Use Your Computer to Make Informed Decisions in Stock Trading: Practical Introduction — Part 6: Boom of IPOs in 2020

This article continues the series of event-driven analytical stories, which we’ve started from the news sentiment analysis (Part 3), then continued with one quarter of EPS vs. returns (Part 4), and finally got the long-term EPS analysis over the last 20 years (Part 5) for the selected set of stocks.

IPOs in 2020

Many things are moving in repetitive patterns: it is either dictated by nature (e.g. the season of a year — sales), or longer periods of people’s activity (e.g. economic cycles). IPOs surprisingly come in a similar manner, although it’s not obvious to notice that.

You may have heard about the Dot-com bubble between 1995 and 2000, when many internet companies went public, reaching its peak with 397 IPOs in 2000. In the end it turned out to be a massive disaster since many companies had a big stock price loss or were out of the business very soon. Twenty years since then were relatively calm with 212 IPOs in average per year.

2020 is already called ‘an avalanche of IPO’ due to 450 IPO deals (statistics for Dec-13th 2020).

There can be many reasons for that: low interest rates, Covid-19 and growth of the digital economy, uncertainty of future and hard time to raise money though VC.

Executive Summary

We’ve done a quick analysis of the last 6 months of IPO data (100 deals until mid-December 2020): selected an easy to read datasource, cleaned and transformed the data, merged it with the daily prices table, and built a powerful dynamic visualisation with Plotly in Python.

Summary of results:

  • Avg. IPO volume is $413m, varying from $2.5m to $3.5b, avg. price per stock is $19.6 (the full range is $2.1 to $120)
  • Stock price change (in 10 to 180 days after IPO) is from -52% to +293% to the IPO price (average is 57%, median=39%)
  • Top-5 largest IPOs came from the Technology sector, had volume $1.9b-$3.5b USD, change from +4% to 274% with average 130%
  • The average price increase during the first day of trade after the listing is 22% (which is a big daily fluctuation)
  • Day1 to Day7 growth is -3% on average (19% Day7 cumulative returns vs. 22% Day0 returns). It is much harder to make a profit in an open market short term investment compared with pre-IPO investment. You may want to consider choosing one of IPO ETFs to be eligible to invest before the IPO happens.

The Visual Story

The distribution of realised amount on IPO

From the pie chart (Fig.1) below one can quickly find that the money raised during the recent IPOs were mostly in Technology (58.3%) and Healthcare (25.6%) sectors by the total amount offered.

This view is a revenue-weighted calculation biased towards large Tech IPOs. It may be better to look at the company level data to find the insights, because smaller companies can grow faster and promise higher returns.

Three views on individual stock growth after the IPO

The next chart (Fig.2) shows the same pie of realised amount split by stock exchange and company name (ticker). The colouring scheme is important: dark green companies are the most profitable IPOs (growth from the initial price of offering to the current day 11-Dec), white are neutral, and red are negative.

At first sight everything seems to be very positive: almost all squares are green or white, NYSE has larger and more green deals (111% average increase) than NASDAQ (57% average increase). The problem is that it is harder or more expensive (higher fees) to invest in those deals before they go to the open market (stock exchange), unless you’re a large institutional investor who has access to the investment banks doing the IPO listing. You can try to buy ETFs focused on IPOs (e.g. Renaissance IPO ETF grew more than 2x in 2020 in less than one year), but it also bears some risks and caveats (Can Mutual Funds and ETFs Invest in IPOs?).

For example, GDRX (GoodRx Holdings, Inc.) in the bottom left corner was a large IPO on NASDAQ ($1.1b), grew 29% from the IPO price to the current price Dec, 11th in just 81 days.

The next graph (Fig.3) represents the same set of tickers, but the growth is calculated to the end of the first trading day vs. IPO price.

This is a more realistic situation, which assumes that you managed to buy the stock during the first trading day on the exchange (it is also an arguable assumption, as many brokers add new tickers only several days after the IPO).

The same example GDRX (GoodRx Holdings, Inc.) is red now: it has only -15% of growth compared to 29% in the previous view. The news article supports the fact that most of the growth happened during the first trading day: GoodRx closes up 53% in market debut.

The average growth (last trading day to the listing day) on NYSE decreased from 111% to 41%, and on NASDAQ from 57% to 7%.

The next idea (Fig.4) is calculate the metric, which is comparable to the market growth (“Is IPO growing faster than the market or not?”):

Adjusted price change = [Stock close price last day]- [Stock close price first day]-[S&P500 growth], where S&P500 is a proxy for a market growth.

A recent IPO company is often a relatively new venture and not even profitable, so it should offer more growth (at least sometimes) to be an interesting investment opportunity. Otherwise, an intelligent investor would simply invest directly to S&P500 (e.g. S&P500 ETF), as it is less risky due to lower volatility and large diversification.

GoodRx Holdings (GDRX) becomes even more negative (saturated red): it shows -28% of adjusted returns.

The market adjusted average growth (last trading day to the listing day adjusted to S&P500 growth) on NYSE decreased from 41% to 34%, and on NASDAQ from 7% to 2%.

IPO price and amount distribution

Sunburst graph (Fig.5) combines both of the chart types above: tree map for tickers and stock exchanges categories, and pie chart for the verticals. Here we focus on the initial price (although there is more information on hover).

Thus, one can find that the Technology sector on NYSE consists of several large companies (DASH, SNOW, LU, PLTR, U), which have a wide range of initial prices from $7.25 for PLTR to $120 for SNOW.

On the other hand, Healthcare IPOs mostly occurred on NASDAQ: they have lower price $10-$33 and smaller size (only GDRX, MRVI, and SHC are different).

NYSE and NASDAQ used to be approximately equal on the absolute size of the realised amount (the inner circle NASDAQ/NYSE divided the area on two large sectors), but the last week Airbnb IPO ($3.5b, which is the largest IPO in the last 6 month) outweighed the NASDAQ region.

NYSE has an average price equal to $55, and NASDAQ is only $29.

By checking hover images on the sunburst chart I’ve noticed that large IPOs tend to have zero or positive returns (if we don’t count SNOW and DASH, which were probably too highly priced at $120 and $102).

The idea of a total size is better visible on the next scatter plot (Fig.6):

  • All data is visually binned into 3 clusters: <$0.5b, $0.5b..$1.5b, >$1.5b
  • Top large IPOs (>$1.5b) are yellow or green (zero or positive growth)
  • For example, Unity Software, Inc. (U) has 46% growth in less than 2 months from September.

We can slightly tweak the graph and substitute the “Realized_offer_amount_m_usd” to “days_from_ipo_to_now” (Fig.7) hoping to find some trend.

You can see that in the last 30 days there were two largest IPOs in the top left corner(DASH — $3.3b, ABNB — $3.5b) and several large deals (OZON, MRVI, SHC). All of them are yellow, yielding slightly positive or negative growth after the first trading day.

Another trend is that earlier IPOs (>50 days ago, happened during the summer) tend to be more profitable (more green and dark green circles).

Sectors performance

This graph should be taken cautiously, because only Healthcare (40) and Technology (26) sectors have enough deals to make conclusions about growth distribution. Other sectors deals: Industrials (4), Consumer Discretionary (7), Communications (1), Financials (4), Consumer Staples (4), Materials (2), Real Estate (2).

Healthcare shows a slightly higher median growth (8% above S&P500) than Technology (2% below S&P500). But Technology has more outliers with abnormal 112%, 120%, 176% growth above S&P500.

Binned bar charts

We can split the data into 10 equal bins on realised amount and initial IPO price.

You can immediately see that the smaller realised amount bins (Fig. 9) have a negative average adjusted return. Probably, a smaller IPO gets less attention in the media and less investors who are willing to buy it. And small companies offering IPOs of such size carry more risks due to less customer base and not tested enough business models. There are also diminishing returns from the larger amounts ($176m-$228m-$309m), with one slightly negative growth group ($612m-$1050m).

But if a deal is in top-10% largest — then it shows high returns, probably because it is ‘too big to fail’, a large customer base, and well-known brand.

Another way to cut the data is to build a bar chart with the Initial IPO price bins (Fig.10), which shows a similar trend: smaller (IPO price = $9-$13) and larger (IPO price =$19-$28) bars are negative, while mid-priced (IPO price =$13–$19) IPOs are positive.

The lowest bar (IPO price = $4-$9) has unexpectedly high average adj. growth 30% and the highest bar ($28 -$120) is moderately positive.

Let’s take a closer look at all deals with IPO price <$9 (Fig.11).
I would rather say this group showed high returns because of two outliers IPOs (PLTR and YALA), which showed an incredible adjusted growth to the original price and moved the whole group up.

PLTR and YALA show a similar daily cumulative growth from the moment of IPO (Fig.12 and Fig.13): they started with a moderate 0–20% in the first 20 days, and then showed an huge jump gaining 80% of the price during day 20–50 after the IPO.


We’ve analysed last 100 IPOs in (mostly) second half of 2020 and showed that they can grow very quickly, especially if you can invest using pre-IPO instruments. Some of large companies continue to grow from the first days of trade on the open market. And some of the smaller companies can start to grow quickly just after 20–30 days.

No doubt 2020 was a very successful year for the most companies going public. It is also a hotly debated topic among investors and it gives a high potential for a quick gain.

2021 may be even better: some well-known companies (e.g. Instacart, Robinhood, Stripe, Coinbase) are also planning to go for an IPO in 2021.

In this Part I didn’t publish any explanations on the technical programming in Python, but you can check the full code on my Github page.
I appreciate you comments and suggestions on the further improvement.



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Ivan Brigida

Ivan Brigida

Data and Product Analyst at Google. I run a website on Python programming, analytics, and stock investing.