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.
WITH TOTAL_UNICORNS AS (
SELECT
i.industry,
COUNT(c.company_id) AS total_unicorns
FROM
public.companies c
INNER JOIN
public.industries i ON i.company_id = c.company_id
INNER JOIN
public.dates d ON c.company_id = d.company_id
WHERE
TO_CHAR(d.date_joined, 'YYYY') IN ('2019', '2020', '2021')
GROUP BY
i.industry
ORDER BY
total_unicorns DESC
LIMIT 3
),
INDUSTRY_DETAILS AS (
SELECT
i.industry,
TO_CHAR(d.date_joined, 'YYYY') AS year,
COUNT(c.company_id) AS num_unicorns,
ROUND(AVG(f.valuation)/1000000000.0, 2) AS average_valuation_billions
FROM
public.companies c
INNER JOIN
public.industries i ON i.company_id = c.company_id
INNER JOIN
public.dates d ON c.company_id = d.company_id
INNER JOIN
public.funding f ON c.company_id = f.company_id
WHERE
TO_CHAR(d.date_joined, 'YYYY') IN ('2019', '2020', '2021')
AND i.industry IN (SELECT industry FROM TOTAL_UNICORNS)
GROUP BY
i.industry, TO_CHAR(d.date_joined, 'YYYY')
),
RANKED_RESULTS AS (
SELECT
industry,
year,
num_unicorns,
average_valuation_billions,
RANK() OVER(PARTITION BY year ORDER BY num_unicorns DESC) AS rank
FROM
INDUSTRY_DETAILS
)
SELECT
industry,
year,
num_unicorns,
average_valuation_billions
FROM
RANKED_RESULTS
WHERE
rank <= 3
ORDER BY
year DESC,
num_unicorns DESC;