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_ind as (
Select
industry,
Count(*) as cnt
From industries
Inner Join dates on industries.company_id = dates.company_id
Where extract('year' from date_joined) in (2019,2020,2021)
Group by 1
Order by 2 desc
Limit 3
),
yearly_ranking as
(
Select
industry,
extract('year' from date_joined) as year,
count(*) as num_unicorns,
avg(valuation) as average_valuation
From
dates d
Inner Join industries i on d.company_id = i.company_id
Inner Join funding f on d.company_id = f.company_id
Group by industry, year
)
Select
industry,
year,
num_unicorns,
round(avg(average_valuation / 1000000000),2) as average_valuation_billions
From
yearly_ranking
Where year in (2019, 2020,2021) and industry in (select industry from top_ind)
Group by 1,2,3
Order by year desc, num_unicorns desc