Web Scraping is a boon for the financial industry. The use of web data is expected to experience excellent growth in financial and market research in the coming years.
The rise of fintech startups, especially in the US, Europe, and Indian markets, added an enormous number of investors in the stock market and various other financial sectors.
With such a humongous presence of investors, it requires research and analysis tailored to various needs, as different parameters are significant for multiple groups of people. This, in turn, will drive the increased usage of web data for better decision-making and informed decisions.
In this article, we will learn the importance of web scraping in the finance industry. We will also explore the challenges and their solutions in extracting financial data from the web.
Let’s get started!!
Web Scraping holds great importance in enhancing the investment strategies of the stakeholders:
Market Monitoring — Financial experts can harness the power of web scraping to identify potential future trends and market sentiment by tracking changes in stocks and commodities pricing, currency exchange rates, and market news.
Competitive Analysis — Web Scraping enables businesses to track the financial condition of their competitors, including balance sheets, quarterly results, and market strategies.
Economic Indicators — Economic indicators like GDP growth, unemployment rate, and tax collection are necessary for informed financial decisions. These data points can be extracted from trusted government sources, private sources, and economic research providers with the help of web scraping.
Web Scraping has several use cases in the Financial Industry:
Algorithmic Trading Strategies — Web Scraping can be used in collecting vast amounts of data to train AI and Machine Learning Models to unleash more advanced trading strategies for quicker and more advanced investment decisions.
Real Estate and Property Market Insights — The Real Estate and Property market data can be extracted for AI-driven analysis to predict more accurate valuation models and investment insights.
Supply Chain Finance — Supply Chain Finance is a solution that helps businesses accelerate their payments to suppliers to improve cash flow and business efficiency. By collecting data using web scraping, businesses can optimize their working capital, reduce financial risks, and enhance financial options and other relevant aspects of the supply chain.
Scraping financial data from the web is a common practice to know more about the financial condition of any entity. But how can this data be collected?
Here are some points that can satisfy your curiosity:
Building your scraper — If you are a learned developer, you can write and design the scraper in a particular programming language to automate the process of extracting data from the web.
Read More: Know The Best Programming Language For Web Scraping
It is crucial to remember not to disturb the website server by firing too many requests, as it can result in IP blockage, preventing you from extracting the data. However, this can make the process unscalable. But, this problem can be solved using an API.
Read More: How To Scrape Google Finance using Python
There are various sources of Finance Data available on the internet:
Stock Exchanges — Several stock exchanges like NSE, NASDAQ, and LSE provide complete financial data, including quarterly results, balance sheets, shareholding, etc., of the publicly traded companies.
Financial News Providers — Financial News providers like Bloomberg, Yahoo Finance, Google Finance, MorningStar, etc., are some of the best financial news providers in the market and are quality sources for scraping financial data.
Trading Platforms — The startup boom became evidence of the rise of many fintech companies and trading platforms. These platforms often provide a wealth of information, including real-time stock pricing, QoQ and YoY reports, historical performances, and much more.
Credit Rating Agencies — Credit Rating Companies like Moody’s, S&P, and Fitch Ratings provide in-depth research analysis on the creditworthiness of companies and governments. These ratings allow investors and financial institutions to calculate the risks associated with bonds, debt instruments, and financial stability.
Extracting financial data at scale can present you with various challenges:
IP Blockage — If you are looking to scrape this data from trusted providers like Google, Bloomberg, or Yahoo then there is a high probability that your scraper might get blocked after a time by the anti-bot mechanism on these websites.
Legal Problems — Extracted data should only be used for legal purposes such as research and analysis. However, if any illegal activity is caught on notice, you must deal with the repercussions.
Scalability — The scraper might face scalability issues if it is not correctly optimized to extract huge amounts of data from the websites. This will cause a disturbance in the data pipeline and there would be no consistent flow of data.
A thorough financial information is always necessary when you are making an investment decision. Financial institutions always use web scraping to calculate the risk, investment returns, and probable losses associated with a specific entity. This data-driven approach of collecting data for in-depth research and analysis empowers investors to turn their fortunes toward success.
In this article, we learned the importance of web scraping for the financial industry.
If you think we can complete your web scraping tasks and help you collect data, feel free to contact us. Please do not hesitate to message us if I missed something.
I have prepared a complete list of blogs to learn web scraping that can give you an idea and help you in your web scraping journey.
