<p>The purpose of this qualitative single-case study was to explore the publicly documented perceptions of the SEC regulators to determine if SOX 404 requirements have been supportive of influencing transparent financial reporting, preventing fraudulent financial statements, or misrepresentation of ICFR. A total of 15 archived, secondary, publicly available documents, representing 15 different SEC regulators? perspectives, were retrieved from the SEC website over a research time frame of July 30, 2002 until November 1, 2010. The samples were explored to identify the common perspectives of the 15 SEC regulators? relative to the effectiveness of SOX 404, and determine the overall perspectives of the holistic SEC entity or single case. Nvivo 8 was used to perform the data analysis and yielded 9 common themes. The findings revealed that although the SEC deemed SOX 404 supportive of influencing transparent financial reporting, preventing fraudulent financial statements, or misrepresentation of ICFR, the Commission was aware that gaps and opportunities for enhancing the law to promote its effectiveness and remove inefficiencies existed. The findings also revealed that the SEC was conscious of the issues, topics, opinions, observations and findings raised by SOX 404 critics and complying companies, and have been attentive, responsive and willing to address to those matters.

Credit Default Swaps (CDS) instruments - as an indicator of credit risk - were one of the most prominent innovations in financial engineering. Very limited literature existed on the drivers of CDS spreads before the financial crisis due to the opacity of this market and its lack of transparency. First, this thesis investigates the drivers of CDS spread in the UK banking sector, by considering the role of the housing market, over the period of 2004-2011. I find that, in the long-run, house price dynamics were the main factor contributing to wider CDS spreads. In addition, I show that a rise in stock prices lead to higher availability of capital and therefore increased bank borrowing activities, which led to lower credit risk. Furthermore, findings show that with higher aggregate bank liquidity, banks tend to grant more loans to low-income consumers, thus increasing bank credit risk. In addition, in the short-run, I employ the Structural VAR by imposing short-run restrictions to identify the five shocks arising from the CDS spread, the house price index, the yield spread, the TED spread, and the FTSE100. The SVAR findings indicate that a positive shock to house prices significantly increases the CDS spread in the medium-term, in the UK banking sector. In addition, apart from its own shock, the house price shock explains a big part of the variance (nearly 20%) in CDS spread. These results remained robust even after changing the ordering of the variables in the Structural VAR. Second, considering the bank-level factors across 30 countries and 115 banks, I find most significant bank-level drivers of the CDS spread were asset quality, liquidity and the operations income ratio. As such, banks with better asset quality, high levels of liquidity and operations income ratio were subject to lower CDS spreads and credit risk. Furthermore, larger banks were found to be more risky than smaller banks. We have conducted the U-test and our results indicate the presence of a U-shape relationship between bank size and bank CDS spread. It should be noted that in order to ensure that our results are robust, we used several estimation frameworks, including the FE, RE and alternative Generalized Method of Moments (GMM) approaches, which all prove the existence of a U-shape relationship between the CDS spread and bank size. In addition, we find a threshold level of bank size, which shows that banks growing beyond this point are subject to wider CDS spreads. Finally, I consider the difference in financial systems at country-level and regulatory structures at bank-level, in a panel setting, over the period of 2004-2011. At country-level, my findings directly link financial deepening to higher credit risk, reflecting a sign of credit bubble. Besides, at bank-level, I confirm my previous findings whereby asset quality, liquidity and operations income remain significant drivers of the CDS spread.