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 i.industry, count(i.*)
from industries as i
inner join dates as d
on i.company_id = d.company_id
where extract(year from d.date_joined) in ('2019', '2020', '2021')
group by industry
order by count desc
limit 3
),
yearly_rankings as (
select
count(i.*) as num_unicorns,
i.industry,
extract(year from d.date_joined) as year,
avg(f.valuation) as average_valuation
from industries as i
inner join dates as d
using(company_id)
inner join funding as f
using(company_id)
group by industry, year
)
select
industry,
year,
num_unicorns,
round(avg(average_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 year desc, num_unicorns desc