Stock Analysis
Bethany Eubank and Hillary Oliver
May 2, 2019
- Q1 Get stock prices from the past 10 years
- Q2 Create a line chart:
- Q3 Calculate the yearly returns:
- Q4 Create a boxplot
- Q5 Which of the two stocks is the typical yearly return expected to be higher?
- Q6 Create a density plot:
- Q7 Create a kurtosis and skewness
- Q8 Would you consider the standard deviation as an appropriate measure of risk for the stocks listed?
- Q9 Import Disneyās Stock
- Q 10a Calculate 20 day moving averages and 20-day running standard deviation:
- Q10b Calculate the Bollinger Bands:
- Q10c Keep variables to build the Bollinger Bands:
- Q10d Transform data to long form from wide form for graphing
- Q10e Visualize data
- Q11 Import Appleās Stock
- Q12a Calculate 20 day moving averages and 20-day running standard deviation:
- Q12b Calculate the Bollinger Bands:
- Q12c Keep variables to build the Bollinger Bands:
- Q12d Transform data to long form from wide form for graphing
- Q12e Visualize data
- Q13 Which of the two stocks is expected to be riskier?
- Q14 Which stock would we buy?
Q1 Get stock prices from the past 10 years
## # A tibble: 5,032 x 8
## # Groups: symbol [2]
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 DIS 2009-05-04 22.0 22.9 21.8 22.9 17603700 19.9
## 2 DIS 2009-05-05 22.7 23.3 22.7 23.2 16806900 20.2
## 3 DIS 2009-05-06 25.0 26.3 24.7 25.9 49777100 22.5
## 4 DIS 2009-05-07 25.8 26.2 25.0 25.3 23981600 22.1
## 5 DIS 2009-05-08 25.7 26 25 25.5 18172900 22.2
## 6 DIS 2009-05-11 25.1 25.2 24.7 24.7 14656600 21.5
## 7 DIS 2009-05-12 24.9 24.9 23.9 24.3 15536900 21.2
## 8 DIS 2009-05-13 23.9 24.0 23.4 23.6 14055900 20.6
## 9 DIS 2009-05-14 23.6 24.0 23.3 23.5 11893400 20.5
## 10 DIS 2009-05-15 23.5 24.2 23.3 23.4 20991800 20.4
## # ... with 5,022 more rowsQ2 Create a line chart:
Q3 Calculate the yearly returns:
## # A tibble: 22 x 3
## # Groups: symbol [2]
## symbol date yearly.returns
## <chr> <date> <dbl>
## 1 DIS 2009-12-31 0.427
## 2 DIS 2010-12-31 0.176
## 3 DIS 2011-12-30 0.0165
## 4 DIS 2012-12-31 0.348
## 5 DIS 2013-12-31 0.553
## 6 DIS 2014-12-31 0.249
## 7 DIS 2015-12-31 0.129
## 8 DIS 2016-12-30 0.00672
## 9 DIS 2017-12-29 0.0476
## 10 DIS 2018-12-31 0.0361
## # ... with 12 more rowsQ4 Create a boxplot
Q5 Which of the two stocks is the typical yearly return expected to be higher?
-Appleās yearly return is expected to be higher than Disney because Appleās median yearly return is higher than Disney.
Q6 Create a density plot:
Q7 Create a kurtosis and skewness
## # A tibble: 2 x 2
## # Groups: symbol [2]
## symbol skewness.1
## <chr> <dbl>
## 1 DIS 0.608
## 2 AAPL -0.442
## # A tibble: 2 x 2
## # Groups: symbol [2]
## symbol kurtosis.1
## <chr> <dbl>
## 1 DIS -0.755
## 2 AAPL -1.05Q8 Would you consider the standard deviation as an appropriate measure of risk for the stocks listed?
-Apple has a negative skewness which means large negative returns will occur more often. Disney has a positive skewness which means large positive returns are likely to occur more often. Apple and Disney both have kurtosis that is below zero so larger returns of both positive and negative are less likely to occur more often (they will have skinnier tails). Because of this information, I have determined that standard deviation is not an appropriate measure of risk for these stocks because they are not normally distributed so there is a greater chance of variation.
Q9 Import Disneyās Stock
## # A tibble: 251 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 100. 101. 99.2 99.6 9286400 98.1
## 2 2018-05-03 99.1 99.2 97.7 98.8 10450800 97.2
## 3 2018-05-04 98.7 101. 98.6 101. 9980100 99.6
## 4 2018-05-07 102. 103. 102. 102. 10181100 101.
## 5 2018-05-08 101. 103. 101. 102. 14229500 100.
## 6 2018-05-09 102. 102. 99.3 100.0 18345700 98.4
## 7 2018-05-10 100. 102. 100. 102. 8927300 100.
## 8 2018-05-11 102. 102. 101. 102. 7036000 100.
## 9 2018-05-14 102. 103. 102. 102. 9891900 101.
## 10 2018-05-15 102. 103. 102. 103. 5762400 101.
## # ... with 241 more rowsQ 10a Calculate 20 day moving averages and 20-day running standard deviation:
## # A tibble: 251 x 9
## date open high low close volume adjusted SMA SD
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 100. 101. 99.2 99.6 9286400 98.1 NA NA
## 2 2018-05-03 99.1 99.2 97.7 98.8 10450800 97.2 NA NA
## 3 2018-05-04 98.7 101. 98.6 101. 9980100 99.6 NA NA
## 4 2018-05-07 102. 103. 102. 102. 10181100 101. NA NA
## 5 2018-05-08 101. 103. 101. 102. 14229500 100. NA NA
## 6 2018-05-09 102. 102. 99.3 100.0 18345700 98.4 NA NA
## 7 2018-05-10 100. 102. 100. 102. 8927300 100. NA NA
## 8 2018-05-11 102. 102. 101. 102. 7036000 100. NA NA
## 9 2018-05-14 102. 103. 102. 102. 9891900 101. NA NA
## 10 2018-05-15 102. 103. 102. 103. 5762400 101. NA NA
## # ... with 241 more rowsQ10b Calculate the Bollinger Bands:
## # A tibble: 251 x 11
## date open high low close volume adjusted SMA SD sd2up
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 100. 101. 99.2 99.6 9.29e6 98.1 NA NA NA
## 2 2018-05-03 99.1 99.2 97.7 98.8 1.05e7 97.2 NA NA NA
## 3 2018-05-04 98.7 101. 98.6 101. 9.98e6 99.6 NA NA NA
## 4 2018-05-07 102. 103. 102. 102. 1.02e7 101. NA NA NA
## 5 2018-05-08 101. 103. 101. 102. 1.42e7 100. NA NA NA
## 6 2018-05-09 102. 102. 99.3 100.0 1.83e7 98.4 NA NA NA
## 7 2018-05-10 100. 102. 100. 102. 8.93e6 100. NA NA NA
## 8 2018-05-11 102. 102. 101. 102. 7.04e6 100. NA NA NA
## 9 2018-05-14 102. 103. 102. 102. 9.89e6 101. NA NA NA
## 10 2018-05-15 102. 103. 102. 103. 5.76e6 101. NA NA NA
## # ... with 241 more rows, and 1 more variable: sd2down <dbl>Q10c Keep variables to build the Bollinger Bands:
## # A tibble: 251 x 5
## date close SMA sd2up sd2down
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 99.6 NA NA NA
## 2 2018-05-03 98.8 NA NA NA
## 3 2018-05-04 101. NA NA NA
## 4 2018-05-07 102. NA NA NA
## 5 2018-05-08 102. NA NA NA
## 6 2018-05-09 100.0 NA NA NA
## 7 2018-05-10 102. NA NA NA
## 8 2018-05-11 102. NA NA NA
## 9 2018-05-14 102. NA NA NA
## 10 2018-05-15 103. NA NA NA
## # ... with 241 more rowsQ10d Transform data to long form from wide form for graphing
## # A tibble: 1,004 x 3
## date type price
## <date> <chr> <dbl>
## 1 2018-05-02 close 99.6
## 2 2018-05-03 close 98.8
## 3 2018-05-04 close 101.
## 4 2018-05-07 close 102.
## 5 2018-05-08 close 102.
## 6 2018-05-09 close 100.0
## 7 2018-05-10 close 102.
## 8 2018-05-11 close 102.
## 9 2018-05-14 close 102.
## 10 2018-05-15 close 103.
## # ... with 994 more rowsQ10e Visualize data
Q11 Import Appleās Stock
## # A tibble: 251 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 175. 178. 174. 177. 66539400 174.
## 2 2018-05-03 176. 178. 174. 177. 34068200 174.
## 3 2018-05-04 178. 184. 178. 184. 56201300 181.
## 4 2018-05-07 185. 188. 185. 185. 42451400 182.
## 5 2018-05-08 185. 186. 184. 186. 28402800 183.
## 6 2018-05-09 187. 187. 185. 187. 23211200 185.
## 7 2018-05-10 188. 190. 188. 190. 27989300 187.
## 8 2018-05-11 189. 190. 187. 189. 26212200 186.
## 9 2018-05-14 189. 190. 188. 188. 20778800 186.
## 10 2018-05-15 187. 187. 185. 186. 23695200 184.
## # ... with 241 more rowsQ12a Calculate 20 day moving averages and 20-day running standard deviation:
## # A tibble: 251 x 9
## date open high low close volume adjusted SMA SD
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 175. 178. 174. 177. 66539400 174. NA NA
## 2 2018-05-03 176. 178. 174. 177. 34068200 174. NA NA
## 3 2018-05-04 178. 184. 178. 184. 56201300 181. NA NA
## 4 2018-05-07 185. 188. 185. 185. 42451400 182. NA NA
## 5 2018-05-08 185. 186. 184. 186. 28402800 183. NA NA
## 6 2018-05-09 187. 187. 185. 187. 23211200 185. NA NA
## 7 2018-05-10 188. 190. 188. 190. 27989300 187. NA NA
## 8 2018-05-11 189. 190. 187. 189. 26212200 186. NA NA
## 9 2018-05-14 189. 190. 188. 188. 20778800 186. NA NA
## 10 2018-05-15 187. 187. 185. 186. 23695200 184. NA NA
## # ... with 241 more rowsQ12b Calculate the Bollinger Bands:
## # A tibble: 251 x 11
## date open high low close volume adjusted SMA SD sd2up
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 175. 178. 174. 177. 6.65e7 174. NA NA NA
## 2 2018-05-03 176. 178. 174. 177. 3.41e7 174. NA NA NA
## 3 2018-05-04 178. 184. 178. 184. 5.62e7 181. NA NA NA
## 4 2018-05-07 185. 188. 185. 185. 4.25e7 182. NA NA NA
## 5 2018-05-08 185. 186. 184. 186. 2.84e7 183. NA NA NA
## 6 2018-05-09 187. 187. 185. 187. 2.32e7 185. NA NA NA
## 7 2018-05-10 188. 190. 188. 190. 2.80e7 187. NA NA NA
## 8 2018-05-11 189. 190. 187. 189. 2.62e7 186. NA NA NA
## 9 2018-05-14 189. 190. 188. 188. 2.08e7 186. NA NA NA
## 10 2018-05-15 187. 187. 185. 186. 2.37e7 184. NA NA NA
## # ... with 241 more rows, and 1 more variable: sd2down <dbl>Q12c Keep variables to build the Bollinger Bands:
## # A tibble: 251 x 5
## date close SMA sd2up sd2down
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2018-05-02 177. NA NA NA
## 2 2018-05-03 177. NA NA NA
## 3 2018-05-04 184. NA NA NA
## 4 2018-05-07 185. NA NA NA
## 5 2018-05-08 186. NA NA NA
## 6 2018-05-09 187. NA NA NA
## 7 2018-05-10 190. NA NA NA
## 8 2018-05-11 189. NA NA NA
## 9 2018-05-14 188. NA NA NA
## 10 2018-05-15 186. NA NA NA
## # ... with 241 more rowsQ12d Transform data to long form from wide form for graphing
## # A tibble: 1,004 x 3
## date type price
## <date> <chr> <dbl>
## 1 2018-05-02 close 177.
## 2 2018-05-03 close 177.
## 3 2018-05-04 close 184.
## 4 2018-05-07 close 185.
## 5 2018-05-08 close 186.
## 6 2018-05-09 close 187.
## 7 2018-05-10 close 190.
## 8 2018-05-11 close 189.
## 9 2018-05-14 close 188.
## 10 2018-05-15 close 186.
## # ... with 994 more rowsQ12e Visualize data
