Financial statements analysis, comparability and interpretation

Introduction

The annual report purpose is to provide stakeholders disclosure about economical and financial activities. The financial statements analysis, comparability and interpretation plays a crucial role in clarifying this disclosure. However, there are some complications and considerations to care about. Ratio analysis is only a small part of the whole, and KPIs should be “key” according to the analysis’ goal and recipient. Finally, we will discuss about quality, because there are poor analysis issues too.

Table of Contents

  1. Introduction
  2. The mental model: a multidimensional approach
  3. The analysis framework
    1. Comparison and (not) homogeneity
  4. Measuring company performance
  5. Analytical tools and techniques
    1. The universe of ratios
    2. Common-size analysis
    3. Trend analysis: time series and graphs
    4. Simulations: scenario and sensitivity analysis
  6. How to develop a proper KPI set
  7. Reporting and quality assessment
  8. Bibliography

The mental model: a multidimensional approach

The step zero could represent a crucial phase for the entire analysis success. A consulting and multidimensional approach is recommended: the consultant starts his assessment venturing from outside into the bowels of the company, with a unbiased view; the assessment has a multidimensional extent due to the countless purposes and to whom is intended for. Robinson et al. (2015) suggest the should-is-cause logic, in order to align the purpose of the analysis with the data needed and with the conclusions that has to be investigated, and hence, to develop a proper KPI set useful for benchmarking and interpretation. In this sense, the information obtained has to be arranged to focus on the SHOULD (goal), IS (current state) and CAUSE (factors that may help or hinder achieving business objectives). Indeed, facing financial analysis bearing in mind final goals from scratch is a mental model that means a better and effective process and so a qualitative reporting.

The analysis framework

In light of multidimensionality, every analysis will be unique. However, there is a framework below that connect them. The financial statement analysis process is articulated as follows (Benninga, Simon and Sarig, 1997):

  • Contextualization and goal(s) setting;
  • Data gathering;
  • Data processing;
  • Data interpretation;
  • Reporting;
  • Follow-up.

The analysis framework implies a circular process: variation over time is what matters, comparing data and analysis among themselves. Analyses are usually performed according to the going concern perspective and this confirms circularity: today’s report will be a data input of tomorrow’s reports and so on.

Comparison and (not) homogeneity

Comparing different companies deserves its particular spot. Indeed, the general framework remains the same but there are same precautions regarding the data gathering, processing and interpretation steps.

Basically, in order to perform a financial statements analysis comparison it is fundamental to primarily investigate accounting principles adopted (Teodori, 2017) and relative singularities. Notes and commentaries (MD&A) represent a valid support. After that, it is possible to apply adjustments and reclassifications when required (e.g. IAS/IFRS tables are already reclassified in some aspects). Starting from IAS 1, international principles allow several degrees of freedom in drawing up statements data, although a minimum content is mandatory. Only after having fixed peculiarities, the analyst will be able to design a KPIs set, where the juxtaposition of the same index(es) from different companies makes sense (e.g. comparing a US-GAAP company’s ROI with a French company’s ROI could be unreasonable).

Measuring company performance

It is a well known fact that numerical fluency is a crucial skill for every business leader (Farris et al., 2010). Authorities and regulators are massively concerned with how companies deliver financial reports and accounting disclosure in general: this denotes the relevance of information on which depend other companies, families, institutions and so on. On the other hand, several researches has established the low value of financial statement information (cfr. Scott, 2015): in particular, they cannot explain all abnormal return variability and it can also affect investors’ perceptions of a firm’s risk, possibly affecting its cost of capital. If cost of capital changes, the effects on share price will create abnormal return volatility.

In this sense, an accurate data-driven approach surely requires proper techniques, but especially great analytical and interpretation skills (e.g. supporting CFOs with data scientists).

Analytical tools and techniques

We can distinguish mainly four types of financial analysis:

  • Ratio analysis
    • Activity ratios
    • Liquidity ratios
    • Solvency ratios
    • Profitability ratios
    • Valuation ratios
  • Common-size analysis
    • Vertical analysis
    • Horizontal analysis
  • Trend analysis
    • Time series
    • Graphs
  • Simulations
    • Scenario analysis
    • Sensitivity analysis

The universe of ratios

Each analysis of the previous paragraph takes advantages of a number of metrics and tools useful to investigate the company’s health. Obviously, the following is a non-exhaustive list of ratios.

Activity ratios

Activity ratios reveal how efficiently a company performs daily operating tasks (e.g. A/R or inventory). Common activity ratios include the following:

  • Inventory turnover: Cost of sales (or cost of goods sold)/Average inventory
  • Days of inventory on hand (DOH): Number of days in period/Inventory turnover
  • Receivables turnover: Net sales/Average receivables
  • Days of sales outstanding (DSO): Number of days in period/Receivables turnover
  • Payables turnover: Purchases/Average trade payables
  • Number of days of payables: Number of days in period/Payables turnover
  • Working capital turnover: Net sales/Average working capital
  • Fixed asset turnover: Net sales/Average net fixed assets
  • Total asset turnover: Net Sales/Average total assets

Liquidity ratios

Liquidity ratios measure the company’s ability to pay off short-term debt ­obligations and to meet unexpected cash requirements (Rist & Pizzica, 2015). Common liquidity ratios include the following:

  • Acid test (or quick ratio): (Current assets – Inventory)/Current liabilities
  • Cash conversion cycle (CCC): Days in inventory + Days sales outstanding – Days payable outstanding
  • Cash ratio: Cash and cash equivalent/Current liabilities
  • Current ratio: Current assets/Current liabilities
  • Working capital: Current assets – Current liabilities
  • Operating cash flow (OCF): NOPAT + Depreciation + Amortization

Solvency ratios

Solvency ratios measure the company’s ability to survive in the long-term period, in specific relation to the debt structure. Common solvency ratios include the following:

  • Debt to assets ratio: Total liabilities/Total assets
  • Debt to equity ratio (D/E): Total liabilities/Total equity
  • Times interest earned (TIE, also called interest coverage): EBIT/Interest expense
  • Free Cash Flow (FCF): Net cash provided by operating activities – Capital expenditures – Cash dividends
  • Financial leverage ratio: Average total assets/Average total equity

Profitability ratios

Profitability ratios can be thought of as the combination of many of the other more specific ratios to show the company’s ­ability to generate profits, in a nutshell. Common profitability ratios include the following:

  • Earnings Per Share (EPS): (Net income – Preferred dividends)/Weighted-average common shares outstanding
  • Price-earnings ratio (P-E): Market price per share/EPS
  • Current yield: Dividend per share/Price per share
  • Gross profit rate: Gross profit/Net sales
  • Profit margin: Net income/Net sales
  • Return on Assets (ROA): Net income/Average total assets
  • Asset turnover: Net sales/Average total assets
  • Payout ratio: Cash dividends declared on common stock/Net income
  • Return on common stockholders’ equity: (Net income – Preferred dividends)/Average common stockholders’ equity
  • ROE: Net income/Average total equity

Valuation ratios

Valuation ratios represent metrics useful to appraise the value of a business in terms of attractiveness and cost-benefit trade off. Common valuation ratios include the following:

  • Earnings Per Share (EPS): (Net income – Preferred dividends)/Weighted-average common shares outstanding
  • Price-earnings ratio (P-E): Market price per share/EPS
  • Current yield: Dividend per share/Price per share
  • Gross profit rate: Gross profit/Net sales
  • Profit margin: Net income/Net sales
  • Return on Assets (ROA): Net income/Average total assets
  • Asset turnover: Net sales/Average total assets
  • Payout ratio: Cash dividends declared on common stock/Net income
  • Return on common stockholders’ equity (ROE): (Net income – Preferred dividends)/Average common stockholders’ equity

Common-size analysis

A common-size analysis represents a tool financial managers adopt to facilitate comparison across time periods (i.e. time series analysis) and across companies (i.e. cross-sectional analysis), because the standardization of each line item removes the effect of size (Robinson, Henry & Broihahn, 2020).

Vertical analysis

A vertical common-size analysis can be performed by stating each line item on a table as a percentage of the total. For instance, it can be useful to represent each balance sheet item of several companies as a percentage of their total assets. This tool can be act as a solid base for further investigation about strategy, pricing, advertising budgeting and so on. Vertical analysis is also used to examine industry data: as an example, comparing median common-size income statement data compiled for the components of the S&P 500 classified into the 10 S&P/MSCI Global Industrial Classification System (GICS), according to operating margin based on EBIT can be meaningful.

Horizontal analysis

A horizontal common-size analysis states quantities in terms of a selected base-year value. Namely, we can compare a specific financial statement with prior or future time periods or to a cross-sectional analysis of one company with another. For instance, an analysis of horizontal common-size balance sheets highlights structural changes that have occurred in a business. Although, past trends are obviously not necessarily an accurate predictor of the future, especially when the macroeconomic or competitive environment changes.

Trend analysis: time series and graphs

The use of time series and graphs as analytical tools is crucial: they allow to notice a comprehensive view over time with changes’ impact and trends. Directors, executives, managers and stakeholders in general, need concise numbers and pictures. We have to re-think information as a pyramid, where the vertex represents the first tier of information, simple, influential and visual, while gradually as you descend the level of detail will get heavy. With the advent of AI and Big Data, financial information available is massive, as a consequence data storytelling has to be modulated in order to satisfy various actors’ interests. In this sense, trend analysis is a powerful help and pulling the strings of time series and graphs is an art.

In general, pie graphs are most useful to communicate the composition of a total value (e.g., assets over a limited amount of time, say one or two periods). Line graphs are useful when the focus is on the change in amount for a limited number of items over a relatively longer time period. When the composition and amounts, as well as their change over time, are all important, a stacked column graph can be useful.

Robinson, Henry & Broihahn, 2020

Simulations: scenario and sensitivity analysis

Uncertainty is a peculiarity of these turbulent days. The economic doctrine provides wide range of instruments to deal with future events. In particular, simulations are useful not merely to financial statement performance’s forecasting, they has to be across-the-board implemented in prudent model building (e.g., in logistic, production, marketing, etc.). There are two main sets of simulations:

  • Scenario analysis: the analyst has to be able to build scenario variations (often pessimistic, neutral and optimistic scenario), according to possible outcomes resulting from given events;
  • Sensitivity analysis: bearing in mind the going-concern basis, a sensitivity analysis allows to investigate what happens when a variable – ceteris paribus – changes. For this reason, it is known as what-if analysis.

How to develop a proper KPI set

From a exquisitely operational perspective, building a KPI set that makes sense is not a cheap task. Computing indexes, metrics and ratios can be automated with a few lines of code or specific software, their architecture, selection and combination cannot. The analyst plays a crucial role in setting meaningful and effective KPIs. First of all, the basket (and its presentation) will rely on the purpose(s) of the analysis (and its addressees): evaluating a short position due to bankruptcy risk is different than evaluating a business. Moreover, people’s involvement it is an extra-gear in order to strengthen commitment and integrate non-financial metrics (e.g., Churn Rate).

“The two most important things in any company do not appear in its balance sheet: its reputation and its people”.

Henry Ford

Building custom dimensions is another useful integration, mostly if there are idiosyncratic investments that need unique KPIs. A long list of numbers is useless if they are not consistent: less is more. Variation is what counts, a fistful of effective metrics and their historical deltas, it is all that matters. Indeed, growth rates can be helpful (Anthony et al.,2016):

  • Average Annual Growth Rate (AAGR): average of each % growth year by year (i.e. series’ arithmetic mean);
  • Compound Annual Growth Rate (CAGR): % growth from the beginning to the ending year value analyzed (i.e. return of value variations over time).

The ideal ranges could be senseless: financial statements are affected by accounting principles and conventions, with a often relevant subjective component. Nevertheless, it can come to the aid of standardization. In order to standardize an index, it is necessary to transform it in a “pure” number, as follows: (Ia – Ῑ)/δ, where: Ia is the index we want to standardize related to the our a-company, Ῑ is the index average and δ is the standard deviation.

A further important point is the alleged inadequacy of the current financial reporting systems for digital companies. They need alternative performance measures (i.e. Non-GAAP financial measures), there are parameters that are commonly used by nearly all companies, while others seem to be more firm-specific according to peculiar needs. Especially with regard to the most-used non-GAAP metrics – the cash flow-based ones – this trend is interpreted as a shared need, among digital companies, to convey additional disclosure and performance measures to the GAAP metrics, which seem to be inadequate to represent their value creation process (Sousa and Rocha, 2019).

Thus, we can infer there is no best KPIs set: certainly, it is possibile to perform a quick check verifying metrics such as ROE, ROI, EBIT, Share capital/Equity, Net sales/Raw materials and consumables used, Trade receivables/Net sales, Borrowing costs/Net sales, Equity/Liabilities, Employee benefits expense, and so on.

As a matter of fact, all that have to be integrated with a proper qualitative analysis with a strong interpretation and commentary according to the recipient. In particular, financial statement analysis has to be intimately related to whom is intended for:

  • Shareholders;
  • Management;
  • Lenders and banks;
  • Competitors;
  • Suppliers;
  • Customers;
  • Investors;
  • Analysts;
  • Employee and labor unions;
  • Auditors;
  • Rating organizations;
  • Fiscal and government authorities;
  • Judge and law;
  • Academics;
  • Stats;
  • News and media;
  • And many others.

Reporting and quality assessment

In October 2000, Enron was named in the top 25 on Fortune magazine’s list of the World’s Most Admired
Companies. Even top management could be affected by blind spots about economic reality, let alone a stakeholder. Quality is the key. Quality of disclosure, of reporting, of earnings. Standard setters promotes financial reports quality, indeed the characteristics of decision-useful information are identical under IFRS and US GAAP: comparability, verifiability, timeliness, and understandability.

Regardless of subjectivity and assumptions (e.g. coin of account stable value), there are some aspects to investigate in order to assess quality: accounting principles, management, accounting areas, policy’s comparison with competitors, ratio benchmarking, inventory turnover, apply probability models such as Beneish Model, Altman Model, Shumway or Bharath and Shumway model, analyze deferred taxes, incentive compensation and so on.

Concluding, it is crucial to bear in mind the fact that auditor’s opinions are often misleading about company risk’s exposure: one thing is accounting conformity, quite another is risk, regardless of going concern assumption. Management Commentary (Management Discussion and Analysis, or MD&A) and risk business models could be a source of risk information as well as financial press.

Bibliography

ANTHONY R. N., HAWKINS D. F., MACRÌ D. M., MERCHANT K. A. (2016). Accounting: text and cases. New York: McGraw-Hill Publishing.

BENNINGA, SIMON Z., and ODED H. SARIG (1997). Corporate Finance: A Valuation Approach. New York: McGraw-Hill Publishing.

FARRIS P. W., BENDLE N. T., PFEIFER P. E., REIBSTEIN D. J. (2010). Marketing Metrics. Upper Saddle River, New Jersey: Pearson Education, Inc.

RIST M. & PIZZICA A. J. (2015). Financial Ratios for Executives: How to Assess Company Strength, Fix Problems, and Make Better Decisions. New York: Springer apress.

ROBINSON T. R., HENRY E. & BROIHAHN M. A., (2020). International Financial Statement Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc.

SCOTT W. R. (2015). Financial accounting theory. Don Mills, Ontario: Pearson Canada Inc.

SOUSA M. & ROCHA A. (2019). Skills for disruptive digital business. Journal of Business Research. 94. 257-263. 10.1016/j.jbusres.2017.12.051.

TEODORI C. (2017). Analisi di bilancio – Lettura e interpretazione. Torino: Giappichelli editore.

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