Report on CAPM Model
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26Capital Pricing Model
Report on CAPM Model
The Name of the School
Q1. Assumption and the implication of CAPM
The introduction of capital asset pricing model (CAPM) was set apart by the disclosure of William Sharpe (1964) and John Lintner (1965) where Sharpe was honored Nobel Prize in 1990 (Barberis, Greenwood & Shleifer 2015). Before their discovery, there exist nothing like (CAPM) which was built on the initial principles concerning the nature of taste and the investment opportunities which was able to give clear and straightforward and testable predictions concerning the return and risk. The major assumption and implication of the CAPM model include:
CAPM assumes that all the asset markets are perfectly competitive with very many investors and the implication of this is that the many investors that exist are the price takers in the market. The model further assumes that the market is frictionless with no taxes and zero cost of transaction (Barberis, Greenwood & Shleifer 2015). The model assumes the economic behaviour of an individual in the market where they draw conclusion about the overall market prices returns and quantities. The implication of the equilibrium assumption is that of the expected asset return which cannot be observed directly in the market (Zabarankin, Pavlikov & Uryasev 2014). Furthermore if every person believes in this particular theory, at that point it is conceivable that there is a focal part of the business sector portfolio which would rearrange the portfolio choice and gives a method of reasoning to a business sector indexing speculation examination (Zabarankin, Pavlikov & Uryasev 2014).
The model moreover expect that the lot of financial specialists that exist in the business sector are appearing to be identical time of arranging skyline. Al the financial specialists in the business sector have measure up to access to every one of the securities with no duties and commissions (Barberis, Greenwood & Shleifer 2015). Financial specialists can obtain and loan at the one risk free rate and ultimately speculators are capable at any offered point to short any advantage, furthermore hold any part of a benefit which exist in the business sector. Another critical ramifications is that there is an unequivocal risk return exchange off for each individual stocks (Zabarankin, Pavlikov & Uryasev 2014). The CAPM show ordinarily determines anticipated that profits would be utilized as a part of the capital planning assessment and direction. Also, the risk premium on an individual security is typically a component of its deliberate risk, measured by the covariance with the business sector.
Q2. Validity and the effect of violation of the CAPM assumption
To a larger extent, we can argue that CAPM is very valid subject to very many arguments. In most cases, investors hold well diversified portfolio and this is done to ensure that the investors has shed out some risk arising from the firm (Fama and French 2015). Therefore, we can conclude that those given portfolio are having high chances of correlating with the CAPM model. In his study, Brennan showed that even if the investors had distinct individual tax rate, still the new version of CAPM will be held constant. In another study, Mayer’s showed that a new version of CAPM will be held in situations where investors were allowed to trade in nontraded assets such as human capital (Zabarankin, Pavlikov & Uryasev 2014). In his case, he derived the CAPM, where high beta securities could have higher than anticipated risk premium and low beta securities lower than anticipated risk premiums.
Fischer and Black proved that even if there is no riskfree rate asset in the market, one is capable of deriving a zero beta version of CAPM, where the intercept might be higher than expected under the normal CAPM model (Dempsey 2013). This further validates the assumption and the expected results of its violation. Lastly, the consumption based CAPM normally allows for the fact that investors horizon are longer than one period and therefore when they choose portfolios they are also thinking of how the present portfolios will hedge risk in future time of assets (Barberis, Greenwood & Shleifer 2015).
Q3: Empirical test of the CAPM
In analyzing the empirical test of CAPM, we uses the data from two industries that is from technology industry and manufacturing industry. Eight regression were done using the excess asset return model
Testing the null hypothesis which is
Test: _{}
The first case we run the regression for the two industries separately using the 30 year time period. The results are shown in the appendix I below. For the first industry which is the technology,
We have _{}= 148372
While _{}
For technology industry, it proves the CAPM theory which states that the assets cannot be fully diversified from the risk. With industry beta of _{}>0 it shows that the asset is exposed to market risk for the entire market (Scott 2014).
For the manufacturing industry the results are shown in the appendix II and it shows that
We have _{}= 0.189993
While _{}
With_{} it further proves the CAPM theory for the manufacturing industry is highly volatile. _{}= 0.189993 which shows presence of volatile market. However, technology industry is more volatile than manufacturing industry with higher beta. Therefore, we can conclude that, regardless of the amount we enhance our speculations, it’s difficult to dispose of all the risk (Scott 2014)
From a nearer investigation of different models, unmistakably the beta and alpha coefficients are more noteworthy than 1 supporting the discoveries from the underlying relapse from index 1 and 2. At the point when the information is assembled into 10 years for each industry it further uncovers that riskless economic situation can’t be achieved supporting the capital resource estimating model found by Fama and French (1992). Despite the fact that present day approach shows that specific risk can be expelled through improvement. The drawback is that expanding still doesn’t deal with the issue of productive risk; even a course of action of all the shares in the offer exchanging framework can’t execute that hazard (Fama and French 2015). Henceforth, while figuring a justified return, proficient risk is the thing that diseases money related experts most. CAPM, along these lines, progressed as a way to deal with measure this efficient risk (Dempsey 2013).
Q4: Background and characteristics of Fama and French three factor model
As a result of numerous issues experienced while utilizing CAPM amid the examination if the acknowledged returns and the impact of other risk elements has put the CAPM model under constant feedback. The value of the model is to a great extent constrained with numerous suspicions of single risk element (Fama and French 2015). Numerous researchers utilizes the CAPM model, the covariance of return portfolio with the business sector portfolio come back to clarify the minor departure from the excess portfolio return. All things considered, in an observational study embraced by Fama and French (1992) demonstrates that the covariance of the arrival portfolio and the business sector return is not ready to completely clarify the progressions on the portfolio over excess returns. They discovered that the covariance have extremely negligible or no force at all as far as clarifying the crosssectional variety in resource/value return (Ai, Croce & Li 2013).
As a result of poor execution of CAPM, Fama and French created three variable model to help in filling the crevice left by CAPM (Fama and French 2015). They contended in their study that the abnormalities which are identifying with CAPM are caught by the three element model. The three component model were created on the premise of the way that normal excess portfolio returns are ordinarily sensible to the three variable model in particular;
Q5: Excess market portfolio return
The difference between Excess return on the course of action of little stocks and over excess return on a game plan of gigantic stocks (SMB). The difference between the Excess return on a course of action of highbook to market stocks and the over excess return on a game plan of low book to market stock (Ai, Croce & Li 2013). The thought about the going with formula to clear up the model. Another important three factor model characteristics is the regression equation which is stated as:
Empirical test
Using the excess return model:
Test the following hypotheses:
_{} , _{} ,_{} ,_{}
In this study, it covers the period from January 1980 to 2009 which is a thirty year period having 12 months each giving a total of 360 months. The study tested CAPM using two industry which includes manufacturing and technology industry. Furthermore, we included Small minus big and high minus low in the model. Since the main aim of this exercise was to test the prediction of CAPM, we used the Black et al (1972). We start with the first portfolio to estimate beta of the industry which was technology then we moved to second industry which was manufacturing industry (Ai, Croce & Li 2013).
From the result of the analysis in Appendix 6, the fit of the model was tested using Rsquare, the results shows that Rsquare is 0.828339 indicating that a total of 82.83% of the total variables in the models were tested and is the model is fit for making conclusion. For the three variables used in the model, SMB, HML and MktRf, the Pvalue which is 3.2E57, 5.55E10 ad 2.5E81 respectively is less than 0.05 critical value hence they are statistically significance at significance level of 95%. The coefficient beta for SMB is 1.256517 indicating high volatility hence high risk. For the HML the beta >0 and is 0.43791 indicating risk free portfolio while MktRf 1.161159 showing high risk market portfolio (Fama &French 2015).
In Appendix 7, the fit of the model was tested using Rsquare, the results shows that Rsquare is 0.819812 indicating that a total of 81.9812% of the total variables in the models were tested and is the model is fit for making conclusion (Fama &French 2015). For the three variables used in the model, SMB, HML and MktRf, the Pvalue which is 5.63E48, 7.4E21 and 4.6E112 respectively is less than 0.05 critical value hence they are statistically significance at significance level of 95%. The coefficient beta for SMB is 0.762542 indicating high volatility hence high risk. For the HML the beta and is 0.47099 indicating low risk portfolio while MktRf 1.058549 showing high risk market portfolio. Along these lines, we can contends that specific theories can be tried independent of whether one trusts in the legitimacy of the basic CAPM or in some other adaptation of the hypothesis (Scott 2014). Firstly, the hypothesis focuses that higher deliberate risk (beta) is connected with a larger amount of return and in this study, we can verification that both the assembling division and the innovation part have higher return that is why it is associated with high risk giving beta > 1(Scott 2014). It can also be noticed that the findings of this study is a proof of CAPM model that explains that risk free portfolio does not exist in real life hence all the two industries post high risk asset portfolio.
Part B: Wage Data
Descriptive statistics
From the study, there are seven variables which was under study, they include Wage, Education, Experience, Gender, Age, Marital status and Union. The researcher carried out descriptive analysis in SPSS and the results are shown in Appendix 1 below. From the analysis, age is having the highest mean of 36.811 followed by experience with 17.8221 union has the lowest mean of 0.179775. The analysis also gives standard deviation of the variables with experience the highest with 12.37971 followed by age 11.72657 with 11.72657 then wage with 7.708645. Union on the other hand gives the smallest standard deviation of 0.38436. This is an indication on low effect of union on wage and salary. Minimum ranges from 0 and maximum is 66.75. The result of descriptive analysis is presented in appendix 1 below.
The study further investigated the effect of education on an employee wage using the equation;
…………………………………………… (1)
The regression output is in the appendix 2 below.
XX states that an Rsquared in the range of 0.10 to 0.15 is reasonable for making decision and indicates that the model is fit, from the analysis, Rsquares is 0.14586446 indicating that the model is fit and conclusion on the relationship of the variables can be reached. This can be represented in the graph below
Furthermore, Pvalue is 5.47^{E20} < 0.05 meaning the result is statistically significance at the significant level of 95%. From the study, the coefficient is of education is 1.125691 implying that one more year of education increases an individual salary by 1.125691 and also there is positively relationship between education and individual salary. From the result we can right the predictive equation as
Wages I = 1.11897 +1.125691educ +Ɛ
Adding experience to the first model, the researcher intended to know how education and experience influences an individual wages. The model two is written as:
…………………………… (2)
The regression result are in the appendix 3 below. From the appendix two, it can be noticed that the model is perfectly fit since its Rsquared is 0.202025 indicating that of 20% of the variables were analyzed in the regression analysis. The fit model line can be represented in the figure below;
On the significance level of the study, education has pvalue of 5.56E27 while experience pvalue is 1.89E09 the pvalue is < 0.05 therefore at significance level of 95%, the analysis is statistically significant. The coefficient of the study gives 1.388 and 0.1576 for education and experience respectively. This indicate that 1 year of education increases salary by 1.388 and 1 more year for experience increases wages by 0.1576
Therefore the predictive equation can be given as
Wage= 7.35672 +1.388 education + 0.1576 experience
To investigate more on factors influencing an individual wage, the third model which includes gender was developed. The model equation is given by,
From this regression was done and the regression output was placed in appendix 4 below.
The model gives Rsquared of 0.253158 indicating that the model is fit since over 25% of the variables were included in the study. This is shown in the graph below
The pvalues of education, experience and gender is gives as 3.28E29, 3.19E11 and 3.19E09 respectively. The pvalue of the three variables are < 0.05 meaning the variables are statistically significance at significance level of 95%. The relationship of the three variables are also shown at the appendix 4 with education, experience and gender coefficients as 1.41076, 0.169951 and 3.50645 respectively. Both experience and education positively influence individual wage while gender negatively influence.
The study further investigated the correlation between the factors influencing the individual wage. This is shown below
EDUCATION 
EXPERIENCE 

EDUCATION 
0.381922 

EXPERIENCE 
0.35268 

0.20537 
0.002031 

0.176967 
0.15002 
0.977961 
0.079179 

0.100579 
0.03552 
0.011225 
0.278947 

0.161766 
0.02389 
0.117926 
0.15703 
0.119466 
0.093164 
The researcher intended to investigate the correlation matrix of all of the variables and use the matrix to discuss the importance of each variable for wages as well as the relationships between the variables. From the above table the relationship between wage and the other variables can be seen clearly with all variables positively related with wage except gender which is negatively related. All variables that is experience, Age, married and union are negatively related with education except gender which is positively related. Experience on the other hand is positively correlated with gender, age, married and union. From the correlation matrix above, gender is positively correlated with age and marital status while negatively related with union. Age on the other hand is positively correlated with marital status and union and lastly married is positively related with being in union. All these factors in one way or the other influence the wage of an individual the difference is that the magnitude which they influence vary from one variable to another.
The researcher lastly formulated the model including all the variables and the model is given as
(4)
The regression results are shown in appendix 5 below
From the regression result in appendix 5, the Rsquare is 0.266 indicating that the model is perfect fit and over 26% of the total variables were included in the analysis. The p value of the variables include education which is 0.2190 > 0.05 hence education is not statistically significance in explaining the effect of the wage in this model, furthermore, experience gives a pvalue of 0.625681> 0.05 hence is not statistically significance. Gender on the other hand gives a pvalue of 5.88E08< 0.05 hence is statistically significance at 95% significance level, age pvalue is 0.691697 which is >0.05, not statistically significance, marital status gives a pvalue of 0.259017 >0.05 indicating that it is not statistically significance and lastly union which gives Pvalue of 0.005088 <0.005 indicating that it is statistically significance at 95% significance level.
Following this outcome, the model 4 can be rewritten as
Wage = α +βgender +βUnion +Ɛ
Therefore, the new regression output can be given as
Coefficients 
Standard Error 

Intercept 
14.36573 
0.484336 
29.66065 
9.4E115 
2.85181 
0.658662 
1.79E05 

0.854716 
3.115935 
0.001933 
Therefore, we can conclude that change in gender influences age by 2.85181 while union increase wage by 2.66324.
In conclusion we can state that all the above five variables influences wages and change in one variables can either increase wage an individual earn or reduce the age.
Bibliography
Ai, H., Croce, M.M. and Li, K., 2013. Toward a quantitative general equilibrium asset pricing model with intangible capital. Review of Financial Studies, 26(2), pp.491530.
Barberis, N., Greenwood, R., Jin, L. and Shleifer, A., 2015. XCAPM: An extrapolative capital asset pricing model. Journal of Financial Economics, 115(1), pp.124.
Dempsey, M., 2013. The capital asset pricing model (CAPM): the history of a failed revolutionary idea in finance? Abacus, 49(S1), pp.723.
Fama, E.F. and French, K.R., 2015. A fivefactor asset pricing model. Journal of Financial Economics, 116(1), pp.122.
Fama, E.F. and French, K.R., 2015. International Tests of a FiveFactor Asset Pricing Model. FamaMiller Working Paper.
Scott, W.R., 2014. Financial accounting theory. Pearson Education Canada.
Zabarankin, M., Pavlikov, K. and Uryasev, S., 2014. Capital asset pricing model (CAPM) with drawdown measure. European Journal of Operational Research, 234(2), pp.508517.
APPENDICES
Part A: Appendix
APPENDIX I SUMMARY OUTPUT TECHNOLOGY INDUSTRY 

Regression Statistics 

Multiple R 
0.759223 

0.576419 

Adjusted R Square 
0.575236 

Standard Error 
5.775994 

Observations 

Significance F 

Regression 
16253.18 
16253.18 
487.1749 
9.23E69 

Residual 
11943.64 
33.36211 

28196.82 

Coefficients 
Standard Error 
Lower 95% 
Upper 95% 

Intercept 
0.148372 
0.306539 
0.484022 
0.628666 
0.45447 
0.751215 
1.464847 
0.066367 
22.07204 
9.23E69 
1.334329 
1.595364 

APPENDIX II SUMMARY OUTPUT MANUFACTURING INDUSTRY 

Regression Statistics 

Multiple R 
0.811292 

0.658195 

Adjusted R Square 

Standard Error 

Observations 

Significance F 

Regression 
8349.018 
8349.018 
1.82E85 

Residual 
4335.699 
12.11089 

12684.72 

Coefficients 
Standard Error 
Lower 95% 
Upper 95% 

Intercept 
0.189993 
0.184692 
1.028703 
0.304314 
0.17322 

1.049882 
0.039986 
26.25607 
1.82E85 
0.971245 
Appendix III 19801989 SUMMARY OUTPUT TECHNOLOGY 

Regression Statistics 

Multiple R 
0.842065 

0.709073 

Adjusted R Square 
0.706607 

Standard Error 
4.012555 

Observations 

Significance F 

Regression 
4630.531 
4630.531 
287.5999 

Residual 
1899.871 

6530.402 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.57413 
0.370171 
1.30717 

1.278496 
0.075389 
16.95877 
1.129207 

SUMMARY OUTPUT MANUFACTURING 

Regression Statistics 

Multiple R 
0.823697 

0.678477 

Adjusted R Square 
0.675753 

Standard Error 
3.423918 

Observations 

Significance F 

Regression 
2919.123 
2919.123 
249.0036 
7.47E31 

Residual 
1383.339 
11.72321 

4302.463 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.069462 
0.315867 
0.219909 
0.826322 
0.55604 
1.015103 
0.064329 
15.77985 
7.47E31 
0.887714 

Appendix IV: 19901999 SUMMARY OUTPUT: TECHNOLOGY 

Regression Statistics 

Multiple R 

0.462999 

Adjusted R Square 
0.458448 

Standard Error 
5.558591 

Observations 

Significance F 

Regression 
3143.522 
3143.522 
101.7389 
1.25E17 

Residual 
3645.956 
30.89793 

6789.478 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.525283 
0.291922 
0.48411 

1.287678 
0.127663 
10.08657 
1.25E17 
1.034871 

SUMMARY OUTPUT: MANUFACTURING 

Regression Statistics 

Multiple R 

0.523799 

Adjusted R Square 
0.519764 

Standard Error 
3.266003 

Observations 

Significance F 

Regression 
129.7946 
9.81E21 

Residual 
10.66678 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.308635 
0.64252 
0.521784 
0.80948 

0.854563 
0.075009 
11.39274 
9.81E21 
0.706024 

Technology 

APPENDIX V SUMMARY OUTPUT TECHNOLOGY 

Regression Statistics 

Multiple R 
0.778492 

Adjusted R Square 
0.602712 

Standard Error 
7.005355 

Observations 

Significance F 

Regression 
8908.616 
8908.616 
181.5307 
1.27E25 

Residual 
5790.849 
49.07499 

14699.47 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.832625 
0.639798 
1.301388 
0.195661 
0.43435 
1.798919 
0.133517 
13.47333 
1.27E25 
1.534519 

SUMMARY OUTPUT MANUFACTURING 

Regression Statistics 

Multiple R 
0.870638 

Adjusted R Square 
0.755959 

Standard Error 
3.430541 

Observations 

Significance F 

Regression 
369.6239 

Residual 
1388.696 
11.76861 

5738.655 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
0.961573 
0.313311 
3.069073 
0.002664 
0.341133 
0.065384 
19.22561 
1.127562 
APPENDIX 6 SUMMARY OUTPUT: TECHNOLOGY INDUSTRY 

Regression Statistics 

Multiple R 
0.910132 

0.828339 

Adjusted R Square 
0.826893 

Standard Error 
3.687316 

Observations 

Significance F 

Regression 
23356.53326 
7785.511 
572.6197 
8.1E136 

Residual 
4840.283668 

28196.81693 

Coefficients 
Standard Error 
Lower 95% 
Upper 95% 

Intercept 
0.198929773 
1.539335 
0.124611 
0.08501 
0.697445 

1.256517 
0.065177073 
19.27851 
1.128336 
1.384697 

0.43791 
0.068651483 
6.37867 
5.55E10 
0.57292 
0.30289 

1.161159 
0.045994987 
25.24533 
1.070703 
1.251615 

APPENDIX 7 MANUFACTURING INDUSTRY SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.905434 

0.819812 

Adjusted R Square 
0.818293 

Standard Error 
2.533838 

Observations 

Significance F 

Regression 
10399.07801 
3466.359 
539.9033 
4.5E132 

Residual 
2285.638633 
6.420333 

12684.71664 

Coefficients 
Standard Error 
Lower 95% 
Upper 95% 

Intercept 
0.13669989 
0.63277 
0.527293 
0.35534 
0.182342 

0.762542 
0.044788161 
17.02554 
5.63E48 
0.850625 

0.047175694 
9.983735 
0.378212 
0.563768 

1.058549 
0.03160668 
4.6E112 
1.120708 
Part B: Appendix
Appendix 1: Descriptive statistic
Descriptive 
Education 
Experience 
Gender 
Married 

13.01872659 
0.458801 
36.83333 
0.655431 
0.179775 

Standard Error 
0.333586 
0.113178243 
0.535722 
0.021584 
0.507458 
0.020584 
0.016633 
0 
0 

0 
0 

Standard Deviation 
7.708645 
2.615372628 
12.37971 
0.498767 
11.72657 
0.475673 

Sample Variance 
59.42321 
6.840173985 
153.2572 
0.248769 
137.5125 
0.226265 
0.147733 
Kurtosis 
4.991768 
0.840774992 
0.38095 
1.97993 
0.58079 
1.57561 
0.800367 
Skewness 
1.697286 
0.203677595 
0.687758 
0.165822 
0.548297 
0.65598 
1.672538 
0 
0 
0 
0 

7228.275 

Confidence Level (95.0%) 
0.655304 
0.222330138 
1.052386 
0.996864 
0.040436 
0.032674 
Appendix 2: Model 1
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.381922 

0.145864 

Adjusted R Square 
0.144259 

Standard Error 

Observations 

Significance F 

Regression 
4619.903 
4619.902851 
90.85197 
5.47E20 

Residual 
27052.67 
50.85088192 

31672.57 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
1.11897 
1.568181 
0.713546101 
0.475821 
4.19956 
EDUCATION 
1.125691 
0.118101 
9.53163003 
5.47E20 
APPENDIX 3: MODEL 2
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.449472 

0.202025 

Adjusted R Square 
0.199019 

Standard Error 
6.899047 

Observations 

Significance F 

Regression 
6398.644 
3199.322 
67.21709 
9.51E27 

Residual 
25273.93 
47.59685 

31672.57 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
7.35672 
1.828386 
4.02362 
6.56E05 
10.9485 
EDUCATION 
1.388947 
0.122105 
5.56E27 
1.149078 

EXPERIENCE 
0.157697 
0.025796 
1.89E09 
0.107022 
Appendix 4: Model 3
Regression Statistics 

Multiple R 
0.503148 

0.253158 

Adjusted R Square 

Standard Error 
6.680641 

Observations 

Significance F 

Regression 
8018.161 
59.88489 
2.37E33 

Residual 
23654.41 
44.63096 

31672.57 

Coefficients 
Standard Error 
Lower 95% 

Intercept 
6.25031 
1.780005 
0.000484 
9.74704 

EDUCATION 
0.118295 
11.92577 
3.28E29 
1.178375 

EXPERIENCE 
0.169951 
0.025062 
6.781113 
3.19E11 
0.120717 
3.50645 
0.582094 
6.02386 
3.19E09 
4.64994 
APPENDIX 4: CORRELATION ANALYSIS
EDUCATION 
EXPERIENCE 

EDUCATION 
0.381922 

EXPERIENCE 
0.35268 

0.20537 
0.002031 

0.176967 
0.15002 
0.977961 
0.079179 

0.100579 
0.03552 
0.011225 
0.278947 

0.161766 
0.02389 
0.117926 
0.15703 
0.119466 
0.093164 
APPENDIX 5: MODEL 4
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.516426 

0.266696 

Adjusted R Square 
0.258347 

Standard Error 

Observations 

Significance F 

Regression 
8446.938 
1407.823 
31.94413 
8.11E33 

Residual 
23225.63 
44.07141 

31672.57 

Coefficients 
Standard Error 
Lower 95% 
Upper 95% 

Intercept 
2.72132 
10.18339 
0.26723 
0.789396 
22.7263 
17.28371 
EDUCATION 
2.054653 
1.669659 
1.230582 
0.219028 
1.22535 
5.334658 
EXPERIENCE 
1.670308 
0.488102 
0.625681 
4.096559 

3.23235 
0.587519 
5.88E08 
4.38652 
2.07819 

0.66227 
1.669139 
0.39677 
0.691697 
3.94125 
2.616717 

0.713341 
1.129937 
0.259017 
0.52685 
1.953535 

2.154617 
0.765886 
2.813234 
0.005088 
0.650052 
3.659181 