Construct an equal-weighted (EW) and value-weighted (VW) portfolio, consisting of AMZN, AABA, and IBM.
In this section, prepare an object, port, which will be used later to build EW and VW portfolio.
Step 0: Import Price Data
# To clean up the memory of your current R session run the following line
rm(list=ls(all=TRUE))
library(quantmod)
library(xts)
#Importing Price Data
data.AMZN <- getSymbols("AMZN", from = "2016-12-30", to = "2017-06-30", auto.assign = FALSE)
data.MSFT <- getSymbols("MSFT", from = "2016-12-30", to = "2017-06-30", auto.assign = FALSE)
data.AAPL <- getSymbols("AAPL", from = "2016-12-30", to = "2017-06-30", auto.assign = FALSE)
data.TSLA <- getSymbols("TSLA", from = "2016-12-30", to = "2017-06-30", auto.assign = FALSE)
#to = "2013-12-31" in the book was replaced by to = "2014-01-01"
#B/c that code doesn't produce the output that includes stock price of 2013-12-31the date. Step 1: Importing the Price Data
data.AMZN[c(1:3, nrow(data.AMZN)), ]
## AMZN.Open AMZN.High AMZN.Low AMZN.Close AMZN.Volume
## 2016-12-30 766.47 767.40 748.28 749.87 4139400
## 2017-01-03 757.92 758.76 747.70 753.67 3521100
## 2017-01-04 758.39 759.68 754.20 757.18 2510500
## 2017-06-29 979.00 987.56 965.25 975.93 4303000
## AMZN.Adjusted
## 2016-12-30 749.87
## 2017-01-03 753.67
## 2017-01-04 757.18
## 2017-06-29 975.93
data.MSFT[c(1:3, nrow(data.MSFT)), ]
## MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume
## 2016-12-30 62.96 62.99 62.03 62.14 25579900
## 2017-01-03 62.79 62.84 62.13 62.58 20694100
## 2017-01-04 62.48 62.75 62.12 62.30 21340000
## 2017-06-29 69.38 69.49 68.09 68.49 28918700
## MSFT.Adjusted
## 2016-12-30 60.78280
## 2017-01-03 61.21319
## 2017-01-04 60.93930
## 2017-06-29 67.78660
data.AAPL[c(1:3, nrow(data.AAPL)), ]
## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume
## 2016-12-30 116.65 117.20 115.43 115.82 30586300
## 2017-01-03 115.80 116.33 114.76 116.15 28781900
## 2017-01-04 115.85 116.51 115.75 116.02 21118100
## 2017-06-29 144.71 145.13 142.28 143.68 31499400
## AAPL.Adjusted
## 2016-12-30 113.9870
## 2017-01-03 114.3118
## 2017-01-04 114.1838
## 2017-06-29 142.6053
data.TSLA[c(1:3, nrow(data.TSLA)), ]
## TSLA.Open TSLA.High TSLA.Low TSLA.Close TSLA.Volume
## 2016-12-30 216.30 217.50 211.68 213.69 4642600
## 2017-01-03 214.86 220.33 210.96 216.99 5923300
## 2017-01-04 214.75 228.00 214.31 226.99 11213500
## 2017-06-29 370.61 371.00 354.10 360.75 8221000
## TSLA.Adjusted
## 2016-12-30 213.69
## 2017-01-03 216.99
## 2017-01-04 226.99
## 2017-06-29 360.75Step 2: Create Object with Only the Relevant Data
port <- data.AMZN[, c(4, 6)]
port <- merge(port, data.MSFT[, c(4, 6)])
port <- merge(port, data.AAPL[, c(4, 6)])
port <- merge(port, data.TSLA[, c(4, 6)])
#port <- cbind(port, data.MSFT[, c(4, 6)], data.AAPL[, c(4, 6)], data.TSLA[, c(4, 6)])
port[c(1:3, nrow(port)), ]
## AMZN.Close AMZN.Adjusted MSFT.Close MSFT.Adjusted AAPL.Close
## 2016-12-30 749.87 749.87 62.14 60.78280 115.82
## 2017-01-03 753.67 753.67 62.58 61.21319 116.15
## 2017-01-04 757.18 757.18 62.30 60.93930 116.02
## 2017-06-29 975.93 975.93 68.49 67.78660 143.68
## AAPL.Adjusted TSLA.Close TSLA.Adjusted
## 2016-12-30 113.9870 213.69 213.69
## 2017-01-03 114.3118 216.99 216.99
## 2017-01-04 114.1838 226.99 226.99
## 2017-06-29 142.6053 360.75 360.75Step 3: Calculate Returns of Each Security
port$AMZN.ret <- Delt(port$AMZN.Adjusted)
port$MSFT.ret <- Delt(port$MSFT.Adjusted)
port$AAPL.ret <- Delt(port$AAPL.Adjusted)
port$TSLA.ret <- Delt(port$TSLA.Adjusted)
port[c(1:3, nrow(port)), ]
## AMZN.Close AMZN.Adjusted MSFT.Close MSFT.Adjusted AAPL.Close
## 2016-12-30 749.87 749.87 62.14 60.78280 115.82
## 2017-01-03 753.67 753.67 62.58 61.21319 116.15
## 2017-01-04 757.18 757.18 62.30 60.93930 116.02
## 2017-06-29 975.93 975.93 68.49 67.78660 143.68
## AAPL.Adjusted TSLA.Close TSLA.Adjusted AMZN.ret
## 2016-12-30 113.9870 213.69 213.69 NA
## 2017-01-03 114.3118 216.99 216.99 0.005067529
## 2017-01-04 114.1838 226.99 226.99 0.004657224
## 2017-06-29 142.6053 360.75 360.75 -0.014540632
## MSFT.ret AAPL.ret TSLA.ret
## 2016-12-30 NA NA NA
## 2017-01-03 0.007080902 0.002849238 0.01544295
## 2017-01-04 -0.004474330 -0.001119264 0.04608507
## 2017-06-29 -0.018767921 -0.014743251 -0.02825663Step 4: Convert to data.frame Object and Subset Data
port <- cbind(index(port), data.frame(port))
names(port)[1] <- "date"
port[c(1:3, nrow(port)), ]
## date AMZN.Close AMZN.Adjusted MSFT.Close MSFT.Adjusted
## 2016-12-30 2016-12-30 749.87 749.87 62.14 60.78280
## 2017-01-03 2017-01-03 753.67 753.67 62.58 61.21319
## 2017-01-04 2017-01-04 757.18 757.18 62.30 60.93930
## 2017-06-29 2017-06-29 975.93 975.93 68.49 67.78660
## AAPL.Close AAPL.Adjusted TSLA.Close TSLA.Adjusted AMZN.ret
## 2016-12-30 115.82 113.9870 213.69 213.69 NA
## 2017-01-03 116.15 114.3118 216.99 216.99 0.005067529
## 2017-01-04 116.02 114.1838 226.99 226.99 0.004657224
## 2017-06-29 143.68 142.6053 360.75 360.75 -0.014540632
## MSFT.ret AAPL.ret TSLA.ret
## 2016-12-30 NA NA NA
## 2017-01-03 0.007080902 0.002849238 0.01544295
## 2017-01-04 -0.004474330 -0.001119264 0.04608507
## 2017-06-29 -0.018767921 -0.014743251 -0.02825663
port <- subset(port,
port$date >= "2016-12-30" &
port$date <= "2017-12-29")
port[c(1:3, nrow(port)), ]
## date AMZN.Close AMZN.Adjusted MSFT.Close MSFT.Adjusted
## 2016-12-30 2016-12-30 749.87 749.87 62.14 60.78280
## 2017-01-03 2017-01-03 753.67 753.67 62.58 61.21319
## 2017-01-04 2017-01-04 757.18 757.18 62.30 60.93930
## 2017-06-29 2017-06-29 975.93 975.93 68.49 67.78660
## AAPL.Close AAPL.Adjusted TSLA.Close TSLA.Adjusted AMZN.ret
## 2016-12-30 115.82 113.9870 213.69 213.69 NA
## 2017-01-03 116.15 114.3118 216.99 216.99 0.005067529
## 2017-01-04 116.02 114.1838 226.99 226.99 0.004657224
## 2017-06-29 143.68 142.6053 360.75 360.75 -0.014540632
## MSFT.ret AAPL.ret TSLA.ret
## 2016-12-30 NA NA NA
## 2017-01-03 0.007080902 0.002849238 0.01544295
## 2017-01-04 -0.004474330 -0.001119264 0.04608507
## 2017-06-29 -0.018767921 -0.014743251 -0.02825663considerd as an approach to capture the small capitalization stock premeum (the belief that small cap stock yield higher returns over larger cap stocks) An example is the S&P 500 Equal Weight Index
Step 1: Keep Only Variables We Need to Construct EW Portfolio
ewport <- port[, c(1, 10:13)]
ewport[c(1:3, nrow(ewport)), ]
## date AMZN.ret MSFT.ret AAPL.ret TSLA.ret
## 2016-12-30 2016-12-30 NA NA NA NA
## 2017-01-03 2017-01-03 0.005067529 0.007080902 0.002849238 0.01544295
## 2017-01-04 2017-01-04 0.004657224 -0.004474330 -0.001119264 0.04608507
## 2017-06-29 2017-06-29 -0.014540632 -0.018767921 -0.014743251 -0.02825663
names(ewport)[2:5] <- c("AMZN", "MSFT", "AAPL", "TSLA")
rownames(ewport) <- seq(1, nrow(ewport), 1)
ewport[c(1:3, nrow(ewport)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 NA NA NA NA
## 2 2017-01-03 0.005067529 0.007080902 0.002849238 0.01544295
## 3 2017-01-04 0.004657224 -0.004474330 -0.001119264 0.04608507
## 125 2017-06-29 -0.014540632 -0.018767921 -0.014743251 -0.02825663Step 2: Convert Net Returns to Gross Returns
ewport[2:5] <- ewport[2:5] + 1
ewport[c(1:3, nrow(ewport)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 NA NA NA NA
## 2 2017-01-03 1.0050675 1.0070809 1.0028492 1.0154429
## 3 2017-01-04 1.0046572 0.9955257 0.9988807 1.0460851
## 125 2017-06-29 0.9854594 0.9812321 0.9852567 0.9717434Step 3: Calculate EW Portfolio Values for 1Q 2013
# Subset ewport by data from December 31, 2012 to March 31, 2013
ewq1 <- subset(ewport,
ewport$date >= "2016-12-30" &
ewport$date <= "2017-06-29")
ewq1[c(1:3, nrow(ewq1)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 NA NA NA NA
## 2 2017-01-03 1.0050675 1.0070809 1.0028492 1.0154429
## 3 2017-01-04 1.0046572 0.9955257 0.9988807 1.0460851
## 125 2017-06-29 0.9854594 0.9812321 0.9852567 0.9717434
# Calculate the cumulative gross returns fro each security for Q1
ewq1[1, 2:5] <- 1
ewq1[2:5] <- cumprod(ewq1[2:5])
ewq1[c(1:3, nrow(ewq1)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 1.000000 1.000000 1.000000 1.000000
## 2 2017-01-03 1.005068 1.007081 1.002849 1.015443
## 3 2017-01-04 1.009748 1.002575 1.001727 1.062240
## 125 2017-06-29 1.301466 1.115227 1.251067 1.688193
# Calculate the index value for each security for Q1
num.sec <- 3
ewq1$AMZN.ind <- ewq1$AMZN / num.sec
ewq1$MSFT.ind <- ewq1$MSFT / num.sec
ewq1$AAPL.ind <- ewq1$AAPL / num.sec
ewq1$TSLA.ind <- ewq1$TSLA / num.sec
ewq1[c(1:3, nrow(ewq1)), ]
## date AMZN MSFT AAPL TSLA AMZN.ind MSFT.ind
## 1 2016-12-30 1.000000 1.000000 1.000000 1.000000 0.3333333 0.3333333
## 2 2017-01-03 1.005068 1.007081 1.002849 1.015443 0.3350225 0.3356936
## 3 2017-01-04 1.009748 1.002575 1.001727 1.062240 0.3365828 0.3341916
## 125 2017-06-29 1.301466 1.115227 1.251067 1.688193 0.4338219 0.3717422
## AAPL.ind TSLA.ind
## 1 0.3333333 0.3333333
## 2 0.3342831 0.3384810
## 3 0.3339089 0.3540799
## 125 0.4170222 0.5627311
# Calculate the agrregate portfolio value on each day
q1.val <- data.frame(rowSums(ewq1[, 6:9]))
q1.val[c(1:3, nrow(q1.val)), ]
## [1] 1.333333 1.343480 1.358763 1.785317
names(q1.val) <- paste("port.val")
q1.val$date <- ewq1$date
q1.val[c(1:3, nrow(q1.val)), ]
## port.val date
## 1 1.333333 2016-12-30
## 2 1.343480 2017-01-03
## 3 1.358763 2017-01-04
## 125 1.785317 2017-06-29
# Pass the aggregate portfolio value at the end of Q1 to Q2
q2.inv <- q1.val[nrow(q1.val), 1]
q2.inv
## [1] 1.785317Step 4: Calculate EW Portfolio Values for 2Q 2013
# Subset ewport by data from April 1, 2013 to June 30, 2013
ewq2 <- subset(ewport,
ewport$date >= "2017-04-01" &
ewport$date <= "2017-06-30")
ewq2[c(1:3, nrow(ewq2)), ]
## date AMZN MSFT AAPL TSLA
## 64 2017-04-03 1.0056061 0.9952930 1.0002784 1.0726554
## 65 2017-04-04 1.0171843 1.0027460 1.0074461 1.0173523
## 66 2017-04-05 1.0027017 0.9974135 0.9948194 0.9713533
## 125 2017-06-29 0.9854594 0.9812321 0.9852567 0.9717434
# Calculate the cumulative gross returns fro each security for Q2
#ewq2[1, 2:4] <- 1 This is the difference from Q1
ewq2[2:5] <- cumprod(ewq2[2:5])
ewq2[c(1:3, nrow(ewq2)), ]
## date AMZN MSFT AAPL TSLA
## 64 2017-04-03 1.005606 0.9952930 1.000278 1.072655
## 65 2017-04-04 1.022887 0.9980261 1.007727 1.091269
## 66 2017-04-05 1.025650 0.9954448 1.002506 1.060007
## 125 2017-06-29 1.100830 1.0458940 1.004267 1.296263
# Calculate the index value for each security for Q2
ewq2$AMZN.ind <- (q2.inv / num.sec) * ewq2$AMZN #a difference from Q1
ewq2$MSFT.ind <- (q2.inv / num.sec) * ewq2$MSFT
ewq2$AAPL.ind <- (q2.inv / num.sec) * ewq2$AAPL
ewq2$TSLA.ind <- (q2.inv / num.sec) * ewq2$TSLA
ewq2[c(1:3, nrow(ewq2)), ]
## date AMZN MSFT AAPL TSLA AMZN.ind MSFT.ind
## 64 2017-04-03 1.005606 0.9952930 1.000278 1.072655 0.5984420 0.5923046
## 65 2017-04-04 1.022887 0.9980261 1.007727 1.091269 0.6087258 0.5939311
## 66 2017-04-05 1.025650 0.9954448 1.002506 1.060007 0.6103705 0.5923949
## 125 2017-06-29 1.100830 1.0458940 1.004267 1.296263 0.6551104 0.6224176
## AAPL.ind TSLA.ind
## 64 0.5952715 0.6383435
## 65 0.5997039 0.6494202
## 66 0.5965971 0.6308164
## 125 0.5976452 0.7714137
# Calculate the agrregate portfolio value on each day
q2.val <- data.frame(rowSums(ewq2[, 6:9]))
q2.val[c(1:3, nrow(q2.val)), ]
## [1] 2.424362 2.451781 2.430179 2.646587
names(q2.val) <- paste("port.val")
q2.val$date <- ewq2$date
q2.val[c(1:3, nrow(q2.val)), ]
## port.val date
## 64 2.424362 2017-04-03
## 65 2.451781 2017-04-04
## 66 2.430179 2017-04-05
## 125 2.646587 2017-06-29
#$1.121 at the end of Q1 and 1.125
#Means the portfolio grown from $1.121 at the end of Q1 to $1.125 at the end of Q2
#Note that it doesn't mean that the portfolio grew 12.5% during Q2 but 12.5% during Q1-Q2
#b/c this is a cumulative measure Step 7: Combine Quarterly EW Portfolio Values into One Data Object
ew.portval <- rbind(q1.val, q2.val)
ew.portval[c(1:3, nrow(ew.portval)), ]
## port.val date
## 1 1.333333 2016-12-30
## 2 1.343480 2017-01-03
## 3 1.358763 2017-01-04
## 1251 2.646587 2017-06-29It’s considerd as an approach to track the changes in the size of the sector. Larger firms are given more weight in VW, whereas equal weights are given in EW portfolio. An example is the S&P 500 Index. It rebalances only at the beginning of each quarter.
Step 1: Keep Only Variables We Need to Construct EW Portfolio
vwport <- port[, c(1, 2, 4, 6, 8, 10:13)] #port is from 3.3 Constructing Benchmark Portfolio Returns
vwport[c(1:3, nrow(vwport)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close
## 2016-12-30 2016-12-30 749.87 62.14 115.82 213.69
## 2017-01-03 2017-01-03 753.67 62.58 116.15 216.99
## 2017-01-04 2017-01-04 757.18 62.30 116.02 226.99
## 2017-06-29 2017-06-29 975.93 68.49 143.68 360.75
## AMZN.ret MSFT.ret AAPL.ret TSLA.ret
## 2016-12-30 NA NA NA NA
## 2017-01-03 0.005067529 0.007080902 0.002849238 0.01544295
## 2017-01-04 0.004657224 -0.004474330 -0.001119264 0.04608507
## 2017-06-29 -0.014540632 -0.018767921 -0.014743251 -0.02825663
rownames(vwport) <- seq(1:nrow(vwport))
vwport[c(1:3, nrow(vwport)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.ret
## 1 2016-12-30 749.87 62.14 115.82 213.69 NA
## 2 2017-01-03 753.67 62.58 116.15 216.99 0.005067529
## 3 2017-01-04 757.18 62.30 116.02 226.99 0.004657224
## 125 2017-06-29 975.93 68.49 143.68 360.75 -0.014540632
## MSFT.ret AAPL.ret TSLA.ret
## 1 NA NA NA
## 2 0.007080902 0.002849238 0.01544295
## 3 -0.004474330 -0.001119264 0.04608507
## 125 -0.018767921 -0.014743251 -0.02825663Step 2: Convert Net Returns to Gross Returns
vwport$AMZN.ret <- vwport$AMZN.ret + 1
vwport$MSFT.ret <- vwport$MSFT.ret + 1
vwport$AAPL.ret <- vwport$AAPL.ret + 1
vwport$TSLA.ret <- vwport$TSLA.ret + 1
vwport[c(1:3, nrow(vwport)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.ret
## 1 2016-12-30 749.87 62.14 115.82 213.69 NA
## 2 2017-01-03 753.67 62.58 116.15 216.99 1.0050675
## 3 2017-01-04 757.18 62.30 116.02 226.99 1.0046572
## 125 2017-06-29 975.93 68.49 143.68 360.75 0.9854594
## MSFT.ret AAPL.ret TSLA.ret
## 1 NA NA NA
## 2 1.0070809 1.0028492 1.0154429
## 3 0.9955257 0.9988807 1.0460851
## 125 0.9812321 0.9852567 0.9717434Step 3: Calculate the Market Capitalization of Each Security in the Portfolio
# Construct Series of Calendar Days
date <- seq(as.Date("2016-12-31"), as.Date("2017-12-31"), by = 1)
date <- data.frame(date)
date[c(1:3, nrow(date)), ]
## [1] "2016-12-31" "2017-01-01" "2017-01-02" "2017-12-31"
# Create Data Object with Daily Prices
PRICE.qtr <- vwport[, 1:5]
PRICE.qtr[c(1:3, nrow(PRICE.qtr)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close
## 1 2016-12-30 749.87 62.14 115.82 213.69
## 2 2017-01-03 753.67 62.58 116.15 216.99
## 3 2017-01-04 757.18 62.30 116.02 226.99
## 125 2017-06-29 975.93 68.49 143.68 360.75
# Filling in Last Available Price on Non-trading Days
PRICE.qtr <- na.locf(merge(x = date, y = PRICE.qtr, by = "date", all.x = TRUE))
#all.x=T tells R to keep all the data in the "by" varialble that is available in x=date
#na.locf tells R to copy over the last available data in y
#when there is a date in x that is not available in y
PRICE.qtr[c(1:3, nrow(PRICE.qtr)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close
## 1 2016-12-31 <NA> <NA> <NA> <NA>
## 2 2017-01-01 <NA> <NA> <NA> <NA>
## 3 2017-01-02 <NA> <NA> <NA> <NA>
## 366 2017-12-31 975.93 68.49 143.68 360.75
# Keep Only Prices at the End of Each Calendar Quarter
PRICE.qtr <- subset(PRICE.qtr,
PRICE.qtr$date == as.Date("2016-12-31") |
PRICE.qtr$date == as.Date("2017-06-30"))
PRICE.qtr
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close
## 1 2016-12-31 <NA> <NA> <NA> <NA>
## 182 2017-06-30 975.93 68.49 143.68 360.75
# Obtain Share Outstanding Data from SEC Filings
#http://www.sec.gov/edgar/searchedgar/companysearch.html
#Interactive Data / Financial Statements / Condensed Consolidated Balance Sheets
PRICE.qtr$AMZN.shout <- c(477000000, 478000000)
PRICE.qtr$MSFT.shout <- c(7808000000, 7723000000)
PRICE.qtr$AAPL.shout <- c(5336166000, 5205815000)
PRICE.qtr$TSLA.shout <- c(161561000, 164164000)
PRICE.qtr
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.shout
## 1 2016-12-31 <NA> <NA> <NA> <NA> 4.77e+08
## 182 2017-06-30 975.93 68.49 143.68 360.75 4.78e+08
## MSFT.shout AAPL.shout TSLA.shout
## 1 7.808e+09 5336166000 161561000
## 182 7.723e+09 5205815000 164164000
# Calculate Market Capitalization of Each Security
str(PRICE.qtr)
## 'data.frame': 2 obs. of 9 variables:
## $ date : chr "2016-12-31" "2017-06-30"
## $ AMZN.Close: chr NA " 975.93"
## $ MSFT.Close: chr NA "68.49"
## $ AAPL.Close: chr NA "143.68"
## $ TSLA.Close: chr NA "360.75"
## $ AMZN.shout: num 4.77e+08 4.78e+08
## $ MSFT.shout: num 7.81e+09 7.72e+09
## $ AAPL.shout: num 5.34e+09 5.21e+09
## $ TSLA.shout: num 1.62e+08 1.64e+08
PRICE.qtr$date <- as.Date(PRICE.qtr$date)
PRICE.qtr$AMZN.Close <- as.numeric(PRICE.qtr$AMZN.Close)
PRICE.qtr$MSFT.Close <- as.numeric(PRICE.qtr$MSFT.Close)
PRICE.qtr$AAPL.Close <- as.numeric(PRICE.qtr$AAPL.Close)
PRICE.qtr$TSLA.Close <- as.numeric(PRICE.qtr$TSLA.Close)
str(PRICE.qtr)
## 'data.frame': 2 obs. of 9 variables:
## $ date : Date, format: "2016-12-31" "2017-06-30"
## $ AMZN.Close: num NA 976
## $ MSFT.Close: num NA 68.5
## $ AAPL.Close: num NA 144
## $ TSLA.Close: num NA 361
## $ AMZN.shout: num 4.77e+08 4.78e+08
## $ MSFT.shout: num 7.81e+09 7.72e+09
## $ AAPL.shout: num 5.34e+09 5.21e+09
## $ TSLA.shout: num 1.62e+08 1.64e+08
weights <- PRICE.qtr
weights$AMZN.mcap <- weights$AMZN.Close * weights$AMZN.shout
weights$MSFT.mcap <- weights$MSFT.Close * weights$MSFT.shout
weights$AAPL.mcap <- weights$AAPL.Close * weights$AAPL.shout
weights$TSLA.mcap <- weights$TSLA.Close * weights$TSLA.shout
weights
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.shout
## 1 2016-12-31 NA NA NA NA 4.77e+08
## 182 2017-06-30 975.93 68.49 143.68 360.75 4.78e+08
## MSFT.shout AAPL.shout TSLA.shout AMZN.mcap MSFT.mcap
## 1 7.808e+09 5336166000 161561000 NA NA
## 182 7.723e+09 5205815000 164164000 466494540000 528948270000
## AAPL.mcap TSLA.mcap
## 1 NA NA
## 182 747971499200 59222163000
# Calculate Quarter-end Aggregate Market Capitalization
weights$tot.mcap <- rowSums(weights[, 10:13])
weights
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.shout
## 1 2016-12-31 NA NA NA NA 4.77e+08
## 182 2017-06-30 975.93 68.49 143.68 360.75 4.78e+08
## MSFT.shout AAPL.shout TSLA.shout AMZN.mcap MSFT.mcap
## 1 7.808e+09 5336166000 161561000 NA NA
## 182 7.723e+09 5205815000 164164000 466494540000 528948270000
## AAPL.mcap TSLA.mcap tot.mcap
## 1 NA NA NA
## 182 747971499200 59222163000 1.802636e+12Step 4: Calculate Quarter-end Weights of Each Security in the Portfolio
weights$AMZN.wgt <- weights$AMZN.mcap / weights$tot.mcap
weights$MSFT.wgt <- weights$MSFT.mcap / weights$tot.mcap
weights$AAPL.wgt <- weights$AAPL.mcap / weights$tot.mcap
weights$TSLA.wgt <- weights$TSLA.mcap / weights$tot.mcap
weights
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.shout
## 1 2016-12-31 NA NA NA NA 4.77e+08
## 182 2017-06-30 975.93 68.49 143.68 360.75 4.78e+08
## MSFT.shout AAPL.shout TSLA.shout AMZN.mcap MSFT.mcap
## 1 7.808e+09 5336166000 161561000 NA NA
## 182 7.723e+09 5205815000 164164000 466494540000 528948270000
## AAPL.mcap TSLA.mcap tot.mcap AMZN.wgt MSFT.wgt AAPL.wgt
## 1 NA NA NA NA NA NA
## 182 747971499200 59222163000 1.802636e+12 0.2587846 0.2934304 0.414932
## TSLA.wgt
## 1 NA
## 182 0.03285308
weights <- weights[, c(1, 10:18)]
weights
## date AMZN.mcap MSFT.mcap AAPL.mcap TSLA.mcap
## 1 2016-12-31 NA NA NA NA
## 182 2017-06-30 466494540000 528948270000 747971499200 59222163000
## tot.mcap AMZN.wgt MSFT.wgt AAPL.wgt TSLA.wgt
## 1 NA NA NA NA NA
## 182 1.802636e+12 0.2587846 0.2934304 0.414932 0.03285308
weights$date <- weights$date + 1 #since the weights are applicable at the start of the next Q
weights
## date AMZN.mcap MSFT.mcap AAPL.mcap TSLA.mcap
## 1 2017-01-01 NA NA NA NA
## 182 2017-07-01 466494540000 528948270000 747971499200 59222163000
## tot.mcap AMZN.wgt MSFT.wgt AAPL.wgt TSLA.wgt
## 1 NA NA NA NA NA
## 182 1.802636e+12 0.2587846 0.2934304 0.414932 0.03285308Step 5: Calculate the Quarterly VW Portfolio Values
q1.vw.wgt <- subset(weights, date == "2017-12-30") #I'm not sure why author took the long way
q2.vw.wgt <- subset(weights, date == "2017-06-29")
q1.vw.wgt
## [1] date AMZN.mcap MSFT.mcap AAPL.mcap TSLA.mcap tot.mcap AMZN.wgt
## [8] MSFT.wgt AAPL.wgt TSLA.wgt
## <0 rows> (or 0-length row.names)
q2.vw.wgt
## [1] date AMZN.mcap MSFT.mcap AAPL.mcap TSLA.mcap tot.mcap AMZN.wgt
## [8] MSFT.wgt AAPL.wgt TSLA.wgt
## <0 rows> (or 0-length row.names)Step 7: Calculate VW Portfolio Values for 1Q 2013
vw.q1 <- subset(vwport,
vwport$date >= as.Date("2016-12-30") &
vwport$date <= as.Date("2017-06-29"))
vw.q1[c(1:3, nrow(vw.q1)), ]
## date AMZN.Close MSFT.Close AAPL.Close TSLA.Close AMZN.ret
## 1 2016-12-30 749.87 62.14 115.82 213.69 NA
## 2 2017-01-03 753.67 62.58 116.15 216.99 1.0050675
## 3 2017-01-04 757.18 62.30 116.02 226.99 1.0046572
## 125 2017-06-29 975.93 68.49 143.68 360.75 0.9854594
## MSFT.ret AAPL.ret TSLA.ret
## 1 NA NA NA
## 2 1.0070809 1.0028492 1.0154429
## 3 0.9955257 0.9988807 1.0460851
## 125 0.9812321 0.9852567 0.9717434
vw.q1 <- vw.q1[, c(1, 6:9)]
vw.q1[c(1:3, nrow(vw.q1)), ]
## date AMZN.ret MSFT.ret AAPL.ret TSLA.ret
## 1 2016-12-30 NA NA NA NA
## 2 2017-01-03 1.0050675 1.0070809 1.0028492 1.0154429
## 3 2017-01-04 1.0046572 0.9955257 0.9988807 1.0460851
## 125 2017-06-29 0.9854594 0.9812321 0.9852567 0.9717434
names(vw.q1)[2:5] <- paste(c("AMZN", "MSFT", "AAPL", "TSLA"))
vw.q1[c(1:3, nrow(vw.q1)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 NA NA NA NA
## 2 2017-01-03 1.0050675 1.0070809 1.0028492 1.0154429
## 3 2017-01-04 1.0046572 0.9955257 0.9988807 1.0460851
## 125 2017-06-29 0.9854594 0.9812321 0.9852567 0.9717434
vw.q1[1, 2:5] <- 1
vw.q1$AMZN <- cumprod(vw.q1$AMZN)
vw.q1$MSFT <- cumprod(vw.q1$MSFT)
vw.q1$AAPL <- cumprod(vw.q1$AAPL)
vw.q1$TSLA <- cumprod(vw.q1$TSLA)
vw.q1[c(1:3, nrow(vw.q1)), ]
## date AMZN MSFT AAPL TSLA
## 1 2016-12-30 1.000000 1.000000 1.000000 1.000000
## 2 2017-01-03 1.005068 1.007081 1.002849 1.015443
## 3 2017-01-04 1.009748 1.002575 1.001727 1.062240
## 125 2017-06-29 1.301466 1.115227 1.251067 1.688193