## # 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 ^IXIC 1990-04-23 423. 425. 418. 420. 125990000 420.
## 2 ^IXIC 1990-04-24 422. 422. 419. 419. 126790000 419.
## 3 ^IXIC 1990-04-25 420 421. 419. 421. 121710000 421.
## 4 ^IXIC 1990-04-26 422. 422 419. 421. 115930000 421.
## 5 ^IXIC 1990-04-27 421. 421. 418. 418 116010000 418
## 6 ^IXIC 1990-04-30 418. 420. 417 420. 105790000 420.
## 7 ^IXIC 1990-05-01 422 422. 421. 422. 124130000 422.
## 8 ^IXIC 1990-05-02 423. 424. 422. 424. 143260000 424.
## 9 ^IXIC 1990-05-03 425 427. 424. 425. 160850000 425.
## 10 ^IXIC 1990-05-04 427. 429. 426. 429. 136810000 429.
## # … with 22,667 more rows
## # A tibble: 93 x 3
## # Groups: symbol [3]
## symbol date yearly.returns
## <chr> <date> <dbl>
## 1 ^IXIC 1990-12-31 -0.110
## 2 ^IXIC 1991-12-31 0.569
## 3 ^IXIC 1992-12-31 0.155
## 4 ^IXIC 1993-12-31 0.147
## 5 ^IXIC 1994-12-30 -0.0320
## 6 ^IXIC 1995-12-29 0.399
## 7 ^IXIC 1996-12-31 0.227
## 8 ^IXIC 1997-12-31 0.216
## 9 ^IXIC 1998-12-31 0.396
## 10 ^IXIC 1999-12-31 0.856
## # … with 83 more rows
## # A tibble: 3 x 2
## symbol returns_avg
## <chr> <dbl>
## 1 ^IXIC 0.137
## 2 MSFT 0.275
## 3 WMT 0.160
The stock that I think is going to have the highest return is Microsoft with .275.
## # A tibble: 3 x 2
## # Groups: symbol [3]
## symbol sd.1
## <chr> <dbl>
## 1 ^IXIC 0.278
## 2 MSFT 0.402
## 3 WMT 0.327
The highest risk when talking about standard deviation is MSFT. It has the highest standard deviation at .402.
## # A tibble: 3 x 2
## # Groups: symbol [3]
## symbol skewness.1
## <chr> <dbl>
## 1 ^IXIC 0.179
## 2 MSFT 0.275
## 3 WMT 1.42
## # A tibble: 3 x 2
## # Groups: symbol [3]
## symbol kurtosis.1
## <chr> <dbl>
## 1 ^IXIC 0.302
## 2 MSFT 0.431
## 3 WMT 1.52
Using standard deviation is not always affective. For example, Walmart’s skewness and kurtosis was way higher than the other stocks, however in question 4 it happened to not be the highest standard deviation. Which means you should look at different things besides just standard diviation.
## [,1] [,2] [,3]
## symbol "^IXIC" "MSFT" "WMT"
## DownsideDeviation(0%) "0.1245" "0.1448" "0.0829"
## DownsideDeviation(MAR=0.833333333333333%) "0.1279" "0.1475" "0.0866"
## DownsideDeviation(Rf=0%) "0.1245" "0.1448" "0.0829"
## GainDeviation "0.2050" "0.3226" "0.3151"
## HistoricalES(95%) "-0.3991" "-0.5362" "-0.2471"
## HistoricalVaR(95%) "-0.3541" "-0.3317" "-0.2197"
## LossDeviation "0.1598" "0.2388" "0.0885"
## MaximumDrawdown "0.6718" "0.6285" "0.3206"
## ModifiedES(95%) "-0.4069" "-0.4899" "-0.5204"
## ModifiedVaR(95%) "-0.2976" "-0.3414" "-0.2176"
## SemiDeviation "0.1892" "0.2659" "0.1707"
Microsoft’s downside risk is greater. It has more volatile monthly returns below the mean than Walmart and NASDAQ, the largest loss one would expect with 95% confidence is larger for NASDAQ and Walmart, and the average of the 5% most negative monthly returns is larger for NASDAQ and Walmart.
## # A tibble: 3 x 4
## # Groups: symbol [3]
## symbol `ESSharpe(Rf=2%,p=5%)` `StdDevSharpe(Rf=2%,p=5%… `VaRSharpe(Rf=2%,p=5%…
## <chr> <dbl> <dbl> <dbl>
## 1 ^IXIC 0.286 0.419 0.392
## 2 MSFT 0.520 0.633 0.746
## 3 WMT 0.269 0.428 0.644
Its all depends on what angle you are investing at. If you are in it for the long run, you possibly want more risk. Maybe if you are investing short term, you cant risk as much. If I were investing long term I would choose microsoft.
## # A tibble: 3 x 4
## # Groups: symbol [3]
## symbol `ESSharpe(Rf=2%,p=1%)` `StdDevSharpe(Rf=2%,p=1%… `VaRSharpe(Rf=2%,p=1%…
## <chr> <dbl> <dbl> <dbl>
## 1 ^IXIC 0.205 0.419 0.243
## 2 MSFT 0.364 0.633 0.428
## 3 WMT 0.140 0.428 1.13
The numbers for the ratios are completely different now that there is a .01 confidence interval. Some of microsofts ratios went up because of standard deviation and some went down because of ES and Var.It all depends on how much time you have want your money sitting around. I could look at walmart now with the .01. But i’d more than likly stay with microsoft.
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