Section 1.1: Key Fundamental Ratios

We will delve into four widely used ratios that form the basis of financial analysis:

1. Price to Earnings Ratio (P/E): This ratio compares a company's share price to its earnings per share (EPS). A high P/E ratio may indicate overvaluation, whereas a low ratio suggests undervaluation. However, it's important to consider other valuation factors alongside the P/E ratio. Typically, a P/E ratio over 30 is regarded as high.
   • Formula: P/E = Stock Price / Earnings per Share (EPS)
2. Price to Book Ratio (P/B): This ratio assesses the relationship between a company's stock price and its book value. A lower P/B ratio signals an undervalued stock. For instance, if a company's book value is 50 and its stock price is 500, the P/B ratio is 10.
   • Formula: P/B = Stock Price / Book Value
3. Price to Sales Ratio (P/S): This straightforward ratio helps investors understand what they are paying for the company's sales. A lower P/S ratio indicates undervaluation, while a higher ratio suggests overvaluation.
   • Formula: P/S = Stock Price / Sales per Share
4. Price-Earnings to Growth Ratio (PEG): An enhanced version of the P/E ratio, the PEG ratio incorporates expected growth, providing a more comprehensive view. A PEG ratio below 1 is indicative of an undervalued stock.
   • Formula: PEG = P/E / EPS Growth

Section 1.2: Loading Required Libraries

To access the fundamental data for various stocks, we will utilize Yahoo Finance. Here’s how to set up your environment:

import pandas as pd

import numpy as np

import yahoo_fin.stock_info as si

import pandas as pd

Configuring Your Data Range

We will analyze data from February 2022 to February 2023:

start_date = '2022-02-02'

end_date = '2023-02-02'

tickers = ['AAPL', 'IBM', 'MSFT', 'WMT', 'AMGN', 'AXP', 'BA',

'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']

ratio_stat = ['Trailing P/E', 'Forward P/E', 'PEG Ratio (5 yr expected)',

'Price/Book (mrq)', 'Price/Sales (ttm)',

'Enterprise Value/EBITDA', 'Enterprise Value/Revenue']

Retrieving Data from Yahoo Finance

The following function allows us to download the necessary data for the selected stocks:

def load_data(self):

try:

data = pd.DataFrame(columns=self.tickers)

for ticker in self.tickers:

data[ticker] = yf.download(self.ticker,

self.start_date,

self.end_date)['Adj Close']

return data

except Exception as e:

print(f'An exception occurred while executing load_data: {e}')

Fetching Fundamental Data

We will utilize the get_stats_valuation function to collect the required data and construct a DataFrame with the ratios for all companies:

def get_fundamental(self):

try:

df_fundamentals = pd.DataFrame()

for ticker in range(len(self.tickers)):

stock_name = str(self.tickers[ticker])

fundamental_ratio = si.get_stats_valuation(stock_name)

fundamental_ratio.index = fundamental_ratio[0]

fundamental_ratio = fundamental_ratio.drop(labels=0, axis=1)

tmp_table = fundamental_ratio.T

tmp_table = tmp_table[self.ratio_stat]

df_fundamentals = df_fundamentals.append(tmp_table)

df_fundamentals.index = self.tickers

df_fundamentals = df_fundamentals.astype('float')

df_fundamentals.dropna(inplace=True)

return df_fundamentals

except Exception as e:

print(f'An exception occurred while executing get_fundamental: {e}')

Practical Applications of Ratios

These ratios can help identify stocks that are either overvalued or undervalued. For a basic analysis, we can use a simple ratio called over_under:

over_under = P/E of a specific stock / mean P/E of all the stocks

• If over_under > 1, the stock is overvalued.
• If over_under < 1, the stock is undervalued.
• If over_under = 1, it is fairly valued.

The denominator typically represents the industry average P/E. To keep it straightforward, we will calculate the mean of the stocks within our DataFrame:

def get_over_under_stocks(self):

try:

df_fundamentals = self.get_fundamental()

df_fundamentals['Trailing P/E'].mean()

df_fundamentals['over_under'] = (df_fundamentals['Trailing P/E']) / (df_fundamentals['Trailing P/E'].mean())

category = []

for i in df_fundamentals['over_under']:

if i < 1: category.append('Under Valued')

elif i > 1: category.append('Over Valued')

else: category.append('Fair Valued')

df_fundamentals['Category'] = category

return df_fundamentals

except Exception as e:

print(f'An exception occurred while executing get_over_under_stocks: {e}')

Closing Thoughts

Fundamental analysis relies on actual data, and when conducted effectively, it can guide sound investment choices. The integration of technology and automation allows for swift analyses using reliable figures. It is crucial to remember that these ratios fluctuate over time and must be recalculated accordingly. They should be regarded as supportive tools rather than standalone indicators for decision-making.

For further reading, I recommend my previous articles on Risk Management of Stocks Using Python and How to Build a Financial Portfolio Using Python, where I delve into return calculations and Value at Risk (VaR) methodologies, along with constructing an optimized financial portfolio.

I hope you found this article insightful and beneficial.

You can connect with me on LinkedIn and GitHub.

Disclaimer

This blog serves educational purposes only and should not be construed as professional financial advice.

References

• Investopedia
• Unsplash

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