Looking Beyond The Numbers

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Financial analysis increasingly extends beyond reviewing reported balances and calculating conventional financial ratios.

In areas such as financial due diligence, M&A, valuation and financial modelling, the analytical challenge is to determine what the underlying data reveals about the economic performance, cash-generating capacity and risk profile of a business.

A clear example is Quality of Earnings (QoE) analysis. Reported EBITDA is rarely considered in isolation. It may require normalisation for non-recurring transactions, discontinued operations, non-operating items, related-party arrangements, exceptional expenditure and other factors that are not representative of sustainable operations. The objective is to establish a maintainable earnings base and assess the extent to which historical profitability is recurring, predictable and sustainable. QoE therefore goes beyond reported earnings to consider the underlying quality of those earnings and their ability to support future cash generation.

However, financial analysis extends considerably beyond EBITDA. Revenue disaggregation, customer and product concentration, gross-margin decomposition, working-capital normalisation, cash conversion, net debt and debt-like items can reveal trends that aggregate financial statements may obscure. Analytical procedures can also identify outliers, discontinuities and accounting anomalies requiring further investgation. Data analytics can facilitate this process through data cleansing, aggregation and reconciliation, while enabling larger datasets to be interrogated for patterns and exceptions. The value of these insights can be further enhanced through effective visualisation, helping stakeholders identify trends, risks and opportunities more clearly, as discussed in our article on Visualisation in Advisory: Moving Beyond Static Reports.
 

The same analytical discipline applies to financial modelling and forward-looking assessments. Scenario analysis, sensitivity testing and stress testing allow individual assumptions to be isolated and their impact on liquidity, leverage, debt-service capacity and investment returns to be quantified. Depending on the nature of the analysis, key variables may include pricing, volume, occupancy, operating costs and capital expenditure, while outputs may incorporate measures such as NPV and IRR.

The distinction between reported performance and underlying economics becomes particularly important when financial information is used to support transactions or investment decisions. Accounting treatment determines how performance is presented, but the analytical question is whether that presentation reflects the underlying economics of the business. This may require assessing the quality and recurrence of earnings, the conversion of EBITDA into cash, the sustainability of working-capital requirements and the extent of obligations not immediately apparent from reported net debt. Considered together, these factors provide a more robust basis for assessing the underlying financial position and performance of a business. 

Look beyond the numbers.

Watch our video to discover how deeper financial analysis can uncover the drivers of performance, risk and business value.