IPO Exploration

Inspired by recent headlines like Fear Overtakes Greed in IPO Market after WeWork Debacle and This Year’s IPO Class is Least Profitable since the Tech Bubble, today we’ll explore historical IPO data and next time we’ll look at the the performance of IPO driven-portfolios constructed during the ten-year period from 2004 – 2014. I’ll admit I’ve often wondered how a portfolio that allocated money to new IPOs each year might perform since this has to be an ultimate example of a few headline gobbling whales dominating the collective consciousness. We hear a lot about a few IPOs each year but there are dozens about which we hear nothing.

Here are the packages we’ll be using today.

library(tidyverse)
library(tidyquant)
library(plotly)
library(riingo)
library(roll)
library(tictoc)

Let’s get all the companies listed on the NASDAQ, NYSE and AMEX exchanges and their IPO dates. That’s not every company that IPO’d in those years, of course, but we’ll go with it as a convenience for today’s purposes. Fortunately, the tq_exchange() function from tidyquant makes is painless to grab this data.

nasdaq <-
  tq_exchange("NASDAQ")

amex  <- 
  tq_exchange("AMEX")

nyse <- 
  tq_exchange("NYSE")

Big time warning alert, not only have we missed companies that IPO’d and are not listed on those exchanges, we have also missed companies that IPO’d and have ceased to exist, i.e. the companies that went bust, i.e. the very companies that would absolutely sacre the heck out of us before we invested in things like recent IPOs. You would need to correct for Major survivor bias should you choose to explore this for actual trading. As we’ll see next time, even without those dead companies, portfolios built upon these IPOs are very risky. And, while we’re on the caveat theme, nothing in this post is financial advice in any way.

Back to the exciting part, the code!

Notice how we pulled in data from three different data sources but our objects have the same column structures. That’s due to some nice work from the tidyquant authors and it makes our lives easier in the next step, wherein we use bind_rows() to combine to one object.

After binding these data togther, we select(symbol, company, ipo.year, sector) to isolate a few columns of interest. We will also filter out any tickers with ipo.year equal to NA with filter(!is.na(ipo.year)).

company_ipo_sector <-
  nasdaq %>% 
  bind_rows(amex) %>% 
  bind_rows(nyse) %>% 
  select(symbol, company, ipo.year, sector) %>% 
  filter(!is.na(ipo.year))


company_ipo_sector %>% 
  head()

# A tibble: 6 x 4
  symbol company                                ipo.year sector           
  <chr>  <chr>                                     <dbl> <chr>            
1 TXG    10x Genomics, Inc.                         2019 Capital Goods    
2 YI     111, Inc.                                  2018 Health Care      
3 PIH    1347 Property Insurance Holdings, Inc.     2014 Finance          
4 FLWS   1-800 FLOWERS.COM, Inc.                    1999 Consumer Services
5 BCOW   1895 Bancorp of Wisconsin, Inc.            2019 Finance          
6 VNET   21Vianet Group, Inc.                       2011 Technology       

In the next installment, Jonathan will explore the data set.

Visit Jonathan’s Reproducible Finance Blog to download the R code and complimentary scripts.