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Responding to the allegedly biased research reports issued by large investment banks, the Global Research Analyst Settlement and related regulations went to great lengths to weaken the conflicts of interest faced by investment bank analysts. In this paper, I investigate the effects of these changes on small and large investor

Responding to the allegedly biased research reports issued by large investment banks, the Global Research Analyst Settlement and related regulations went to great lengths to weaken the conflicts of interest faced by investment bank analysts. In this paper, I investigate the effects of these changes on small and large investor confidence and on trading profitability. Specifically, I examine abnormal trading volumes generated by small and large investors in response to security analyst recommendations and the resulting abnormal market returns generated. I find an overall increase in investor confidence in the post-regulation period relative to the pre-regulation period consistent with a reduction in existing conflicts of interest. The change in confidence observed is particularly striking for small traders. I also find that small trader profitability has increased in the post-regulation period relative to the pre-regulation period whereas that for large traders has decreased. These results are consistent with the Securities and Exchange Commission's primary mission to protect small investors and maintain the integrity of the securities markets.
ContributorsDong, Xiaobo (Author) / Mikhail, Michael (Thesis advisor) / Hwang, Yuhchang (Committee member) / Hugon, Artur J (Committee member) / Arizona State University (Publisher)
Created2011
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Description
During the past decade, the Chinese bond market has been rapidly developing. The percentage of bond to total social funding is constantly increasing. The structure and behavior of investors are crucial to the construction of China’s bond market. Due to specific credit risks, bond market regulation usually involves in rules

During the past decade, the Chinese bond market has been rapidly developing. The percentage of bond to total social funding is constantly increasing. The structure and behavior of investors are crucial to the construction of China’s bond market. Due to specific credit risks, bond market regulation usually involves in rules to control investor adequancy. It is heatedly discussed among academia and regulators about whether individual investors are adequate to directly participate in bond trading. This paper focuses on the comparison between individual and institutional bond investors, especially their returns and risks. Based on the comparison, this paper provides constructive suggestions for China’s bond market development and the bond market investor structure.
ContributorsLiu, Shaotong (Author) / Gu, Bin (Thesis advisor) / Zhu, Ning (Thesis advisor) / Yan, Hong (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection

Using historical data from the third-party payment acquiring industry, I develop a statistical model to predict the probability of fraudulent transactions by the merchants. The model consists of two levels of analysis – the first focuses on fraud detection at the store level, and the second focuses on fraud detection at the merchant level by aggregating store level data to the merchant level for merchants with multiple stores. My purpose is to put the model into business operations, helping to identify fraudulent merchants at the time of transactions and thus mitigate the risk exposure of the payment acquiring businesses. The model developed in this study is distinct from existing fraud detection models in three important aspects. First, it predicts the probability of fraud at the merchant level, as opposed to at the transaction level or by the cardholders. Second, it is developed by applying machine learning algorithms and logistical regressions to all the transaction level and merchant level variables collected from real business operations, rather than relying on the experiences and analytical abilities of business experts as in the development of traditional expert systems. Third, instead of using a small sample, I develop and test the model using a huge sample that consists of over 600,000 merchants and 10 million transactions per month. I conclude this study with a discussion of the model’s possible applications in practice as well as its implications for future research.
ContributorsZhou, Ye (Author) / Chen, Hong (Thesis advisor) / Gu, Bin (Thesis advisor) / Chao, Xiuli (Committee member) / Arizona State University (Publisher)
Created2015