library(tidyverse)
library(tidyquant)Hint: Add group_by(symbol) at the end of the code so that calculations below will be done per stock.
from = today() - years(10)
Stocks <-
tq_get(c("AAPL", "AMZN", "MSFT"), get = "stock.prices", from = from) %>%
group_by(symbol)
Stocks
## # A tibble: 7,551 x 8
## # Groups: symbol [3]
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2010-05-10 35.8 36.4 35.5 36.3 246076600 31.5
## 2 AAPL 2010-05-11 36.0 37.1 35.8 36.6 212226700 31.8
## 3 AAPL 2010-05-12 37.0 37.6 37.0 37.4 163594900 32.5
## 4 AAPL 2010-05-13 37.6 37.9 36.6 36.9 149928100 32.0
## 5 AAPL 2010-05-14 36.5 36.6 35.6 36.3 189840700 31.5
## 6 AAPL 2010-05-17 36.4 36.6 35.4 36.3 190708700 31.5
## 7 AAPL 2010-05-18 36.7 36.9 35.8 36.1 195669600 31.3
## 8 AAPL 2010-05-19 35.6 36.1 35.0 35.5 256431700 30.8
## 9 AAPL 2010-05-20 34.6 34.8 33.7 34.0 320728800 29.5
## 10 AAPL 2010-05-21 33.3 34.9 33.0 34.6 305972800 30.0
## # … with 7,541 more rowsHint: Take the adjusted variable from Stocks, and calculate yearly returns using ***tq_transmute().
returns_yearly <-
Stocks %>%
tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "yearly")
returns_yearly
## # A tibble: 33 x 3
## # Groups: symbol [3]
## symbol date yearly.returns
## <chr> <date> <dbl>
## 1 AAPL 2010-12-31 0.270
## 2 AAPL 2011-12-30 0.256
## 3 AAPL 2012-12-31 0.326
## 4 AAPL 2013-12-31 0.0807
## 5 AAPL 2014-12-31 0.406
## 6 AAPL 2015-12-31 -0.0301
## 7 AAPL 2016-12-30 0.125
## 8 AAPL 2017-12-29 0.485
## 9 AAPL 2018-12-31 -0.0539
## 10 AAPL 2019-12-31 0.890
## # … with 23 more rowsHint: Take returns_yearly and pipe it to summarise. Calculate the mean yearly returns.
returns_yearly %>%
summarise(returns_avg = mean(yearly.returns))
## # A tibble: 3 x 2
## symbol returns_avg
## <chr> <dbl>
## 1 AAPL 0.254
## 2 AMZN 0.345
## 3 MSFT 0.222
returns_yearly
## # A tibble: 33 x 3
## # Groups: symbol [3]
## symbol date yearly.returns
## <chr> <date> <dbl>
## 1 AAPL 2010-12-31 0.270
## 2 AAPL 2011-12-30 0.256
## 3 AAPL 2012-12-31 0.326
## 4 AAPL 2013-12-31 0.0807
## 5 AAPL 2014-12-31 0.406
## 6 AAPL 2015-12-31 -0.0301
## 7 AAPL 2016-12-30 0.125
## 8 AAPL 2017-12-29 0.485
## 9 AAPL 2018-12-31 -0.0539
## 10 AAPL 2019-12-31 0.890
## # … with 23 more rowsHint: Take returns_yearly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute sd (standard deviation).
returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = sd)
## # A tibble: 3 x 2
## # Groups: symbol [3]
## symbol sd.1
## <chr> <dbl>
## 1 AAPL 0.275
## 2 AMZN 0.368
## 3 MSFT 0.194Hint: when the return distribution is not normal, the standard deviation is not an appropriate measure of risk. One can use skewness and kurtosis to detect non-normal returns. Take returns_yearly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute skewness. Do the same for kurtosis.
yearly_skewness <- returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = skewness)
yearly_kurtosis <- returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = kurtosis)Hint: Take returns_yearly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute table.DownsideRisk. Fully interpret downside risk measures in at least 100 words.
Downside_risk <- returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = table.DownsideRisk) %>%
t()Hint: Make your argument based on the three Sharpe Ratios. Fully interpret Sharpe Ratios in at least 100 words.
returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = SharpeRatio, Rf = .02) %>%
t()
## [,1] [,2] [,3]
## symbol "AAPL" "AMZN" "MSFT"
## ESSharpe(Rf=2%,p=95%) "1.160834" "1.304840" "1.853873"
## StdDevSharpe(Rf=2%,p=95%) "0.8501845" "0.8821035" "1.0439292"
## VaRSharpe(Rf=2%,p=95%) "2.533286" "2.155536" "3.018652"As we know any sharpe ratio greater than 1.0 is considered acceptable. Also, we know that the higher the sharpe ratio th better. The higher a sharpe ratio is means that there is a higher degree of expected return and a more relatively low amount of risk in most cases. For this reason, I decided that if I were to choose one of these three stocks I would choose microsoft because they have the highest sharpe ratio. Since their sharpe ratio is the highest that means the stock would give me a more favorable ratio the degree of expected return and the degree of risk. Obviously I would be looking for the highest degree of return and the lowest degree of risk and microsoft would give me the best balance between the two.
Hint: Make your argument based on the three Sharpe Ratios. Fully interpret Sharpe Ratios in at least 100 words.
returns_yearly %>%
tq_performance(Ra = yearly.returns,
Rb = NULL,
performance_fun = SharpeRatio, p= .01, Rf = .02) %>%
t()
## [,1] [,2] [,3]
## symbol "AAPL" "AMZN" "MSFT"
## ESSharpe(Rf=2%,p=1%) "0.2336311" "0.9015037" "0.5226757"
## StdDevSharpe(Rf=2%,p=1%) "0.8501845" "0.8821035" "1.0439292"
## VaRSharpe(Rf=2%,p=1%) "2.649956" "1.221477" "1.664520"After making the desired changes to the confidence interval my results and my decision did in fact change. After making the changes and going back to interpret the sharpe ratios once again I was able to come to the conclusion that out of the three stocks I would buy Amazon. I would buy amazon instead of the other two because in this case amazon has the highest sharpe ratio. Seeing that Amazon has the highest sharpe ratio it is clear to me that to make the safest and most profitable decision I must buy amazon. Amazon will give me a better balance between expected degree or return and amount of risk so for that reason, it is the safest and smartest option to buy of the three stocks listed.
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