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.
--Query to identify top 3 industries by number of unicorns in years 2021,2020,2019--
WITH top_industries AS (
SELECT i.industry, COUNT(i.*)
FROM public.industries AS i
INNER JOIN public.dates AS d
USING(company_id)
WHERE EXTRACT(YEAR FROM d.date_joined)
IN ('2021', '2020', '2019')
GROUP BY industry
ORDER BY 2 DESC
LIMIT 3),
--Query to identify average valuation across all industries with no specific year--
yearly_rankings AS (
SELECT i.industry, EXTRACT(year FROM d.date_joined) AS year,
COUNT(i.*) AS num_unicorns, AVG(f.valuation) AS avg_valuation
FROM public.industries as i
INNER JOIN public.dates AS d
USING(company_id)
INNER JOIN public.funding AS f
USING(company_id)
GROUP BY 1,2)
--This query identifies the average valuation in billions the top 3 industries by the number of unicorns in years 2021, 2020 and 2019--
SELECT industry, year,num_unicorns,
ROUND(AVG(avg_valuation/1000000000), 2) AS average_valuation_billions
FROM yearly_rankings
WHERE year IN ('2021','2020','2019')
AND industry IN (SELECT industry
FROM top_industries)
GROUP BY 1,2,3
ORDER BY 2 DESC, 3 DESC