Did you know that the average return from investing in stocks is 10% per year (not accounting for inflation)? But who wants to be average?!
You have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.
You have been given access to their unicorns database, which contains the following tables:
dates
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
date_joined | The date that the company became a unicorn. |
year_founded | The year that the company was founded. |
funding
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
valuation | Company value in US dollars. |
funding | The amount of funding raised in US dollars. |
select_investors | A list of key investors in the company. |
industries
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
industry | The industry that the company operates in. |
companies
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
company | The name of the company. |
city | The city where the company is headquartered. |
country | The country where the company is headquartered. |
continent | The continent where the company is headquartered. |
The output
Your query should return a table in the following format:
| industry | year | num_unicorns | average_valuation_billions |
|---|---|---|---|
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
Where industry1, industry2, and industry3 are the three top-performing industries.
--Top industries CTE
WITH top_industries AS
(SELECT
i.industry AS industry,
COUNT(*) AS count
FROM industries AS i
LEFT JOIN dates AS d
ON i.company_id=d.company_id
WHERE EXTRACT(year FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY count DESC
LIMIT 3
),
--Unicorns CTE
unicorns AS
(SELECT
COUNT(*) AS num_unicorns,
i.industry AS industry,
EXTRACT(year FROM d.date_joined) AS year,
AVG(f.valuation) AS average_valuation
FROM industries AS i
LEFT JOIN dates AS d
ON i.company_id=d.company_id
LEFT JOIN funding AS f
ON i.company_id=f.company_id
GROUP BY industry, year
)
--Main Query
SELECT
unicorns.industry AS industry,
unicorns.year AS year,
unicorns.num_unicorns AS num_unicorns,
ROUND(AVG(unicorns.average_valuation)/1000000000,2) AS average_valuation_billions
FROM unicorns
WHERE unicorns.year IN ('2019','2020','2021')
AND unicorns.industry IN
(SELECT industry
FROM top_industries)
GROUP BY industry, year, num_unicorns
ORDER BY year DESC, num_unicorns DESC