
Correlation is not Causation but…

The financial statements of a business might give you a basic overview of how your business is performing. But on a decision making level, one cannot uncover patterns or recognize relationships between all financial variables involved. Financial Statements don’t tell a story. To do such analysis, it requires a lot of manual effort.
Many prospective company acquisition targets are smaller, more private, and more difficult to locate using standard methods. Companies may develop a set of acquisition alternatives that bring the talents they need in-house using powerful data sets and machine learning techniques.
Traditionally, in M&A transactions, purchasers will generally value the company they are purchasing based on a multiple of its earnings. However, with technological transactions, things might be different, as revenues and, more crucially, revenue growth are commonly utilised as a value benchmark.
Let’s take EBITDA for instance. EBITDA is highly essential in M&A deals, particularly in determining the acquisition price. EBITDA is a measure of a company’s overall financial performance and is used as an alternative to net income, in certain circumstances. It is a more perfect indicator of a company’s success than revenue or net sales as it may indicate earnings before accounting and financial deductions. But again any metric in isolation do not convey a story – instead we need the ability to do the following
1. Gather all Financial Ratios properly, accurately and on time
2. Measure, Monitor and most importantly look at ways to correlate the ratios to see if they tell a consistent, cohesive story
A good correlation framework can aid in the diagnosis of performance factors and the formulation of hypotheses for future investigation. QuarkCube’s platform provides the necessary correlation framework for companies to not only to collect and report on ratios but also do a comprehensive correlation analysis
#data #machinelearning #quarkmerge #mergersandacquisitions #correlation #analytics