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The role of financial data in business decision-making can’t be understated. Financial data refers to essential pieces of information that help to provide a clear picture of the financial well-being of an organization or business. If you’re determined to collect comprehensive data, consider using an AI-powered proxy solution for block-free scraping. Financial data can be derived from traditional sources such as a business’s balance sheet, income statement, and cash flow statement, as well as alternative data sources originating outside the company, also commonly referred to as “external data”.
Investors, managers, and regulators analyze these sets of data for different purposes. For example, investors rely on financial data when deciding whether to invest in a business and track its performance in the post-investment stage. On the other hand, internal management teams favor analyzing financial data to evaluate business performance and measure the success of their strategies.
This article will discuss different types of financial data, their use cases, and financial data management and analysis tools.
Financial data can be divided into two broad categories: traditional financial data and alternative data.
Traditional data refers to traditional sources of financial data such as financial statements, press releases, and SEC filings. Some of the common types of traditional financial data include assets, liabilities, equity, income, expenses, and cash flow.
A company’s assets comprise everything it owns, including its real, personal, tangible, and intangible property. In contrast, liabilities are a business’s financial obligations or what it owes to others.
As for income, it refers to the increase of wealth generated from sales and investments. Expenses, on the contrary, refer to the cost of operations of a business.
Equity refers to the amount of money left over if all of a company’s assets were liquidated and all its debts were paid off, while cash flow shows where a business’s income and expenses are coming from.
In today’s data-driven world, many investors and analysts are increasingly turning to alternative data sources to gain a unique view of investment opportunities. According to research by Forrester, 56% of decision-makers indicate that their businesses are increasing their ability to source external data.
Grand View Research states that the alternative data market was valued at $1.72 billion in 2020 and is expected to grow at a compound annual rate of 58.5% from 2021 to 2028. These findings indicate that alternative data is gaining momentum and turning into a considerable point when making business decisions.
Alternative data includes external data from non-traditional sources such as financial transactions, web data, and geolocation data. Such alternative data often provides more granular insights into the performance and financial details of a business. In one of our previous articles, we already explored the topic of alternative data.
Alternative data is generated by individuals, business processes, and sensors. Some examples of alternative data include web data such as page views, click-through rates, bounce rates, and search data. Other types of alternative data include social media data, geolocation data, credit card transactions, email receipts, product reviews, and satellite imagery.
There are three major ways to acquire alternative data: web scraping, raw data acquisition, and third-party licensing.
Web scraping involves the use of software tools to extract relevant unstructured data from web pages. Such data can then be processed into structured, readable formats such as CSV and JSON for investors and analysts to interpret.
Raw data can also be obtained from different sources, such as sensors. This type of alternative data is obtained as a straight data feed and has to be cleaned up and processed before analysis.
Alternative data can also be derived from licensed third parties. These intermediaries obtain raw data such as credit card and POS transaction data from other companies. They then process the obtained data sets into usable formats and sell them to investors.
Web scraping is one of the ways to gather alternative data
Financial data management is the process through which organizations measure and analyze their financial information with specialized tools. Let’s look at what financial analysis involves and the various tools used to collect, analyze, and manage financial data.
Financial analysis is the process of evaluating and analyzing a company’s financial data to assess its performance. It involves thoroughly examining the financial statements of a business.
Analysis of financial data can be conducted internally by a company’s management to review past trends and make future decisions based on the business’s financial data. This kind of analysis is known as corporate financial analysis.
On the other hand, investment financial analysis is conducted externally by investors looking to determine if a business is worth investing in.
The two major types of financial analysis are fundamental analysis and technical analysis. Fundamental analysis involves using ratios from the business’s financial data to determine its value. Fundamental analysts study a wide range of factors that can affect the value of the company.
Technical analysis makes use of statistical trends collected from the trading activity of the company’s stock. Thus, technical analysis focuses on analyzing price movements and looking for patterns and trends to predict future activity.
Other financial analysis methods include vertical analysis, horizontal analysis, leverage ratios analysis, growth rates analysis, and profitability analysis. Financial analysts also assess liquidity, efficiency ratios, cash flow, rates of return, valuation, and variance.
Financial analysis is often performed using tools such as Microsoft Excel or specialized financial data management software applications. Analysts need to be attentive to detail when working with large data sets and make sound assumptions based on the data.
Some financial analytics tools enable analysts to track a wide variety of metrics, including income, expenses, and profits. In addition, these systems help to simplify the data collection and preparation process. Some solutions also prove helpful for generating insights that can help businesses to improve their operations.
The process of extracting useful insights from alternative data can be quite challenging. Alternative data sets are often very large and complex and require advanced software and skilled data scientists to process. Nevertheless, with the right strategy, investors can reduce costs while gaining a competitive advantage.
Data analysis is a challenging process that requires special tools
Financial statements are documents that contain the financial performance of a business over a time period. These statements are often audited by accountants, firms, and government agencies. Financial analysts and investors rely on the data in these statements to ascertain the financial health of businesses and make decisions accordingly.
Financial statements are like the scorecard of a company. They are used to record vital financial data on different aspects of an organization’s activities. Thus, they can be assessed based on the company’s current, past, or projected performance.
Examples of financial statements include the balance sheet, cash flow statement, and income sheet.
Balance sheet. A balance sheet contains an overview of a business’s assets, liabilities, and equity at a specific time. Such important financial data enables investors to evaluate the performance of potential investments quickly.
Income statement. Income statements provide an overview of sales, expenses, and the total net income for a time period. It is also referred to as the profit and loss statement. The net income value is derived by subtracting the company’s expenses from the generated revenue.
Cash flow statement. The cash flow statement estimates how well a business can generate cash to cover its liabilities and fund operating expenses. This statement complements the other two financial statements and gives investors a quick overview of how money flows through the business.
Financial statements are maintained by businesses daily and used by internal management teams for decision-making. Financial statement analysts often use three techniques to analyze data from a company’s financial statements:
Horizontal analysis. This technique involves comparing historical financial data over two or more years to detect growth trends.
Vertical analysis. This method looks at how items on a financial statement compare to each other.
Ratio analysis. It helps compare the data from the financial statements to measure how the company is performing in different areas. Some examples of financial ratios are liquidity ratios, solvency ratios, profitability ratios, efficiency ratios, and coverage ratios.
While financial statements provide a lot of information on a company’s finances, they’re open to interpretation by individual investors. As a result, investors might draw different conclusions about the financial health of a business.
Financial data is an invaluable resource for analysts and investors who need to ascertain companies’ financial health and performance. Alternative data is proving to be a gold mine for investors and hedge funds looking for ways to improve alpha. Read another similar article about data aggregation in finance and other industries here.
About the author
Maryia Stsiopkina is a Content Manager at Oxylabs. As her passion for writing was developing, she was writing either creepy detective stories or fairy tales at different points in time. Eventually, she found herself in the tech wonderland with numerous hidden corners to explore. At leisure, she does birdwatching with binoculars (some people mistake it for stalking), makes flower jewelry, and eats pickles.
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