The PE Band Chart Analysis - Visualize Historical Stock Valuation

What Idea about PE ROE CHART ?

In this article, we will explore the idea of using a Price-to-Earnings (PE) and Return on Equity (ROE) band chart to analyze financial data. We will use Python and some popular libraries to read and process the data, and then create a band chart to visualize it.

The code provided imports the necessary libraries, reads data from an Excel file, and stores it in a pandas DataFrame. It then sorts the DataFrame by market capitalization (MCap) and selects the top 50 companies.

Importing Libraries:

First, let’s import the necessary libraries:

import pandas as pd
import numpy as np
import plotly.express as px

Loading the Data:

Next, we will read the data from an Excel file hosted on Google Drive and store it in a pandas DataFrame:

path = 'https://drive.google.com/uc?expoert=download&id=1EqhMOcAPX7WfW1gObCPPsFq99D5RfA42'
df=pd.read_excel(path)
df

Sorting by Market Capitalization:

We will now sort the DataFrame by the market capitalization (MCap) column in descending order and select the top 50 rows:

set50df = df.sort_values(['MCap (M)'],ascending=False)[:50]
set50df

set50df.columns
Index(['Name', 'No.', 'Links', 'Sign', 'Last', 'Chg%', 'Volume', 'Value (k)','MCap (M)', 'P/E', 'P/BV', 'D/E', 'DPS', 'EPS', 'ROA%', 'ROE%', 'NPM%','Yield%', 'FFloat%', 'MG%', 'Magic1', 'Magic2', 'PEG', 'CG'],dtype='object')

set50df['Yield%']=set50df['Yield%'].astype(float)
# must be type float for plotly display

set50df.dtypes
    Name          object
    No.          float64
    Links         object
    Sign          object
    Last         float64
    Chg%         float64
    Volume       float64
    Value (k)    float64
    MCap (M)     float64
    P/E          float64
    P/BV          object
    D/E          float64
    DPS           object
    EPS          float64
    ROA%         float64
    ROE%         float64
    NPM%         float64
    Yield%       float64
    FFloat%      float64
    MG%          float64
    Magic1       float64
    Magic2       float64
    PEG          float64
    CG           float64
    dtype: object

Creating the PE Band Chart:

With our data now sorted, we can create a PE band chart to visualize the relationship between the Price-to-Earnings ratio (P/E) and the Return on Equity (ROE). We will use the Plotly Express library to achieve this:

fig = px.scatter(set50df,x='ROE%',y='P/E',color='Yield%',text='Name',hover_data=['Name','PEG'],title='FUNDAMENTAL PE : ROE SET50')

This will create a line chart that plots the Price-to-Earnings ratio on the x-axis and the Return on Equity percentage on the y-axis. The chart will help us visualize the relationship between these two financial metrics and analyze trends or potential outliers in the data.

Conclusion

In this article, we have shown how to use Python and popular libraries to load financial data, process it, and create a PE band chart to visualize the relationship between the Price-to-Earnings ratio and the Return on Equity. This can be a helpful tool for analyzing trends, identifying potential opportunities, or spotting potential issues in a company’s financial performance.