Tech Dividends – Part II - Jonathan Regenstein on IBKR Quant Blog

See part I for instructions on required packages and datasets: https://tradersinsight.news/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 MSFT, AMZN, GOOG, FB and CSCO. Let’s save that as an object called nasdaq_wrangled.

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()

[1] "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 [128]
$ 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 ungroup() first.

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 geom_point().

Any stock, options or futures symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

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Posted: October 21, 2019

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Disclosure: Interactive Brokers