Normalizing financial data is an essential analysis required to determine the true value of a company for a sale or acquisition of another company, from the largest Fortune 500 corporations to small family owned businesses. This is usually a first step in understanding how efficiently the company operates on an ongoing basis before other bench-marking analysis is undertaken. Outputs from the normalization process are used to develop valuation estimates and other metrics for comparisons to similar companies.
Financial statements are “normalized” to make adjustments for non-operating assets and liabilities and other nonrecurring and unusual items that may distort results on an ongoing basis. Removing these items helps to establish an “apples to apples” comparison between companies by eliminating anomalies. For example, a common income statement adjustment considered in normalization includes corporate restructuring costs, which are typically one-time charges that cover costs for employee separations, restructured contracts and facility charges. Since these line items are not ongoing business expenses, they need to be removed for valuation purposes. A prospective buyer would first eliminate these charges before establishing the company’s value.
Normalization is also applied to develop statements that are GAAP compliant so buyers don’t need to rely solely on company reporting which may include non-GAAP adjustments that may put the company in the best light possible. From a buyer’s viewpoint, these adjusted statements are good indicators that estimated cash flow projections are likely to occur into the future.
Normalization adjustments fall into a broad range of categories that depend on specific facts and circumstances. Income statement adjustments generally fall into the following areas:
- Ownership characteristics (controlling vs. non-controlling interest)
- Differences in GAAP (Generally Accepted Accounting Principles) reporting
- Extraordinary or non-recurring income or expenses, and
- Income and expenses related to non-operating assets or liabilities
The normalization process can be a both an art and science, requiring a fair amount of professional expertise to ensure adjustments to financial statements are not applied arbitrarily or incorrectly. Some firms have set rules for normalization while others apply line item adjustments on a case-by-case basis.
It’s no surprise this process is time consuming. Analysts need to crawl through financial documents and footnote references to arrive at an understanding and potential anomalies. Accuracy and interpretation of the data within filings can be an issue as well, especially if rules are not applied consistently.
Until recently, normalization has been the domain of analysts and accountants applying judgments to adjust line items. Even the most sophisticated rules based engines have been unable to automate the complex thought processes behind this relatively straight forward analysis performed by humans.
Artificial Intelligence (AI) is now emerging as a powerful technology that can replicate human thought processes in helping to automate this type of work. AI based “smart” financial analysis tools are now available to help analysts streamline complex analysis and automate rules based processes. In an M&A environment this translates to eliminating much of the “grunt work” involved in identifying potential line item adjustments for financial statements calculated across multiple reporting periods. In equity investing, deriving a normalized result for EBITDA and free cash flow are essential starting points in fundamental analysis.
Reducing the time spent on financial analyses from hours to only a few minutes has certainly captured the attention of professionals doing this work. But the benefits extend beyond cost alone using AI based platforms to include:
- Simplification of the business valuation process by performing a range of time consuming calculations easily with standard metrics
- Improved organization of the valuation process with audible results that can be easily tracked over reporting periods
- Greater accuracy in calculations that reduce and even eliminate time spent verifying revisiting data pulled from reports
- Better communication of results using AI capabilities that are integrated with reporting tools, including Excel and PowerPoint.
We’re using Kognetics to support our own valuation work done for clients in investment banking and private equity. We’ve experienced productivity increases of up to 70% for common tasks with increased accuracy and consistency in reporting. Kognetics enables analysts to automate their workflow for complex processes like normalization but keeps them firmly in control. All decisions on what line items to adjust and by how much are determined by the analyst.
Click here to learn more about Kognetics and how AI can be applied to complex financial analysis.