See part I for instructions on required packages and datasets: https://ibkrcampus.com/ibkr-quant-news/tech-dividends-part-i/. The below is the full call:
nasdaq %>% clean_names() %>% mutate(market_cap = if_else(str_detect(market_cap, "M|B", negate = TRUE), str_remove_all(market_cap, "\\$") %>% as.numeric() %>% `/`(1000), if_else(str_detect(market_cap, "B"), str_remove_all(market_cap, "\\$|B") %>% as.numeric() %>% `*`(1000), str_remove_all(market_cap, "\\$|M") %>% as.numeric()))) %>% arrange(desc(market_cap))
# A tibble: 3,547 x 7 symbol company last_sale_price market_cap ipo_year sector industry <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 MSFT Microsof… 133. 1018490 1986 Techno… Computer S… 2 AAPL Apple In… 203. 915770 1980 Techno… Computer M… 3 AMZN Amazon.c… 1750. 865460 1997 Consum… Catalog/Sp… 4 GOOGL Alphabet… 1154. 799890 NA Techno… Computer S… 5 GOOG Alphabet… 1151. 798300 2004 Techno… Computer S… 6 FB Facebook… 178. 507110 2012 Techno… Computer S… 7 CSCO Cisco Sy… 46.6 199520 1990 Techno… Computer C… 8 INTC Intel Co… 45.0 199170 NA Techno… Semiconduc… 9 CMCSA Comcast … 42.4 192840 NA Consum… Television… 10 PEP Pepsico,… 130. 182140 NA Consum… Beverages … # … with 3,537 more rows
That finally looks how we were expecting, the top five by market cap are
CSCO. Let’s save that as an object called
nasdaq_wrangled <- nasdaq %>% clean_names() %>% mutate(market_cap = if_else(str_detect(market_cap, "M|B", negate = TRUE), str_remove_all(market_cap, "\\$") %>% as.numeric() %>% `/`(1000), if_else(str_detect(market_cap, "B"), str_remove_all(market_cap, "\\$|B") %>% as.numeric() %>% `*`(1000), str_remove_all(market_cap, "\\$|M") %>% as.numeric()))) %>% arrange(desc(market_cap))
Now let’s dig in to the dividends paid by these NASDAQ-listed companies that have IPO’d in the last ten years. It’s a bit anticlimactic because most haven’t paid any dividends but here we go. First, let’s pull just the tickers for companies that IPO’d after 2007, by setting
filter(ipo_year > 2007).
nasdaq_tickers <- nasdaq_wrangled %>% filter(ipo_year > 2007) %>% pull(symbol) nasdaq_tickers %>% head()
 "FB" "AVGO" "JD" "TSLA" "PDD" "TEAM"
We will import the dividend data using
tq_get(source = 'dividends'), which is a wrapper for
quantmod::getDividends() and sources dividend data from Yahoo! Finance.
We are passing 1120 symbols to this function but only those that pay a dividend will come back to us. It takes a while to run this because we still have to check on all 1120.
nasdaq_dividends <- nasdaq_tickers %>% tq_get(get = 'dividends') %>% select(-value)
After a huge data import task like that, I like to use
slice(1) to grab the first observation from each group, which in this case will be each
symbol. We can count the number symbols for which we have a dividend and it’s 130.
nasdaq_dividends %>% group_by(symbol) %>% slice(1) %>% glimpse()
Observations: 128 Variables: 3 Groups: symbol  $ symbol <chr> "AGNC", "AMAL", "ATAI", "AVGO", "AY", "BKEP", "BLMN", … $ date <date> 2009-03-31, 2018-11-15, 2011-06-28, 2010-12-13, 2014-… $ dividends <dbl> 0.850, 0.060, 0.430, 0.070, 0.037, 0.110, 0.060, 0.003…
We could also get a sense for how these first dividend payments cluster into years by using
count(year). Note we need to
nasdaq_dividends %>% group_by(symbol) %>% slice(1) %>% mutate(year = year(date)) %>% ungroup() %>% count(year)
# A tibble: 11 x 2 year n <dbl> <int> 1 2009 3 2 2010 8 3 2011 6 4 2012 8 5 2013 14 6 2014 12 7 2015 14 8 2016 12 9 2017 11 10 2018 28 11 2019 12
And a chart will help to communicate these yearly frequencies.
nasdaq_dividends %>% group_by(symbol) %>% slice(1) %>% mutate(year = year(date)) %>% ungroup() %>% count(year) %>% ggplot(aes(year, n)) + geom_col(fill = "cornflowerblue", width = .5) + scale_x_continuous(breaks = 2008:2019) + scale_y_continuous(breaks = scales::pretty_breaks(n = 15)) + labs(y = "number of first dividends by year", x = "")
Stay tuned for the next installment in which Jonathan will create a quick chart of the last dividend paid by each of these 130 companies, using
slice(n()) and will plot a dot with
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