Quiz on Ch1: Prices
Tim Larocque
9/18/2018
- Q1 What is the closing price of the stock on December 13, 2002?
- Q2 How many variables (columns) does your data have after conversion?
- Q3 During what year (of the study period) did the stock reach the highest price?
- Q4 What is the highest price the stock ever reached? Consider only the study period.
- Hint: Use the closing price in the summary statistics below.
```
Q1 What is the closing price of the stock on December 13, 2002?
The closing price of the stock was .892857
# Load csv file
Data.NFLX <- read.csv("/resources/rstudio/FinModeling/Data/NFLX.csv", header = TRUE)
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 the date variable from a Factor to a Date.
class(Data.NFLX$Date)
## [1] "factor"
date <- as.Date(Data.NFLX$Date, format = "%Y-%m-%d")
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 14085400Convert data.frame object to xts object.
Q2 How many variables (columns) does your data have after conversion?
After the conversion, the data now has 6 variables (columns).
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?
The stock reached its highest price in July of 2018.
plot(Data.NFLX$NFLX.Close)
Q4 What is the highest price the stock ever reached? Consider only the study period.
Hint: Use the closing price in the summary statistics below.
The highest price that the stock has ever reached is 423.21. However, the highest clsoing price of the stock is 418.97.
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```