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.
Exercice
WITH industry_unicorns_ranking_cte AS (
SELECT
i.industry,
COUNT(*) AS num_unicorns,
RANK() OVER (ORDER BY COUNT(*) DESC) AS num_unicorns_rank
FROM
dates AS d
INNER JOIN
industries AS i
ON
d.company_id = i.company_id
WHERE
EXTRACT(YEAR FROM date_joined) BETWEEN 2019 AND 2021
GROUP BY
i.industry
LIMIT 3
),
average_unicorn_valuation_ranking_cte AS (
SELECT
c.company,
ROUND(AVG(f.valuation) / 1000000000, 2) AS average_valuation,
RANK () OVER (ORDER BY ROUND(AVG(f.valuation) / 1000000000, 2) DESC) as average_valuation_rank
FROM
funding AS f
INNER JOIN
companies AS c
ON
c.company_id = f.company_id
GROUP BY
c.company
)
SELECT
industry,
EXTRACT(YEAR FROM date_joined) AS year,
COUNT(i.company_id) AS num_unicorns,
ROUND(AVG(valuation) / 1000000000, 2) AS average_valuation
FROM
industries AS i
INNER JOIN
dates AS d
ON d.company_id = i.company_id
INNER JOIN
funding AS f
ON f.company_id = i.company_id
WHERE
industry IN (SELECT industry FROM industry_unicorns_ranking_cte
ORDER BY num_unicorns_rank LIMIT 3)
AND EXTRACT(YEAR FROM date_joined) in ('2019', '2020', '2021')
GROUP BY
industry,
year
ORDER BY
num_unicorns DESC,
year DESC
;