Yunqi Fan, Guanglei Hu and Xiaoxue Chen
This study aims to examine whether mandatory audit partner rotation is associated with future stock price crash risk.
Abstract
Purpose
This study aims to examine whether mandatory audit partner rotation is associated with future stock price crash risk.
Design/methodology/approach
This study makes use of a regulatory change from the Ministry of Finance of China and the China Securities Regulation Commission, which requires mandatory rotation of audit partners since 2004, as a natural experiment to establish causality and applies a difference-in-difference research design.
Findings
Audit partner rotation leads to a significant decrease in future stock price crash risk in the departing partner’s final year of tenure preceding mandatory rotation, consistent with peer monitoring argument of mandatory rotation. Inconsistent with other arguments, including client-specific knowledge, fresh perspective and auditor independence, no significant effect takes a place in the incoming partner’s first year of tenure following mandatory rotation. Mechanism analysis documents that mandatory audit partner rotation reduces stock price crash risk by improving audit quality and constraining managerial empire building.
Originality/value
The results shed new light on the capital market consequence of mandatory audit partner rotation and the cause of stock price crash risk.
Details
Keywords
The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.
Abstract
Purpose
The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.
Design/methodology/approach
This paper begins its research with the statistics and analysis of plagiarism data and a review of expert interviews regarding online literature publishing. All of these data materials were collected from anti-plagiarism platforms, online literature websites, news report websites and judiciary office websites.
Findings
The paper provides empirical insights into why the plagiarism is so rampant in the publishing of online literature in China. It suggests that the current task of controlling network literature plagiarism is arguably created by the literary production platform, which leads to the problem of the validity of the “self-monitoring model.” In fact, controlling plagiarism must be emphasized by means of external monitoring, because strict supervision and various external punitive measurements for committing plagiarism can force literature-generating platforms to strengthen their own internal monitoring.
Research limitations/implications
Online plagiarism occurs almost constantly, but it rarely results in court cases over copyright because of the lack of a robust copyright ecology in China. This paper considers large amounts of data and cases from self-publishing media platforms.
Practical implications
The paper includes implications for the development of plagiarism management in online literature publishing from the publishing Association, media and government.
Social implications
This paper suggests to online literature users that plagiarism will be controlled when certain active measures against it are taken. The authors hope that this view will promote the development of original online literature.
Originality/value
This paper points out that China must strengthen supervision that comes from outside the online literature generate platforms to control the current rampant plagiarism that occurs on these platforms.
Details
Keywords
Shutian Ma, Yingyi Zhang and Chengzhi Zhang
The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys.
Abstract
Purpose
The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys.
Design/methodology/approach
Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation.
Findings
Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set.
Originality/value
For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.