Goal

Visualize and compare skewness of your portfolio and its assets.

Choose your stocks.

from 2012-12-31 to 2017-12-31

1 Import stock prices

2 Convert prices to returns (monthly)

3 Assign a weight to each asset (change the weigting scheme)

## [1] "AMZN" "GOOG" "NFLX"
## [1] 0.4 0.3 0.3
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.4
## 2 GOOG        0.3
## 3 NFLX        0.3

4 Build a portfolio

## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.216  
##  2 2013-02-28  0.0545 
##  3 2013-03-28  0.00262
##  4 2013-04-30  0.0315 
##  5 2013-05-31  0.0539 
##  6 2013-06-28 -0.00525
##  7 2013-07-31  0.0791 
##  8 2013-08-30  0.00290
##  9 2013-09-30  0.0784 
## 10 2013-10-31  0.122  
## # … with 50 more rows

5 Compute Skewness

## # A tibble: 1 × 1
##   Skewness
##      <dbl>
## 1    0.188
## [1] NA

6 Plot: Skewness Comparison

## # A tibble: 4 × 2
##   asset      skew
##   <chr>     <dbl>
## 1 AMZN      0.187
## 2 GOOG      0.784
## 3 NFLX      0.909
## 4 Portfolio 0.188

Is any asset in your portfolio more likely to return extreme positive returns than your portfolio collectively? Discuss in terms of skewness. You may also refer to the distribution of returns you plotted in Code along 4.

Google and Netflix are more likely to have extreme positive returns than the portfolio as a whole. The portfolio has a skewness of 0.2 and GOOG and NFLX have skewness of 0.8 and 0.9 respectively.