Financial analysis is a useful tool in evaluating a company’s performance and trends. The primary source of data is the company’s annual reports, financial statements, and MD&A. An analyst must be capable of using a company’s financial statements along with other information such as economy/industry trends to make projections and reach valid conclusions.
Before beginning any financial analysis, an analyst must clarify the purpose and context of why it is needed. Once the purpose is defined, an analyst can choose the right techniques for the analysis. For example, the level of detail required for a substantial long-term investment in equities will be higher than one needed for a short-term investment in fixed income.
This reading focuses on steps 3 and 4 of the financial analysis framework in detail: how to adjust financial statements, compute ratios, and produce graphs and forecasts. The processed data is then analyzed to arrive at a conclusion.
Financial Analysis Framework | |
Phase | Output of the phase |
1. Define purpose and context based on the analyst’s function, client input, and organizational guidelines.
|
Objective
Questions to be answered Nature and content of report to be provided Timetable and budget |
2. Collect data: financial statements, other financial data, industry/economic data, discussions with management, suppliers, customers, and competitors. | Organized financial statements
Financial tables Completed questionnaires
|
3. Process data | Adjusted financial statements
Common-size statements Ratios and graphs Forecasts |
4. Analyze and interpret processed data | Analytical results |
5. Develop and communicate conclusions and recommendations
|
Report answering questions from phase 1
Recommendation regarding the purpose of the analysis |
6. Follow-up | Updated recommendations |
An effective analysis is not just a compilation of various pieces of information, tables, and graphs. It includes both calculations and interpretations. For analyzing past performance, an analyst computes several ratios, compares them against benchmarks, evaluates how the company performed, and determines the reasons behind its good/bad performance. Similarly, for a forward-looking analysis, an analyst must forecast and make recommendations after analyzing trends, management quality, etc.
Various tools and techniques such as ratios, common size analysis, graphs, and regression analysis help in evaluating a company’s performance. Evaluations require comparisons, but to make a meaningful comparison of a company’s performance, the data needs to be adjusted first. An analyst can then compare a company’s performance to other companies at any point in time (cross-section analysis) or its own performance over time (time-series analysis).
A ratio is an indicator of some aspect of a company’s performance like profitability or inventory management that tells us what happened, but not why it happened. Ratios help in analyzing the current financial health of a company, evaluate its past performance, and provide insights for future projections. Calculating ratios is straightforward, but interpreting them is subjective.
Uses of ratio analysis
Ratios allow us to evaluate:
Limitations of ratio analysis
Ratio analysis also has certain limitations. Some of the factors to consider include:
Common-size financial statements are used to compare the performance of different companies within an industry or a company’s performance over time. Common size statements are prepared by expressing every item in a financial statement as a percentage of a base item.
Common-Size Analysis of the Balance Sheet
There are two types of common-size balance sheets: vertical and horizontal.
Vertical common-size balance sheet
A vertical common-size balance sheet is prepared by dividing each item on the balance sheet by the total assets for a period and expressed as a percentage. This highlights the composition of the balance sheet.
Vertical common-size balance sheet account (in %) =
A simple common-size vertical balance sheet for Everest Inc. is shown below:
Vertical common-size (partial) balance sheet for Everest Inc. | ||
ASSETS | 2015 | 2014 |
Cash and cash equivalents | 10.81% | 13.12% |
Short-term marketable securities | 1.24% | 0.62% |
Accounts receivable | 7.50% | 4.80% |
Inventory | 25.32% | 25.97% |
Other current assets | 3.37% | 2.14% |
Property, plant, and equipment (PPE) | 38.76% | 40.06% |
…………. | …… | ….. |
Other non- current assets | 0.03% | 0.02% |
Total | 100.00% | 100.00% |
EQUITY and LIABILITIES | ||
Short-term borrowing | 0.46% | 0% |
Deferred tax liabilities | 4.01% | 4.20% |
….. | … | … |
Stockholder’s equity | 58.88% | 60.13% |
Equity and Liabilities | 100.00% | 100.00% |
Time-Series Analysis
Trend analysis or time-series analysis provides information on historical performance and growth. It indicates how a particular item is changing – whether it is improving or deteriorating – relative to total assets over multiple periods. For the data given above, we can observe that inventory decreased as a percentage of total assets in 2015, while accounts receivable increased as a percentage of total assets.
Cross-Sectional Analysis
The vertical common-size balance sheet can be used in cross-sectional analysis (also called relative analysis) to compare a specific metric of one company with the same metric for another company or companies for a single time period. As illustrated in the table below, this method allows comparison across companies which might be of significantly different sizes and/or operate in different currencies.
Everest Inc. | Alps Corp. | |||
2015 | 2015 | |||
Cash and cash equivalents | 10.81% | $3,500 | 9.00% | €1,755.00 |
Short-term marketable securities | 1.24% | $400 | 4.00% | €780.00 |
Accounts receivable | 7.50% | $2,430 | 5.20% | €1,014.00 |
Inventory | 25.32% | $8,200 | 20.10% | €3,919.50 |
Other non-current assets | 0.03% | $10 | 0.50% | €97.50 |
Total Assets | 100.00% | $32,382 | 100.00% | €19,500.00 |
This presentation makes it easy to see that Alps Corp. has lower receivables as a percentage of total assets relative to Everest Inc. Alps Corp. also has lower inventory as a percentage of total assets relative to Everest Inc.
Horizontal Common-Size Balance Sheet
In a horizontal common-size balance sheet, each balance sheet item is shown in relation to the same item in a base year. Consider the following balance sheet excerpt for Everest Inc.:
2014 (base year) | 2015 | |
Cash and cash equivalents | $3,800 | $3,500 |
Short-term marketable securities | $180 | $400 |
Inventory | $7,520 | $8,200 |
The corresponding horizontal common size balance sheet will look like this:
2014 (base year) | 2015 | |
Cash and cash equivalents | 1.0 | 0.9 |
Short-term marketable securities | 1.0 | 2.2 |
Inventory | 1.0 | 1.1 |
Notice that the base-year value for all balance sheet items is set to 1. This makes it easy to see the percentage change in each item relative to the base year. For the data given above, cash decreased by 10% and inventory increased by 10%. An analysis of horizontal common-size balance sheets highlights structural changes that have occurred in a business.
Common-size Analysis of the Income Statement
A vertical common-size income statement divides each income statement element by revenue.
Vertical common-size income statement account (in %) =
Relationships among Financial Statements
Comparing the trend data of a horizontal common-size analysis across financial statements will give some insight into a company’s financial standing. Consider the following percentage changes for a company to identify some potential issues:
Revenue: +15%, Operating income: +15%, Operating cash flow: -10%, Inventory: +60%, Receivables: +40%, Total assets: +30%
Some of the potential issues based on these numbers are:
Graphs can be considered an extension of the financial analysis. It is a pictorial representation of the analysis done, be it ratio analysis or trend analysis. Analysts use appropriate graphs such as line charts and bar graphs based on the type of data to be shown. This helps in quick comparison of financial performance and structure over time.
Regression analysis, described in detail in Level II, is a statistical method of analyzing relationships (correlations) between variables.