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Delisting in Athens Stock Exchange
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作者 Yiannis K. Yiannoulis 《Journal of Modern Accounting and Auditing》 2019年第7期343-366,共24页
The scope of this paper is to contribute to the question of delisting (both voluntary and involuntary), with particular emphasis on the initial conditions leading up to the Initial Public Offering (IPO) of the firms. ... The scope of this paper is to contribute to the question of delisting (both voluntary and involuntary), with particular emphasis on the initial conditions leading up to the Initial Public Offering (IPO) of the firms. The case of Athens Stock Exchange (ASE) is examined, specifically the IPOs of years 1996-2004 and their subsequent performance (delisting or not) till the end of 2017. We find that the probability of delisting is positively associated with the size of the issuance and the degree of earnings’ manipulation and negatively associated with audit quality, information transparency, and market timing. The effects of audit quality, size of issuance, and earnings’ manipulation are accentuated in instances of voluntary delisting while the effect of market timing is more clearly pronounced in instances of involuntary delisting. Our results are robust to a number of confounding factors including size, profitability, leverage, liquidity, growth options, and corporate governance. Our work is not replication of existing studies;in addition, we choose ASE as in that capital market there were many delistings the last two decades. 展开更多
关键词 delisting ATHENS STOCK Exchange(ASE) Initial Public Offering(IPO) ACCOUNTING ratios family form of business CORPORATE GOVERNANCE EARNINGS management
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Procedure for Delisting a Site From the World Heritage List: Is Delisting With Consent or Against the Wish of a State Party Possible?
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作者 Eike Albrecht Benedicte Gaillard 《Journal of Tourism and Hospitality Management》 2015年第1期15-21,共7页
As of 2015 (UNESCO, 2015), the over-40-year-old Convention Concerning the Protection of the World Cultural and Natural Heritage (1972) has been ratified by 191 nations, making it a universal and internationally re... As of 2015 (UNESCO, 2015), the over-40-year-old Convention Concerning the Protection of the World Cultural and Natural Heritage (1972) has been ratified by 191 nations, making it a universal and internationally recognised instrument for the protection of cultural and natural heritage. In the meantime, 1007 World Heritage Sites have been inscribed on the World Heritage List (as of February 2015) and two sites have been delisted. This paper discusses the delisting procedure of a site from the World Heritage List based on the Operational Guidelines for the Implementation of the World Heritage Convention (henceforth OG). First of all, the question of the possibility to delist a site from the World Heritage List, in general, is addressed. And based on this discussion, the necessity of the consent of the State Party to the WHC on whose territory the site to be delisted is located is then examined. 展开更多
关键词 World Heritage Site delisting World Heritage in Danger Dresden Elbe Valley cultural landscape World Heritage Convention (WHC) World Heritage Committee Operational Guidelines for the Implementation of the WHC (OG)
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On modeling acquirer delisting post-merger using machine learning techniques
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作者 Ephraim Kwashie Thompson Changki Kim So-Yeun Kim 《Journal of Management Analytics》 2024年第2期247-275,共29页
We test the comparative ability of representative machine-learning algorithms–Logistic Regression,Random Forest Classifier,Adaboost Classifier and Multi-Layer Perceptron Classifier–to predict the likelihood that an ... We test the comparative ability of representative machine-learning algorithms–Logistic Regression,Random Forest Classifier,Adaboost Classifier and Multi-Layer Perceptron Classifier–to predict the likelihood that an acquirer will be forcibly delisted for performance reasons after the close of a deal.We find that the Multi-Layer Perceptron Classifier,Adaboost and Random Forest have similar performance in terms of performance but the Logistic Regression is the poorest performing among the models we study.For feature importance,the results suggest that firm size,leverage,and profitability are the most informative features for the models in predicting the likelihood of performance-induced delisting.Deal-related characteristics and agency problems do not drive performance-induced involuntary delisting of acquirers.The results taken together suggest that acquirers delisted within five years post-merger for performance-induced reasons were already poorperforming firms pre-merger,their state likely worsened by undertaking a merger they were originally not supposed to undertake. 展开更多
关键词 Mergers and acquisitions machine learning performance-induced delisting involuntary delisting forced delisting
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2024 Top Ten News of China’s Paper Industry
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《造纸信息》 2025年第10期158-159,共2页
Vinda International Holdings Limited was acquired by Golden Eagle Group On March 12,2024,Vinda International Holdings Limited issued an announcement,stating that Singapore-based RGE Group had completed the acquisition... Vinda International Holdings Limited was acquired by Golden Eagle Group On March 12,2024,Vinda International Holdings Limited issued an announcement,stating that Singapore-based RGE Group had completed the acquisition of Vinda International whit a price of HK$23.5 per share and a total consideration of approximately HK$26.1 billion.On August 16,aft er the acquisition was completed,Vinda International was offi cially delisted from the Hong Kong Stock Exchange. 展开更多
关键词 vinda international holdings limited stock exchange vinda international acquisition delisting golden eagle group rge group
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2024 Top Ten News of China's Paper Industry
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作者 《造纸信息》 2025年第10期145-157,共13页
Top 1 Vinda International Holdings Limited was acquired by Golden Eagle Group On March 12,2024,Vinda International Holdings Limited issued an announcement,stating that Singaporebased RGE Group had completed the acquis... Top 1 Vinda International Holdings Limited was acquired by Golden Eagle Group On March 12,2024,Vinda International Holdings Limited issued an announcement,stating that Singaporebased RGE Group had completed the acquisition of Vinda International whit a price of HK$23.5 per share and a total consideration of approximately HK$26.1 billion.On August 16,a?t er the acquisition was completed,Vinda International was offi cially delisted from the Hong Kong Stock Exchange. 展开更多
关键词 Golden Eagle Group vinda international Hong Kong Stock Exchange Vinda International Holdings Limited delisting RGE Group acquisition golden eagle group
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An Early Warning Model of Financial Distress Prediction Based on Logistic-AHP-BP Neural Network Model 被引量:1
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作者 Yifan Wu 《经济管理学刊(中英文版)》 2018年第2期184-194,共11页
Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies ... Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies in China has never exceeded 1% each year.The number of delisted companies in the security market is far less than the number of companies with financial distress.The capital market lacks a good delisting system and investors lack risk identification capabilities.Financial risk is directly related to delisting risk.Therefore,an early warning model of financial distress prediction for China.s stock market can provide guidance to stakeholders such as listed companies and capital markets.This paper first explains the immature delisting system of China.s capital market and the overall high risk of listed companies.financial distress.Then,the paper further elaborates previous research on financial distress prediction model of listed companies and analyzes the advantages and disadvantages of different models.This paper chooses the Analytic Hierarchy Process(AHP)to screen out the main factors that affect the risk of financial distress.The main factors are included in Logistic regression model and BP neural network model for predicting financial distress of listed companies.The overall effect of two models are assessed and compared.Finally,this paper proposes policy implications according to empirical results. 展开更多
关键词 FINANCIAL DISTRESS Risk of delisting LOGISTIC Regression BP NEURAL Network Model
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