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 TopIndustries AS (
-- Step 1: Find the top 3 industries based on the number of unicorns from 2019-2021
SELECT
i.industry,
COUNT(d.company_id) AS num_unicorns
FROM
industries i
JOIN
dates 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
num_unicorns DESC
LIMIT 3
),
IndustryData AS (
-- Step 2: Retrieve the number of unicorns, year they became unicorns, and their average valuation
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(d.company_id) AS num_unicorns,
ROUND(AVG(f.valuation) / 1000000000, 2) AS average_valuation_billions
FROM
industries i
JOIN
dates d ON i.company_id = d.company_id
JOIN
funding f ON i.company_id = f.company_id
WHERE
EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
AND i.industry IN (SELECT industry FROM TopIndustries)
GROUP BY
i.industry, year
)
-- Step 3: Sort by year and number of unicorns
SELECT
industry,
year,
num_unicorns,
average_valuation_billions
FROM
IndustryData
ORDER BY
year DESC,
num_unicorns DESC;