Failings of Financial Institutions such as UBS Bank, Barclays, and Citigroup Which Involved Rigging of Labor - Research Paper Example

Report Table of Contents Purpose of Research and its Background 3 Data Collection 3 Primary Data 3 Secondary Data 4 Descriptive Analysis 4 Establishing Hypotheses 7 Area #1 of Investigation 7 Statistical Testing: Area #1 7 Area #2 of Investigation 8 Correlation Matrix 8 Scatterplots of Independent Variables vs. Dependent Variable 9 Regression Analysis 11 SHARE_RANGE and RISKATT_AFTER 11 SD_SHARE and RISKATT_AFTER 12 Conclusions and Limitations 13 Suggestions for Further Study 14 List of References 15 Appendix A 16 Appendix B 17 Appendix C 18 Appendix D 19 Report Purpose of Research and its Background The motivation for this research has been the recent failings of major financial institutions such as UBS Bank, Barclays, and Citigroup which involved rigging of labor. These cases involved unethical practices at the top management level and unacceptable risk taking by companies’ CEOs in order to attain high level of financial gains. The underlying aim of the current quantitative research is to investigate the effects of risk attitude on company performance indicators and whether or not this has led to a change in attitudes of companies’ CEOs. Thus, the quantitative research performed in this report focus on two relationships as indicated below: 1. Whether or not there has been a change in attitudes to risk before and after the UBS bank scandal; 2. Whether or not there is a correlation between the risk attitude of companies and their volatility, and if so to measure this effect. Data Collection The present study uses data from both primary and secondary sources of information. Primary Data The research uses quantitative approach for data collection and analysis. The primary data involves collection of responses of selected companies CEOs regarding their risk attitudes after Barings crash. For this purpose, a sample of 100 CEOs of various large financial organizations presently operating in London, UK has been selected. In this study, a non-probability purposive sampling method has been used that involved acquiring a list of 100 largest companies on the basis of the size of their workforce from the Financial Times. These companies were then approached requesting CEOs to complete a short questionnaire in order to assess their attitude to risk. The attitude to risk is measured on the basis of a ranking from 1 to 30 with 1 being the most conservative and 30 being the most risky. This data makes up values for one variable RISKATT_AFTER. The selection of a sample of largest financial organizations operating in London could be considered to have certain limitations such as similarities in their responses suggesting possible biasness as they may have common approach to managing their businesses due to demographics and business context. The selection of only large companies avoids inclusion of views of smaller organizations which may have different and prudent attitude towards risk-taking measures. Their input would have been beneficial to include their views related to risk before and after crash of Barings. Secondary Data The research also makes use of secondary data that is obtained from two published articles from years 2007 and 2009, which provided measures of the risk attitude of the selected 100 organizations. This data is included in the present study for another variable RISKATT_BEFORE using the same scale from 1 to 30. The limitation associated with the inability of the researcher to evaluate the accuracy of this information is included in the present study, which could have direct implications for its outcome. Furthermore, the secondary data pertaining to stock’s volatility has been obtained for the selected companies. This information includes minimum (SHARE_MIN) and maximum (SHARE_MAX) share prices and their standard deviation (SD_SHARE) during period before and after Barings crash. Descriptive Analysis This section provides descriptives pertaining to two variables RISKATT_AFTER and RISKATT-BEFORE. This highlights the difference in the mean value of these variables before and after Barings crash, which clearly indicates that CEOs had more risky attitude before Barings crash (µ=20.72>14.78) and thus, implying that they were engaging more in risk-taking measures than after Barings crash. Moreover, the table below indicates high value of standard deviation for both variables. This implies greater variations within selected UK companies despite of common characteristics in term of firm size. This is also supportive of differences in views related to the risk associated with financial transactions at the time and also in the approach of CEOs confronting similar economic and financial decisions. Descriptive Statistics N Minimum Maximum Mean Std. Deviation CEOs Attitude to risk before Barings crash 100 15 25 20.72 3.134 CEOs Attitude to risk after Barings crash 100 4 27 14.78 4.807 Valid N (listwise) 100 Table 1: Descriptive Statistics of RISKATT_BEFORE and RISKATT_AFTER The table also indicates maximum and minimum values of both variables which clearly show that before Barings crash CEOs had much aggressive attitude to risk as compared to after Barings crash. This is derived from the minimum values on the scale obtained for before and after Barings crash periods. Moreover, the concentration of values is graphical represented before and after Barings crash pertaining to both variables in the following using histograms. Figure 1: CEOs Attitude to risk before Barings Crash Figure 2: CEOs Attitude to risk after Barings Crash Establishing Hypotheses Area #1 of Investigation This is related to the significance of differences between attitudes to risk before and after the UBS bank scandal measured by two variables RISKATT_BEFORE and RISKATT_AFTER respectively. To test out the differences in the mean values pertaining to the selected variables the following null and alternative hypotheses are stated: H0: There is no significant difference between attitudes to risk before and after Barings crash. Ha: There is a significant positive difference between attitudes to risk before and after Barings crash. The above stated hypotheses are tested out for validation on the basis of the difference between sample means for both variables and also by comparing p Read More