Relationship between Risk attitude and Organizational Vulnerability - Assignment Example

Relationship between Risk attitude and Organizational Vulnerability. Purpose of Research and its Background The report is aimed at highlighting the relationship between the risk factor an organization has to face and the performance it in turn exhibits. More specifically, the study revolves around the risk taking aptitude and the stability that they exhibit as a result of it. It also aims at evaluating the degree of the effect of the risk taking aptitude, that is, if there is any. Background The background of the research at hand revolves around the recent aptitude of financial organizations towards taking higher degrees of risk in their dealings. The fact that this change in their aptitude has had mixed results towards the final outcomes of their endeavors and their overall performance has been evident owing to certain happenings in the recent past. For example, a financial institution named UBS (Union Bank of Switzerland) was charged with the allegation of being involved in global interest rate manipulation and was penalized under the criminal act. This was the resultant of a high aptitude of the company towards risk taking. The resultant of the matter was that the company ended up paying around 1.5 Million Dollars to the US, UK and Swiss authorities. This matter was seen as a blow in the financial world. The happening created instant awareness amongst peer organizations and general public. The resultant of this awareness is still unknown. Whether organizations have lessened taking risks or not and whether this taking or not taking of risks has affected the productivity of the organizations is still unknown. Introduction and Data Collection In line with the background discussed above, the report aims to study the relationship between the fluctuations in adoptions of risk factor and the resultant vulnerability or stability of an organization. In order to measure the relationship data from 100 renowned financial organizations from the city of London was taken. The Chief Executive Officers of these companies were contacted and their co-operation resulted in the availability of real world data for evaluation and research purposes. The hundred companies whose data were collected were amongst the top largest financial organizations being measured with respect to their size of workforce. This list of companies was generated by the renowned Financial Times. The CEOs of the organizations were requested to fill out a questionnaire which was aimed at assessing their inclination towards risk taking. This data collection and the data further collected after consultation with two different articles published in the years 2007 and 2009 subsequently laid the road to the onset of analysis. The report furthers to narrate how the analysis progressed and what were the eventual findings. Primary Data The questionnaires presented to the Chief Executive Officers of the hundred companies were responded to by them. The responses were used to assess the risk taking aptitudes of the companies. Each response was measured on a scale of one to thirty. 'Thirty' was considered as the most risky while the opposite end 'one' was considered as the most conservative response. This primary data is denoted by the variable named "RISKATT_AFTER" in the calculations. Secondary Data The risk attitudes of the organizations under discussion was obtained via data collected from two articles published in the years 2007 and 2009 respectively. These values were denoted by a variable named "RISKATT_BEFORE". These values were also measured on a scale similar to that used for primary data, that is, 'Thirty' was considered as the most risky while the opposite end 'one' was considered as the most conservative response. Research Question(s) Following are the research questions that have been formulated after the initial study of the scenario, 1. Whether there is a significant difference between the attitudes of CEOs towards risk after the UBS Bank scandal or not? 2. Is there a correlation between the risk attitude of the companies and the respective fluctuation in their share prices? If yes then would there be any way to measure the volatility? Area #1 of Investigation This is related to the exploration of the answer to first research question mentioned above. The answer to this question is particularly related to the study of two initial variables namely RISKATT_BEFORE and RISKATT_AFTER. The rest of the discussion is as follows, Descriptive Statistics The descriptive statistics regarding the variables “RISKATT_BEFORE” and “RISKATT_AFTER” are presented in Table 1.0. Measures of central tendency focused for the study include Mean, Median, Mode and Standard Deviation. Considering the readings on the scale of 1 – 30 (1 being most conservative and 30 being most risky) the mean values representing CEOs’ attitude towards risk appear to have considerable difference after the incident of UBS bank. Moreover the other central tendency measures that are Median and Mode appear to converge towards Mean value which is more towards conservative attitude after the UBS failure incident. The RISKATT_AFTER variable exhibits a broader range that may be a result of nervousness. The standard deviation also appears higher for the readings taken after the incident. Table 1.0: Descriptive Statistics CEO's Attitude to risk before Barings crash CEO's Attitude to risk after Barings crash N Valid 100 N Valid 100 Missing 0 Missing 0 Mean 19.78 Mean 15.14 Median 20.00 Median 15.50 Mode 23 Mode 16 Std. Deviation 3.007 Std. Deviation 5.345 Range 10 Range 24 The graphical narration of these facts is done through box plot presented as figure 1.0. Values on vertical axis represent the risk attitude ratings. The expansion in range of values with a comparatively lower mean in the second is clearly evident. Figure 1.0 Establishing Hypotheses Let µ1 and µ2 represent the mean values of risk attitude. The null and alternative hypotheses as per the research question would be as follows, H0: µ1 = µ2 (There is no difference between mean values of CEO’s attitude towards risk before and after the incident) H1: µ1 ≠ µ2 (There mean value of CEO’s attitude towards risk has changed significantly after the UBS failure) Statistical Testing: Area #1 In order to analyze and test Hypothesis 1, the means values of CEO’s attitude towards risk are compared under Paired Sample t – test. With 95% level of significance it was found that the p-value (0.000) < α. Hence the null hypothesis is rejected and it is concluded that there is a significant difference between the mean values of CEO’s attitude towards risk before and after the UBS failure incident. The test values are presented in Table 1.1. Table 1.1: Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 CEO's Attitude to risk before Barings crash - CEO's Attitude to risk after Barings crash 4.640 6.127 .613 3.424 5.856 7.572 99 .000 It appears that there is a general shift in the attitude of CEOs towards risk and most of them have become more conservative after the incident of UBS failure. However a few of the CEOs exhibited otherwise. The shift seems to be a bit drastic due to the higher standard deviation and range values. The volume of surges and spikes may be due to the instant reaction or nervousness of organization heads. Area #2 of Investigation The study under this area is to explore the answer of second question. In order to achieve the results it is imperative (as per the given instructions) to calculate a new variable namely SHARE_RANGE (for knowing the range of share prices). This is done to explore the extent of volatility of company in terms of its share prices. The SHARE_RANGE is calculated according to the given formula i.e. Independent and Dependent Variables The variable RISKATT_AFTER is taken as an independent variable while vari≠ables SHARE_RANGE and SD_SHARE are taken as dependent variables. This is to figure out the impact of change in attitude (of respective CEO towards risk) on company’s volatility in terms of change reflected through its share prices. Correlation Matrix Table 1.2 presents the correlation matrix containing the respective values regarding independent and dependent variables. The correlation of CEO’s Attitude to risk after Barings Crash with Range of Share Prices and Standard Deviation of Share Prices seems significant i.e. r1 (54%) and r2 (65%) respectively. With null and alternative hypotheses as, H0: ρ = 0 (There is NO actual correlation) H1: ρ ≠ 0 (There is a correlation) Referring Table 1.2, In this case with 99% level of confidence and p-value (0.000) < 0.01 the null hypothesis is rejected. Hence there exists a correlation between studied independent and dependent variables. Table 1.2: Correlation Matrix CEO's Attitude to risk after Barings crash Range of share prices in last 30 days Standard deviation of share prices in the last 30 days CEO's Attitude to risk after Barings crash Pearson Correlation 1 .537** .650** Sig. (2-tailed) .000 .000 N 100 100 100 N 100 100 100 **. Correlation is significant at the 0.01 level (2-tailed). Scatterplots of Independent Variables vs. Dependent Variable The scatter plots of RISKATT_AFTER with SHARE_RANGE and SD_SHARE are added as figures 1.1 and 1.2 below. Figure 1.1 Figure 1.2 The scatter plots highlighted the fact that higher the risk value (CEOs attitude to risk) higher would be the volatility of the company in terms of its share prices. Regression Analysis The two measures of volatility that are SHARE_RANGE and SD_SHARE are analyzed through regression against the independent variable RISKATT_AFTER. The analysis is presented in the text below. SHARE_RANGE and RISKATT_AFTER Linear Regression Model (See Table 1.3 and 1.4) can be formulated as, Y = 3.037 x + 493.325 with coefficient of determination (r2) = 0.28. With p-value (0.00) < 0.05, the null hypothesis is rejected and the model seems suitable for predictions. Respective Residual Plots are included as figure 1.3 in Appendix A. Table 1.3: Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .537a .288 .281 25.659 .288 39.628 1 98 .000 a. Predictors: (Constant), CEO's Attitude to risk after Barings crash b. Dependent Variable: Range of share prices in last 30 days Table 1.4: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 493.325 7.742 63.717 .000 477.960 508.690 CEO's Attitude to risk after Barings crash 3.037 .482 .537 6.295 .000 2.080 3.995 a. Dependent Variable: Range of share prices in last 30 days SD_SHARE and RISKATT_AFTER Linear Regression Model (See Tables 1.5 and 1.6) can be formulated as, Y = 9.344 x + 111.240 with coefficient of determination (r2) = 0.42. With p-value (0.00) < 0.05, the null hypothesis is rejected and the model seems suitable for predictions. Respective Residual Plots are included as figure 1.4 in Appendix A. Table 1.5: Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .650a .423 .417 58.620 .423 71.855 1 98 .000 a. Predictors: (Constant), CEO's Attitude to risk after Barings crash b. Dependent Variable: Standard deviation of share prices in the last 30 days Table 1.6: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95.0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 111.240 17.688 6.289 .000 76.137 146.342 CEO's Attitude to risk after Barings crash 9.344 1.102 .650 8.477 .000 7.156 11.531 a. Dependent Variable: Standard deviation of share prices in the last 30 days Conclusions and Limitations The study reveals that after the incident of Barings Crash the CEOs of 100 organizations (under study) have changed their attitude towards risk. Most of them preferred to be more conservative in order to remain less vulnerable against risk. Few acquired a risky posture though. It also highlighted the fact that the fluctuation in CEOs’ attitude towards risk had been impactful on company’s performance in terms of their share prices. The volatility is measured in terms of share prices in last 30 days (range) and standard deviation in share prices (dispersion). It is observed that conservative attitude ended up in stable share prices with less range and standard deviation values. While higher risk attitude of CEOs reflected unstable share prices with higher standard deviations and share price range. References: MOORE, D. S., & MCCABE, G. P. (1993). Introduction to the practice of statistics. New York, Freeman. WAGNER, S. F. (1992). Introduction to statistics. New York, HarperPerennial SALKIND, N. J. (2000). Statistics for people who (think they) hate statistics. Thousand Oaks, Calif, Sage Publications, Inc. BAUM, S., GABLE, R. K., & LIST, K. (1987). Chi square, pie charts and me. Monroe, N.Y., Trillium Press, Inc. SHOTTER, J. (2012). UBS to pay $1.5bn in Libor settlement. Available: http://www.ft.com/cms/s/0/0c8bd408-4945-11e2-b25b-00144feab49a.html#axzz2FVA9ayGD. Last accessed 22nd January 2013. Appendix A Figure 1.3 Figure 1.4 Read More