Hager
Keyword(s):  
Logistic Regression ◽  
Empirical Study ◽  
Regression Model ◽  
Cultural Values ◽  
Logistic Regression Model ◽  
Demographic Variables ◽  
Contextual Variables ◽  
Cultural Aspects ◽  
Variable Influence ◽  
Elder People

The reality of volunteering is highly complex. This concept is difficult to define and typify because of the great variety of interpretations, motivations, socio-demographic variables and cultural aspects that shape the volunteer profile. This work aims to analyze the differential and inter-related impact of socio-demographic and contextual variables, and cultural values on elder volunteer profile in Europe. We thus conduct an empirical study involving the use of a logistic regression model that shows, in probabilistic terms, traits that characterize senior and retired volunteers. Further, we study which variables motivate senior volunteers to a determined type of volunteering. Results from the European Value Study help to explain variable influence on volunteering and confirm that cultural values impact among elder people, both, election to volunteering activities and decisions regarding which kind of activity volunteers are drawn to. By analyzing two types of volunteering (religious and secular) that are supposed to be motivated by different forces, we conclude that certain values encourage religious volunteering while others stimulate secular volunteering.

scholarly journals A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

2020 ◽  
Vol 10 (5) ◽  
pp. 1874 ◽  
Author(s):  
José M. Bolarín ◽  
F. Cavas ◽  
J.S. Velázquez ◽  
J.L. Alió
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Web Application ◽  
High Performance ◽  
Logistic Regression Model ◽  
Early Stage ◽  
Disease Classification ◽  
Multivariate Logistic Regression Model ◽  
Detection Model

This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.

Study on Agricultural Investment Value of Listed Companies Based on Logistic Regression Model

2014 ◽  
Vol 651-653 ◽  
pp. 1647-1650
Author(s):  
Hai Lun Huang ◽  
Wu Xue Jiang ◽  
Yin Zhen Zhong
Keyword(s):  
Factor Analysis ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Listed Companies ◽  
T Test ◽  
Test Method ◽  
Agricultural Investment ◽  
Investment Value ◽  
Financial Perspective

This article is from the financial perspective to analyze the investment value of listed companies. Using independent two-sample T-test method and factor analysis of selected financial ratios, by building a logistic regression model for agricultural investment value of listed companies to judge, provides a method for determining the value of investments to investors.

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