Building a Web Scraper for Financial Statement Analysis

Web scraping provides powerful capabilities for automating data collection from online sources. This technique allows analysts to gather information systematically without manual intervention.

At its core, a scraper functions as a programmatically controlled virtual user that navigates web pages. This automated user reads the content of target pages and searches for specific elements of interest. When these elements are located, the scraper extracts them for further processing.

The implementation begins by defining a base URL that points to the financial statements to be analyzed. A dedicated function is created to handle the scrolling behavior, simulating how a human user would navigate the page.

The scraper fetches the webpage and extracts specific content – in this case, links that redirect to financial statements. Once these links are collected, the program accesses the most recent statement and extracts its contents for analysis.

Beyond web scraping, the solution incorporates stock data retrieval using the Financial Modeling Prep (FMP) API. API access represents a common method for obtaining financial data, where providers offer programmatic access to their resources for a fee.

This approach eliminates the need for manual data entry and creates opportunities for automated financial analysis. FMP is recognized in financial circles as a reliable provider of stock data, though it does impose limits on the number of requests that can be made within a given timeframe.

By combining web scraping techniques with API integration, analysts can build powerful tools for gathering and processing financial information efficiently.