I expected the numbers to steadily increase year by year as companies became more mature and cash flow positive but that is not the pattern we see in that plot.
Now let’s create a quick chart of the last dividend paid by each of these 130 companies, using
slice(n()). This time we’ll plot a dot with
nasdaq_dividends %>% group_by(symbol) %>% slice(n()) %>% mutate(year = year(date)) %>% ggplot(aes(x = year, y = dividends, color = symbol)) + geom_point() + scale_y_continuous(labels = scales::dollar) + scale_x_continuous(breaks = 2008:2019) + labs(x = "", y = "div/share", title = "Nasdaq dividends") + theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
Not quite as useful as a lof of the dots are right on top of eachother. We do see a couple of massive outliers. Let’s add the label for each symbol with
geom_label(aes(label = symbol)).
nasdaq_dividends %>% group_by(symbol) %>% slice(n()) %>% ggplot(aes(x = date, y = dividends, color = symbol)) + geom_point() + geom_label(aes(label = symbol)) + scale_y_continuous(labels = scales::dollar) + scale_x_date(breaks = scales::pretty_breaks(n = 10)) + labs(x = "", y = "div/share", title = "Nasdaq dividends") + theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
MACK and FWP paid some huge dividends. Let’s investigate.
nasdaq_dividends %>% filter(symbol == "MACK" | symbol == "FWP")
# A tibble: 2 x 3 symbol date dividends <chr> <date> <dbl> 1 MACK 2017-05-30 106. 2 FWP 2017-09-12 23.1
In the next installment of this series, Jonathan will remove the
geom_label so we see just the dots again, this time by date instead of year.
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