Financial Modelling with Excel Modeling - a Responsible Investment Strategy - Statistics Project Example

S1126948 B024874 Financial Modelling with Excel Tatiana Rodionova 27th April Modelling: a Responsible Investment Strategy Table of Contents McWilliams, A. &D. Siegel. 2000. Corporate Social Responsibility and Financial Performance: Correlation or Misspecification? Strategic Management Journal, 21: 603-609. 16 Section A 1. A brief overview of the key arguments regarding responsible investment Since the early 1990s, responsible investment has been on the rise. While making key investment decisions, most investors are increasingly bearing in mind corporate social responsibility as a significant element. Many investors, however, do not fully comprehend the whole issue of responsible investment, because of varying opinions. Regardless of the ambiguity and difference of opinions, understanding the potential returns and risks involved in making key investment decisions remains a crucial need for all responsible institutional investors. Company screening should be given priority in a case where socially responsible investment (SRI) funds are being considered. It is, however, a very challenging process to implement. The screening requires companies to be effectively assessed and differentiated according to their Corporate Social Responsibility (CSR) level (Wilson et al. 2007, p.305). The underlying reason is that CSR involves many dimensions that do not have any undisputed aggregative model. When evaluating the rankings of CSR companies, various modelling assumptions ought to be made. There is an increasing concern about ethical or Socially Responsible Investment (SRI), and it calls for comprehensive CSR-related information. It helps to single out the firms that are suitable for socially responsible investment. Every form of ethical investment funds requires sufficient measurement of CSR. The criterion of selection lies in the quality of each investment, and this is determined through the screening of all the investments prior to the actual investment decision (Barnett 2007, p.796). This essay closely examines the degree to which certain assets that are either incorporated or excluded may be validated (Barnett and Salomon 2006, p.1103). There is a huge difficulty when making a choice on whether to incorporate an asset or to exclude it in the terms of SRI. This is because the standard of selection in CSR is very complex and multi-dimensional. It comprises of a wide range of single dimensional indicators. Every screening process requires the reduction of data from various indicators. The companies should be ranked on the basis of construction of company-specific composite CSR indices. According to McWilliams and Siegel (2000), Bowen in 1953 defined CSR as “the obligations of businessmen to pursue those policies, to make those decisions, or to follow those lines of action which are desirable in terms of the objectives and values of our society” Choosing to use composite scores that combine various components of CSR into a single number rests on the fact that CSR’s multi-dimensionality can sometimes obstruct an adequate comparison among companies. Usually, a composite score enables faster and more efficient evaluation of CSR performance of a company. It also makes it possible for the companies in a certain segment to be benchmarked (Jensen 2002, p.237). Schafer (2005) observes that “while CSR rating institutions calls for corporate transparency and operates as ‘social accountants, the industry itself is currently lacking sufficient transparency.” Section B 2. Selection of the companies It may be appropriate to improve the general CSR performance of a company. If composite scores are incorporated in investment screening, these concerns automatically become more vital (Orlitzky et al. 2003, p.409). For the purposes of simplicity, there may only be three indicators which need to be taken into consideration, each of which captures a significant CSR feature. These may include; • The ‘People’ indicator, which shows the ratio of females to males in the high calibre jobs • The ‘Planet’ indicator, which may be based on a quantitative rating by experts from NGOs of the companies’ environmental policies • The ‘Profit’ indicator, which shows the data for a certain percentage like the return on assets. The summary table below shows the indicator scores for three firms. Indicators may however not perform on the same scale. Note that for each of the three indicators, the higher the value of the indicator, the better the performance of that indicator and vice versa. The aim is to come up with the best company from the overall performance analysis. People Planet Profit 1stCompany 0.9 0.6 0.06 2ndCompany 0.6 0.2 0.10 3rdCompany 0.2 1 0.11 For illustration purposes, only three companies were used though there is a difficulty in ranking them according to all the indicators simultaneously together. They can only be ranked if they are aggregated. A composite index may be constructed to overcome this challenge. The generic form of the composite indicator representing the overall performance of a company on Corporate Social Responsibility is recognized. Weights are attached to the indicators, and the normalized value for the company is determined. One direct way of constructing a composite index is by using the simple arithmetic average of standardized numbers (McWilliams and Siegel 2000, p.608). Here the original indicators are normalized using the standardization method. In this method, the sample average and standard deviation are computed for the assets and then represented. The method yields a scenario where various companies will be ranked in an A-B-C format. The best company may then be selected, out of the many chosen. The process relies on the choice of certain modelling assumptions made. Composite indices may be a useful tool if their respective outcomes are passed through in-depth robustness and sensitivity analysis. From a large number , of the world’s largest stocks that have high environmental, social and governance scores, the companies that have robust corporate social responsibility practices are; Google, Walt Disney, BMW, Apple, Volkswagen, Intel, Rolex Daimler and Microsoft. Section C 3. Potential benefits and tradeoffs of the selected portfolio and a hypothesis as to how it will perform One key advantage of the selected portfolio is that it is selected using a method that allows security assessment. It therefore, ensures efficiency of the optimal portfolio. This method also distinguishes macroeconomic and sole-business sources of return disparity. It ensures that there is consistency in all areas. The underlying hypothesis is that macroeconomic assessment is applied to estimate the risk premium and risk of the market index. Statistical analysis is applied to estimate all forms of security and their lasting variances (Saisana et al. 2005, p.312). Portfolio managers ought to use the estimates for risk premium to determine the expected return of the portfolio. It is a return caused by the market and may at times be regarded as a benchmark. Section D 4. The responsible investment portfolio, constructed using five stocks In constructing the responsible investment portfolio, five stocks have been used. The stocks include; MKS.L, LLOY.L, HSBA.L, NXT.L, SMIN.L. The period chosen for analysis is from 22nd January 2015 to 5th March 2015. The complete data concerning these stocks is found in the appendix section of this report. The standard deviation, variance and average returns of these stocks can be obtained by using formulas shown in the following section. According to Renneboog et al. (2008), a good stock portfolio comprises of five to ten stocks. Selection of the five stocks ensures diversity in the portfolio, which is associated with an increased investment security. The economic justification is that a well-diversified portfolio will always help to lower risks of many kinds. When various stocks are picked indiscriminately and combined, as happened in this case, into a portfolio, there is a significant risk reduction effect. Analyzing stocks daily helps to identify the assets that show the highest profitability potential. The daily stock analysis may also enable investors to understand the major shifts in the market. Section E 5. Analysis of the constructed stocks portfolio (including the comparisons of the performance of the responsible portfolio to the benchmark portfolio) The average returns, variances, standard deviations, variance-covariance matrix and the efficient frontier of these stocks have been computed for one full month. The data used is daily data. The figures for the expected return are computed in percentages. Stock values are computed in whole numbers. For MKS.L MKS.L Date Close Return 22/01/2015 to 05/03/2015   Average return 0.26% Standard Deviation 1.16% Variance 1.3456% LLOY.L LLOY.L Date Close Return 22/01/2015 to 05/03/2015   Average return 0.23% Standard Deviation 0.89% Variance 0.7921% For HSBA.L HSBA.L Date Close Return 22/01/2015 to 05/03/2015   Average return -0.27% Standard Deviation 1.28% Variance 1.6384% SMIN.L SMIN.L Date Close Return 22/01/2015 to 05/03/2015 Average return 0.13% Standard Deviation 1.23% Variance 1.5129% NXT.L NXT.L Date Close Return 22/01/2015 to 05/03/2015 Average return 0.25% Standard Deviation 0.91% Variance 0.8281% The Variance-Covariance Matrix MKS.L LLOY.L HSBA.L SMIN.L NXT.L MKS.L 5.41649 5.46762 -6.91 -1.7731 2.64259 LLOY.L 5.46762 3.17596 3.72 1.65432 3.0841 HSBA.L -6.9118 3.71894 6.52 1.99146 -5.6182 SMIN.L -1.7731 1.65432 1.99 6.05678 -7.5952 NXT.L 2.64259 3.08418 -5.62 -7.5952 3.30344 Using Blacks theorem to Derive the Efficient Frontier Proportion of x (α) 0.5 Ps mean return - E(Rp) 24.18% Ps sigma 9.56% 0.5 24.18% 9.56% Of all the five stocks, HSBA scores highest in terms of standard deviation and variance. Of all the five stocks, LLOY.L has the smallest variance and standard deviation. MKS.L has a smaller variance and standard deviation compared to SMIN.L. SMIN.L has a higher standard deviation compared to NXT.L. The standard deviation also helps to evaluate the levels of risk of a given stock portfolio. The standard deviation of the five stocks, as computed, can be used to ascertain their volatility. Usually, an increase in the variation of a stock’s returns will increase its volatility. As additional assets are included in the portfolio, the portfolio variance is expected to decrease. However, forming a separate or new portfolio with an equal investment in all the five other stocks cannot reduce the variance. On a daily basis, the daily returns of LLOY.L ranges from -0.08%to 1.76%, returns of MKS.L varies from -0.06% to 4.88%, returns HSBA.L varies from -4.63%to 1.21, returns of SMIN.L varies from -2.24% to 2.98% and returns of NXTL.L varies from -1.23% to 2.40%. Average returns of stocks have been calculated to determine the profitability of different stocks. Higher returns mean higher profitability. Of all the five stocks, MKS.L has the highest profitability while HSBA.L has the least profitability. The calculated variances reveal the level of variation between the anticipated rates of return and the expected returns. Usually, higher variances are indications of high variation and hence such stocks are unattractive. HSBA.L is, therefore, the most adverse. As mentioned above, the calculated standard deviations can be used to tell the level of risk linked to each stock. LLOY.L is the stock with the smallest standard deviation (of 0.89%). It therefore, means that LLOY.L is the stock with the lowest risk, and hence the best to choose. The stock having the biggest standard deviation is HSBA.L and is, therefore, the riskiest to choose. Therefore, a responsible investment portfolio structure that gives competitive returns compared to the broad market and the benchmark portfolio, and minimum risk is LLOY.L. Section F 8. Comparing the performance of the portfolio above to the performance of the appropriate broad market portfolio (FTSE100, S&P 500) The average expected return of the broad market portfolio is about 0.1372% while that of the constructed portfolio is 0.12%. It shows that stocks in the broad market portfolio are more profitable than those in the portfolio above. The variance of the general market portfolio is 0.040% while that of the portfolio constructed ranges from 0.89% to 1.51%. It means the stocks in the constructed portfolio are more inconsistent. The standard deviation of the general market portfolio is 0.2002% while that of the constructed portfolio ranges from 0.91% to 1.64%. Some stocks in the constructed portfolio are associated with lower risk compared to the general market portfolio. Section G 9. A benchmark portfolio of 5 stocks with no CSR screens, based on the risk and return characteristics Week FUSEX BUDTX VUSTX MERTX ENGTX Portfolio returns 1-Dec-10 0.35441 0.1143 8-Dec-10 0.3436 -0.03051 0.1133 -0.00875 0.025362 -0.01866 15-Dec-10 0.3412 -0.00698 0.1129 -0.00353 0.034562 -0.00481 22-Dec-10 0.3375 -0.01084 0.1127 -0.00177 0.080962 -0.00603 29-Dec-10 0.3515 0.041481 0.1108 -0.01686 0.010962 0.014093 5-Jan-11 0.3345 -0.04836 0.1138 0.027076 0.000962 -0.01326 12-Jan-11 0.3468 0.036771 0.1127 -0.00967 0.010984 0.014615 19-Jan-11 0.3453 -0.00433 0.115 0.020408 0.056062 0.006097 26-Jan-11 0.3537 0.024327 0.1124 -0.02261 0.031009 0.003216 2-Feb-11 0.3654 0.033079 0.1178 0.048043 0.023433 0.035853 Average return 0.36% 0.28% 0.12% -0.03% 0.025% 0.1471 Variance 4.67% 0.48% 5.48% 4.12% 3.31% 0.09810 Standard Deviation 2.16% 0.69% 2.34% 2.03% 1.82% 0.3132 Section H 10. Comparison of the performance of the responsible portfolio to the benchmark portfolio The average expected return of the benchmark portfolio is about 0.151% while that of the responsible portfolio is 0.12%. It shows that stocks in the responsible portfolio are not as profitable. The variance of the benchmark portfolio ranges from 0.48% to 5.48%while that of the responsible portfolio ranges from 0.89% to 1.51%. It means the stocks in the responsible portfolio are less inconsistent. The standard deviation of the benchmark portfolio ranges from 0.69% to 2.16% while that of the responsible portfolio ranges from 0.91% to 1.64%.Most stocks in the benchmark portfolio are associated with lower risk compared to those in the responsible portfolio. Section I 11. Using Microsoft Excel Solver to find the efficient or responsible investment portfolio structure Here, Microsoft Excel Solver is applied to determine a responsible investment portfolio structure that gives competitive returns compared to the broad market, my benchmark portfolio, and minimum risk (Saisana et al. 2005, p.285). Usually, a responsible investment portfolio minimizes the variance of the portfolio. The following table shows the initial data that will be considered (for the five stocks); Stock E(R) VAR(R) 1 (MKS.L) 0.26 1.3456 2 (LLOY.L) 0.23 0.7921 3 (HSBA.L) -0.27 1.6384 4 (SMIN.L) 0.13 1.5129 5 (NXT.L) 0.25 0.8281 The excel spreadsheet appears as shown below (All constraints and optimality requirements are fulfilled): Target Portfolio weights mu0 X1 X2 X3 X4 X5 Constraint 1 Constraint 2 Var(Rp) 0.053 0.20 0.10 0.20 0.20 0.30 1.000 0.1230 0.254 The most efficient portfolio has weights X1 = 0.20, X2 = 0.10, X3 = 0.20, X4 = 0.20 and X5 = 0.30. The variance of the portfolio is 0.254while the portfolio expected return is 0.053. Section J 12. Implications of the analysis, introducing CSR screens The implications of the analysis when the CSR screens are introduced may also be assessed. The results of the sensitivity analysis may either be adopted or disregarded. Moreover, internal deliberations may be increased to the specific scenario that will be selected as an option (Renneboog et al. 2008, p.1730). The greatest challenge comes when maintaining the agreement on such an option. Construction of a CSR index is usually associated with uncertainty (Saisana et al. 2005, p.310). However, suppose that if different alternatives yield company-specific ranges of CSR index values and rankings, the SRI screening process must establish the possibility of making assumption-driven categorization mistakes. Reduction of such possibilities is then determined. It involves ascertaining how to build a robust CSR classification of companies (Porter and Kramer 2006, p.82). From the robustness analysis, it is clear that the impact of the modelling uncertainty problem is insignificant for companies at the ends (top or at the bottom of the original CSR position). Companies that are ranked above the standard set in every situation may then be identified. In conclusion, understanding the potential returns and risks involved in making key investment decisions is a crucial need for all responsible institutional investors. Company screening should be given priority in a case where socially responsible investment (SRI) funds are being considered. To overcome the challenge of ranking various companies according to all the indicators simultaneously together, they can only be ranked if they are aggregated. A composite index may be constructed to overcome this challenge. Portfolio managers ought to use the estimates for risk premium to determine the expected return of the portfolio. From the robustness analysis, it is clear that the impact of the modelling uncertainty problem is insignificant for companies at the top or at the bottom of the original CSR position. References Barnett, M. 2007.Stakeholder Influence Capacity and the Variability of Financial Returns to Corporate Social Responsibility.Academy of Management Review, 32: 794-816. Barnett, M. L..and R. M. Salomon. 2006. Beyond Dichotomy: the Curvilinear Relationship between Social Responsibility and Financial Performance. Strategic Management Journal, 27: 1101-1122. Jensen, Michael C. 2002.Value Maximization, Stakeholder Theory and The Corporate Objective Function. Business Ethics, Quarterly 235-256. McWilliams, A. &D. Siegel. 2000. Corporate Social Responsibility and Financial Performance: Correlation or Misspecification? Strategic Management Journal, 21: 603-609. Orlitzky, M., F. U. Schmidt, and S. L.Rynes.2003. Corporate Social and Financial Performances: A Meta-Analysis. Organizational Studies, 24: 403-41. Porter, Michael E. and Mark R. Kramer. 2006.Strategy and Society: The Link between Competitive Advantage and Corporate Social Responsibility. Harvard Business Review, 78-92. Renneboog, L., J. Ter-Horst, and C. Zhan. 2008. Socially Responsible Investments: Institutional Aspects, Performance, and Investor Behaviour. Journal of Banking and Finance, 32: 1723-1742. Saisana, M.,Saltelli, A. and Tarantola, S. 2005. Uncertainty and Sensitivity Analysis as Tools for the Quality Assessment of Composite Indicators. Journal of the Royal Statistical Society Series A, 168(2):307-323. Schäfer, H., 2005. International Corporate Social Responsibility Rating Systems: Conceptual Outline and Empirical Results, Journal of Corporate Citizenship 20:107-120. Wilson, J., Tyedmers, P., and Pelot, R., 2007. Contrasting and Comparing Sustainable Development Indicator Metrics. Ecological Indicators, 7(2):299-314. Appendices MKS.L Date Close Return 5-Mar-15 506.5 0.20% 4-Mar-15 505.5 -0.49% 3-Mar-15 508 -0.39% 2-Mar-15 510 1.09% 27-Feb-15 504.5 0.20% 26-Feb-15 503.5 -0.20% 25-Feb-15 504.5 0.10% 24-Feb-15 504 0.92% 23-Feb-15 499.4 0.34% 20-Feb-15 497.7 0.08% 19-Feb-15 497.3 0.53% 18-Feb-15 494.7 1.14% 17-Feb-15 489.1 -0.89% 16-Feb-15 493.5 -0.10% 13-Feb-15 494 0.51% 12-Feb-15 491.5 -0.39% 11-Feb-15 493.4 -1.06% 10-Feb-15 498.7 4.88% 9-Feb-15 475.5 -1.21% 6-Feb-15 481.3 -0.06% 5-Feb-15 481.6 0.25% 4-Feb-15 480.4 0.69% 3-Feb-15 477.1 -0.19% 2-Feb-15 478 -1.32% 30-Jan-15 484.4 -0.70% 29-Jan-15 487.8 2.14% 28-Jan-15 477.6 0.61% 27-Jan-15 474.7 -0.27% 26-Jan-15 476 0.11% 23-Jan-15 475.5 1.26% 22-Jan-15 469.6 -   Average return 0.26%   SD 1.16% LLOY.L Date Close Return 5-Mar-15 80.94 1.34% 4-Mar-15 79.87 0.54% 3-Mar-15 79.44 -0.69% 2-Mar-15 79.99 1.25% 27-Feb-15 79 0.64% 26-Feb-15 78.5 -0.93% 25-Feb-15 79.24 0.04% 24-Feb-15 79.21 0.24% 23-Feb-15 79.02 1.33% 20-Feb-15 77.98 0.97% 19-Feb-15 77.23 -0.08% 18-Feb-15 77.29 0.34% 17-Feb-15 77.03 1.72% 16-Feb-15 75.73 0.50% 13-Feb-15 75.35 1.24% 12-Feb-15 74.43 0.05% 11-Feb-15 74.39 -0.83% 10-Feb-15 75.01 0.12% 9-Feb-15 74.92 -1.17% 6-Feb-15 75.81 0.37% 5-Feb-15 75.53 1.31% 4-Feb-15 74.55 -0.56% 3-Feb-15 74.97 1.76% 2-Feb-15 73.67 -0.11% 30-Jan-15 73.75 -1.48% 29-Jan-15 74.86 0.13% 28-Jan-15 74.76 -0.49% 27-Jan-15 75.13 -1.14% 26-Jan-15 76 0.62% 23-Jan-15 75.53 -0.16% 22-Jan-15 75.65 -   Average return 0.23%   SD 0.89% HSBA.L Date Close Return 5-Mar-15 571 -2.64% 4-Mar-15 586 0.90% 3-Mar-15 581 -0.34% 2-Mar-15 583 0.97% 27-Feb-15 577 -0.74% 26-Feb-15 582 0.73% 25-Feb-15 577 -0.72% 24-Feb-15 582 0.76% 23-Feb-15 577 -4.63% 20-Feb-15 605 0.55% 19-Feb-15 602 -0.59% 18-Feb-15 606 0.45% 17-Feb-15 603 0.28% 16-Feb-15 601 0.48% 13-Feb-15 598 0.30% 12-Feb-15 596 0.73% 11-Feb-15 592 -0.95% 10-Feb-15 598 -2.10% 9-Feb-15 611 -1.64% 6-Feb-15 621 1.21% 5-Feb-15 613 -0.36% 4-Feb-15 616 -0.31% 3-Feb-15 618 0.83% 2-Feb-15 612 0.46% 30-Jan-15 610 -1.38% 29-Jan-15 618 -0.02% 28-Jan-15 618.2 0.03% 27-Jan-15 618 -1.45% 26-Jan-15 627.1 0.37% 23-Jan-15 624.8 0.73% 22-Jan-15 620.3 -   Average return -0.27%   SD 1.28% SMIN.L Date Close Return 5-Mar-15 1,179.00 1.55% 4-Mar-15 1,161.00 0.00% 3-Mar-15 1,161.00 -0.77% 2-Mar-15 1,170.00 1.30% 27-Feb-15 1,155 0.43% 26-Feb-15 1,150.00 1.14% 25-Feb-15 1,137 -2.24% 24-Feb-15 1,163 -1.11% 23-Feb-15 1,176 -1.34% 20-Feb-15 1,192 0.51% 19-Feb-15 1,186 -0.92% 18-Feb-15 1,197 1.44% 17-Feb-15 1,180 0.00% 16-Feb-15 1,180 -0.51% 13-Feb-15 1,186 1.28% 12-Feb-15 1,171 1.04% 11-Feb-15 1,159 -0.43% 10-Feb-15 1,164 0.17% 9-Feb-15 1,162 -2.11% 6-Feb-15 1,187 0.68% 5-Feb-15 1,179 0.68% 4-Feb-15 1,171 -0.34% 3-Feb-15 1,175 2.98% 2-Feb-15 1,141 1.24% 30-Jan-15 1,127 1.53% 29-Jan-15 1,110 -0.54% 28-Jan-15 1,116 -0.09% 27-Jan-15 1,117 -1.41% 26-Jan-15 1,133 1.16% 23-Jan-15 1,120 -1.41% 22-Jan-15 1,136 -   Average return 0.13%   SD 1.23% NXT.L Date Close Return 5-Mar-15 7,555.00 0.73% 4-Mar-15 7,500.00 1.08% 3-Mar-15 7,420.00 -1.20% 2-Mar-15 7,510.00 0.20% 27-Feb-15 7,495.00 0.94% 26-Feb-15 7,425.00 0.20% 25-Feb-15 7,410.00 -0.07% 24-Feb-15 7,415.00 0.34% 23-Feb-15 7,390.00 0.89% 20-Feb-15 7,325.00 -0.61% 19-Feb-15 7,370.00 1.31% 18-Feb-15 7,275.00 0.48% 17-Feb-15 7,240.00 0.14% 16-Feb-15 7,230.00 0.07% 13-Feb-15 7,225.00 -1.23% 12-Feb-15 7,315.00 0.97% 11-Feb-15 7,245.00 -0.07% 10-Feb-15 7,250.00 2.40% 9-Feb-15 7,080.00 -1.19% 6-Feb-15 7,165.00 0.35% 5-Feb-15 7,140.00 0.35% 4-Feb-15 7,115.00 0.28% 3-Feb-15 7,095.00 -0.98% 2-Feb-15 7,165.00 -0.97% 30-Jan-15 7,235.00 -0.82% 29-Jan-15 7,295.00 0.90% 28-Jan-15 7,230.00 1.05% 27-Jan-15 7,155.00 -0.21% 26-Jan-15 7,170.00 0.28% 23-Jan-15 7,150.00 2.00% 22-Jan-15 7,010.00 -   Average return 0.25%   SD 0.91% Market Portfolio Efficiency Frontier Stock Returns Date MKS.L LLOY.L HSBA.L SMIN.L NXT.L S & P500 5-Mar-15 0.20% 1.34% -2.64% 1.55% 0.73% -0.0718 4-Mar-15 -0.49% 0.54% 0.90% 0.00% 1.08% 0.0656 3-Mar-15 -0.39% -0.69% -0.34% -0.77% -1.20% 0.1844 2-Mar-15 1.09% 1.25% 0.97% 1.30% 0.20% 0.3242 27-Feb-15 0.20% 0.64% -0.74% 0.43% 0.94% -0.0491 26-Feb-15 -0.20% -0.93% 0.73% 1.14% 0.20% 0.2141 25-Feb-15 0.10% 0.04% -0.72% -2.24% -0.07% 0.2251 24-Feb-15 0.92% 0.24% 0.76% -1.11% 0.34% 0.1275 23-Feb-15 0.34% 1.33% -4.63% -1.34% 0.89% -0.0718 20-Feb-15 0.08% 0.97% 0.55% 0.51% -0.61% 0.0656 19-Feb-15 0.53% -0.08% -0.59% -0.92% 1.31% 0.1844 18-Feb-15 1.14% 0.34% 0.45% 1.44% 0.48% 0.3242 17-Feb-15 -0.89% 1.72% 0.28% 0.00% 0.14% -0.0491 16-Feb-15 -0.10% 0.50% 0.48% -0.51% 0.07% 0.2141 13-Feb-15 0.51% 1.24% 0.30% 1.28% -1.23% 0.2251 12-Feb-15 -0.39% 0.05% 0.73% 1.04% 0.97% 0.1459 11-Feb-15 -1.06% -0.83% -0.95% -0.43% -0.07% -0.0758 10-Feb-15 4.88% 0.12% -2.10% 0.17% 2.40% 0.0213 9-Feb-15 -1.21% -1.17% -1.64% -2.11% -1.19% 0.1276 6-Feb-15 -0.06% 0.37% 1.21% 0.68% 0.35% 0.0712 5-Feb-15 0.25% 1.31% -0.36% 0.68% 0.35% 0.1372 4-Feb-15 0.69% -0.56% -0.31% -0.34% 0.28% 0.0215 3-Feb-15 -0.19% 1.76% 0.83% 2.98% -0.98% 0.2002 2-Feb-15 -1.32% -0.11% 0.46% 1.24% -0.97% 0.0913 30-Jan-15 -0.70% -1.48% -1.38% 1.53% -0.82% 0.2243 29-Jan-15 2.14% 0.13% -0.02% -0.54% 0.90% 0.0913 28-Jan-15 0.61% -0.49% 0.03% -0.09% 1.05% 0.2243 27-Jan-15 -0.27% -1.14% -1.45% -1.41% -0.21% -0.3446 26-Jan-15 0.11% 0.62% 0.37% 1.16% 0.28% 0.0213 23-Jan-15 1.26% -0.16% 0.73% -1.41% 2.00% 0.1276 22-Jan-15 - - - - - Average return 0.26% 0.23% -0.27% 0.13% 0.25% 0.1372 Variance 1.35% 0.89% 1.28% 1.51% 0.83% 0.0400 Standard Deviation 1.16% 0.79% 1.64% 1.23% 0.91% 0.2002 Read More