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 company_level as
(select
dates.company_id,
extract(year from dates.date_joined) as year,
funding.valuation as valuation,
industries.industry
from dates
left join funding on dates.company_id = funding.company_id
left join companies on companies.company_id = dates.company_id
left join industries on industries.company_id = dates.company_id),
year_filtered as (
select
industry,
year,
count(*) as num_unicorns,
avg(valuation) as average_valuation
from company_level
where year in (2019,2020,2021)
group by industry, year ),
top_three as (
select
industry,
sum(num_unicorns) as sum_unicorns
from year_filtered
group by industry
order by sum_unicorns desc
limit 3
)
select year_filtered.industry,
year_filtered.year,
year_filtered.num_unicorns,
round((year_filtered.average_valuation / 1000000000), 2) as average_valuation_billions
from year_filtered
right join top_three on top_three.industry = year_filtered.industry
order by year, industry desc;