Using financial data to mine for insights, present reports and handle other critical tasks has become a major reason for the popularity of computer-driven analytics. That has led to immense competition, and consequently, there is immense value in digging through financial data to find what may be hiding. Let’s take a closer look at what financial data is, how it is used and what hidden value may be lurking within it.
What is Financial Data?
Financial data generally refers to information that can be derived from accounts and securities. The most basic forms of financial data include things like cash flow, net income and total assets. Notably, the idea extends far beyond those three items, but these three provide the most accessible way to understand what financial data is. This information is useful in:
- Providing credit to individuals and companies
- Establishing buy and sell points on stocks
- Financial planning
- Placing valuations on businesses
- Determining interest rates
- Forecasting future economic conditions
- Detecting misrepresentations and fraud
In other words, you likely interact with lots of financial data on a daily basis even if you’ve never invested a dollar.
How is Financial Data Used?
Utilizing financial data is increasingly about feeding information into machines. For example, credit card companies regularly monitor transactions worldwide to detect patterns of theft, fraud and misuse. Your bank account might be flagged because your ATM card was detected in use in a geographic location you’ve never been to.
Analysis of financial data is performed using an array of mathematical, statistical and programming tools. The loan officer assigned to determining whether you might get a mortgage may use a computer model that compares your financial situation to similar customers to assess what your risk of default is. That requires access to large datasets, and it’s essential to have enough processing power to make the comparisons rapidly enough for them to be relevant.
Sources of data are also quickly becoming more diverse. Where financial data was once limited to banks and stock traders, we’re seeing actionable information come from previously unthinkable sources. For example, a mercantile exchange trader may gather data from farmers in the Midwest to determine whether crop yields will be up this summer. Similarly, farmers can become sellers of their data, transmitting information from IoT sensors placed in field to co-ops that then monetize the data by selling it to traders.
Where is the Hidden Value?
Data is rarely is a true representation of a thing in the real world, and that means getting at what might be hiding requires some tricks. For example, Bayesian analysis is frequently used in assessing medium- and long-term risks in stock markets, bonds and other financial instruments. Traders configure their models and buying programs according to a wide range of variables, including their comfort with risks and how concerned they might be by potential shocks in the market.
Differences in how analysis is done mean there will always be parties with different opinions of the future. These differences are often referred to as market inefficiencies, and much of the hidden value in financial data lies here. For example, there will always be differences in the estimates of the core brand values of different products. Someone using social media analytics might identify an old brand that’s making a comeback and buy into its parent company’s stock to leverage that advantage. That’s hidden value.
It takes time to become familiar with the tools and techniques used to assess financial data. With time, though, you can utilize it to begin maker better decisions about assets, liabilities and risks.
