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.
-- This table is date industry.
WITH yd AS (SELECT d.company_id AS id1,EXTRACT ('year' FROM d.date_joined) AS year,i.industry
FROM dates AS d
INNER JOIN industries AS i
ON d.company_id = i.company_id),
cf AS (SELECT f.company_id AS id2,c.company,f.valuation
FROM companies AS c
INNER JOIN funding AS f
ON c.company_id = f.company_id
),
Z AS(
SELECT industry,year,COUNT(industry) AS num_unicorns,ROUND((AVG(valuation)/1000000000),2) average_valuation_billions,RANK() OVER(PARTITION BY year ORDER BY COUNT(industry) DESC) AS rank
FROM yd
INNER JOIN cf
ON yd.id1=cf.id2
WHERE year IN ('2019','2020','2021')
GROUP BY industry,year
ORDER BY year DESC,num_unicorns DESC)
SELECT*
FROM Z
WHERE rank<=3
;