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 c.company,
MAX(f.valuation) AS valuation
FROM companies AS c
INNER JOIN funding AS f
ON c.company_id = f.company_id
join public.dates as d
on d.company_id = i.company_id and d.year_founded in (2019,2020,2021)
GROUP BY c.company
LIMIT 3
)
with top_industries as
(select count(d.date_joined) as cnt, industry from public.industries as i
join public.funding as f
on f.company_id = i.company_id
where d.date_joined is not null
group by i.industry
order by cnt desc
limit 3
)
, num_unicorns as
(select count(c.company_id) as num_unicorns
from public.companies as c
join top_industries as i
on c. = i.company_id
group by i.industry
select i.industry, d.year_founded as year,, round(avg(f.valuation) over (partition by c.company_id), 2) as average_valuation_billions
from public.companies as c
join public.industries as i
on c.company_id = i.company_id and i.industry in (select industry from top_industries)
join public.funding as f
on f.company_id = c.company_id
join public.dates as d
on d.company_id = i.company_id and d.year_founded in (2019,2020,2021)
where d.date_joined is not null
order by average_valuation_billions desc;