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.
-- new unicorns created in 2019, 2020, and 2021 combined
WITH top_3_inductries AS(
SELECT industry, COUNT(d.company_id) AS num_unicorns
FROM industries i
JOIN dates d
ON i.company_id = d.company_id
WHERE DATE_PART('year', date_joined) IN (2019, 2020, 2021)
GROUP BY 1
ORDER BY 2 DESC
LIMIT 3)
-- Top 3 industries are Fintech, Internet software & services, E-commerce & direct-to-consumer
SELECT i.industry, DATE_PART('year', date_joined) AS year, COUNT(d.company_id) AS num_unicorns,
ROUND(AVG(valuation/1000000000), 2) AS average_valuation_billions
FROM industries i
JOIN dates d
ON i.company_id = d.company_id
JOIN top_3_inductries top
ON top.industry = i.industry
JOIN funding f
ON f.company_id = i.company_id
WHERE DATE_PART('year', date_joined) IN (2019, 2020, 2021)
GROUP BY 1, 2
ORDER BY 3 DESC