Q1 Import stock prices of NASDAQ Compsite Index, Microsoft and Walmart for the last 30 years.

Hint: Add group_by(symbol) at the end of the code so that calculations below will be done per stock.

## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1
## # A tibble: 22,677 x 8
## # Groups:   symbol [3]
##    symbol date        open  high   low close   volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 WMT    1990-04-24  6.09  6.12  6.03  6.03  2812000     3.64
##  2 WMT    1990-04-25  6.06  6.19  6.06  6.17  3591200     3.72
##  3 WMT    1990-04-26  6.19  6.20  6.09  6.14  4599200     3.70
##  4 WMT    1990-04-27  6.14  6.17  6     6.03  3448800     3.64
##  5 WMT    1990-04-30  6.03  6.22  6     6.20  5861600     3.74
##  6 WMT    1990-05-01  6.25  6.34  6.25  6.28  5718400     3.79
##  7 WMT    1990-05-02  6.28  6.41  6.28  6.41  6201600     3.86
##  8 WMT    1990-05-03  6.41  6.45  6.20  6.22 12880000     3.75
##  9 WMT    1990-05-04  6.28  6.34  6.22  6.33  5635200     3.82
## 10 WMT    1990-05-07  6.33  6.41  6.30  6.41  5196800     3.86
## # … with 22,667 more rows

Q2 Calculate yearly returns.

Hint: Take the adjusted variable from Stocks, and calculate yearly returns using ***tq_transmute().

## # A tibble: 93 x 3
## # Groups:   symbol [3]
##    symbol date       yearly.returns
##    <chr>  <date>              <dbl>
##  1 WMT    1990-12-31         0.278 
##  2 WMT    1991-12-31         0.977 
##  3 WMT    1992-12-31         0.103 
##  4 WMT    1993-12-31        -0.212 
##  5 WMT    1994-12-30        -0.138 
##  6 WMT    1995-12-29         0.0640
##  7 WMT    1996-12-31         0.0398
##  8 WMT    1997-12-31         0.761 
##  9 WMT    1998-12-31         1.09  
## 10 WMT    1999-12-31         0.706 
## # … with 83 more rows

Q3 Which of the three stocks has the highest expected yearly return?

Hint: Take returns_yearly and pipe it to summarise. Calculate the mean yearly returns.

## # A tibble: 3 x 2
##   symbol returns_avg
##   <chr>        <dbl>
## 1 ^IXIC        0.137
## 2 MSFT         0.275
## 3 WMT          0.160

This would make it so Microsfoft has the highest yearly return.

Q4 Calculate standard deviation of the yearly returns. Which of the three stocks is the riskiest in terms of standard deviation?

Hint: Take returns_yearly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute sd (standard deviation).

## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol  sd.1
##   <chr>  <dbl>
## 1 WMT    0.328
## 2 MSFT   0.403
## 3 ^IXIC  0.278

Microsoft has the highest standard deviaton which in turn makes it the most riskiest.

Q5 Is the standard deviation appropriate measure of risk for the three stocks? Calculate skewness and kurtosis, and discuss them in your answer.

Hint: 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.

## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 WMT         1.42 
## 2 MSFT        0.272
## 3 ^IXIC       0.179
## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol kurtosis.1
##   <chr>       <dbl>
## 1 WMT         1.52 
## 2 MSFT        0.424
## 3 ^IXIC       0.304

I don’t beleive standard deviation is a good measure of this because they are all in different markets you are comparing apples to oranges. I think if you were comparing similiar things it would be good to use. There is also a lot more factors to look at too.

Q6 Which of the three stocks poses greater downside risk? Calculate HistoricalES(95%), HistoricalVaR(95%), and SemiDeviation, and discuss them in your answer.

Hint: Take returns_yearly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute table.DownsideRisk.

##                                           [,1]      [,2]      [,3]     
## symbol                                    "WMT"     "MSFT"    "^IXIC"  
## DownsideDeviation(0%)                     "0.0829"  "0.1448"  "0.1244" 
## DownsideDeviation(MAR=0.833333333333333%) "0.0866"  "0.1475"  "0.1279" 
## DownsideDeviation(Rf=0%)                  "0.0829"  "0.1448"  "0.1244" 
## GainDeviation                             "0.3157"  "0.3227"  "0.2050" 
## HistoricalES(95%)                         "-0.2471" "-0.5362" "-0.3991"
## HistoricalVaR(95%)                        "-0.2197" "-0.3317" "-0.3541"
## LossDeviation                             "0.0885"  "0.2388"  "0.1599" 
## MaximumDrawdown                           "0.3206"  "0.6285"  "0.6718" 
## ModifiedES(95%)                           "-0.5226" "-0.4907" "-0.4068"
## ModifiedVaR(95%)                          "-0.2183" "-0.3419" "-0.2974"
## SemiDeviation                             "0.1707"  "0.2663"  "0.1891"

Microsoft’s downside risk is greater. It has more uneasy on yearly returns below the mean than Walmart and NASDAQ, the largest loss one would expect with 95% confidence is larger for NASDAQ and Walmart, but with the largest loss could come the largest profit if your a gambling man.

Q7 Which of the three stocks would you choose? Calculate the Sharpe ratios with an annualized risk-free rate of 2% and a default confidence interval of 0.95.

Hint: Make your argument based on the three Sharpe Ratios.

## # A tibble: 3 x 4
## # Groups:   symbol [3]
##   symbol `ESSharpe(Rf=2%,p=95%… `StdDevSharpe(Rf=2%,p=95… `VaRSharpe(Rf=2%,p=95…
##   <chr>                   <dbl>                     <dbl>                  <dbl>
## 1 WMT                     0.267                     0.426                  0.639
## 2 MSFT                    0.519                     0.633                  0.745
## 3 ^IXIC                   0.287                     0.420                  0.392

For me I could go more risky at my point in life living at home no bills no kids so I would go with microsoft but some people like more stable stocks.

Q7.a Repeat Q7 but at a confidence interval of 0.99. Does it change your answer in Q7?

Hint: Make your argument based on the three Sharpe Ratios.

## # A tibble: 3 x 4
## # Groups:   symbol [3]
##   symbol `ESSharpe(Rf=2%,p=99%… `StdDevSharpe(Rf=2%,p=99… `VaRSharpe(Rf=2%,p=99…
##   <chr>                   <dbl>                     <dbl>                  <dbl>
## 1 WMT                     0.140                     0.426                  1.13 
## 2 MSFT                    0.364                     0.633                  0.428
## 3 ^IXIC                   0.205                     0.420                  0.243

It changed some of microsofts standerd deviation and other components but for me I like microsoft and beleive I would stay with them.

Q8 Hide the messages and the code, but display results of the code from the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.