Tech Dividends – Part I

In a previous post, we explored the dividend history of stocks included in the SP500. Today we’ll extend that anlaysis to cover the Nasdaq because, well, because in the previous post I said I would do that. We’ll also explore a different source for dividend data, do some string cleaning and check out ways to customize a tooltip in plotly. Bonus feature: we’ll get into some animation too. We have a lot to cover, so let’s get to it.

We need to load up our packages for the day.

library(tidyverse)
library(tidyquant)
library(janitor)
library(plotly)

First we need all the companies listed on the Nasdaq. Not so long ago, it wasn’t easy to import that information into R. Now we can use tq_exchange("NASDAQ") from the tidyquant package.

nasdaq <-
  tq_exchange("NASDAQ")

nasdaq %>% 
  head() 

# A tibble: 6 x 7
  symbol company     last.sale.price market.cap ipo.year sector  industry  
  <chr>  <chr>                 <dbl> <chr>         <dbl> <chr>   <chr>     
1 YI     111, Inc.              2.69 $219.66M       2018 Health… Medical/N…
2 PIH    1347 Prope…            4.85 $29.16M        2014 Finance Property-…
3 PIHPP  1347 Prope…           25.7  $17.96M          NA Finance Property-…
4 TURN   180 Degree…            2.09 $65.04M          NA Finance Finance/I…
5 FLWS   1-800 FLOW…           15.3  $983.42M       1999 Consum… Other Spe…
6 BCOW   1895 Banco…            9.33 $45.5M         2019 Finance Banks     

Notice how the market.cap column is of type character? Let’s coerce it to a dbl with as.numeric and while we’re at it, let’s remove the periods in all the column numes with clean_names from the janitor package.

nasdaq %>% 
  clean_names() %>%
  mutate(market_cap = as.numeric(market_cap)) %>% 
  select(symbol, market_cap) %>% 
  head()

# A tibble: 6 x 2
  symbol market_cap
  <chr>       <dbl>
1 YI             NA
2 PIH            NA
3 PIHPP          NA
4 TURN           NA
5 FLWS           NA
6 BCOW           NA

Not exactly what we had in mind. The presence of those M, B and $ characters are causing as.numeric() to coerce the column to NAs. If we want to do any sorting by market cap, we’ll need to clean that up and it’s a great chance to explore some stringr. Let’s start with str_remove_all and remove those non-numeric characters. The call is market_cap %>% str_remove_all("\\$|M|B"), and then an arrange(desc(market_cap)) so that the largest cap company is first.

nasdaq %>% 
  clean_names() %>%
  mutate(market_cap = market_cap %>% str_remove_all("\\$|M|B") %>% as.numeric()) %>%
  arrange(desc(market_cap)) %>% 
  head()

# A tibble: 6 x 7
  symbol company    last_sale_price market_cap ipo_year sector  industry   
  <chr>  <chr>                <dbl>      <dbl>    <dbl> <chr>   <chr>      
1 CIVBP  Civista B…            66      667920        NA Finance Major Banks
2 ASRVP  AmeriServ…            29.6    621600        NA Finance Major Banks
3 ESGRP  Enstar Gr…            26.6    425920        NA Finance Property-C…
4 AGNCN  AGNC Inve…            26.0    337870        NA Consum… Real Estat…
5 SBFGP  SB Financ…            15.8    237221.       NA Finance Major Banks
6 ESGRO  Enstar Gr…            26.4    115984        NA Finance Property-C…

Well, that wasn’t too bad!

Wait, that looks weird, where’s AMZN and MSFT shouldn’t they be at the top of the market cap? Look closely at market_cap and notice it’s been coerced to a numeric value as we intended but we didn’t account for the fact that those M and B letters were abbreviating values and standing place for a whole bunch of zeroes. The first symbol above, CIVBP, didn’t have an M or B because it’s market cap is low, so it didn’t have any zeroes lopped off of it.

We need a way to remove the M and the B account for those zeroes that got removed. Here’s how I chose to tackle this.

1. Find all the cells that do not have an M or a B, remove the $ sign, convert to numeric and divide by 1000. We do that with if_else(str_detect(market_cap, "M|B", negate = TRUE), str_remove_all(market_cap, "\\$") %>% as.numeric() %>%/(1000).
2. Find all the cells that have a B, remove the B and the $ sign, convert to numeric and multiply by 1000. We do that with if_else(str_detect(market_cap, "B"), str_remove_all(market_cap, "\\$|B") %>% as.numeric() %>%*(1000).
3. Find all the cells that have an M, remove the M and the $ sign, convert to numeric and don’t multiply or divide. We do that with str_remove_all(market_cap, "\\$|M") %>% as.numeric())).

Stay tuned for the next installment, in which Jonathan will continue coding this tibble in R. To download the full R script, visit his blog http://www.reproduciblefinance.com/2019/08/25/tech-dividends/ .

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