A deep dive analysis on Snowflake using Fidap
Snowflake (Wikipedia) is a data warehouse company that has built one of the leading cloud native data warehouses. It’s a cool product and we use it here at Fidap to store our data that our users can actually query. In this article, we’ll use Fidap’s data to find some interesting insights into Snowflake as an investment.
This analysis is as of market close on Fri, Mar 11, 2022.
Let’s just start out with some basic information about the company. We’ll use the TICKERS table to extract some fields (Snowflake’s ticker is SNOW) —
Here are the fields we grabbed (note there are a total of 209 fields in this table — so a lot more available)
- Name: The company name, Snowflake Inc.
- Ticker: As appears on the exchange, SNOW.
- Market cap (mc_bn): The market cap in billions, currently $55bn.
- 1Y percent change (pc_1y): The percent change in the stock price over 1 year, -22%
- 3M percent change (pc_3m): Same as above, for 3 months, -47%
- Revenue (rev_mn): Total revenue in USD from latest quarterly earnings in millions, $334m.
- Sector: The broad sector category, Technology.
- Industry: The industry category within the sector, Software — Application
- First price date: The date we have our first price (usu the IPO date), Sep 16, 2020.
SELECT
ticker,
name,
round(marketcap / 1e9, 2) AS mc_bn,
sector,
industry,
firstpricedate,
revenueusd / 1e6 AS rev_mn,
round(pc_1y * 100, 2) AS pc_1y,
round(pc_3m * 100, 2) AS pc_3m
FROM
fidap.US_EQUITIES.TICKERS
WHERE
ticker = 'SNOW'
Price history
Let’s look at Snowflake’s price history (here’s the query on Fidap) —
As you can see, the price has collapsed recently to well below the IPO date’s closing price as well as below the high close at around $400.
We can also look at Snowflake’s historical volatility (the chart here is on std_3m or historical 3 month standard deviation of daily returns)—
Historical volatility had dropped towards 2% in Sep and Nov 2021 from over 6% immediately after IPO, but then spiked back up. Interesting to compare to the price chart above where we see the low in volatility corresponded to the high in price.
Fundamentals
Let’s take a look at some fundamental factors. To do this, we’ll leverage the FUNDAMENTALS table. Let’s just grab the historical data first —
We have 10 rows of quarterly earnings stretching back to Jan 2019. We grabbed revenue, EBIDTA, and QoQ and QoY rev growth numbers.
And here’s the query used —
SELECT
ticker,
datekey,
revenueusd/1e6 as rev_mn,
round(rev_qoq*100,2) as rev_qoq,
round(rev_yoy*100,2) as rev_yoy,
ebitdausd/1e6 as ebitda_mn
FROM
fidap.US_EQUITIES.FUNDAMENTALS
WHERE
ticker = 'SNOW'
AND dimension = 'MRQ'
ORDER BY
datekey asc
Here’s Snowflake’s quarterly revenue —
Quarterly revenue started at $73m and exploded since to $334m at the latest reading.
Here’s Snowflake’s quarter over year growth rates —
Snowflake has managed to maintain consistent >100% growth rates so far.
Here’s Snowflake’s EBITDA —
While it increased the first few quarters, it has since peaked and looks like it is decreasing. Still a ways away from profitability though — seems Snowflake is prioritizing maintaining high growth rates vs a quick move to profits.
What we find overall when looking at Snowflake’s fundamentals is a very consistently high growth company with still large but slowly narrowing losses.
Insider transactions
Let’s move on to looking at insider transactions in Snowflake. Insider trades / transactions occur when an important person close to the company makes a trade. When this happens, a X is filed with the SEC that is then made public, and captured within our dataset. Here’s the Fidap insider trades table. Definition here.
Here are the latest insider trades in SNOW (over $100k in total value) —
Interestingly, there have been a lot of buys by key insiders in the last few months.
Here are the sells totals by month —
Here’s the insiders query on Fidap.
As you can see — Dec 2021 saw the heaviest insider selling just as it was coming off its all time highs.
Here are the 10 biggest sellers over the last 12 months —
The CEO, Frank Slootman, has offloaded $742m of stock, while the CFO, Michael Scarpelli, has sold $445m.
Portfolios
Large institutional shareholders must declare their equity holdings with the SEC on a quarterly basis. Using these filings, called 13Fs, we can find out large shareholders of Snowflake’s stock.
Here are the largest institutional shareholders, as declared on 13Fs —
We can also look at firms that hold a disproportionate amount of SNOW vs their other holdings. Here are the most overweight on Snowflake —
Two investors in particular — Altimeter and Iconiq — had over 50% of their share assets in SNOW! However, it might be the case that these were VC investors from when SNOW was private.
Correlations
Finally, let’s look at stocks that are most correlated with Snowflake. We’ve done a small analysis here previously. Stocks that are most correlated price wise with Snowflake also seem to have similar business models and other characteristics (eg generally recently IPOed high growth enterprise software) —
This is a sample of some of the analysis that Fidap makes possible. But the platform was built to be flexible — so you can do much deeper analysis than what’s presented here.