Monthly excess return data

Attached the detail of the question that i need help with

Finance Department
College of Business Administration
Muzahimiyah Campus
Alhudhaif, FINA423 (Fall 2023)
Assignment 2
November 26, 2023
Deadline: December 8, 2023.
Please submit your answers using either an Excel file (.xlsx), a Jupyter file (.ipynb), or a Python file (.py).
Either case, you must show your work to receive credit. Showing your work means that I will be able to
run all cells (or code lines) on my computer.
The excel file provided to you contains monthly excess returns for Bank of America (BAC), FedEx (FDX),
Pepsi (PEP), Nike (NKE), Chevron (CVX), and the market index (W5000) from January 1996 to November 2019. Use this data to answer the following questions:
1. (5 points) Show the descriptive statistics for these securities. Specifically, show the mean, median,
standard deviation, max, and min.
(a) If the average excess return is used to estimate the risk premiums for these securities, which
security has the highest risk premium?
(b) Which security has the highest estimated risk?
2. (14 points) Estimate the index model for each security. Specifically, for each security, run the
following regression:
Ri − Rf = α̂i + β̂i [Rm − Rf ]
(a) Find the estimated alpha (α), beta (β), standard error (σe ), and the r-squared for each security.
Try to organize your findings in a nice table
(b) Which security has the highest beta? And what does a high beta mean for the security’s
monthly risk premium?
(c) Which security has the highest alpha? And what does a high alpha mean for the security’s
monthly risk premium?
(d) Rank the securities from the highest standard error to the lowest one, and explain what does a
high standard error mean?
(e) Rank the securities from the highest R-squared to the lowest one, and explain what does a high
R-squared mean?
3. In your table, create 2 new columns:
(a) Portfolio_1: The monthly risk premium for this portfolio will be the result if you invested
52% in CVX and the rest in PEP:
i. (4 points) Run a regression on this portfolio similar to what is requested in question 2,
and show the estimated alpha (α), beta (β), standard error (σe ), and the R-squared
ii. (2 points) Was there another way to find the portfolio’s alpha and beta? If so, how can
it be done?
iii. (2 points) Compare the standard error of this portfolio with the standard error of both
CVX and PEP. Why do you think it is lower than them?
(b) Portfolio_2: The monthly risk premium for this portfolio will be the result if you invested
70% in BAC and the rest in NKE:
i. (4 points) Run a regression similar to what is requested in question 2 on this portfolio,
and show the estimated alpha (α), beta (β), standard error (σe ), and the r-squared
ii. (2 points) Compare the regression R-squared with that of BAC and NKE. Rank them
from highest to lowest
iii. (2 points) From the regression results, if you had money to invest will you invest in BAC,
in NKE, or in Portfolio 2? Explain your choice
1
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BAC
0.051276
0.090778
-0.00723
0.011485
0.014291
0.04383
-0.01444
0.01629
0.088215
0.095255
-0.06086
0.109311
0.10566
-0.07923
0.08937
-0.02913
0.092378
0.104528
-0.16489
0.031345
-0.03207
-2E-05
0.00823
-0.01359
0.139788
0.060556
0.043564
-0.01075
0.011564
0.041042
-0.28314
-0.0736
0.078164
0.130064
-0.08123
0.116343
-0.02704
0.077643
0.016364
-0.09977
0.129589
-0.09197
-0.09239
CVX
0.068109
0.013928
0.029294
0.026008
-0.00835
-0.02332
0.013013
0.069359
0.04568
0.014796
-0.02603
0.016979
-0.03242
0.08382
-0.02042
0.017617
0.060739
0.064274
-0.024
0.076541
-0.0057
-0.03736
-0.03731
-0.0335
0.077677
-0.0033
0.025373
-0.03819
0.051928
-0.0176
-0.10777
0.139588
-0.03425
0.022441
-0.00424
-0.1054
0.028202
0.159702
0.120258
-0.07639
0.03054
-0.04392
0.007762
FDX
-0.03209
-0.05987
0.151521
-0.05525
0.065948
-0.05608
-0.04124
0.054163
0.011553
0.095164
0.001488
0.14751
0.000711
0.007906
0.031706
-0.03437
0.100786
0.111355
0.024824
0.199919
-0.16982
0.000483
-0.09373
0.061231
-0.0254
0.112556
-0.04814
-0.06116
-0.02561
-0.03704
-0.17922
-0.10891
0.169175
0.230614
0.371191
-0.09057
0.165932
-0.02732
0.20868
-0.03142
-0.01401
-0.17893
-0.05556
NKE
-0.07228
0.24587
0.073399
0.142978
0.019463
-0.00266
0.045552
0.123047
-0.03262
-0.04227
0.050783
0.136467
0.047021
-0.14336
-0.09448
0.017942
0.010992
0.069515
-0.15192
-0.01125
-0.1142
0.028957
-0.20195
0.022742
0.090901
0.004323
0.076354
-0.04082
0.054259
-0.08893
-0.22465
0.057222
0.176457
-0.08144
0.010494
0.119661
0.170773
0.075836
0.075447
-0.0238
0.036255
-0.18242
-0.10484
PEP
0.056617
-0.00413
0.002856
0.04308
0.06347
-0.1068
-0.09875
-0.02166
0.048683
0.016882
-0.03722
0.192739
-0.05793
-0.02317
0.07678
0.049482
0.017884
0.017501
-0.06304
0.122516
-0.09215
0.082317
-0.02121
-0.00423
0.006118
0.165296
-0.07157
0.024172
0.005023
-0.05586
-0.28825
0.052014
0.147453
0.142665
0.052975
-0.04638
-0.04054
0.039565
-0.0579
-0.04262
0.086044
0.006465
-0.12748
^W5000
0.011182
0.005167
0.019276
0.020788
-0.01387
-0.05956
0.025667
0.047275
0.008444
0.060127
-0.01703
0.048268
-0.00633
-0.04999
0.038122
0.064769
0.040356
0.071375
-0.04313
0.05341
-0.03851
0.027067
0.01278
0.000307
0.066999
0.044571
0.006749
-0.03202
0.029714
-0.02699
-0.161
0.059826
0.06956
0.057971
0.059097
0.032313
-0.0411
0.033659
0.043437
-0.02676
0.046925
-0.03511
-0.01599
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-0.08345
0.161801
-0.09454
-0.14793
-0.03093
-0.05482
0.135318
-0.06
0.126597
-0.22201
0.09688
0.125996
-0.01778
-0.0874
-0.17414
0.159753
0.168429
-0.06237
0.087738
0.019151
0.054706
0.019705
0.056836
-0.03622
-0.05307
0.017039
0.038646
0.023973
0.009538
0.013174
0.072315
0.064098
0.044473
-0.07333
-0.04861
0.052431
-0.091
0.102554
0.002765
-0.0084
0.015277
-0.01256
-0.03564
0.117424
-0.03631
0.024876
-0.03421
-0.01928
-0.039
-0.11137
0.243498
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0.081123
-0.08081
-0.07358
0.065089
0.011577
-0.04172
-0.00813
0.034294
-0.01847
0.024175
0.028757
0.096093
-0.00861
-0.05439
0.006866
-0.0099
-0.06215
0.042554
-0.04185
0.060753
-0.0663
0.006215
0.076752
-0.04091
0.004756
0.020843
-0.15398
0.020331
-0.08944
-0.02475
-0.01012
0.001221
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0.017553
-0.02942
-0.08786
0.10372
-0.02441
-0.03387
-0.03795
-0.1214
0.106285
-0.0337
-0.06282
0.065641
0.037898
0.013355
0.09386
0.051769
0.017535
-0.17113
0.130876
-0.10246
0.014293
0.005677
-0.05253
0.00193
0.026137
0.014748
-0.12974
0.11554
0.114489
0.129664
0.030727
0.079063
0.002695
-0.11214
0.042655
-0.01164
-0.04646
-0.07206
0.056061
0.062076
-0.0129
0.030263
-0.02997
-0.02381
0.070426
0.087437
0.212668
-0.01829
-0.18266
0.07321
-0.08501
-0.3795
0.388778
0.093499
-0.01773
-0.07621
0.095729
-0.1003
0.007212
-0.00663
0.062206
0.304362
-0.01782
-0.29489
0.029789
0.034209
-0.02031
0.018585
0.131069
0.048627
-0.06646
0.053494
0.071672
0.059727
0.064967
-0.01895
0.018086
-0.11191
0.006404
-0.0033
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0.093038
-0.05232
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0.002192
0.040095
0.107931
0.041586
-0.11014
0.137874
-0.0077
0.015655
-0.03273
-0.0631
0.080975
0.051917
0.104248
0.087386
0.02961
-0.07418
0.073758
0.051384
-0.06831
0.087264
-0.11275
0.041207
-0.05026
-0.00381
0.018355
-0.01558
0.055375
0.005037
0.029255
0.005134
-0.00351
-0.00037
0.030376
0.006763
0.01835
0.009205
6.97E-05
-0.07416
-0.10797
-0.08035
-0.06712
0.196827
-0.03798
-0.00724
-0.03886
-0.05435
0.042866
0.085358
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0.058801
0.028241
0.070519
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0.016696
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0.041406
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0.002902
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0.073729
0.057311
-0.05804
-0.02715
-0.01976
0.008966
0.079776
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0.001089
0.064214
0.052885
-0.04099
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0.065512
0.022731
0.004886
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0.057083
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0.028917
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0.036003
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0.013329
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0.033125
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0.104486
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0.000175
0.067278
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0.047967
0.001508
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0.128501
0.027497
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0.009757
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0.039095
0.009978
0.160752
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0.022401
0.001143
0.04159
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0.048395
0.015345
0.018196
0.108238
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0.027487
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0.034151
0.139206
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0.031889
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0.001133
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0.041305
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0.175938
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0.020048
0.09358
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0.022407
0.109311
0.002235
0.000198
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0.017237
0.017597
0.032106
0.016712
0.005879
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-0.01929
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