Comparing Industry Profitability Ratios with Python

Profitability ratios are a good way to measure how effective companies are managing their operations. When looking into company profitability ratios such as gross profit margin, we need to compare them with industry data to evaluate the relative performance of the company.

In this post, we will calculate the median of the main profitability ratios for 8 different industries on companies operating in Nasdaq. We will then see how profitability ratios vary across industries.

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What are Profitability Ratios?

With margin ratios we are able to measure how a company is able to generate income relative to sales. In general, all financial ratios facilitate the comparison across time periods and companies.

In this post, we will focus on below three profitability ratios which we can extract from the income statement.

Gross profit margin: This is the profit the company makes after deducting costs associated to make and sell products. Gross profit margin let us know how effective a company is to manufacture or purchase its products. Gross Profit Margin = Gross Profit / Sales

Operating profit margin. Once we get our profit margin, we can then move on and calculate the operating margin. That is the revenue from operations minus the operating costs that a company has to incur in order to be able to continue with its day to day operations. Operating profit margin let us know how effective the company is at operations. Operating Profit Margin = Operating Profit / Sales

Net income margin. It measure the net income produce by a company as a percentage of revenue. Net income margin is a good indicator of the overall effectiveness of the company. Net Income Margin = Net Income / Sales

Comparing Profitability Ratios Across Companies

These three ratios can be calculate for an individual company. But as with the rest of financial ratios, they need to be compared to other companies or across time in order to make sense from them.

That is, we need to compare the individual metrics of a company with an industry or comparable company to understand better how it is performing.

Therefore, what we will do in the next section using Python is the following:

• Retrieve the income statement from 10 companies in 8 different sectors.

• Calculate the gross profit, operating profit and net income margins ratios with Python for each individual company.

• Compute the median gross profit margin, operating margin and net profit margin for each of the sectors

Profitability Ratios with Python

Lets move to the code. For our margin ratio calculations, we will use Company Financial Statements Endpoint.

First, we will need to define in a Python list, the list of sectors that we will be calculating the ratios for. Then, we also pass the number of companies that we want to include. To speed up the code, I will only include 10 companies per sector. Feel free to add many more to it.

import requests

import numpy as np

import pandas as pd

api_key = 'your api key'

sectors = ['Technology','Consumer Cyclical','Industrials','Basic Materials','Communication Services','Consumer Defensive','Healthcare','Real Estate','Utilities','Financial','Services']

exchange = 'NASDAQ'

marketcapmorethan = '1000000000'

number_of_companies = 10

symbols = []

overview_Nasdaq = {}

Once we have defined our code set up, we will need to make an API call to retrieve the 10 company tickers from the API for each of the industries. We will use a for loop to do so (see section 1 in the code) and append the extracted tickers to a symbol list.

Next, within the sector loop (section 2 in the code) we will loop through each of the tickers in order to retrieve the income statement from the last quarter. Remember, that our margin ratios are computed out of income statement items.

Then, in section 3 of the code below, we simply calculate the margin ratios and append the result to a list. Then, we convert the Python lists into Numpy arrays to calculate the median. That will give us the gross profit, operating profit and net margin medians for each of the industries.

Finally, in section 4, we add the median of each of the industry ratios into a Python dictionary.

Then, we convert it into a Pandas DataFrame in order to better visualise the data.

### 1 - request list of tickers for each industry

for sector in sectors:

symbols = []

gross_Margin = []

operating_Margin = []

net_Profit_Margin = []

overview_Nasdaq[sector] = {}

n = 0

screener = requests.get(f'https://financialmodelingprep.com/api/v3/stock-screener?{marketcapmorethan}=1000000000&volumeMoreThan=10000§or={sector}&exchange={exchange}&limit={number_of_companies}&apikey={api_key}').json()

for item in screener:

symbols.append(item['symbol'])

### 2 extract Income Statement for each of the companies

for company in symbols:

try:

n = n + 1

IS = requests.get(f'https://financialmodelingprep.com/api/v3/income-statement/{company}?period=quarter&apikey={api_key}').json()

### 3 compute margin ratios and append to list

gross_Margin.append(IS[0]['grossProfit'] / IS[0]['revenue'])

operating_Margin.append( (IS[0]['operatingIncome']) / IS[0]['revenue'])

net_Profit_Margin.append(IS[0]['netIncome'] / IS[0]['revenue'])

#NP to be able to calculate mean

except:

pass

gross_Margin = np.array(gross_Margin)

gross_Margin = np.median(gross_Margin)

operating_Margin = np.array(operating_Margin)

operating_Margin = np.median(operating_Margin)

net_Profit_Margin = np.array(net_Profit_Margin)

net_Profit_Margin = np.median(net_Profit_Margin)

### 4 - Add Ratios to a Dictionary and Convert to Pandas

overview_Nasdaq[sector]['Num. Companies'] = n

overview_Nasdaq[sector]['Gross Margin'] = gross_Margin * 100

overview_Nasdaq[sector]['Operating Margin'] = operating_Margin* 100

overview_Nasdaq[sector]['Net Profit Margin'] = net_Profit_Margin* 100

median_by_Industry = pd.DataFrame.from_dict(overview_Nasdaq,orient='columns')

median_by_Industry

The code above makes you able to compare different industries with each others and make