Quiz on Ch1: Prices
Jason Blais
Go to Yahoo Finance and import Netflix stock prices from December 13, 2002 to July 31, 2018.
Q1 What is the closing price of the stock on December 13, 2002?
The price of the stock at the end of the day on December 13, 2002 was about $0.893.
# Load csv file
data.NFLX <- read.csv("/resources/rstudio/Financial Modeling/Data/NFLX.csv", header = TRUE)
head(data.NFLX)
## Date Open High Low Close Adj.Close Volume
## 1 12/13/2002 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 12/16/2002 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 12/17/2002 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 12/18/2002 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 12/19/2002 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 12/20/2002 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## Date Open High Low Close Adj.Close Volume
## 3929 7/24/2018 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 7/25/2018 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 7/26/2018 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 7/27/2018 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 7/30/2018 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 7/31/2018 331.51 342.50 328.00 337.45 337.45 14085400Convert the date variable from a Factor to a Date.
class(data.NFLX$Date)
## [1] "factor"
date <- as.Date(data.NFLX$Date, format = "%m/%d/%Y")
class(date)
## [1] "Date"Combine date and data.NFLX.
data.NFLX <- cbind(date, data.NFLX[, -1])
head(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 1 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 3929 2018-07-24 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 2018-07-25 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 2018-07-26 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 2018-07-27 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 2018-07-30 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 2018-07-31 331.51 342.50 328.00 337.45 337.45 14085400Sort the data in chronological order.
data.NFLX <- data.NFLX[order(data.NFLX$date),]
head(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 1 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 3929 2018-07-24 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 2018-07-25 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 2018-07-26 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 2018-07-27 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 2018-07-30 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 2018-07-31 331.51 342.50 328.00 337.45 337.45 14085400Convert data.frame object to xts object.
Q2 How many variables (columns) does your data have after conversion?
There are six colums in my data.
class(data.NFLX)
## [1] "data.frame"
library(xts)
data.NFLX <- xts(data.NFLX[, 2:7],order.by = data.NFLX[, 1])
class(data.NFLX)
## [1] "xts" "zoo"Rename variables
names(data.NFLX)
## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume"
names(data.NFLX) <- paste(c("NFLX.Open", "NFLX.High", "NFLX.Low",
"NFLX.Close", "NFLX.Adjusted", "NFLX.Volume"))
head(data.NFLX)
## NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Adjusted
## 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857
## 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571
## 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429
## 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000
## 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429
## 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286
## NFLX.Volume
## 2002-12-13 1395800
## 2002-12-16 2098600
## 2002-12-17 1680000
## 2002-12-18 13378400
## 2002-12-19 3781400
## 2002-12-20 4384800
tail(data.NFLX)
## NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Adjusted
## 2018-07-24 366.94 367.40 354.56 357.32 357.32
## 2018-07-25 357.57 363.28 355.65 362.87 362.87
## 2018-07-26 358.19 365.54 356.63 363.09 363.09
## 2018-07-27 366.85 367.00 351.65 355.21 355.21
## 2018-07-30 351.93 352.03 334.02 334.96 334.96
## 2018-07-31 331.51 342.50 328.00 337.45 337.45
## NFLX.Volume
## 2018-07-24 12851500
## 2018-07-25 8467800
## 2018-07-26 6993700
## 2018-07-27 8949500
## 2018-07-30 18260700
## 2018-07-31 14085400Plot the data
Q3 During what year (of the study period) did the stock reach the highest price?
plot(data.NFLX$NFLX.Close)
Q4 What is the highest price the stock ever reached? Consider only the study period.
The high of the data was $423.210.
summary(data.NFLX)
## Index NFLX.Open NFLX.High
## Min. :2002-12-13 Min. : 0.6929 Min. : 0.7086
## 1st Qu.:2006-11-08 1st Qu.: 3.7343 1st Qu.: 3.8243
## Median :2010-10-06 Median : 10.6107 Median : 10.8643
## Mean :2010-10-07 Mean : 45.9126 Mean : 46.6075
## 3rd Qu.:2014-09-03 3rd Qu.: 60.8068 3rd Qu.: 61.5161
## Max. :2018-07-31 Max. :421.3800 Max. :423.2100
## NFLX.Low NFLX.Close NFLX.Adjusted
## Min. : 0.6843 Min. : 0.6907 Min. : 0.6907
## 1st Qu.: 3.6429 1st Qu.: 3.7286 1st Qu.: 3.7286
## Median : 10.3607 Median : 10.6179 Median : 10.6179
## Mean : 45.1841 Mean : 45.9355 Mean : 45.9355
## 3rd Qu.: 60.1032 3rd Qu.: 60.7368 3rd Qu.: 60.7368
## Max. :413.0800 Max. :418.9700 Max. :418.9700
## NFLX.Volume
## Min. : 866300
## 1st Qu.: 7126450
## Median : 12444600
## Mean : 18683695
## 3rd Qu.: 22590575
## Max. :323414000