# Load packages
library(tidyquant)
library(tidyverse)

# Import stock prices and calculate returns
returns_quarterly <- c("^DJI", "^GSPC", "^IXIC") %>%
    tq_get(get  = "stock.prices",
           from = "1990-01-01",
           to   = "2020-11-01") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = quarterlyReturn)
returns_quarterly
## # A tibble: 372 x 3
## # Groups:   symbol [3]
##    symbol date       quarterly.returns
##    <chr>  <date>                 <dbl>
##  1 ^DJI   1990-03-30          -0.0366 
##  2 ^DJI   1990-06-29           0.0641 
##  3 ^DJI   1990-09-28          -0.149  
##  4 ^DJI   1990-12-31           0.0739 
##  5 ^DJI   1991-03-28           0.106  
##  6 ^DJI   1991-06-28          -0.00244
##  7 ^DJI   1991-09-30           0.0378 
##  8 ^DJI   1991-12-31           0.0504 
##  9 ^DJI   1992-03-31           0.0210 
## 10 ^DJI   1992-06-30           0.0257 
## # ... with 362 more rows
# See options for the `performance_fun` argument
tq_performance_fun_options()
## $table.funs
##  [1] "table.AnnualizedReturns" "table.Arbitrary"        
##  [3] "table.Autocorrelation"   "table.CAPM"             
##  [5] "table.CaptureRatios"     "table.Correlation"      
##  [7] "table.Distributions"     "table.DownsideRisk"     
##  [9] "table.DownsideRiskRatio" "table.DrawdownsRatio"   
## [11] "table.HigherMoments"     "table.InformationRatio" 
## [13] "table.RollingPeriods"    "table.SFM"              
## [15] "table.SpecificRisk"      "table.Stats"            
## [17] "table.TrailingPeriods"   "table.UpDownRatios"     
## [19] "table.Variability"      
## 
## $CAPM.funs
##  [1] "CAPM.alpha"       "CAPM.beta"        "CAPM.beta.bear"   "CAPM.beta.bull"  
##  [5] "CAPM.CML"         "CAPM.CML.slope"   "CAPM.dynamic"     "CAPM.epsilon"    
##  [9] "CAPM.jensenAlpha" "CAPM.RiskPremium" "CAPM.SML.slope"   "TimingRatio"     
## [13] "MarketTiming"    
## 
## $SFM.funs
## [1] "SFM.alpha"       "SFM.beta"        "SFM.CML"         "SFM.CML.slope"  
## [5] "SFM.dynamic"     "SFM.epsilon"     "SFM.jensenAlpha"
## 
## $descriptive.funs
## [1] "mean"           "sd"             "min"            "max"           
## [5] "cor"            "mean.geometric" "mean.stderr"    "mean.LCL"      
## [9] "mean.UCL"      
## 
## $annualized.funs
## [1] "Return.annualized"        "Return.annualized.excess"
## [3] "sd.annualized"            "SharpeRatio.annualized"  
## 
## $VaR.funs
## [1] "VaR"  "ES"   "ETL"  "CDD"  "CVaR"
## 
## $moment.funs
##  [1] "var"              "cov"              "skewness"         "kurtosis"        
##  [5] "CoVariance"       "CoSkewness"       "CoSkewnessMatrix" "CoKurtosis"      
##  [9] "CoKurtosisMatrix" "M3.MM"            "M4.MM"            "BetaCoVariance"  
## [13] "BetaCoSkewness"   "BetaCoKurtosis"  
## 
## $drawdown.funs
## [1] "AverageDrawdown"   "AverageLength"     "AverageRecovery"  
## [4] "DrawdownDeviation" "DrawdownPeak"      "maxDrawdown"      
## 
## $Bacon.risk.funs
## [1] "MeanAbsoluteDeviation" "Frequency"             "SharpeRatio"          
## [4] "MSquared"              "MSquaredExcess"        "HurstIndex"           
## 
## $Bacon.regression.funs
##  [1] "CAPM.alpha"       "CAPM.beta"        "CAPM.epsilon"     "CAPM.jensenAlpha"
##  [5] "SystematicRisk"   "SpecificRisk"     "TotalRisk"        "TreynorRatio"    
##  [9] "AppraisalRatio"   "FamaBeta"         "Selectivity"      "NetSelectivity"  
## 
## $Bacon.relative.risk.funs
## [1] "ActivePremium"    "ActiveReturn"     "TrackingError"    "InformationRatio"
## 
## $Bacon.drawdown.funs
## [1] "PainIndex"     "PainRatio"     "CalmarRatio"   "SterlingRatio"
## [5] "BurkeRatio"    "MartinRatio"   "UlcerIndex"   
## 
## $Bacon.downside.risk.funs
##  [1] "DownsideDeviation"     "DownsidePotential"     "DownsideFrequency"    
##  [4] "SemiDeviation"         "SemiVariance"          "UpsideRisk"           
##  [7] "UpsidePotentialRatio"  "UpsideFrequency"       "BernardoLedoitRatio"  
## [10] "DRatio"                "Omega"                 "OmegaSharpeRatio"     
## [13] "OmegaExcessReturn"     "SortinoRatio"          "M2Sortino"            
## [16] "Kappa"                 "VolatilitySkewness"    "AdjustedSharpeRatio"  
## [19] "SkewnessKurtosisRatio" "ProspectRatio"        
## 
## $misc.funs
## [1] "KellyRatio"   "Modigliani"   "UpDownRatios"

Q1 Create a density plot for the returns of the given stocks.

Hint: Refer to the ggplot2 cheatsheet. Look for geom_density under One Variable. Use the fill argument to create the plot per each stock.

ggplot(returns_quarterly, aes(x = quarterly.returns, fill = symbol)) +
  geom_density(alpha = 0.3)

Q2 Which stock has higher expected quarterly return?

Hint: Discuss your answer in terms of the mean. Take returns_quarterly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute the mean.

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = mean
    )
## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol mean.1
##   <chr>   <dbl>
## 1 ^DJI   0.0212
## 2 ^GSPC  0.0212
## 3 ^IXIC  0.0333

The NASDAQ has the highest expected quarterly return because they have a mean of 0.0333 over the Dow and S&P 500’s mean of 0.0212.

Q3 Which stock is riskier?

Hint: Discuss your answer in terms of the standard deviation. Take returns_quarterly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute sd (standard deviation).

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = sd
    )
## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol   sd.1
##   <chr>   <dbl>
## 1 ^DJI   0.0762
## 2 ^GSPC  0.0794
## 3 ^IXIC  0.122

The NASDAQ is riskier than both the Dow and S&P 500. Usually higher return means more risk so this makes sense. Standard deviation is a good way to assess risk, the NASDAQ has a SD of 0.122 over the Dow’s 0.0762 and the S&P 500’s 0.0794.

Q4 Is the standard deviation enough as a risk measure? Or do you need additional downside risk measurements? Why? Or why not?

Hint: Discuss your answer in terms of the skewness and the kurtosis. Take returns_quarterly and pipe it to tidyquant::tq_performance. Use the performance_fun argument to compute the skewness. Do the same for the kurtosis.

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = skewness)
## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol skewness.1
##   <chr>       <dbl>
## 1 ^DJI       -0.734
## 2 ^GSPC      -0.601
## 3 ^IXIC      -0.106
returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = kurtosis)
## # A tibble: 3 x 2
## # Groups:   symbol [3]
##   symbol kurtosis.1
##   <chr>       <dbl>
## 1 ^DJI        0.811
## 2 ^GSPC       0.770
## 3 ^IXIC       1.81

All three indexes have negative skewness meaning large losses are more likely than large gains. Also kurtosis show that the NASDAQ is 1.81 meaning they have bigger tails then normal distribution. Based on this downside risk measures are very helpful in assessing risk along with standard deviation.

Q5 Calculate the downside risk measures. Which stock has the greatest downside risk? Discuss HistoricalES(95%), HistoricalVaR(95%), and SemiDeviation.

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

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = table.DownsideRisk) %>% 
  t()
##                                          [,1]      [,2]      [,3]     
## symbol                                   "^DJI"    "^GSPC"   "^IXIC"  
## DownsideDeviation(0%)                    "0.0492"  "0.0512"  "0.0734" 
## DownsideDeviation(MAR=3.33333333333333%) "0.0528"  "0.0548"  "0.0770" 
## DownsideDeviation(Rf=0%)                 "0.0492"  "0.0512"  "0.0734" 
## GainDeviation                            "0.0428"  "0.0463"  "0.0814" 
## HistoricalES(95%)                        "-0.1664" "-0.1686" "-0.2565"
## HistoricalVaR(95%)                       "-0.1235" "-0.1394" "-0.1954"
## LossDeviation                            "0.0596"  "0.0621"  "0.0882" 
## MaximumDrawdown                          "0.4524"  "0.4774"  "0.7437" 
## ModifiedES(95%)                          "-0.1658" "-0.1724" "-0.2550"
## ModifiedVaR(95%)                         "-0.1175" "-0.1207" "-0.1658"
## SemiDeviation                            "0.0587"  "0.0607"  "0.0885"

The NASDAQ has the greatest downside risk. It’s historicalES(95%)is -0.2565 over the Dow and S&P 500’s that are both in the -0.16’s. It is the same case with historicalVar(95%). SemiDeviation shows the same with NASDAQ being a greater then both other indexes.

Q6 Which stock would you choose? Calculate and interpret the Sharpe Ratio.

Hint: Assume that the risk free rate is zero and 95% confidence level. Note that the Sharpe Ratios are calculated using different risk measures: ES, VaR and semideviation. Make your argument based on all three Sharpe Ratios.

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = SharpeRatio) 
## # A tibble: 3 x 4
## # Groups:   symbol [3]
##   symbol `ESSharpe(Rf=0%,p=95%~ `StdDevSharpe(Rf=0%,p=95~ `VaRSharpe(Rf=0%,p=95~
##   <chr>                   <dbl>                     <dbl>                  <dbl>
## 1 ^DJI                    0.128                     0.278                  0.181
## 2 ^GSPC                   0.123                     0.266                  0.175
## 3 ^IXIC                   0.131                     0.273                  0.201

My results show that the nasdaq would be the best to choose because they have the highest number in 2 of the 3 sharpe ratios.

Q7 Redo Q6 at the 99% confidence level instead of the 95% confidence level. Which stock would you choose now? Is your answer different from Q6? Why? Or why not?

Hint: Google tq_performance(). Discuss in terms of ES, VaR and semideviation and their differences between 95% and 99%.

returns_quarterly %>%
    tq_performance(
        Ra = quarterly.returns, 
        Rb = NULL, 
        performance_fun = SharpeRatio, p = 0.99) 
## # A tibble: 3 x 4
## # Groups:   symbol [3]
##   symbol `ESSharpe(Rf=0%,p=99%~ `StdDevSharpe(Rf=0%,p=99~ `VaRSharpe(Rf=0%,p=99~
##   <chr>                   <dbl>                     <dbl>                  <dbl>
## 1 ^DJI                   0.0784                     0.278                  0.109
## 2 ^GSPC                  0.0799                     0.266                  0.105
## 3 ^IXIC                  0.0814                     0.273                  0.108

Redoing Q6 with 99% confidence makes it so the Dow would have the highest number in 2 of the 3 sharpe ratios. Narrowly taking the lead in the VaRSharp ratio 0.109 to 0.108.

Q8 Hide the messages, but display the code and its results on 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.