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