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.
-- Get top 3 industries PER YEAR by unicorn count
WITH industry_counts AS (
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(*) AS num_unicorns
FROM industries AS i
LEFT JOIN dates AS d ON d.company_id = i.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN ('2019', '2020', '2021')
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
),
top_3 AS (
SELECT *
FROM (
SELECT
industry,
year,
num_unicorns,
ROW_NUMBER() OVER (PARTITION BY year ORDER BY num_unicorns DESC) AS rn
FROM industry_counts
) sub
WHERE rn <= 3
)
-- Now fetch average valuations
SELECT
t.industry,
t.year,
COUNT(*) AS num_unicorns,
ROUND(AVG(f.valuation) / 1000000000.0, 2) AS average_valuation_billions
FROM top_3 AS t
JOIN industries AS i ON i.industry = t.industry
JOIN dates AS d ON d.company_id = i.company_id AND EXTRACT(YEAR FROM d.date_joined) = t.year
JOIN funding AS f ON f.company_id = i.company_id
GROUP BY t.industry, t.year
ORDER BY t.year DESC, num_unicorns DESC;print(df)