• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.156-163
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• 2012

The price stabilization policies which require the underwriters to maintain post-IPO prices at some level (stabilization duty) or give put-back options to investors, have survived for eight years until June, 2006. The purpose of this study is to analyze the effect of price stabilization policies upon IPO underpricing in KOSDAQ. The study decomposes the influence of the price stabilization policies on IPO underpricing into ex-ante and ex-post effect. The price stabilization policies have shown to increase the IPO underpricing both at ex-ante and ex post bases. And the ex-ante effect is greater than ex-post effect. Among policies, the price stabilization duty magnifies the underpricing more than put-back option. This study differentiate from the precedented studies by including the whole period in which those policies are in act and by decomposing the IPO underpricing effects into ex-ante and ex-post bases.

• The Journal of Industrial Distribution & Business
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• v.13 no.2
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• pp.45-57
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• 2022

Purpose: This paper examines whether or not universal banking operating in the asset management business tend to IPO underpricing when they are hosting IPOs in favor of their private interests. Previous studies suggest evidence which indicates that the universal banking operating in the asset management business tend to underestimate offering prices. This paper compares and analyzes the data before and after June 2007 to examine the influence of put-back option on IPO underpricing. Research design, data, and methodology: This paper compares the underwritten prices of IPOs of universal banking with and without asset management business in Korea in order to test such tendency actually exists. Result: We can find that such tendency is not correlated with first-day stock returns but correlated with put-back options. Our paper concludes that the hypothesis that "the universal banking's subsidiary asset management business influences the IPO underpricing" is found to be statistically insignificant. Conclusion: According to our analysis, it cannot be concluded that the interests of operating asset management do not conflict with the ones of underwriting business. However, it is so possible that the asset management companies try to harm the customers' interests, for instances churning and stuffing, it is necessary to scrutinize their behaviors and review the related regulations.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.