Web scraping is being used by every industry to aggregate data and find usable information from it. Data-driven decisions are the new norm, and the best source of ever-updated data is the web. Be it news media or retail and manufacturing, market research. Or even for keeping a track of the financial industry. Webscraper is what fuels big data and data science across industries today. When it comes to the financial sector, the scope of web scraper service is very vast. From scraping news media articles to understanding the background of a company to scouring websites. Like Yahoo Finance to get a more in-depth look into the stock prices. And other related historical data of a company. There is no limit to the data that one can get his hands into.
While there are a variety of data scraped from the web consumed by the financial industry. We shall go over the different types of data consumed one by one-
Aggregated News Data:
For companies involved in stock markets, investments, and insurance, the news media is a vast source of information. The decision of whether to bet a million dollars on a company might completely change based on a single piece of breaking news. Traders who make it big time often use the latest news to their advantage and before their competitors to get an edge in the market.
However, it is not possible to keep track of every piece of news article 24×7. So a better way would be to make a list of companies that you want to keep an eye on and feed it to a web scraping engine. The scraper can scrape the web and search for the names of the companies or any related bits of information that it can find. This can lead you to both breaking news that everyone will act on or even smaller news bits that may fall through the radar but have impactful changes in the world of investing.
Aggregation of Financial Industry Data:
When it comes to market data, the internet lined with thousands of webpages, and going through each of them manually will take you years. A better way to get market data would be to use an automated scraper that can scrape, clean, and store market data from different websites into databases so that you can plug the data directly into your business systems. Actionable intelligence extracted from the information when you run machine learning models on them. You may also build prediction models that use historical data to predict the future of the market.
Extraction of Company Data:
When analyzing companies, different types of data such as financial statements, the size of the company. When hiring took place last, may be relevant, especially if you are a prospective investor. All publicly owned companies publish data like financial statements. You can scrape company websites to get such data. Government websites also have such information saved for different purposes.
Alternative Data Sources:
A growing use for alternative data sources has seen in many industries. But none can benefit to the extent that the insurance sector can. From data collected from IoT devices to social media information- different alternative data is being collected and studied to create new dynamic insurance policies that would benefit customers and take into account the risk factors when companies need to make decisions.
Stock Market and Trading:
Stock market data is one of the most sought after data and made available to you by various service providers. If you want to get the data via APIs, the APIs exposed to customers but usually come at a cost. Suppose a millisecond’s accuracy is not what you are after. But it is building models on historical data or capturing data over long periods of time to understand stock prices better interested in. In that case. You can quickly get the data by that displays the values for different stocks across various exchanges.
Keeping Track Of Other Financial Products Like Real Estate And Gold:
The pandemic has caused the prices of gold to skyrocket. The same seen during the 2008 financial crisis when investors scampered to stabler investment opportunities. Such economic activities can easily track down if you are scraping data from the web and matching it with historical data. Real estate is another sector where different types of data can be of use. Be it for buying and selling real estate and deciding on prices or understanding whether there is another real estate bubble waiting to break. The industry is best understood through data.
Some Of The Sources Of Financial Industry Data:
When it comes to scraping financial data, some of the common sources are worth mentioning, and we will be discussing these and the benefits of scraping data from them-
Yahoo! Finance – A personal favorite and one of the most popular websites among traders. The website provides not only financial data but also the latest news. Stock data, both current and historical, press releases and reports found here as well.
Google Finance – Launched in 2006, Google finance contains the current status of different indexes such as the S&P 500, the NASDAQ-100, and the NIFTY-50. The rates of specific stocks and other latest news in the market found here.
Graphs and information like the one above. Along with different articles from experts can give you a feel of how the US market is performing and which companies and sectors are currently in focus.
There are more websites like the Wall Street Journal and Bloomberg Markets that can provide you with financial news and latest updates 24×7 and you can scrape these to be aware of the latest happenings and to remain on top of the game.
The Risks And Constraints Of Scraping Financial Industry Data:
Financial markets follow no specific rules even though certain patterns seen if you view the data covering a long period of time, say 25-30 years or more. While in many scenarios historical data can help you in making decisions. The political and socio-economic factors at play can also render the predictions wrong. What factors affecting the market at any given point of time guessed but never known for sure until much later? However, the greater the amount of data you have at hand, the better your chances of understanding the market.
When it comes to constraints, it is to remember that when you are scraping the web for financial data, some ethical rules followed. If a website’s robot.txt restricts you from scraping specific webpages, it is better you don’t scrape those particular webpages. At the same time, even if you scrape data from websites that display financial information. You cannot build products on top of the data that would compete directly from websites you are scraping data from.
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