1 Import stock prices of your choice

2 Convert prices to returns by quarterly

## # A tibble: 60 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 MSFT  2012-03-30  0.213 
##  2 MSFT  2012-06-29 -0.0455
##  3 MSFT  2012-09-28 -0.0207
##  4 MSFT  2012-12-31 -0.0951
##  5 MSFT  2013-03-28  0.0800
##  6 MSFT  2013-06-28  0.216 
##  7 MSFT  2013-09-30 -0.0297
##  8 MSFT  2013-12-31  0.133 
##  9 MSFT  2014-03-31  0.104 
## 10 MSFT  2014-06-30  0.0245
## # ℹ 50 more rows

3 Make plot

4 Interpret the plot

As the plot shows data on quarterly returns only, I can only interpret the plot based on quarterly and not monthly.

NVDA has the widest distribution out of all these stocks, which would indicate a higher volatility, but also potential higher returns in some quarters. GOOGL and MSFT have narrower distributions which would suggest more stability in their returns. As we see in NVDA’s plot, its peak is indeed higher than the peaks of the other stocks. The peak of NVDA’s curve being more to the right than the other curves and peaks also indicates that NVDA typically has higher returns.

Based on this plot, I would expect NVDA to make more money and have larger quarterly returns than MSFT and GOOGL.

5 Change the global chunck options

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