final exam

##Question 1: Import Data #Download ETF daily data from yahoo with ticker names of SPY, QQQ, EEM, IWM, EFA, TLT, IYR and GLD from 2010 to current date:

# Load libraries
library(tidyquant)

## Loading required package: lubridate

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

## Loading required package: PerformanceAnalytics

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
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##     as.Date, as.Date.numeric

## 
## Attaching package: 'PerformanceAnalytics'

## The following object is masked from 'package:graphics':
## 
##     legend

## Loading required package: quantmod

## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(fPortfolio)

## Loading required package: timeDate

## 
## Attaching package: 'timeDate'

## The following objects are masked from 'package:PerformanceAnalytics':
## 
##     kurtosis, skewness

## Loading required package: timeSeries

## 
## Attaching package: 'timeSeries'

## The following object is masked from 'package:zoo':
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##     time<-

## Loading required package: fBasics

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## Attaching package: 'fBasics'

## The following object is masked from 'package:TTR':
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##     volatility

## The following objects are masked from 'package:PerformanceAnalytics':
## 
##     kurtosis, skewness

## Loading required package: fAssets

library(PerformanceAnalytics)
library(xts)
library(readr)
library(dplyr)

## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
## #                                                                             #
## ###############################################################################

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:timeSeries':
## 
##     filter, lag

## The following objects are masked from 'package:xts':
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##     first, last

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lubridate)
library(timetk)
library(purrr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(tibble)

#Define the symbols we want to take 
symbols <- c("SPY", "QQQ", "EEM", "IWM", "EFA", "TLT", "IYR", "GLD")

#Extract data from yahoo finance and feed the data in prices
prices <- 
  getSymbols(symbols, src = 'yahoo', from = "2010-01-01", 
             auto.assign = TRUE, warnings = FALSE) %>% 
  map(~Ad(get(.))) %>% 
  reduce(merge) %>%
  `colnames<-`(symbols)

#Show the data prices
head(prices)

##                 SPY      QQQ      EEM      IWM      EFA      TLT      IYR
## 2010-01-04 88.11789 41.00547 32.42902 53.08978 37.99448 63.06114 28.77184
## 2010-01-05 88.35115 41.00547 32.66441 52.90724 38.02797 63.46846 28.84091
## 2010-01-06 88.41336 40.75814 32.73274 52.85746 38.18871 62.61877 28.82835
## 2010-01-07 88.78658 40.78463 32.54291 53.24746 38.04136 62.72409 29.08586
## 2010-01-08 89.08202 41.12030 32.80107 53.53788 38.34276 62.69604 28.89117
## 2010-01-11 89.20645 40.95247 32.73274 53.32212 38.65753 62.35201 29.02935
##               GLD
## 2010-01-04 109.80
## 2010-01-05 109.70
## 2010-01-06 111.51
## 2010-01-07 110.82
## 2010-01-08 111.37
## 2010-01-11 112.85

Question 2: Calculate weekly and monthly returns using log returns

#Change data from daily to weekly
prices_weekly <- to.weekly(prices, indexAt = "last", OHLC = FALSE)

#Change data from daily to monthly
prices_monthly <- to.monthly(prices, indexAt = "last", OHLC = FALSE)

#Calculate weekly retun
asset_returns_wk_xts <- na.omit(Return.calculate(prices_weekly))

#Calculate monthly return
asset_returns_mon_xts <- na.omit(Return.calculate(prices_monthly))

#Weekly return
head(asset_returns_wk_xts)

##                     SPY          QQQ         EEM         IWM          EFA
## 2010-01-15 -0.008116797 -0.015037793 -0.02893522 -0.01301922 -0.003493967
## 2010-01-22 -0.038983075 -0.036859225 -0.05578049 -0.03062183 -0.055740367
## 2010-01-29 -0.016665099 -0.031023387 -0.03357769 -0.02624335 -0.025802771
## 2010-02-05 -0.006797524  0.004440318 -0.02821289 -0.01397446 -0.019054774
## 2010-02-12  0.012938234  0.018147752  0.03333322  0.02952601  0.005244576
## 2010-02-19  0.028692895  0.024451495  0.02445386  0.03343177  0.022995164
##                      TLT          IYR          GLD
## 2010-01-15  2.004711e-02 -0.006304427 -0.004579349
## 2010-01-22  1.010052e-02 -0.041785110 -0.033285246
## 2010-01-29  3.369748e-03 -0.008447772 -0.011290465
## 2010-02-05 -5.402774e-05  0.003223827 -0.012080019
## 2010-02-12 -1.946057e-02 -0.007574064  0.022544905
## 2010-02-19 -8.206145e-03  0.050185157  0.022701796

#Monthly return
head(asset_returns_mon_xts)

##                    SPY         QQQ         EEM         IWM          EFA
## 2010-02-26  0.03119487  0.04603901  0.01776393  0.04475145  0.002667395
## 2010-03-31  0.06087923  0.07710890  0.08110877  0.08230675  0.063854315
## 2010-04-30  0.01547026  0.02242496 -0.00166157  0.05678449 -0.028045908
## 2010-05-28 -0.07945455 -0.07392336 -0.09393613 -0.07536631 -0.111927878
## 2010-06-30 -0.05174116 -0.05975683 -0.01398644 -0.07743408 -0.020619342
## 2010-07-30  0.06830044  0.07258278  0.10932508  0.06730912  0.116104016
##                     TLT         IYR          GLD
## 2010-02-26 -0.003424582  0.05457081  0.032748219
## 2010-03-31 -0.020572510  0.09748503 -0.004386396
## 2010-04-30  0.033217552  0.06388095  0.058834363
## 2010-05-28  0.051083386 -0.05683594  0.030513147
## 2010-06-30  0.057978757 -0.04670060  0.023553189
## 2010-07-30 -0.009464803  0.09404778 -0.050871157

Question 3: Convert weekly,monthly returns into tibble format.

#Weekly return to tibble format
weekly_return <- as_tibble(asset_returns_wk_xts)

kbl(weekly_return, caption = "Weekly return of portfilio")%>%
  row_spec(row =0, bold= TRUE, color = "#34495E", background = "#BFC9CA") %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "center")

Weekly return of portfilio

SPY	QQQ	EEM	IWM	EFA	TLT	IYR	GLD
-0.0081168	-0.0150378	-0.0289352	-0.0130192	-0.0034940	0.0200471	-0.0063044	-0.0045793
-0.0389831	-0.0368592	-0.0557805	-0.0306218	-0.0557404	0.0101005	-0.0417851	-0.0332852
-0.0166651	-0.0310234	-0.0335777	-0.0262434	-0.0258028	0.0033697	-0.0084478	-0.0112905
-0.0067975	0.0044403	-0.0282129	-0.0139745	-0.0190548	-0.0000540	0.0032238	-0.0120800
0.0129382	0.0181478	0.0333332	0.0295260	0.0052446	-0.0194606	-0.0075741	0.0225449
0.0286929	0.0244515	0.0244539	0.0334318	0.0229952	-0.0082061	0.0501852	0.0227018
-0.0035988	-0.0015610	-0.0106656	-0.0041230	-0.0060448	0.0248190	0.0085883	-0.0003654
0.0316954	0.0375330	0.0510781	0.0608280	0.0448500	-0.0121479	0.0358080	0.0126108
0.0105913	0.0198108	0.0102563	0.0165114	0.0123681	0.0023276	0.0339373	-0.0258100
0.0085532	0.0038151	-0.0043509	-0.0045773	-0.0052103	0.0086207	0.0226304	0.0030570
0.0052601	0.0107394	-0.0021850	0.0084387	0.0025285	-0.0253127	0.0087320	0.0028629
0.0104649	0.0033332	0.0515815	0.0091436	0.0263017	0.0037234	-0.0019953	0.0153790
0.0148557	0.0180648	0.0129568	0.0265963	0.0031594	0.0003381	0.0355926	0.0306548
-0.0015898	0.0101978	-0.0301505	0.0172244	-0.0075238	0.0098891	-0.0289626	-0.0211193
0.0205264	0.0199880	0.0141311	0.0373637	-0.0045842	0.0027823	0.0674085	0.0175297
-0.0246285	-0.0253368	-0.0234556	-0.0334550	-0.0363089	0.0224171	-0.0134126	0.0191713
-0.0635469	-0.0777820	-0.0917955	-0.0877878	-0.0955706	0.0410627	-0.0632553	0.0252253
0.0236384	0.0334726	0.0340401	0.0642593	0.0158502	-0.0073234	0.0370890	0.0177560
-0.0419703	-0.0445340	-0.0544443	-0.0645486	-0.0274053	0.0374158	-0.0520894	-0.0427848
0.0023830	0.0169489	0.0203535	0.0181346	-0.0061702	-0.0196083	0.0241948	0.0317653
-0.0233154	-0.0111845	-0.0236218	-0.0406038	-0.0298014	0.0168026	-0.0546543	0.0026077
0.0267739	0.0090931	0.0419353	0.0217117	0.0433022	-0.0038850	0.0709449	0.0068798
0.0235376	0.0349246	0.0299279	0.0286418	0.0347577	0.0028745	0.0249161	0.0234980
-0.0345475	-0.0349143	-0.0057346	-0.0324852	-0.0178901	0.0139214	-0.0302945	-0.0005699
-0.0525634	-0.0618514	-0.0426070	-0.0713781	-0.0360284	0.0206753	-0.0724458	-0.0347834
0.0563599	0.0506236	0.0588080	0.0521567	0.0630710	-0.0155755	0.0519257	-0.0010971
-0.0120411	-0.0062750	-0.0330245	-0.0297111	-0.0095887	0.0159238	-0.0137808	-0.0142785
0.0351587	0.0387915	0.0646828	0.0640251	0.0405405	-0.0101195	0.0575601	-0.0049713
-0.0012689	-0.0054274	0.0060754	0.0006153	0.0062029	0.0070148	0.0185369	-0.0051684
0.0192260	0.0207373	0.0164250	0.0018458	0.0354459	-0.0005577	0.0121973	0.0203481
-0.0363024	-0.0436268	-0.0332702	-0.0624809	-0.0565583	0.0218778	-0.0353864	0.0076375
-0.0072011	0.0044722	0.0083581	0.0013098	-0.0078879	0.0366602	-0.0031722	0.0103588
-0.0062308	-0.0189225	-0.0129201	0.0081767	0.0041742	-0.0065072	0.0141236	0.0086688
0.0377126	0.0440209	0.0380336	0.0434718	0.0389946	-0.0138759	0.0582578	0.0070242
0.0053209	0.0128237	0.0054722	-0.0094826	0.0060963	-0.0121650	-0.0168675	-0.0010668
0.0144618	0.0322392	0.0179841	0.0233835	0.0155271	-0.0063520	0.0180997	0.0230839
0.0207130	0.0347996	0.0255695	0.0298773	0.0326311	0.0179997	0.0001104	0.0172635
-0.0018290	-0.0130894	0.0296917	0.0129866	0.0016251	0.0144930	-0.0074713	0.0175231
0.0168398	0.0150992	0.0176093	0.0209255	0.0252390	-0.0037281	0.0222057	0.0213327
0.0099534	0.0349747	0.0105990	0.0145786	0.0116053	-0.0379002	0.0134387	0.0153425
0.0055226	0.0029130	-0.0147684	0.0004270	-0.0041716	0.0110701	0.0179838	-0.0295482
0.0011828	0.0104573	0.0019552	-0.0002841	-0.0048876	-0.0094694	-0.0192723	0.0222770
0.0356994	0.0285548	0.0513877	0.0493593	0.0384143	-0.0211888	0.0483991	0.0283518
-0.0205345	-0.0216137	-0.0428955	-0.0235865	-0.0297299	-0.0221468	-0.0461648	-0.0197243
0.0007488	-0.0007617	0.0021545	0.0058307	0.0057454	0.0081407	-0.0223794	-0.0111452
-0.0123867	0.0057176	-0.0367662	0.0106282	-0.0415442	0.0049696	0.0111668	0.0068835
0.0344275	0.0208452	0.0522323	0.0334608	0.0368433	-0.0190689	0.0182222	0.0372625
0.0129385	0.0115095	-0.0116678	0.0274878	0.0038320	-0.0183375	-0.0117502	-0.0192656
0.0038054	0.0025342	-0.0040777	0.0034724	-0.0010413	0.0009667	-0.0084140	-0.0089359
0.0104585	0.0040350	0.0123942	0.0135406	0.0120423	-0.0033252	0.0264579	0.0034278
0.0011941	-0.0051147	0.0226482	-0.0060974	0.0086622	0.0162743	0.0165305	0.0301500
0.0110537	0.0258904	-0.0081862	0.0035788	-0.0145999	-0.0188061	-0.0041101	-0.0370531
0.0169895	0.0202254	0.0143914	0.0257253	0.0362561	-0.0055232	0.0120222	-0.0066627
-0.0071930	-0.0231577	-0.0302523	-0.0415937	-0.0035324	-0.0034838	-0.0008867	-0.0125104
-0.0050632	0.0008978	-0.0247420	0.0028507	-0.0052327	0.0043710	0.0165043	-0.0057239
0.0268554	0.0296071	0.0258106	0.0317784	0.0315625	-0.0304320	0.0101256	0.0105926
0.0149447	0.0184731	-0.0187097	0.0275448	0.0006580	0.0073181	0.0259245	0.0050129
0.0106681	0.0049625	0.0149024	0.0155965	0.0179188	0.0003355	0.0045485	0.0233524
-0.0163532	-0.0183891	-0.0170591	-0.0140370	-0.0148578	0.0311770	-0.0048633	0.0145484
0.0010577	0.0055508	0.0303166	0.0031632	0.0037705	-0.0127570	-0.0089315	0.0143398
-0.0123045	-0.0255305	-0.0185502	-0.0274135	-0.0308673	0.0081495	0.0020406	-0.0081091
-0.0192983	-0.0347526	-0.0173796	-0.0089801	-0.0239298	0.0208629	-0.0108607	0.0010852
0.0277081	0.0438935	0.0466504	0.0368952	0.0252071	-0.0138032	0.0040414	0.0064320
0.0140898	0.0109078	0.0445712	0.0282165	0.0213878	0.0044257	0.0261976	-0.0004308
-0.0021781	-0.0088759	0.0119310	-0.0066239	0.0103873	-0.0250574	-0.0183067	0.0320403
-0.0061718	-0.0052673	-0.0181852	-0.0055966	-0.0053849	0.0310412	0.0203590	0.0096756
0.0131779	0.0298321	0.0213720	0.0112558	0.0183755	-0.0007548	0.0165997	0.0116512
0.0198088	0.0126842	-0.0035871	0.0229726	0.0223941	0.0139308	0.0254002	0.0383671
-0.0163454	-0.0103249	-0.0350002	-0.0361155	-0.0293098	0.0169001	-0.0191414	-0.0464002
-0.0011924	-0.0010263	-0.0275649	0.0028824	-0.0160713	0.0001051	-0.0027876	0.0022712
-0.0032079	-0.0109572	0.0031971	-0.0077833	-0.0021452	0.0025245	0.0036178	0.0127721
-0.0007487	-0.0058855	0.0144466	0.0095338	0.0052912	0.0120648	0.0113059	0.0149840
-0.0231444	-0.0188051	0.0025129	-0.0331140	0.0021381	0.0021971	-0.0132855	0.0034736
-0.0216223	-0.0303457	-0.0292459	-0.0356082	-0.0295420	0.0084081	-0.0397371	-0.0065237
0.0006262	-0.0133494	-0.0146329	0.0029488	-0.0079488	-0.0024702	0.0164155	0.0046904
-0.0018891	0.0109689	0.0036034	0.0218585	-0.0076792	0.0008257	-0.0024324	-0.0245432
0.0560681	0.0649136	0.0584619	0.0519139	0.0647986	-0.0312488	0.0445883	-0.0090934
0.0035843	0.0193401	-0.0047759	0.0154105	-0.0159538	0.0185841	0.0215052	0.0367074
-0.0201634	-0.0199895	-0.0264967	-0.0272522	-0.0275783	0.0083878	-0.0240828	0.0329451
0.0219449	0.0302506	0.0212173	0.0149746	0.0343761	-0.0012479	0.0274554	0.0059278
-0.0390100	-0.0268458	-0.0113329	-0.0512793	-0.0244267	0.0194688	-0.0388102	0.0138996
-0.0715225	-0.0718965	-0.0961578	-0.1054681	-0.0846533	0.0484149	-0.1128580	0.0218587
-0.0163224	-0.0048301	-0.0307657	-0.0215892	-0.0156306	0.0317638	0.0201455	0.0508192
-0.0463935	-0.0660820	-0.0385270	-0.0646229	-0.0516072	0.0533285	-0.0310839	0.0587162
0.0473186	0.0619631	0.0211696	0.0586706	0.0302969	-0.0243705	0.0313264	-0.0137816
-0.0010170	0.0028234	0.0256662	-0.0094056	0.0007742	0.0401838	0.0045743	0.0325126
-0.0163768	-0.0018770	-0.0372956	-0.0140226	-0.0576067	0.0106654	-0.0049178	-0.0138616
0.0537329	0.0660956	0.0129969	0.0595558	0.0441024	-0.0129277	0.0377080	-0.0258439
-0.0656686	-0.0431172	-0.1147297	-0.0856745	-0.0728881	0.0765327	-0.0854929	-0.0922002
-0.0034347	-0.0306556	-0.0217393	-0.0128953	0.0125025	-0.0002482	-0.0140380	-0.0108886
0.0226249	0.0301009	0.0381769	0.0186626	0.0251156	-0.0187062	-0.0199719	0.0070859
0.0592859	0.0760125	0.0864433	0.0861067	0.0641076	-0.0362822	0.0623485	0.0265109
0.0114221	-0.0151257	-0.0184388	-0.0001404	-0.0007675	-0.0071962	0.0324786	-0.0237453
0.0373478	0.0286214	0.0910961	0.0688875	0.0608677	-0.0147611	0.0669609	0.0633149
-0.0242614	-0.0193415	-0.0285377	-0.0188085	-0.0624436	0.0477418	-0.0234481	0.0072516
0.0094039	0.0008651	-0.0097112	-0.0029489	0.0000000	-0.0071261	-0.0051203	0.0182031
-0.0369490	-0.0423511	-0.0551602	-0.0322667	-0.0463318	0.0319064	-0.0315880	-0.0364452
-0.0462374	-0.0454874	-0.0633111	-0.0744649	-0.0597166	0.0122344	-0.0544255	-0.0251760
0.0732335	0.0707260	0.1016622	0.1032722	0.0934336	-0.0154589	0.0594963	0.0392902
0.0095308	0.0070650	-0.0100581	0.0141495	0.0049223	-0.0162675	0.0135356	-0.0201390
-0.0292648	-0.0350434	-0.0469898	-0.0305874	-0.0528996	0.0480672	0.0007219	-0.0671274
0.0394768	0.0222386	0.0356851	0.0367240	0.0365189	-0.0331094	0.0407209	0.0069574
-0.0070419	-0.0035876	-0.0142891	-0.0107316	0.0004039	0.0285016	-0.0052527	-0.0276373
0.0176097	0.0354650	0.0073800	0.0141013	-0.0076720	-0.0207836	0.0003518	0.0342785
0.0088483	0.0064000	0.0279957	0.0213935	0.0091555	0.0181078	0.0151325	0.0131043
0.0241383	0.0273293	0.0531942	0.0243492	0.0469759	-0.0322632	0.0228811	0.0176442
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-0.0145830	-0.0224745	-0.0065801	0.0145869	-0.0175625	0.0033044	0.0132965	-0.0127495
0.0191108	0.0434810	0.0272309	0.0201660	0.0100808	0.0003950	0.0163141	0.0161865
-0.0334569	-0.0333722	-0.0451369	-0.0439070	-0.0367266	0.0007904	-0.0080260	-0.0074180
0.0476666	0.0534079	0.0549614	0.0782740	0.0320484	-0.0249898	0.0347092	-0.0162216
0.0127157	0.0153609	0.0277382	0.0253100	0.0214164	-0.0062150	0.0147326	0.0051823
-0.0066473	-0.0161392	-0.0046713	-0.0091084	-0.0007863	-0.0261029	-0.0092697	-0.0215596
-0.0247931	-0.0509938	-0.0660525	-0.0305520	-0.0293770	-0.0010470	-0.0153306	-0.0311358
0.0085972	-0.0173808	0.0018612	-0.0027482	0.0083772	-0.0282477	-0.0116772	-0.0165008
0.0271876	0.0219645	0.0029723	0.0729409	0.0221090	-0.0205169	0.0525889	0.0147669
-0.0083855	-0.0073542	0.0016670	-0.0284686	0.0019666	-0.0096283	-0.0091342	0.0108366
0.0166890	0.0104077	-0.0149778	-0.0260647	0.0022241	0.0141743	0.0350689	-0.0061259
0.0116925	0.0271201	0.0110756	0.0141879	0.0033944	0.0075961	0.0090575	-0.0016026
0.0271587	0.0386358	-0.0057556	-0.0051401	0.0147020	0.0000000	0.0040607	0.0079640
0.0140222	0.0145355	0.0149393	0.0092549	0.0166689	0.0127266	0.0251169	0.0188645
-0.0012462	-0.0075728	0.0051519	0.0049410	-0.0027748	0.0057447	0.0195185	0.0003005
0.0013435	-0.0042131	-0.0118983	-0.0038536	-0.0121410	-0.0101385	0.0100815	-0.0044471
0.0115506	-0.0112133	0.0131529	0.0022232	0.0294456	0.0057281	-0.0060492	0.0357962
-0.0131242	-0.0233693	-0.0318156	-0.0193886	-0.0105708	-0.0154424	-0.0127800	0.0064107
-0.0039368	0.0018996	0.0035882	-0.0047508	0.0059076	0.0043042	0.0100687	0.0193989
0.0122910	0.0211614	0.0325556	0.0251398	0.0099962	0.0055930	0.0191233	0.0132925
0.0060945	0.0050012	0.0204120	0.0084257	0.0105158	0.0120008	0.0260505	-0.0068393
0.0040464	0.0168056	-0.0081783	0.0204402	0.0052356	0.0172267	0.0211608	-0.0080153
-0.0189307	0.0040733	-0.0191717	-0.0413036	-0.0306173	0.0240319	-0.0291047	-0.0615114
0.0281740	0.0211172	0.0234187	0.0456941	0.0185942	-0.0255265	0.0179468	0.0100649
0.0166663	0.0262691	-0.0129729	-0.0133031	-0.0080023	0.0226683	-0.0053279	0.0042019
0.0041502	0.0066083	-0.0224535	-0.0122605	0.0027730	0.0102732	0.0264901	0.0114772
-0.0095978	-0.0094456	0.0007470	-0.0504904	-0.0155858	0.0114652	0.0036053	0.0011819
0.0199378	0.0296420	-0.0201532	0.0214004	0.0114913	0.0019567	0.0044433	-0.0050174
-0.0032504	-0.0098589	-0.0173300	0.0068321	0.0034081	0.0068687	0.0044234	0.0074751
0.0090762	0.0095456	0.0065892	0.0105859	0.0103158	-0.0104327	0.0033734	-0.0305029
0.0077516	0.0020921	-0.0040432	-0.0101170	0.0138214	0.0052106	-0.0002802	0.0106293
-0.0057410	-0.0029554	-0.0431085	-0.0253695	-0.0241956	0.0134631	-0.0004671	0.0018631
0.0155405	0.0225982	0.0422222	0.0505289	0.0157332	-0.0072401	0.0015888	0.0209358
0.0062854	0.0147058	0.0296569	0.0068460	0.0169766	-0.0073033	0.0382571	0.0051119
-0.0168624	-0.0130513	-0.0118599	-0.0278121	-0.0110882	0.0014844	-0.0379259	-0.0226821
-0.0058952	-0.0073289	-0.0213375	0.0038806	-0.0081320	0.0051887	-0.0024287	-0.0203971
0.0056865	-0.0002303	-0.0114854	0.0055994	-0.0024845	-0.0151502	-0.0104253	-0.0028699
-0.0217837	-0.0352235	-0.0088616	-0.0034522	-0.0261521	-0.0093628	-0.0148024	0.0078995
0.0083364	0.0027209	0.0097357	-0.0031491	-0.0028132	-0.0238732	-0.0048154	-0.0021873
0.0182934	0.0215417	0.0220386	0.0161566	0.0256479	0.0222016	0.0371626	0.0066980
0.0162604	0.0139860	0.0019252	0.0099930	0.0065015	-0.0062054	0.0265932	0.0147584
0.0135285	0.0321036	-0.0215219	0.0028144	-0.0001244	0.0246997	-0.0016362	-0.0066759
0.0202068	0.0323485	0.0000000	0.0603376	0.0166482	0.0121896	0.0036418	0.0191419
-0.0026893	-0.0097844	0.0212098	-0.0095529	-0.0054993	-0.0131950	-0.0018142	0.0271432
0.0034671	0.0235369	-0.0207693	-0.0283091	-0.0127795	0.0069916	-0.0014541	-0.0105474
-0.0211564	-0.0316591	-0.0435977	-0.0424113	-0.0399552	0.0146263	-0.0146523	-0.0333700
-0.0120922	-0.0206288	0.0045174	-0.0365269	-0.0012966	0.0266232	-0.0000923	-0.0013186
0.0381985	0.0388379	0.0237122	0.0242188	0.0264832	-0.0355684	0.0269721	-0.0003001
-0.0196592	-0.0329137	-0.0245680	-0.0187191	-0.0137419	0.0143756	0.0106050	0.0073238
0.0233328	0.0325192	0.0116279	0.0320722	0.0242661	-0.0153152	0.0107905	0.0069726
0.0092646	0.0023430	0.0032053	0.0018466	0.0042343	-0.0022220	0.0331820	0.0117773
-0.0186752	-0.0452180	0.0008189	-0.0286805	0.0011440	-0.0400164	-0.0454624	-0.0187764
-0.0029393	0.0003949	0.0249540	-0.0081456	0.0029197	-0.0011246	-0.0141620	0.0114456
-0.0575400	-0.0745244	-0.0221513	-0.0808174	-0.0336709	0.0107674	-0.0358679	0.0083692
0.0090642	0.0003126	-0.0348979	-0.0088329	-0.0210897	-0.0034814	-0.0037011	-0.0233210
0.0152733	0.0176522	0.0289702	0.0160307	0.0179310	-0.0274327	0.0014287	0.0105326
-0.0183643	-0.0305858	0.0012331	0.0151226	-0.0051267	-0.0054667	-0.0198801	0.0293142
-0.0141442	-0.0159914	0.0000000	-0.0094845	-0.0050211	-0.0001451	-0.0148486	0.0190438
0.0081063	0.0124739	-0.0281199	0.0151902	-0.0087651	-0.0098386	0.0186188	-0.0032181
-0.0127469	-0.0244959	-0.0576558	-0.0189630	-0.0751607	0.0259787	0.0110252	0.0403851
-0.0279983	-0.0382450	-0.0459434	-0.0097152	-0.0055048	-0.0380062	-0.0162618	0.0076764
0.0614919	0.0835080	0.0655392	0.0535760	0.0745812	-0.0108959	0.0290744	-0.0312820
0.0183794	0.0236259	-0.0068343	-0.0035936	-0.0001355	-0.0358214	0.0045434	0.0171779
0.0005081	0.0069570	0.0188680	0.0070348	0.0071854	0.0304895	0.0392935	-0.0157912
-0.0118122	-0.0350975	-0.0211330	-0.0467312	-0.0188450	-0.0548422	-0.0016359	0.0109749
-0.0218513	-0.0307031	-0.0155798	0.0055087	-0.0119358	-0.0349265	-0.0122896	0.0141621
-0.0268395	-0.0385014	-0.0418268	-0.0315641	-0.0215217	-0.0062941	0.0097697	-0.0203760
-0.0329546	-0.0373385	0.0000000	-0.0401184	-0.0259685	-0.0045004	-0.0530303	-0.0187475
-0.0016019	-0.0127694	-0.0342143	-0.0128143	-0.0199593	-0.0467685	-0.0377830	-0.0084224
-0.0233869	-0.0236378	-0.0107501	-0.0247014	-0.0041624	0.0203220	-0.0306522	-0.0377950
-0.0300957	-0.0439161	0.0160534	-0.0111755	0.0116436	0.0218142	-0.0195307	0.0191955
0.0657803	0.0707359	0.0221195	0.0656520	0.0392504	0.0048095	0.0577571	0.0047667
-0.0113663	-0.0093821	0.0076100	-0.0030912	-0.0103649	-0.0238795	-0.0200279	-0.0015621
-0.0505189	-0.0566950	-0.0210166	-0.0427635	-0.0513465	-0.0194777	-0.0564318	0.0113570
-0.0573616	-0.0489891	-0.0354973	-0.0750881	-0.0496213	-0.0148545	-0.0520168	-0.0187349
0.0662001	0.0745796	0.0247039	0.0599951	0.0372479	0.0042826	0.0727811	-0.0068898
-0.0226620	-0.0423609	-0.0196803	-0.0210177	-0.0177199	0.0309998	-0.0035092	-0.0104062
0.0194891	0.0468578	0.0060227	0.0243859	-0.0046297	-0.0286652	-0.0074698	-0.0357652
-0.0091079	-0.0117827	-0.0364181	-0.0142376	-0.0152365	0.0323555	-0.0041930	-0.0202712
0.0258613	0.0346729	0.0204504	0.0370904	0.0325733	0.0207509	0.0294752	0.0104396
0.0427750	0.0446042	0.0136987	0.0431175	0.0365930	-0.0094474	0.0501311	0.0213482
0.0035923	0.0199390	0.0025025	0.0189586	-0.0108033	-0.0063729	-0.0157794	0.0072516
0.0329649	0.0268533	0.0247129	0.0501048	0.0269189	-0.0078991	0.0420090	0.0156089
-0.0116131	-0.0227913	-0.0280146	-0.0284987	-0.0310066	-0.0217222	-0.0197682	-0.0306785
-0.0398684	-0.0477606	0.0027568	-0.0291292	-0.0306076	0.0051309	-0.0373535	-0.0058997
-0.0322469	-0.0399102	-0.0312421	-0.0470420	-0.0357199	-0.0278737	-0.0380804	-0.0154550
0.0366103	0.0403835	0.0061919	0.0405909	0.0325781	-0.0173290	0.0440940	0.0035163
-0.0478509	-0.0578657	-0.0310256	-0.0448773	-0.0326713	-0.0114486	-0.0596999	-0.0249031
-0.0456738	-0.0460214	-0.0478963	-0.0652550	-0.0602650	-0.0127955	-0.0623977	-0.0181597
-0.0292703	-0.0299444	-0.0305724	-0.0091861	-0.0132135	-0.0307473	-0.0410685	0.0108490
0.0157064	0.0068847	0.0220757	0.0223745	0.0146403	-0.0118976	-0.0342667	0.0207539
-0.0142231	-0.0310665	-0.0403928	-0.0106755	-0.0144291	-0.0239627	-0.0211115	-0.0310363
0.0465845	0.0563013	0.0309852	0.0353096	0.0353509	-0.0547833	0.0246850	0.0076480
0.0393544	0.0210587	-0.0277856	0.0605674	0.0324194	0.0389610	0.0661848	-0.0064223
-0.0325690	-0.0588151	0.0557013	-0.0244595	0.0146985	-0.0242570	-0.0171244	0.0216114
0.0588814	0.0879553	0.0541437	0.0460040	0.0775308	0.0389513	0.0652148	0.0517032
-0.0062232	-0.0109043	-0.0034068	-0.0170145	-0.0047356	0.0178772	-0.0164698	-0.0107560
0.0159078	0.0073730	-0.0068367	0.0101242	0.0225633	0.0327179	0.0185933	0.0026415
0.0113837	0.0196221	0.0468627	0.0133096	0.0165115	0.0431968	0.0065760	0.0247518
-0.0334964	-0.0359254	-0.0131513	-0.0501463	-0.0119609	-0.0070967	-0.0190358	-0.0011957
-0.0209775	-0.0276203	-0.0157203	-0.0192893	-0.0183486	0.0097723	-0.0207588	-0.0016162
-0.0009392	-0.0227892	-0.0007931	0.0001147	0.0071844	-0.0462142	0.0001184	0.0028179
-0.0012537	-0.0040394	0.0026455	-0.0001719	-0.0037942	-0.0254503	-0.0040222	0.0142294
0.0147739	0.0094636	0.0572559	0.0184675	0.0379341	0.0564485	0.0239933	0.0239920
0.0268502	0.0452754	0.0306963	0.0533281	0.0352267	0.0149268	0.0440785	0.0290714
-0.0065746	0.0060860	0.0104116	-0.0107459	0.0068057	-0.0051523	-0.0059993	0.0029649
0.0247549	0.0480402	0.0136593	0.0245353	0.0083086	0.0048023	0.0271599	-0.0003904
0.0164416	0.0334841	-0.0335697	0.0390864	-0.0025140	0.0024787	0.0153428	-0.0321392
-0.0104523	-0.0211640	-0.0168787	-0.0339104	-0.0105012	-0.0310215	-0.0203622	-0.0005765
-0.0019116	0.0048715	-0.0126896	0.0148180	0.0049526	-0.0097689	-0.0082049	-0.0121136
-0.0267152	-0.0309137	-0.0347783	-0.0290997	-0.0278794	-0.0137722	-0.0405911	-0.0169916
0.0197033	0.0268288	0.0323760	0.0212789	0.0266511	0.0115981	0.0154057	0.0245916
-0.0452262	-0.0371396	-0.0429945	-0.0800001	-0.0314615	0.0363136	-0.0704258	0.0080004
0.0144292	0.0582567	-0.0060782	-0.0280962	-0.0214130	0.0119330	-0.0004871	0.0569391
0.0147696	0.0196861	0.0281839	0.0069724	0.0244121	0.0000000	-0.0110957	-0.0006530
0.0344662	0.0322943	0.0204293	0.0384772	0.0392327	-0.0044922	0.0521751	-0.0023414
-0.0004886	-0.0089739	-0.0017739	-0.0252803	0.0058726	0.0228932	-0.0080095	0.0178474
0.0079914	0.0016350	0.0083778	0.0150670	0.0177926	-0.0317884	-0.0137734	-0.0006971
-0.0006303	-0.0061526	-0.0178752	0.0059487	0.0055995	-0.0064713	0.0163737	-0.0113222
0.0090490	0.0187929	0.0030762	-0.0132913	-0.0001357	0.0197318	0.0149254	0.0029851
-0.0079340	0.0010231	0.0066446	-0.0042808	0.0035315	-0.0122608	-0.0063025	0.0143940
-0.0025204	0.0066276	-0.0213253	-0.0099169	-0.0100162	-0.0059109	-0.0113930	-0.0034675
0.0170801	0.0353199	0.0111544	0.0196271	0.0071097	-0.0304019	-0.0191279	-0.0169691
0.0033443	0.0353332	0.0005131	0.0002271	-0.0206354	-0.0000989	-0.0134448	-0.0148116
0.0188087	0.0179392	0.0138462	0.0333240	0.0070696	0.0115844	0.0327810	0.0007186
0.0046270	-0.0004229	0.0159907	0.0193517	0.0024916	-0.0006863	0.0061540	0.0054680
0.0090719	0.0168970	0.0020070	0.0041615	0.0043345	0.0029435	0.0011871	-0.0008789

#Monthly return to tibble format
monthly_return <- as_tibble(asset_returns_mon_xts)

kbl(monthly_return, caption = "Monthly return of portfilio")%>%
  row_spec(row =0, bold= TRUE, color = "#34495E", background = "#BFC9CA") %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "center")

Monthly return of portfilio

SPY	QQQ	EEM	IWM	EFA	TLT	IYR	GLD
0.0311949	0.0460390	0.0177639	0.0447514	0.0026674	-0.0034246	0.0545708	0.0327482
0.0608792	0.0771089	0.0811088	0.0823068	0.0638543	-0.0205725	0.0974850	-0.0043864
0.0154703	0.0224250	-0.0016616	0.0567845	-0.0280459	0.0332176	0.0638810	0.0588344
-0.0794546	-0.0739234	-0.0939361	-0.0753663	-0.1119279	0.0510834	-0.0568359	0.0305131
-0.0517412	-0.0597568	-0.0139864	-0.0774341	-0.0206193	0.0579788	-0.0467006	0.0235532
0.0683004	0.0725828	0.1093251	0.0673091	0.1161040	-0.0094648	0.0940478	-0.0508712
-0.0449802	-0.0512990	-0.0323674	-0.0744387	-0.0379504	0.0839094	-0.0129718	0.0570613
0.0895550	0.1317282	0.1175735	0.1244531	0.0997196	-0.0252033	0.0462967	0.0477556
0.0382018	0.0633790	0.0301545	0.0414820	0.0380555	-0.0446925	0.0393340	0.0368227
0.0000000	-0.0017250	-0.0290545	0.0348508	-0.0482373	-0.0168924	-0.0158297	0.0211130
0.0668522	0.0475714	0.0727777	0.0803330	0.0830808	-0.0369910	0.0457410	0.0243687
0.0233004	0.0282775	-0.0384133	-0.0037066	0.0209549	-0.0308116	0.0357400	-0.0637976
0.0347371	0.0316071	-0.0004364	0.0554206	0.0354980	0.0165014	0.0452032	0.0599831
0.0001201	-0.0044830	0.0628957	0.0252301	-0.0238828	0.0002933	-0.0109344	0.0159814
0.0289617	0.0287309	0.0273272	0.0263755	0.0562583	0.0233908	0.0466330	0.0894465
-0.0112149	-0.0121866	-0.0294002	-0.0179420	-0.0220611	0.0342276	0.0101336	-0.0179169
-0.0168696	-0.0202548	-0.0093546	-0.0240452	-0.0121075	-0.0232303	-0.0310601	-0.0243250
-0.0200043	0.0166519	-0.0102943	-0.0340616	-0.0237779	0.0441457	0.0021561	0.0841780
-0.0549759	-0.0506897	-0.0925494	-0.0889141	-0.0875489	0.0966755	-0.0531194	0.1227494
-0.0694207	-0.0449081	-0.1789475	-0.1115030	-0.1080831	0.1320613	-0.1066825	-0.1106235
0.1091472	0.1040197	0.1626781	0.1510107	0.0962747	-0.0382925	0.1318968	0.0587119
-0.0040638	-0.0269200	-0.0196032	-0.0037830	-0.0217637	0.0198912	-0.0366875	0.0166727
0.0104482	-0.0061418	-0.0426478	0.0051502	-0.0217497	0.0344489	0.0410744	-0.1066243
0.0463748	0.0841842	0.1099104	0.0714578	0.0526951	-0.0032994	0.0660088	0.1139548
0.0434050	0.0641006	0.0527190	0.0256899	0.0483315	-0.0259704	-0.0069353	-0.0296498
0.0321638	0.0505302	-0.0311300	0.0248471	0.0042081	-0.0423594	0.0449298	-0.0132083
-0.0066755	-0.0116944	-0.0169967	-0.0161815	-0.0207690	0.0483422	0.0258429	-0.0014803
-0.0600555	-0.0704017	-0.1070582	-0.0657912	-0.1114419	0.0902537	-0.0420904	-0.0633803
0.0405809	0.0362143	0.0503536	0.0505210	0.0704332	-0.0167825	0.0536526	0.0235458
0.0118299	0.0099752	-0.0002554	-0.0152090	0.0008006	0.0381734	0.0233029	0.0083768
0.0250524	0.0518517	0.0040898	0.0353547	0.0319998	-0.0131891	0.0030569	0.0493961
0.0253506	0.0088844	0.0521893	0.0326090	0.0271319	-0.0253280	-0.0111774	0.0467056
-0.0181979	-0.0527930	-0.0043547	-0.0216921	0.0109437	-0.0048406	-0.0059024	-0.0294374
0.0056596	0.0130869	0.0155525	0.0055128	0.0278086	0.0115928	-0.0057808	-0.0046754
0.0089347	-0.0046693	0.0680360	0.0361883	0.0438117	-0.0248775	0.0288543	-0.0242698
0.0511900	0.0267162	-0.0029309	0.0623817	0.0372843	-0.0318535	0.0402043	-0.0050611
0.0127588	0.0034389	-0.0228406	0.0100468	-0.0128854	0.0123873	0.0126357	-0.0508685
0.0379711	0.0302481	-0.0101827	0.0465902	0.0130536	-0.0041941	0.0286496	0.0094771
0.0192124	0.0253733	0.0121580	-0.0034944	0.0501867	0.0468490	0.0572830	-0.0756231
0.0236096	0.0357748	-0.0482791	0.0393197	-0.0301905	-0.0676097	-0.0649337	-0.0619879
-0.0133441	-0.0239527	-0.0532577	-0.0081804	-0.0267792	-0.0327128	-0.0238272	-0.1105884
0.0516771	0.0631404	0.0132469	0.0733421	0.0532286	-0.0225794	0.0024087	0.0743011
-0.0299928	-0.0039595	-0.0253785	-0.0316419	-0.0195526	-0.0134042	-0.0653249	0.0520475
0.0316465	0.0483220	0.0720673	0.0648915	0.0782488	0.0065178	0.0345015	-0.0478384
0.0463068	0.0495690	0.0417081	0.0242001	0.0326021	0.0143042	0.0373101	-0.0034326
0.0296376	0.0355113	-0.0025908	0.0395642	0.0054646	-0.0270322	-0.0463958	-0.0551120
0.0259264	0.0292713	-0.0040661	0.0202111	0.0217613	-0.0186971	0.0090472	-0.0379453
-0.0352481	-0.0192133	-0.0863635	-0.0277391	-0.0520119	0.0630279	0.0344007	0.0341887
0.0455159	0.0515240	0.0337783	0.0477889	0.0613113	0.0052402	0.0475097	0.0627030
0.0082955	-0.0273357	0.0387539	-0.0074849	-0.0045921	0.0073218	0.0014515	-0.0314214
0.0069515	-0.0031937	0.0078028	-0.0374763	0.0166667	0.0209519	0.0302942	0.0049349
0.0232063	0.0448564	0.0295188	0.0078586	0.0159542	0.0294957	0.0279690	-0.0305104
0.0206449	0.0312381	0.0240178	0.0527201	0.0092077	-0.0025132	0.0101260	0.0631902
-0.0134377	0.0118200	0.0136476	-0.0605431	-0.0260349	0.0066418	-0.0011144	-0.0363167
0.0394633	0.0500948	0.0282979	0.0482957	0.0018021	0.0472375	0.0347230	0.0038091
-0.0137962	-0.0075665	-0.0776741	-0.0592792	-0.0388248	-0.0211234	-0.0589056	-0.0617633
0.0235509	0.0264194	0.0141962	0.0659351	-0.0026513	0.0281859	0.0835257	-0.0305481
0.0274722	0.0454636	-0.0154209	0.0011154	0.0006257	0.0296735	0.0272074	-0.0048820
-0.0025367	-0.0224062	-0.0396356	0.0289294	-0.0399278	0.0325141	0.0080499	0.0131121
-0.0296292	-0.0208232	-0.0068718	-0.0327706	0.0062460	0.0981576	0.0571315	0.0868991
0.0562044	0.0722060	0.0440798	0.0594643	0.0633779	-0.0613838	-0.0259754	-0.0590522
-0.0157057	-0.0235899	-0.0149731	0.0176999	-0.0142857	0.0109221	0.0107452	-0.0215220
0.0098341	0.0192233	0.0685274	-0.0256493	0.0364657	-0.0342515	-0.0481592	-0.0016717
0.0128559	0.0224846	-0.0410447	0.0223635	0.0019545	-0.0237246	-0.0033113	0.0055521
-0.0203121	-0.0248408	-0.0293085	0.0078293	-0.0311823	-0.0407197	-0.0439795	-0.0151621
0.0225893	0.0455775	-0.0630996	-0.0110393	0.0203181	0.0454595	0.0495092	-0.0662099
-0.0609500	-0.0682448	-0.0883620	-0.0631100	-0.0742513	-0.0068950	-0.0581320	0.0370723
-0.0255159	-0.0220728	-0.0313241	-0.0493629	-0.0441890	0.0196689	0.0161538	-0.0180114
0.0850597	0.1136986	0.0637586	0.0562267	0.0661201	-0.0040883	0.0621562	0.0228336
0.0036552	0.0060886	-0.0252364	0.0325994	-0.0075274	-0.0086762	-0.0019900	-0.0675206
-0.0172825	-0.0159478	-0.0381984	-0.0503042	-0.0233188	-0.0029724	0.0110527	-0.0045133
-0.0497869	-0.0691043	-0.0503261	-0.0857750	-0.0551772	0.0557308	-0.0410229	0.0541100
-0.0008259	-0.0156535	-0.0081781	-0.0022341	-0.0333452	0.0308598	-0.0075001	0.1093034
0.0672664	0.0685261	0.1296176	0.0801347	0.0658214	-0.0009333	0.1025511	-0.0084289
0.0039410	-0.0318681	0.0040876	0.0167224	0.0222182	-0.0073654	-0.0166965	0.0510881
0.0170115	0.0437003	-0.0369294	0.0224038	-0.0008556	0.0080543	0.0220742	-0.0613830
0.0034759	-0.0227801	0.0455911	-0.0001737	-0.0241940	0.0693422	0.0624312	0.0896950
0.0364713	0.0715083	0.0538414	0.0587488	0.0397707	0.0210252	0.0365335	0.0198466
0.0011974	0.0105006	0.0088376	0.0178407	0.0053410	-0.0101350	-0.0334894	-0.0325632
0.0000581	0.0221354	0.0251845	0.0107385	0.0133676	-0.0150835	-0.0147270	0.0068921
-0.0173371	-0.0145723	-0.0082776	-0.0459704	-0.0221544	-0.0438067	-0.0496032	-0.0294492
0.0368385	0.0043596	-0.0441572	0.1106328	-0.0178140	-0.0821376	-0.0233562	-0.0835657
0.0202716	0.0113223	-0.0025269	0.0288445	0.0270627	-0.0046258	0.0423402	-0.0191499
0.0178948	0.0514010	0.0665527	0.0028177	0.0329119	0.0081424	0.0013000	0.0541922
0.0392916	0.0437505	0.0174075	0.0193005	0.0119066	0.0158457	0.0437434	0.0318477
0.0012499	0.0202689	0.0368516	0.0002602	0.0323169	-0.0065510	-0.0145300	-0.0042775
0.0099264	0.0272698	0.0170095	0.0114927	0.0242413	0.0157320	0.0056059	0.0172675
0.0141127	0.0389733	0.0284575	-0.0197037	0.0354233	0.0188788	-0.0011405	-0.0012420
0.0063749	-0.0231795	0.0092663	0.0337439	0.0029603	0.0079189	0.0208005	-0.0215553
0.0205541	0.0406131	0.0582269	0.0085251	0.0265337	-0.0065835	0.0111572	0.0231317
0.0029178	0.0207359	0.0235158	-0.0125789	-0.0004482	0.0340873	0.0066946	0.0419876
0.0201493	-0.0029521	-0.0004459	0.0630413	0.0236173	-0.0232294	-0.0078508	-0.0336989
0.0235642	0.0460637	0.0328046	0.0072882	0.0167931	-0.0003853	0.0005009	-0.0074848
0.0305657	0.0197176	-0.0038891	0.0294122	0.0068937	0.0074318	0.0256506	0.0035634
0.0121282	0.0060310	0.0376156	-0.0039849	0.0134886	0.0181259	-0.0012325	0.0210570
0.0563593	0.0875707	0.0829797	0.0255804	0.0502062	-0.0325555	-0.0302431	0.0323494
-0.0363605	-0.0129279	-0.0589850	-0.0384371	-0.0483479	-0.0304145	-0.0665736	-0.0207599
-0.0274104	-0.0407883	0.0054144	0.0121754	-0.0083960	0.0285968	0.0377193	0.0063200
0.0051679	0.0050580	-0.0281692	0.0098138	0.0152122	-0.0208814	0.0022526	-0.0095397
0.0243091	0.0567292	-0.0262146	0.0616355	-0.0189425	0.0200442	0.0337124	-0.0119592
0.0057512	0.0114506	-0.0454570	0.0061435	-0.0158410	0.0064576	0.0405943	-0.0361494
0.0370464	0.0279639	0.0353105	0.0164906	0.0285202	-0.0143687	0.0083150	-0.0224189
0.0319199	0.0578069	-0.0376729	0.0431061	-0.0223576	0.0131206	0.0237537	-0.0213811
0.0059456	-0.0028166	-0.0057910	-0.0232503	0.0096524	-0.0286431	-0.0285341	-0.0066073
-0.0691044	-0.0859569	-0.0876050	-0.1098783	-0.0813356	-0.0293054	-0.0238689	0.0211955
0.0185496	-0.0026498	0.0490298	0.0172634	0.0049633	0.0178723	0.0469849	0.0033869
-0.0880487	-0.0866072	-0.0348890	-0.1197218	-0.0534976	0.0585347	-0.0782224	0.0494201
0.0800656	0.0901075	0.1034306	0.1132189	0.0663490	0.0037859	0.1147582	0.0288660
0.0324156	0.0299120	-0.0153132	0.0517913	0.0253669	-0.0137640	0.0073019	-0.0060922
0.0181010	0.0392458	0.0113101	-0.0209332	0.0091800	0.0557164	0.0417092	-0.0159690
0.0408519	0.0549928	0.0235321	0.0339670	0.0292939	-0.0199109	-0.0004596	-0.0066388
-0.0637710	-0.0822519	-0.0732984	-0.0785268	-0.0503296	0.0683926	-0.0014942	0.0175743
0.0695862	0.0759002	0.0620776	0.0698491	0.0591165	0.0095012	0.0127906	0.0800291
0.0151196	0.0233481	-0.0265672	0.0068167	-0.0194735	0.0025651	0.0238234	0.0000751
-0.0167434	-0.0189954	-0.0378263	-0.0493105	-0.0192397	0.1104919	0.0343436	0.0791231
0.0194580	0.0091789	0.0169197	0.0203710	0.0316404	-0.0267997	0.0189005	-0.0339479
0.0221046	0.0438006	0.0418399	0.0271573	0.0338905	-0.0111320	0.0076972	0.0256355
0.0361982	0.0406941	-0.0009393	0.0406560	0.0112727	-0.0040714	-0.0112458	-0.0320859
0.0290554	0.0388638	0.0773024	0.0278655	0.0299539	-0.0319748	0.0085758	0.0365588
-0.0004039	0.0303841	-0.0615108	-0.0310255	-0.0282256	0.0769115	0.0160079	0.0449966
-0.0791658	-0.0605740	-0.0377583	-0.0884571	-0.0776527	0.0662649	-0.0758169	-0.0063617
-0.1248713	-0.0728586	-0.1576999	-0.2147709	-0.1410669	0.0637668	-0.1963243	-0.0022240
0.1269837	0.1497375	0.0735425	0.1384764	0.0581744	0.0121892	0.0931436	0.0726106
0.0476451	0.0660090	0.0297487	0.0659197	0.0542691	-0.0176188	0.0180144	0.0258816
0.0177343	0.0628668	0.0660492	0.0342995	0.0351118	0.0033662	0.0247163	0.0273770
0.0588924	0.0734652	0.0825206	0.0291940	0.0193856	0.0443513	0.0412384	0.1079046
0.0697967	0.1094471	0.0288751	0.0547639	0.0472201	-0.0504654	0.0023155	-0.0032357
-0.0374437	-0.0577862	-0.0101034	-0.0326132	-0.0204679	0.0077345	-0.0234139	-0.0417140
-0.0249335	-0.0304491	0.0140621	0.0220308	-0.0355067	-0.0338527	-0.0291832	-0.0051942
0.1087769	0.1122578	0.0899129	0.1824419	0.1426943	0.0166394	0.0857951	-0.0540863
0.0370482	0.0490242	0.0712944	0.0864644	0.0501542	-0.0122766	0.0250338	0.0701386
-0.0101904	0.0026138	0.0317402	0.0484546	-0.0078126	-0.0363276	-0.0043199	-0.0322382
0.0278054	-0.0013353	0.0078783	0.0620257	0.0223787	-0.0573298	0.0242731	-0.0625688
0.0453993	0.0171679	-0.0072585	0.0139504	0.0251317	-0.0524530	0.0577405	-0.0114331
0.0529103	0.0590980	0.0119985	0.0178781	0.0295242	0.0249517	0.0788558	0.0356339
0.0065663	-0.0120124	0.0164876	0.0027123	0.0348226	0.0000214	0.0100816	0.0767838
0.0224274	0.0626215	0.0095040	0.0186899	-0.0107801	0.0441911	0.0225842	-0.0714766
0.0244125	0.0286092	-0.0643699	-0.0362733	0.0077332	0.0371665	0.0468902	0.0252974
0.0297598	0.0421869	0.0156978	0.0220313	0.0144672	-0.0034016	0.0193965	-0.0007655
-0.0466055	-0.0568316	-0.0387331	-0.0288036	-0.0326139	-0.0290701	-0.0562687	-0.0322353
0.0701637	0.0786400	0.0107185	0.0425143	0.0317906	0.0246500	0.0727609	0.0147972
-0.0080349	0.0199685	-0.0408484	-0.0433240	-0.0453472	0.0277148	-0.0241259	-0.0069007
0.0462477	0.0115234	0.0150707	0.0227151	0.0439094	-0.0201378	0.0946916	0.0329910
-0.0527412	-0.0874703	-0.0002046	-0.0953472	-0.0363498	-0.0391392	-0.0823144	-0.0167876
-0.0295170	-0.0447597	-0.0432023	0.0103359	-0.0342918	-0.0163296	-0.0458811	0.0612173
0.0375904	0.0466789	-0.0338112	0.0115752	0.0051898	-0.0544440	0.0686462	0.0127256
-0.0877691	-0.1359574	-0.0613511	-0.0989916	-0.0673913	-0.0942387	-0.0413048	-0.0207030
0.0022572	-0.0158660	0.0061349	0.0019465	0.0199591	-0.0225331	-0.0444337	-0.0326155
-0.0824604	-0.0890788	-0.0515766	-0.0836635	-0.0876664	-0.0127457	-0.0689109	-0.0156597
0.0920874	0.1255174	-0.0034913	0.1056330	0.0516883	0.0243104	0.0886061	-0.0258815
-0.0408020	-0.0513219	-0.0132633	-0.0200267	-0.0611686	-0.0455311	-0.0586237	-0.0294333
-0.0924458	-0.1053554	-0.1153943	-0.0966045	-0.0922204	-0.0823510	-0.1271344	-0.0288818
0.0812755	0.0399986	-0.0197822	0.1116300	0.0589181	-0.0596444	0.0315648	-0.0178444
0.0555916	0.0554415	0.1558936	0.0220366	0.1316811	0.0715107	0.0616740	0.0849187
-0.0576282	-0.0901367	-0.0263750	-0.0651412	-0.0181291	-0.0262005	-0.0473349	0.0293065
0.0628874	0.1064292	0.0912928	0.0981875	0.0900365	0.0764364	0.0997743	0.0575926
-0.0251427	-0.0035978	-0.0756770	-0.0172341	-0.0307478	-0.0485055	-0.0596176	-0.0536760
0.0370778	0.0949272	0.0321737	-0.0484734	0.0312905	0.0483935	-0.0193699	0.0791613
0.0159749	0.0050790	-0.0083629	-0.0179372	0.0293626	0.0033835	0.0091872	0.0086235
0.0046162	0.0788380	-0.0240225	-0.0081620	-0.0400707	-0.0301529	-0.0402660	-0.0134199
0.0381715	0.0359206	0.0540167	0.0721904	0.0350460	-0.0048385	0.0305016	-0.0024134

#Question 4: Download Fama French 3 factors data and change to digit numbers (not in percetage)

Fama_French <- read_csv("F-F_Research_Data_Factors.CSV")

## New names:
## • `` -> `...1`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

## Rows: 1261 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ...1
## dbl (4): Mkt-RF, SMB, HML, RF
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

show_col_types = FALSE

library(readr)

# Retrieve column specification
column_spec <- spec(read_csv("F-F_Research_Data_Factors.CSV"))

## New names:
## • `` -> `...1`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

## Rows: 1261 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): ...1
## dbl (4): Mkt-RF, SMB, HML, RF
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Print the column specification
print(column_spec)

## cols(
##   ...1 = col_character(),
##   `Mkt-RF` = col_double(),
##   SMB = col_double(),
##   HML = col_double(),
##   RF = col_double()
## )

#Change to digit numbers
Fama_French <- Fama_French %>% mutate(`Mkt-RF` = `Mkt-RF`/100) %>% mutate(SMB = SMB/100) %>% mutate(HML= HML/100)%>% mutate(RF= RF/100)

#Digit numbers data
head(Fama_French)

## # A tibble: 6 × 5
##   ...1   `Mkt-RF`     SMB     HML     RF
##   <chr>     <dbl>   <dbl>   <dbl>  <dbl>
## 1 192607   0.0296 -0.0256 -0.0243 0.0022
## 2 192608   0.0264 -0.0117  0.0382 0.0025
## 3 192609   0.0036 -0.014   0.0013 0.0023
## 4 192610  -0.0324 -0.0009  0.007  0.0032
## 5 192611   0.0253 -0.001  -0.0051 0.0031
## 6 192612   0.0262 -0.0003 -0.0005 0.0028

data.frame("F-F_Research_Data_Factors.CSV")

##   X.F.F_Research_Data_Factors.CSV.
## 1    F-F_Research_Data_Factors.CSV

names(Fama_French)

## [1] "...1"   "Mkt-RF" "SMB"    "HML"    "RF"

#Question 5: Merge monthly return data in question 3 and 4 into tibble format.

#Digit numbers data
head(Fama_French)

## # A tibble: 6 × 5
##   ...1   `Mkt-RF`     SMB     HML     RF
##   <chr>     <dbl>   <dbl>   <dbl>  <dbl>
## 1 192607   0.0296 -0.0256 -0.0243 0.0022
## 2 192608   0.0264 -0.0117  0.0382 0.0025
## 3 192609   0.0036 -0.014   0.0013 0.0023
## 4 192610  -0.0324 -0.0009  0.007  0.0032
## 5 192611   0.0253 -0.001  -0.0051 0.0031
## 6 192612   0.0262 -0.0003 -0.0005 0.0028