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 top_industries AS (
SELECT industry, COUNT(i.company_id) as num_unicorns
FROM industries AS i
INNER JOIN dates AS d ON i.company_id = d.company_id
WHERE year_founded IN (2019,2020,2021)
GROUP BY i.industry
ORDER BY num_unicorns DESC
LIMIT 3
), ranking AS (
SELECT i.industry, EXTRACT(YEAR FROM d.date_joined) AS year, AVG(f.valuation) AS avg_valuation, COUNT(i.company_id) as num_unicorns
FROM funding AS f
INNER JOIN industries AS i ON f.company_id = i.company_id
INNER JOIN dates AS d ON f.company_id = d.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019,2020,2021)
GROUP BY i.industry, year
ORDER BY i.industry DESC, year DESC
)
SELECT r.industry, r.year, r.num_unicorns, ROUND(r.avg_valuation/1000000000,2) AS average_valuation_billions
FROM ranking r
WHERE r.year IN (2019,2020,2021) AND r.industry IN (SELECT industry FROM top_industries)
ORDER BY industry DESC, year DESC