scholarly journals Performance Of Auto-Callable Reverse Convertibles, Information Disclosure Prescribed By Regulation S-K Change In 2013 Under U.S. Security Act: An Empirical Study

2021 ◽  
Author(s):  
Cheuk Lim Lai

This thesis studies the effect of the estimated value disclosure imposed in 2013 on the realized return of the auto-callable reverse convertibles (ACRCs) in the U.S. retail market. The sample of this study consists of about 3,700 issues of ACRCs during the period from 2011 to 2015, which is collected from the Edgar database of the U.S. Security and Exchange Committee (www.sec.gov). The comparison between product realized return and the return of underlying assets reveals that the ACRCs are underperformed by 5% on average, while further analysis shows that the return difference was broadened after the disclosure regulation. It is found that the statistical attributes of the underlying assets are critical to the product performance while they are hidden by the issuer of ACRCs. The disclosure regulation is presumed to enhance information disclosure and to further protect the investors, but the deteriorated performance of ACRCs indicates a failure of the regulation. To protect the anonymity and confidentiality, the identity of the issuer of ACRCs in our sample is removed without compromising the validity of our research. The original data is available upon request.

2021 ◽  
Author(s):  
Cheuk Lim Lai

This thesis studies the effect of the estimated value disclosure imposed in 2013 on the realized return of the auto-callable reverse convertibles (ACRCs) in the U.S. retail market. The sample of this study consists of about 3,700 issues of ACRCs during the period from 2011 to 2015, which is collected from the Edgar database of the U.S. Security and Exchange Committee (www.sec.gov). The comparison between product realized return and the return of underlying assets reveals that the ACRCs are underperformed by 5% on average, while further analysis shows that the return difference was broadened after the disclosure regulation. It is found that the statistical attributes of the underlying assets are critical to the product performance while they are hidden by the issuer of ACRCs. The disclosure regulation is presumed to enhance information disclosure and to further protect the investors, but the deteriorated performance of ACRCs indicates a failure of the regulation. To protect the anonymity and confidentiality, the identity of the issuer of ACRCs in our sample is removed without compromising the validity of our research. The original data is available upon request.


Author(s):  
Amanda M. Y. Chu ◽  
Benson S. Y. Lam ◽  
Agnes Tiwari ◽  
Mike K. P. So

Patient data or information collected from public health and health care surveys are of great research value. Usually, the data contain sensitive personal information. Doctors, nurses, or researchers in the public health and health care sector do not analyze the available datasets or survey data on their own, and may outsource the tasks to third parties. Even though all identifiers such as names and ID card numbers are removed, there may still be some occasions in which an individual can be re-identified via the demographic or particular information provided in the datasets. Such data privacy issues can become an obstacle in health-related research. Statistical disclosure control (SDC) is a useful technique used to resolve this problem by masking and designing released data based on the original data. Whilst ensuring the released data can satisfy the needs of researchers for data analysis, there is high protection of the original data from disclosure. In this research, we discuss the statistical properties of two SDC methods: the General Additive Data Perturbation (GADP) method and the Gaussian Copula General Additive Data Perturbation (CGADP) method. An empirical study is provided to demonstrate how we can apply these two SDC methods in public health research.


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