6 pages Finance Question - Financial markets
Capital asset pricing model (CAPM) is a widely accepted, though controversial, theory of asset pricing in the capital market. According to CAPM, the expected return of any asset in the capital market is a linear function of the expected return on the whole market and the expected return of the risk-free rate. Mathematically, the model is stated as per Equation 1:E(Re) = E(RFR) + β *E(Rm – RFR) (1) Where E(Re) is the expected rate of return on a specific asset, E(RFR) is the expected risk-free rate, β is the sensitivity of the stock return with respect to the overall market return, and E(Rm – RFR) is the expected capital market risk premium.Empirical verification of CAPM is done by running a regression model of historical returns of stocks against historical returns of the overall market.In this assignment, your professor will assign you the stock of a publicly-traded company to conduct the following:
Download the stock’s last 10 years monthly price history from yahoo finance or other sources.
Select a broad stock market index, such as the S&P 500 Index or the Russell 3000 Index, etc., and download its last 10 years of monthly price history.
Use the adjusted closing prices of the stock and the index and calculate the monthly rate of return of each.
Consider the stock’s monthly return as the dependent variable and the index’s monthly return as the independent variable and run the following regression model:
Re = α + β*Rm + ε (2)Where Re is the realized monthly rate of return of your stock, Rm is the realized monthly rate of return on the overall capital market, and ε is the error term.
After estimating the regression coefficients of equation (2) through the ordinary least square (OLS) method, conduct a test of hypothesis and determine if the estimates of α and β are statistically significant at the 5\% level and report their t statistics and p values.
Determine if the F value for the correlation coefficient is statistically significant at the 5\% level.
What is your interpretation of the R-square value? Explain to what extent your regression estimates can predict the future return of your stock against your index’s movements.
What is the estimate of RFR?
Calculate the value of the error term for each year and construct the histogram of the error terms.
Using the Explore feature in SPSS (in Excel: Data Analysis, Regression, Normal Probability Plots), conduct a test for normality of the error terms and exhibit the normality plot.
Does the result of the test for normality of the error terms affect the validity of your regression model? Explain.
Present an APA-formatted write-up of your finding in one paragraph.
Sheet: IBM
Date
Adj Close
Year
Return By Price Change
Dividends
Rate of Return
IBM
S&P 500
Date
Adjusted Close
Year
Return By Price Change
Rate of Return
36526.0
68.961266
36892.0
69.128227
2000.0
0.16696100000000058
0.51
0.009816539620951864
-28.450000000000045
-0.02040216284439858
37257.0
66.918533
2001.0
-2.209693999999999
0.55
-0.024008918961569765
-235.80999999999995
-0.17262684753405902
37622.0
48.860226
2002.0
-18.058307
0.59
-0.26103840321783506
-274.5
-0.24287736683772781
37987.0
62.472309
2003.0
13.612083000000005
0.63
0.2914862284918618
37987.0
1131.13
2003.0
275.43000000000006
0.3218768259904173
38353.0
59.26865
2004.0
-3.203659000000002
0.7000000000000001
-0.040076300045192845
50.13999999999987
0.04432735406186722
38718.0
52.06414
2005.0
-7.204509999999999
0.78
-0.10839642880342304
98.80999999999995
0.08364726099875554
39083.0
64.347694
2006.0
12.283554000000002
1.0999999999999999
0.25705896611372053
158.16000000000008
0.12355477782638592
39448.0
70.491203
2007.0
6.143508999999995
1.5000000000000002
0.1187845053157615
-59.690000000000055
-0.041502113694515556
39814.0
61.372772
2008.0
-9.118431000000001
1.9
-0.10240186991843508
-552.67
-0.4009067498458525
40179.0
83.593674
2009.0
22.220901999999995
2.1500000000000004
0.39709632147624024
40179.0
1073.87
2009.0
247.9899999999999
0.3002736475032691
40544.0
112.764786
2010.0
29.171112000000008
2.5
0.37886972164903304
40544.0
1286.12
2010.0
212.25
0.19764962239377207
40909.0
136.332062
2011.0
23.567276000000007
2.9
0.2347122442993862
26.29000000000019
0.02044132740335287
41275.0
146.163132
2012.0
9.831069999999983
3.3
0.0963168150423778
185.69999999999982
0.14149541682858238
41640.0
129.63613900000001
2013.0
-16.526992999999976
3.6999999999999997
-0.08775806063050139
284.48
0.18989259800682196
42005.0
115.220001
2014.0
-14.416138000000018
4.25
-0.07842055524347279
212.4000000000001
0.11915246916004246
42370.0
96.848412
2015.0
-18.371589
5.0
-0.1160526721397963
-54.75
-0.027443746585195914
42736.0
140.604599
2016.0
43.75618700000001
5.499999999999999
0.5085905487020274
338.6299999999999
0.17452995505710628
43101.0
137.002747
2017.0
-3.601852000000008
5.9
0.016344756973418716
544.94
0.2391272867693199
43466.0
117.586418
2018.0
-19.416329000000005
6.21
-0.0963946292259381
-119.71000000000004
-0.04239307885445552
43831.0
131.760895
2019.0
14.17447700000001
6.430000000000001
0.17522837544043574
521.4200000000001
0.192825709108391
44197.0
114.917534
2020.0
-16.843361
6.51
-0.07842509721871578
488.7199999999998
0.15151665467893544
Regression in which S&P 500 is independent and IBM is dependent
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.5825945062723332
R Square
0.33941635873870374
Adjusted R Square
0.30464879867231975
Standard Error
0.17189746289636001
Observations
21.0
ANOVA
df
SS
MS
F
Significance F
Regression
1.0
0.2884678980449005
0.2884678980449005
9.762444016509457
0.005581713989890132
Residual
19.0
0.5614260172539038
0.029548737750205466
Total
20.0
0.8498939152988043
Coefficients
Standard Error
t Stat
P-value
Lower 95\%
Upper 95\%
Lower 95.0\%
Upper 95.0\%
Intercept
0.0278778367354707
0.03997123302969305
0.6974475046782109
0.4939696716240518
-0.05578291548003679
0.11153858895097819
-0.05578291548003679
0.11153858895097819
X Variable 1
0.6699638309871861
0.214423351109762
3.1244910011887455
0.005581713989890142
0.22117059928761557
1.1187570626867567
0.22117059928761557
1.1187570626867567
Regression is significant as its p-value is less than 0.05
Intercept is not significant as its p-value is more than 0.05
PROBABILITY OUTPUT
Co-efficient of x is significant as its p-value is less than 0.05
Percentile
Y
Final Regresssion Equation
2.380952380952381
-0.26103840321783506
Rate of Return of IBM = (0.66996383)*(Rate of Return of S&P 500)
7.142857142857142
-0.1160526721397963
11.904761904761905
-0.10839642880342304
Co-efficient of x is less than 1, that means IBM security is less volatile than S&P 500
16.666666666666664
-0.10240186991843508
Note: If co-efficient is not significant, we do not include in final reegression equation
21.428571428571427
-0.0963946292259381
26.19047619047619
-0.08775806063050139
30.95238095238095
-0.07842509721871578
35.714285714285715
-0.07842055524347279
40.476190476190474
-0.040076300045192845
45.238095238095234
-0.024008918961569765
50.0
0.009816539620951864
54.76190476190476
0.016344756973418716
59.52380952380952
0.0963168150423778
64.28571428571429
0.1187845053157615
69.04761904761905
0.17522837544043574
73.80952380952381
0.2347122442993862
78.57142857142857
0.25705896611372053
83.33333333333333
0.2914862284918618
88.09523809523809
0.37886972164903304
92.85714285714286
0.39709632147624024
97.61904761904762
0.5085905487020274
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