Mastering Stock Fundamental Analysis: A Comprehensive Guide to Financial Metrics and Data Scraping Techniques

Introduction to Stock Fundamental Analysis

Fundamental analysis is a method used to evaluate the intrinsic value of a stock. By examining various financial metrics, investors can make informed decisions about which stocks to buy, hold, or sell. This guide provides a deep dive into the key metrics used in fundamental analysis, along with a step-by-step tutorial on scraping stock data, cleaning datasets, and conducting exploratory data analysis (EDA).

Why Fundamental Analysis Matters

Fundamental analysis goes beyond price trends and examines the underlying factors that affect a company's financial health and potential for growth. Some of the primary benefits include:

Long-Term Investment Insights: Evaluate companies for their growth potential and stability.

Informed Decision-Making: Use data-driven insights to reduce investment risk.

Valuation Accuracy: Calculate fair market values and spot undervalued or overvalued stocks.

Key Financial Metrics in Stock Analysis

The following metrics provide insight into a company's performance and value:

1. Market Capitalization: Reflects the total value of a company's outstanding shares and offers a quick gauge of its size.

2. Current Price: The latest trading price for the stock.

3. 52-Week High/Low: Measures the stock's price movement over a year.

4. Debt: Total loans and liabilities taken by the company.

5. Dividend Yield: Shows the cash dividends distributed to shareholders relative to the share price.

6. Book Value: Represents the net value of a company's assets.

7. Promoter Holding: Stake held by promoters, often used as a confidence indicator.

8. Sales and Profit: Revenue and profit figures help assess the company’s growth and profitability.

9. Return on Capital Employed (ROCE): Measures profitability against the capital invested.

10. Return on Equity (ROE): Indicates a company's ability to generate returns from shareholders' equity.

11. Price-to-Earnings (PE) Ratio: Valuation metric that shows if a stock is over or undervalued.

12. Compound Annual Growth Rate (CAGR): Represents the mean annual growth rate over a specified period.

13. Earnings per Share (EPS): Net profit divided by the number of shares.

14. Asset Turnover Ratio: Efficiency of a company in generating revenue from assets.

15. Quick Ratio: Company’s ability to cover short-term liabilities with liquid assets.

Step-by-Step Guide to Scraping Financial Data for Analysis

Step 1: Importing Required Libraries

We use libraries like Selenium for web scraping, Pandas for data manipulation, and Matplotlib and Seaborn for visualization.

from selenium import webdriver

from selenium.webdriver.chrome.service import Service

from selenium.webdriver.common.by import By

from selenium.webdriver.common.keys import Keys

import time

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns        

Step 2: Setting Up the Stock List and Required Data Fields

We analyze a selection of stocks, scraping the necessary financial metrics to analyze each company comprehensively.

stocks = ['BAJFINANCE', 'DMART', 'RELIANCE', 'ASIANPAINT', 'TATAMOTORS', 'TCS', 'Pidilite Inds.', 'Nestle India']

data_fields = [

    'COMPANY', 'NSE_CODE', 'BSE_CODE', 'SECTOR', 'MARKET_CAP', 'CURRENT_PRICE', 'DOWN_FROM_52w_HIGH',

    'UP_FROM_52w_LOW', 'DEBT', 'DIV_YIELD', 'BOOK_VALUE', 'PROMOTER_HOLDING', 'HOLDING_PLEDGED', 

    'SALES', 'SALES_GROWTH_5yrs', 'PROFIT', 'PROFIT_GROWTH_5yrs', 'ROCE', 'ROE', 'PE', 'CAGR_5yr',

    'CAGR_10yr', 'EPS', 'ASSET_TURNOVER', 'QUICK_RATIO', 'DEBT_EQUITY'

]        

Step 3: Data Scraping with Selenium

Using Selenium, we scrape stock data from [Screener.in](https://www.screener.in/). Ensure login credentials are handled securely for authenticated scraping.

 service = Service('path/to/chromedriver')

driver = webdriver.Chrome(service=service)

driver.maximize_window()

driver.get('https://www.screener.in/explore/')

# Loop through each stock and scrape the required data

for stock in stocks:
 stock_search = driver.find_element(By.XPATH, '//input[@placeholder="Search for companies"]')

    stock_search.send_keys(stock)

    stock_search.send_keys(Keys.ENTER)

    time.sleep(2)

    # Scrape data fields using XPath for each financial metric

    company_name = driver.find_element(By.XPATH, '//h1').text

    market_cap = driver.find_element(By.XPATH, 'xpath_to_market_cap').text

    # Continue scraping each required field

    # Append the data to the respective list for each metric        

Step 4: Creating a DataFrame and Saving Data

Once the data is collected, we use Pandas to create a DataFrame, organize the data, and save it as a CSV file.

data = {

    'COMPANY': COMPANY, 'NSE_CODE': NSE_CODE, 'BSE_CODE': BSE_CODE, 'SECTOR': SECTOR, 'MARKET_CAP': MARKET_CAP,

    'CURRENT_PRICE': CURRENT_PRICE, 'DEBT': DEBT, 'DIV_YIELD': DIV_YIELD, 'BOOK_VALUE': BOOK_VALUE, 'PROMOTER_HOLDING': PROMOTER_HOLDING

}

df = pd.DataFrame(data)

df.to_csv('stock_data.csv', index=False)        

Data Cleaning and Transformation

Removing Unnecessary Characters and Converting Data Types

It’s crucial to clean the dataset by removing commas from numerical fields and converting strings to integers or floats.

df['MARKET_CAP'] = df['MARKET_CAP'].str.replace(',', '').astype(float)

df['CURRENT_PRICE'] = df['CURRENT_PRICE'].astype(float)        

Handling Missing Values

Replace null values with the mean or median of the column to maintain data consistency.

df.fillna(df.mean(), inplace=True)        

Exploratory Data Analysis (EDA)

Analyzing Sector-Wise Data

Visualize the distribution of market capitalization across different sectors.

plt.figure(figsize=(12, 6))

sns.barplot(x='SECTOR', y='MARKET_CAP', data=df)

plt.title('Market Capitalization by Sector')

plt.xlabel('Sector')

plt.ylabel('Market Cap in Cr.')

plt.xticks(rotation=45)

plt.show()        

Profit and Loss Analysis by Sector

loss_sector = df[df['PROFIT'] < 0]

profit_sector = df[df['PROFIT'] > 0]

        

Identify sectors with the highest profit and losses.

Financial Metric Analysis

1. Debt-to-Equity Ratio: Determines financial leverage and risk level.

2. Return on Equity (ROE): A higher ROE indicates efficient management of shareholder funds.

3. Price-to-Earnings Ratio (PE): Assess if a stock is undervalued or overvalued based on earnings.

4. Quick Ratio: Indicates the company's ability to meet short-term obligations.

Insights and Applications of Fundamental Analysis

Portfolio Diversification

Using the insights from fundamental analysis, investors can diversify their portfolio by selecting stocks across different sectors.

Long-Term Value Investing

Identify stocks with strong fundamentals for long-term investment, such as high ROCE and low debt.

Risk Assessment

Assessing the debt-equity ratio and quick ratio helps investors understand the financial risk associated with each stock.

Conclusion

Stock fundamental analysis enables investors to evaluate a company's financial health and make informed investment decisions. By scraping, cleaning, and analyzing financial data, investors can gain valuable insights that contribute to building a balanced and profitable portfolio.