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Hong Kong Stock 3
by Yeung Ka Ming, CFA
Summary
Compare return to market portfolio. The market portfolio for Hong Kong stock market is HSI Tracker ETF, 2800.HK
R Libraries
library(quantmod)
library(tidyverse)
library(ggplot2)
Preparation of data
Direct download by getSymbols
tickers <- c("0005.HK","9988.HK", "2800.HK")
hkport <- new.env()
getSymbols(tickers,
env=hkport,
src = "yahoo",
index.class = "POSIXct",
from = "2022-01-01")
## [1] "0005.HK" "9988.HK" "2800.HK"
# auto.assign is FALSE, so an environment is used here.
# Unlike US stocks, # the tickers in HK stock market is
# by number, which cannot be the variable
# name in R. Further fine tune in next step.
Rename column names
HSBC<-hkport$`0005.HK`
BABA<-hkport$`9988.HK`
HKTR<-hkport$`2800.HK`
# substitute numeric ticker name to character name. It is inconvenient to handle
# variable name starts with number.
colnames(BABA) <- sub("9988.HK","BABA",colnames(hkport$`9988.HK`))
head(hkport$`9988.HK`)
## 9988.HK.Open 9988.HK.High 9988.HK.Low 9988.HK.Close 9988.HK.Volume 9988.HK.Adjusted
## 2022-01-03 117.0 117.5 114.0 115.0 22176946 115.0
## 2022-01-04 118.4 118.9 115.7 116.9 23228903 116.9
## 2022-01-05 119.0 119.1 113.9 114.5 30717509 114.5
## 2022-01-06 117.5 121.1 117.3 121.0 47231895 121.0
## 2022-01-07 126.5 128.8 122.8 128.8 58778943 128.8
## 2022-01-10 128.8 129.5 125.4 127.6 36814976 127.6
head(BABA)
## BABA.Open BABA.High BABA.Low BABA.Close BABA.Volume BABA.Adjusted
## 2022-01-03 117.0 117.5 114.0 115.0 22176946 115.0
## 2022-01-04 118.4 118.9 115.7 116.9 23228903 116.9
## 2022-01-05 119.0 119.1 113.9 114.5 30717509 114.5
## 2022-01-06 117.5 121.1 117.3 121.0 47231895 121.0
## 2022-01-07 126.5 128.8 122.8 128.8 58778943 128.8
## 2022-01-10 128.8 129.5 125.4 127.6 36814976 127.6
colnames(HSBC) <- sub("0005.HK","HSBC",colnames(hkport$`0005.HK`))
head(hkport$`0005.HK`)
## 0005.HK.Open 0005.HK.High 0005.HK.Low 0005.HK.Close 0005.HK.Volume 0005.HK.Adjusted
## 2022-01-03 47.00 47.15 46.65 46.85 6711558 46.67877
## 2022-01-04 47.10 47.80 47.00 47.80 25500155 47.62530
## 2022-01-05 49.35 49.75 48.75 49.15 59292303 48.97037
## 2022-01-06 49.05 49.05 48.35 49.00 24559582 48.82091
## 2022-01-07 50.10 50.15 49.75 50.10 67407695 49.91689
## 2022-01-10 50.80 51.75 50.60 51.70 59640904 51.51105
head(HSBC)
## HSBC.Open HSBC.High HSBC.Low HSBC.Close HSBC.Volume HSBC.Adjusted
## 2022-01-03 47.00 47.15 46.65 46.85 6711558 46.67877
## 2022-01-04 47.10 47.80 47.00 47.80 25500155 47.62530
## 2022-01-05 49.35 49.75 48.75 49.15 59292303 48.97037
## 2022-01-06 49.05 49.05 48.35 49.00 24559582 48.82091
## 2022-01-07 50.10 50.15 49.75 50.10 67407695 49.91689
## 2022-01-10 50.80 51.75 50.60 51.70 59640904 51.51105
colnames(HKTR) <- sub("2800.HK","HKTR",colnames(hkport$`2800.HK`))
head(hkport$`2800.HK`)
## 2800.HK.Open 2800.HK.High 2800.HK.Low 2800.HK.Close 2800.HK.Volume 2800.HK.Adjusted
## 2022-01-03 23.66 23.76 23.34 23.40 100227198 23.40
## 2022-01-04 23.52 23.58 23.28 23.44 74708335 23.44
## 2022-01-05 23.44 23.44 23.00 23.04 149530742 23.04
## 2022-01-06 23.00 23.22 22.86 23.22 116238361 23.22
## 2022-01-07 23.40 23.66 23.28 23.64 169287010 23.64
## 2022-01-10 23.62 23.92 23.54 23.84 187033452 23.84
head(HKTR)
## HKTR.Open HKTR.High HKTR.Low HKTR.Close HKTR.Volume HKTR.Adjusted
## 2022-01-03 23.66 23.76 23.34 23.40 100227198 23.40
## 2022-01-04 23.52 23.58 23.28 23.44 74708335 23.44
## 2022-01-05 23.44 23.44 23.00 23.04 149530742 23.04
## 2022-01-06 23.00 23.22 22.86 23.22 116238361 23.22
## 2022-01-07 23.40 23.66 23.28 23.64 169287010 23.64
## 2022-01-10 23.62 23.92 23.54 23.84 187033452 23.84
Prepare a dataframe with portfolio value and plot graph
Prepare dataframe
# Suppose $100,000 for investment on the first trading
# day of 2022.
# Use around half of the amount for each stock (50/50)
moneyseed <- 100000
portion <- 0.5
shares <- c(portion*moneyseed/HSBC$HSBC.Adjusted[[1]],
portion*moneyseed/BABA$BABA.Adjusted[[1]],
moneyseed/HKTR$HKTR.Adjusted[[1]])
# need stock adjusted prices and number of shares to make a portfolio
prices.tbl <- as_tibble(fortify(merge(HSBC, BABA, HKTR)))
port.tbl <- prices.tbl %>%
select(Index,HSBC.Adjusted,BABA.Adjusted,HKTR.Adjusted)
port.tbl
## # A tibble: 45 x 4
## Index HSBC.Adjusted BABA.Adjusted HKTR.Adjusted
## <dttm> <dbl> <dbl> <dbl>
## 1 2022-01-03 00:00:00 46.7 115 23.4
## 2 2022-01-04 00:00:00 47.6 117. 23.4
## 3 2022-01-05 00:00:00 49.0 114. 23.0
## 4 2022-01-06 00:00:00 48.8 121 23.2
## 5 2022-01-07 00:00:00 49.9 129. 23.6
## 6 2022-01-10 00:00:00 51.5 128. 23.8
## 7 2022-01-11 00:00:00 51.8 126. 23.9
## 8 2022-01-12 00:00:00 52.6 133 24.5
## 9 2022-01-13 00:00:00 53.5 132. 24.6
## 10 2022-01-14 00:00:00 54.4 129. 24.5
## # ... with 35 more rows
port.tbl <- port.tbl %>%
mutate(port.value = shares[1]*HSBC.Adjusted +
shares[2]*BABA.Adjusted) %>%
mutate(mktport.value = shares[3]*HKTR.Adjusted)
port.tbl
## # A tibble: 45 x 6
## Index HSBC.Adjusted BABA.Adjusted HKTR.Adjusted port.value mktport.value
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2022-01-03 00:00:00 46.7 115 23.4 100000 100000
## 2 2022-01-04 00:00:00 47.6 117. 23.4 101840. 100171.
## 3 2022-01-05 00:00:00 49.0 114. 23.0 102237. 98462.
## 4 2022-01-06 00:00:00 48.8 121 23.2 104903. 99231.
## 5 2022-01-07 00:00:00 49.9 129. 23.6 109469. 101026.
## 6 2022-01-10 00:00:00 51.5 128. 23.8 110654. 101880.
## 7 2022-01-11 00:00:00 51.8 126. 23.9 110052. 101966.
## 8 2022-01-12 00:00:00 52.6 133 24.5 114123. 104701.
## 9 2022-01-13 00:00:00 53.5 132. 24.6 114735. 104957.
## 10 2022-01-14 00:00:00 54.4 129. 24.5 114435. 104615.
## # ... with 35 more rows
Plot graph
plot(x = port.tbl$Index,
y = port.tbl$mktport.value,
type="l", col="red", ylim = c(90000,120000))
lines(x = port.tbl$Index, y = port.tbl$port.value,
type="l", col="blue")
legend(x = "topright",
legend=c("Market Portfolio", "Portfolio"),
lty=c(1,1),col=c("red", "blue"))
port.long.tbl <- port.tbl %>%
pivot_longer(c(port.value,mktport.value),
names_to = "Portfolio",
values_to = "Value") %>%
select(c(Index,Portfolio,Value))
port.long.tbl
## # A tibble: 90 x 3
## Index Portfolio Value
## <dttm> <chr> <dbl>
## 1 2022-01-03 00:00:00 port.value 100000
## 2 2022-01-03 00:00:00 mktport.value 100000
## 3 2022-01-04 00:00:00 port.value 101840.
## 4 2022-01-04 00:00:00 mktport.value 100171.
## 5 2022-01-05 00:00:00 port.value 102237.
## 6 2022-01-05 00:00:00 mktport.value 98462.
## 7 2022-01-06 00:00:00 port.value 104903.
## 8 2022-01-06 00:00:00 mktport.value 99231.
## 9 2022-01-07 00:00:00 port.value 109469.
## 10 2022-01-07 00:00:00 mktport.value 101026.
## # ... with 80 more rows
p2 <- ggplot(port.long.tbl,
aes(x=Index, y=Value, color=Portfolio))
p2 + xlab("Date") + ylab("Portfolio Value") + geom_line()
