1 Import stock prices of your choice

2 Convert prices to returns by quarterly

## # A tibble: 100 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AMZN  2020-03-31  0.0269
##  2 AMZN  2020-06-30  0.347 
##  3 AMZN  2020-09-30  0.132 
##  4 AMZN  2020-12-31  0.0338
##  5 AMZN  2021-03-31 -0.0513
##  6 AMZN  2021-06-30  0.106 
##  7 AMZN  2021-09-30 -0.0461
##  8 AMZN  2021-12-31  0.0149
##  9 AMZN  2022-03-31 -0.0226
## 10 AMZN  2022-06-30 -0.428 
## # ℹ 90 more rows

3 Make plot

4 Interpret the plot

GOOGL had the highest typical return of 17.5%, which was the case for five quarters within this time period. NFLX had the second highest typical return at 25% (4 quarters). With that being said, if these returns were consecutive, then GOOGL would yield an 87.5% return while NFLX would return 100% despite having a lower frequency of occurrence. META came in around 15% (4 quarters), AMZN had a typical return of 5% (4 quarters) with AAPL coming in last with a return of 25% for 3 quarters. It’s important to note that AAPL had identical frequencies of returns at 10% and -10% as well.

META had the potential to have the highest return at roughly 55% whereas NFLX had the largest loss within the same time frame, just shy of -80% (around 75%). Both of these instances occurred only once during the calculated time period.

5 Change the global chunck options

Hide the code, messages, and warnings