Overvalued or Undervalued Stock

5 min readApr 4, 2021

Introduction

Recently, more and more people have been interested in stock investing. However, these new investors wonder how to figure out if a company’s stock is overvalued or undervalued and should put in their portfolio. The P/E (Price-to-Earnings) ratio is a easy way to estimate a company’s value. When a company’s P/E ratio is higher than other companies in same sector or peer group, it means the company’s stock is overvalued. Also, P/S (Price-to-Sales) ratio, P/B (Price-to-Book) ratio, and PEG (Price / Earnings-to-Growth) ration can be a way to check whether a stock is overvalued or undervalued. In this post I will explore how to get these ratios, and find out a company’s stock value is overvalued or undervalued by extracting fundamentals data from Yahoo Finance by using the yahoo_fin package.

What is P/E, P/S, P/B and PEG Ratios

P/E Ratio

The price-to-earnings (P/E ) ratio is an investment valuation ratio that measures its current stock price relative to its earnings per share (EPS).

P/S Ratio

The price-to-sales (P/S) ratio is an investment valuation ratio that compares a company’s stock price to its revenues. It is an indicator of the value that financial markets have placed on each dollar of a company’s sales or revenues.

P/B Ratio

The price-to-book (P/B) is an investment valuation ratio ratio that compares a company’s market value to its book value. The market value of a company is its share price multiplied by the number of outstanding shares. The book value is the net assets of a company.

PEG Ratio

The price/earnings to growth (PEG) ratio is an investment valuation ratio ratio that a stock’s price-to-earnings (P/E) ratio divided by the growth rate of its earnings for a specified time period. The PEG ratio is used to determine a stock’s value while also factoring in the company’s expected earnings growth.

Import stock_info Module from yahoo_fin Package

Install yahoo_fin package by using pip

pip install yahoo_fin

If you already have a previous version of yahoo_fin package, then you can upgrade package

pip install yahoo_fin --upgrade

Import the stock_info module from yahoo_fin to get fundamentals data from Yahoo Finance

import yahoo_fin.stock_info as si
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

Get P/E, P/S, P/B, and PEG ratios of a Company

Use the get_quote_table method to extract the data of a stock from Yahoo Finance

quote = si.get_quote_table("nvda")

P/E ratio

PE_ratio = quote["PE Ratio (TTM)"] #80.07

P/S ratio

val = si.get_stats_valuation("nvda")
val = val.iloc[:,:2]
val.columns = ["Attribute", "Recent"]PS_ratio = float(val[val.Attribute.str.contains("Price/Sales")].iloc[0,1]) #19.51

P/B ratio

PB_ratio = float(val[val.Attribute.str.contains("Price/Book")].iloc[0,1]) #19.01

PEG ratio

PEG_ratio = float(val[val.Attribute.str.contains("PEG Ratio")].iloc[0,1]) #3.32

Get Competitors Ratio in S&P 500 List

Get list of companies in S&P 500 from Wikipedia page

payload=pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
df = payload[0]

Find Nvidia’s GICS Sector, GICS Sub-Industry

df[df["Symbol"] == "NVDA"]

Nvidia’s Information

Find companies’ symbol list which GICS Sector is information technology, and GICS Sub-Industry is semiconductors

# get list of competitors tickers
competitors = list(df.loc[(df["GICS Sector"] == "Information Technology") & (df["GICS Sub-Industry"] == "Semiconductors")]["Symbol"].values)
# ['AMD', 'ADI', 'AVGO', 'INTC', 'MXIM', 'MCHP', 'MU', 'MPWR', 'NVDA', 'NXPI', 'QRVO', 'QCOM', 'SWKS', 'TXN', 'XLNX']

Get fundamentals stats of competitors

# Get data in the current column for each stock's valuation table
competitors_stats = {}
for ticker in competitors:
    temp = si.get_stats_valuation(ticker)
    temp = temp.iloc[:,:2]
    temp.columns = ["Attribute", "Recent"]
 
    competitors_stats[ticker] = temp
 
 
# combine all the stats valuation tables into a single data frame
combined_stats = pd.concat(competitors_stats)
combined_stats = combined_stats.reset_index()
 
del combined_stats["level_1"]
 
# update column names
combined_stats.columns = ["Ticker", "Attribute", "Recent"]

Drop Nvidia’s stats and NXP Semiconductors NV stats because NXPI’s P/E ratio is extremely high compare other competitors

# drop Nvidia's stats
combined_stats= combined_stats[combined_stats['Ticker'] != 'NVDA']# drop NXPI's stats - because the value is extremly high
combined_stats= combined_stats[combined_stats['Ticker'] != 'NXPI']

Get the lists of P/E, P/S, P/B, and PEG ratios of competitors

# get the list of P/E ratio of competitors
com_PE = combined_stats[combined_stats.Attribute.str.contains("Trailing P/E")].Recent.values.astype(float)
# array([ 39.36,  40.81,  52.74,  13.06,  35.62, 119.13,  34.87, 
          100.92, 42.9 ,  22.48,  28.24,  32.17,  51.94])# get the list of P/S ratio of competitors
com_PS = combined_stats[combined_stats.Attribute.str.contains("Price/Sales")].Recent.values.astype(float)
# array([10.03, 10.19,  7.82,  3.51, 10.96,  7.5 ,  4.74, 19.66,  
         5.6 , 5.65,  7.62, 12.39, 10.56])# get the list of P/B ratio of competitors
com_PB = combined_stats[combined_stats.Attribute.str.contains("Price/Book")].Recent.values.astype(float)
# array([16.87,  4.7 ,  7.77,  3.23, 13.28,  7.63,  2.59, 16.67,  
         4.5 , 20.21,  6.71, 19.3 , 12.28])# get the list of PEG ratio of competitors
com_PEG = combined_stats[combined_stats.Attribute.str.contains("PEG")].Recent.values.astype(float)
# array([1.51, 2.26, 1.25, 2.84, 2.7 , 1.42, 0.9 , 3.61, 1.31, 0.75, 
         1.24, 2.92, 3.61])

Result

Create box plots using Matplotlib

# create plot
fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10,10))axs[0,0].boxplot(com_PE)
axs[0,0].set_xticklabels(['P/E ratio'])
axs[0,0].plot(1, PE_ratio, marker='o', markersize=10)
axs[0,0].set_title('P/E ratio')axs[0,1].boxplot(com_PS)
axs[0,1].set_xticklabels(['P/S ratio'])
axs[0,1].plot(1, PS_ratio, marker='o', markersize=10)
axs[0,1].set_title('P/S ratio')axs[1,0].boxplot(com_PB)
axs[1,0].set_xticklabels(['P/B ratio'])
axs[1,0].plot(1, PB_ratio, marker='o', markersize=10)
axs[1,0].set_title('P/B ratio')axs[1,1].boxplot(com_PEG)
axs[1,1].set_xticklabels(['PEG ratio'])
axs[1,1].plot(1, PEG_ratio, marker='o', markersize=10)
axs[1,1].set_title('PEG ratio')# show plot
plt.show()

Conclusion

In this post, I wanted to check Nvidia’s stock price is overvalued or undervalued. According to the result, I can say Nvidia’s stock is overvalued because its P/E ratio and P/S ratio are considerably higher than its competitors’. In addition, P/B ratio and PEG ratio of Nvidia also in the range between 75 percent and 100 percent of other competitors’ distribution. Checking competitors’ ratios is a easy way to figure out a company’s stock values is overvalued or undervalued. I used these codes to check Nvidia’s stock value but these codes also can use for any companies to check their stock value.