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 unicorns_2019_2021 AS (
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year
FROM industries i
JOIN dates d ON i.company_id = d.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
),
top_3_industries AS (
SELECT
industry,
COUNT(*) AS unicorn_count
FROM unicorns_2019_2021
GROUP BY industry
ORDER BY unicorn_count DESC
LIMIT 3
),
all_years AS (
SELECT DISTINCT year FROM (
SELECT 2019 AS year UNION ALL
SELECT 2020 UNION ALL
SELECT 2021
) y
),
industry_year_combinations AS (
SELECT
ti.industry,
y.year
FROM top_3_industries ti
CROSS JOIN all_years y
),
industry_data AS (
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(*) AS num_unicorns,
ROUND(SUM(f.valuation) / 1000000000.0 / COUNT(*), 2) AS avg_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 i.industry IN (SELECT industry FROM top_3_industries)
AND EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry, year
)
SELECT
c.industry,
c.year,
COALESCE(d.num_unicorns, 0) AS num_unicorns,
COALESCE(d.avg_valuation_billions, 0) AS average_valuation_billions
FROM industry_year_combinations c
LEFT JOIN industry_data d
ON c.industry = d.industry AND c.year = d.year
ORDER BY c.year DESC, num_unicorns DESC;