FSA 11 Financial Analysis Techniques

Analysts convert raw financial statements and other inputs into measures that support decisions: how well a company has performed against its own history and against rivals, how it is likely to perform ahead, and what that implies for the value of the company and its securities. The same toolkit supports equity valuation, credit assessment, acquisition due diligence, and general performance review.

There is no single blueprint for structuring an analysis, but a workable framework moves through six phases. The heart of the work sits in phases three and four, where data are processed into ratios and common-size statements and then interpreted.

Computation is not analysis

A useful study separates the two. Compiling tables and running numbers is only the raw material; genuine analysis knits those pieces together and asks, beyond the bare outcome, why the result occurred and whether it added value. Sound narrative work usually draws on three to ten years of data and matches the technique to the purpose of the report.

Everything rests on comparison

Calling a result good or bad means little without a benchmark. Two comparisons dominate. Cross-sectional analysis lines a company up against one or more peers measured at the same time, and trend (time-series) analysis follows a single company across periods. Because peers may differ in size or reporting currency, comparing reported net income directly can mislead; ratios and common-size statements strip size out and make the comparison fair. Where currencies differ, an analyst can translate figures into one currency, or, if the focus is ratios, skip translation entirely because a ratio is unit-free.

A ratio places one quantity over another and is normally written as a quotient. Academic research has linked financial ratios both to future stock returns and to the prediction of credit failure, and practitioners rely on them to value companies and securities. Several points shape how ratios should be handled.

First, the computed number is not the answer; it is an indicator that reports what happened, not why. Suppose Company A earns EUR100,000 of net income on EUR2,000,000 of revenue, a net profit margin of 5 percent, while Company B earns EUR200,000 on EUR6,000,000, a margin of 3.33 percent. Company B books more absolute profit, yet Company A keeps EUR5 of every EUR100 of sales against Company B’s EUR3.33, so Company A is the more profitable in percentage terms. The ratio does not say why: pricing, cost control, or tax could each be at work.

Second, accounting policies that differ from one company to the next, or that shift over time within a single company, can skew ratios, so a fair comparison may call for adjustments. Third, not every ratio is relevant to every question; choosing the right one is itself a skill. Fourth, the work does not end at computation. Interpretation is situation specific, and small differences can carry real meaning.

When to use an average denominator

A helpful rule: when the numerator comes from the income statement or cash flow statement (a flow across a period) and the denominator comes from the balance sheet (a snapshot at one date), use an average for the denominator. When both numerator and denominator are balance sheet items measured at the same date, averaging is generally unnecessary, although some balance-sheet-only ratios are still averaged for consistency inside a decomposition. Most databases use a simple average of opening and closing balances; a seasonal business may warrant averaging over interim periods.

No official rulebook

No authority fixes the exact formula or name of a ratio, so definitions vary by analyst and by database. Faced with an unfamiliar ratio, examine the numerator and denominator to see what it captures. Consider operating income divided by average total assets: since it measures operating profit generated per unit of assets, 12 percent beats 8 percent, and the ratio clearly gauges profitability and asset efficiency. This particular measure is one version of return on assets; results differ if ending or beginning assets replace average assets, especially when the asset base is growing or shrinking.

Ratios can be pulled from filings directly or from vendors such as Bloomberg, Compustat, FactSet, or Thomson Reuters. Vendors offer long histories and periods beyond the fiscal year, such as trailing twelve months (TTM) or most recent quarter (MRQ), but their formulas and item classifications differ, so an analyst should confirm the definition and use one source consistently when comparing companies or tracking a single company over time.

Common-size analysis expresses each financial statement item in relation to a single base, most often total assets or revenue. In effect it forms a ratio between every line and the base, which lets an analyst read the composition of a statement and compare companies of different sizes.

Vertical common-size balance sheet

Dividing each balance sheet line by the same period’s total assets shows the asset mix and the financing structure. In the partial balance sheet below, receivables jump from 35 percent to 57 percent of total assets, a rise of about 63 percent between the two periods. That could signal more credit sales, a shift out of another current asset such as inventory, looser credit standards, or more aggressive revenue recognition; the analyst would probe further, for instance by comparing receivables growth with sales growth.

Vertical common-size income statement

Dividing each income statement line by revenue (or by total assets for some financial institutions) shows where profit is won or lost. In the example below, Service A grows from 30 percent to 45 percent of revenue, yet EBITDA falls from 53 percent to 45 percent of sales, so the shift did not lift profitability. The culprit is visible: salaries and employee benefits rise from 15 percent to 25 percent of revenue. Note also that income tax drops from 15 percent to 8 percent of sales, and the effective tax rate (tax divided by pretax profit, the more telling comparison) falls from 36 percent to 24 percent.

Horizontal common-size statements

A horizontal common-size statement indexes each item to a base year, or reports the percentage change from the prior period, so structural shifts stand out. Dividing each line by its base-year value gives a series such as cash at 1.00, 0.74, 0.69, 0.49, 0.41 across five periods, while the period-over-period version reports the annual change directly. The two presentations answer different questions, and the analyst picks the one that fits the period of interest.

Ratios and common-size statements earn their value through comparison. Cross-sectional analysis, at times labeled relative analysis, places one company’s metric next to the equivalent figure for a rival or a peer group observed at the same moment, even where sizes and currencies differ. In two hypothetical firms, Company 1 holds 38 percent of assets in cash against Company 2’s 12 percent, so Company 1 looks far more liquid; the analyst then asks why so much sits in low-yielding cash (perhaps a pending acquisition or a volatile operating setting), and why Company 2 carries a heavier receivables load.

Trend analysis

Direction matters as much as level. When reviewing changes, watch both the percentage move and the absolute currency move: a large percentage shift on a tiny base can matter less than a modest percentage shift on a large one. In one five-period balance sheet the biggest percentage change was investments, down 33.3 percent, but that was only a USD2 million move, whereas receivables rose USD12 million, the more meaningful change.

Relationships across statements

Growth rates from different statements can be read together. If revenue grows faster than assets, efficiency may be improving. Consider a company reporting revenue up 20 percent, net income up 25 percent, operating cash flow down 10 percent, and total assets up 30 percent. Net income outpacing revenue hints at rising profitability, but the fall in operating cash flow alongside rising earnings raises an earnings-quality flag, and assets growing faster than revenue points to weakening efficiency. Each strand needs follow-up.

Graphs and regression

Graphs make trends and structure visible: pie charts for composition at a point in time, line graphs for a few items over a longer span, and stacked columns when composition, level, and change all matter at once. For more complex relationships, regression analysis can measure how variables move together, for example relating a company’s sales to GDP to judge cyclicality or to build a sales forecast, and it flags items behaving differently from their historical pattern.

Because the number of possible ratios is effectively unlimited, it helps to sort them by the aspect of performance they detect. Four broad categories recur across analysts and vendors, though the exact names and members vary.

Interpretation needs context

A ratio speaks only alongside other information. Even a stable ratio can hide something, since consistency may reflect accounting choices that smooth earnings. Read ratios against five reference points:

1. Prior-period results, to see whether the position is strengthening or weakening.
2. Expectations set before the numbers were published, with any surprise scrutinized.
3. Industry peers, taking care where companies span several lines of business, follow different strategies, or use different accounting methods.
4. Company goals and strategy, to test whether results match the stated plan.
5. Economic conditions, since cyclical companies look better in expansions and worse in recessions.

The aim throughout is to explain the causes of any divergence separating a company’s ratios from those of peers or from its own earlier results.

Activity ratios, also called asset utilization or operating efficiency ratios, gauge how well a company uses its working capital and longer-term assets. They typically pair an income statement figure in the numerator with an average balance sheet figure in the denominator.

Interim ratios need care. A first-quarter inventory turnover of 3.18 times is a quarterly figure, not comparable with an annual 12 times; annualize it by multiplying by 4 (or by 4.06 using 365/90) to get roughly 12.72 to 12.90. Days of inventory on hand then works out near 28.3 either way (90 / 3.18 or 365 / 12.90). For 52- or 53-week years and leap years, use the actual number of days rather than 365.

Reading the ratios

A high inventory turnover (and low DOH) relative to peers can mean tight inventory management or, less happily, stock too lean to meet demand; comparing revenue growth with the industry helps tell them apart. A low turnover (high DOH) can signal slow-moving or obsolete goods. High receivables turnover (low DSO) means faster collection, but if it comes from harsh credit terms the company may be shedding sales. High payables turnover (low days payable) may mean the company is not using available credit, or is capturing early-payment discounts; low turnover (high days payable) may mean payment trouble or generous supplier terms. Working capital turnover shows revenue generated per unit of working capital, but it breaks down when working capital is near zero or negative, where fixed asset and total asset turnover are more informative. Fixed asset turnover can be distorted by the age of assets (older, more-depreciated assets carry lower book value and lift the ratio) and by lumpy capital spending. Total asset turnover captures overall revenue per unit of assets and reflects strategic choices about capital versus labor intensity.

Liquidity analysis measures a company’s capacity to meet near-term obligations. Short-term sources include cash, marketable securities, and the ability to issue debt; over a longer horizon, operating cash flow and the timing of liability maturities matter. Because these ratios describe a position at a point in time, they normally use ending balance sheet values rather than averages.

A higher current ratio signals more liquidity, though it assumes inventory and receivables are genuinely liquid, which low turnover ratios would call into question. The quick ratio is stricter, dropping inventory and prepaid items that cannot readily convert to cash, so it can be the better gauge when inventory is illiquid. The cash ratio, counting only cash and marketable securities, is the most conservative and the most reliable in a crisis, although a severe market shock can still erode the value of those securities.

The defensive interval ratio counts how many days a company could cover daily cash spending from its liquid assets alone, with no new inflows; a value of 50 means about 50 days of runway. Daily cash expenditures are approximated by summing cash operating expenses (such as cost of goods sold and selling, general, and administrative and research costs), subtracting non-cash charges like depreciation, and dividing by the days in the period.

The cash conversion cycle (net operating cycle) measures the time between investing cash in working capital and collecting cash back. A shorter cycle means greater liquidity and less need to finance inventory and receivables; a longer cycle points to lower liquidity and a heavier funding need.

Solvency is the capacity of a company to satisfy its long-term obligations, meaning interest and principal payments as they fall due. Solvency ratios describe how much debt sits in the capital structure and whether earnings and cash flow are sufficient to service fixed charges. Greater operating leverage raises the risk of the operating income stream and so limits how much financial leverage a company can safely carry.

Two families exist. Debt ratios work off the balance sheet and compare debt with equity; coverage ratios work off the income statement and compare earnings with debt-service obligations. Here total debt is taken as interest-bearing short-term plus long-term borrowings, leaving out items such as accounts payable, accrued expenses, and leases; other definitions are broader (all liabilities) or narrower (long-term debt only), and some analysts net out cash and marketable securities to work with net debt.

For the debt ratios, higher generally means higher financial risk and weaker solvency. Debt-to-assets is the share of assets funded by debt; debt-to-capital is debt as a share of debt plus equity; debt-to-equity of 1.0 matches equal debt and equity and equals a debt-to-capital of 50 percent. The financial leverage ratio shows how many units of assets each unit of equity supports, so a value of 3 means each EUR1 of equity carries EUR3 of assets. Debt-to-EBITDA approximates how many years of operating cash flow would repay total debt and features in covenants. For the coverage ratios, higher is stronger: interest coverage (times interest earned) counts how many times EBIT covers interest, and fixed charge coverage extends this to interest plus lease payments.

The capacity to earn profit on invested capital drives a company’s value, so profitability is often the center of an equity analyst’s work. Return-on-sales ratios express income subtotals as a share of revenue; return-on-investment ratios relate income to assets, equity, or total capital.

For each of these, higher is better. Gross profit margin shows what remains to cover other costs and reflects pricing power and product cost advantage. Operating margin, being gross profit less operating costs, rising faster than gross margin points to better cost control. Pretax margin adds the effect of leverage and non-operating items, so a rise driven mainly by non-operating income deserves scrutiny. Net profit margin is usually adjusted for non-recurring items to show sustainable profitability.

Return on assets, three ways

Standard ROA divides net income by average total assets, but net income is a return to equity only, while assets are financed by both creditors and owners. Some analysts add back after-tax interest to reconcile numerator and denominator, and others measure ROA before interest and taxes entirely.

Return on invested capital measures after-tax profit on all capital employed (short-term debt, long-term debt, and equity), with the return taken before deducting interest. Return on equity measures net income against average equity, and return on common equity narrows this to common shareholders by removing preferred dividends. Whichever form is used, it must be applied consistently across companies and periods.

No single ratio, and no single category, tells the whole story. The clearest picture comes from combining evidence across activity, liquidity, solvency, and profitability, because a question raised by one category is often answered by another.

Comparing several companies on a full ratio set works the same way. In one comparison, Anson Industries lifts total asset turnover from 0.84 to 1.11 over four years while Clarence Corporation slides from 1.38 to 1.06, so Anson is the one improving efficiency, even though Clarence carries a higher net profit margin and more leverage. Those same building blocks feed directly into DuPont analysis, the subject of the next section.

Return on equity is net income divided by average shareholders’ equity. DuPont analysis, named for the company that developed it, breaks ROE into component ratios, each capturing a different aspect of performance. The decomposition explains why ROE changed over time or why two companies differ, and it guides management toward the levers that move the return to owners.

Two-way decomposition

Multiplying and dividing by average total assets splits ROE into ROA and leverage.

With no liabilities the leverage factor equals 1.0 and ROE equals ROA. Taking on debt raises leverage, and as long as the company earns more on borrowed funds than it pays for them, higher leverage lifts ROE; if borrowing costs exceed the return earned, leverage drags ROE down.

Three-way decomposition

Splitting ROA further separates profitability from efficiency.

Read left to right, these are net profit margin (profitability), total asset turnover (efficiency), and financial leverage (solvency).

Five-way decomposition

Splitting net profit margin into tax, interest, and operating effects gives the five-component form used in databases such as Bloomberg.

In order, these are the tax burden, interest burden, EBIT margin, total asset turnover, and leverage. The tax burden is one minus the average tax rate expressed as a fraction, so a 30 percent rate produces 0.70; a higher tax burden means the company keeps more of its pretax profit. The interest burden captures how borrowing costs cut into profit, the EBIT margin captures operating profitability, turnover captures efficiency, and leverage captures the asset base relative to equity. Some analysts substitute operating income for EBIT, which folds non-operating items into the interest-burden term.

The five-way form is not the limit. EBIT margin can be split into operating and non-operating pieces, and efficiency can be traced back to inventory management (DOH) and collections (DSO), so the tree extends as far as the question requires.

Because a metric that is central in one sector can be beside the point elsewhere, analysts add industry-specific ratios. Retailers report same-store (comparable-store) sales to separate growth from new stores from growth at existing ones; such metrics are especially important for young, not-yet-profitable industries. Regulated sectors, above all banking, must also satisfy specific regulatory ratios covering liquidity, reserves, and capital adequacy that tie solvency requirements to risk exposure.

Modeling and forecasting

Ratio and common-size work feeds directly into forecasts of future performance, alongside economic, industry, and company inputs and the analyst’s judgment. Given expected growth and expected relationships among the statements, an analyst can build a model, and pro forma statements are widely used inside companies and in presentations to credit providers. Expenses might be budgeted from expected common-size percentages, and balance sheet and cash flow items from expected ratios such as DSO.

Forecasts should span a range of outcomes rather than a single point estimate. Three techniques help:

• Sensitivity analysis (what-if analysis), which shows how outcomes move as specific assumptions change.
• Scenario analysis, which shows the effect of defined events such as losing a major customer or a supply source; if the scenarios are mutually exclusive, exhaustive, and probability-weighted, the analyst can compute expected values and other statistics.
• Simulation, a computer-driven version that assigns probabilities to the drivers and runs many trials to produce an expected outcome.