Taming Big Data with the Interactive Financial Statement Analysis Module

Big data refers to the amount of data a typical organization now processes to solve business problems.  This rapidly developing area is reshaping approaches to business education as has recently been discussed in Business Education Journals such as BizEd (November/December 2012).  For example, in their lead article “Where Technology meets Business” it was observed that:

 “… in today’s world, where everyone can buy databases, technology alone isn’t a competitive advantage.  The advantage rests in how an organization uses it. …. Tomorrow’s CEOs won’t need to connect wires and switches---but they will need to connect the dots…”

The Interactive Financial Statement Analysis (IFSA) is designed to meet these demands.  First, it provides immediate access to big data.  This data includes the interactive financial reports now required by the SEC from publicly listed corporations.  Second, IFSA combines big data with technology to provide the conceptual framework necessary for connecting the dots.  This conceptual framework steps you through a structured and visual approach to understanding a company from the financial data it generates.  These steps include:

·         Analyzing financial reports including the business model and strategy

·         Common Size Analysis (horizontal and vertical analysis)

·         Analyzing Profitability

·         Analyzing Operations

·         Analyzing Risk

·         Analyzing Reporting Quality

·         Analyzing price ratios to connect the dots between fundamentals and returns

Today’s business environment places increasing demands on the user to combine business analytics with big data because the sheer complexity of the problem forces a user to understand the underlying economic drivers of the data.  In the blogs on this site we illustrate how business analytics lets you work with big data to generate increasingly finer information.  For example, Understanding the Degree of Operating Leverage blog introduces you to the problem of analyzing operating risk.  This is the risk associated with operating leverage, measured by combining business analytics with the horizontal analysis of a corporation’s cost behavior.  The technology lets you pull of this analysis quickly for any publicly listed corporation and its competitors, but as the quote in the introduction asserts, the comparative advantage is not created from the database and the technology, but from knowing how to use this information.  The Interactive Financial Statement Module shows you how to acquire these skills -- by doing!

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Understanding the Degree of Financial Leverage

The financial risk a company faces results from its financing decisions, for example, whether it chooses to borrow to finance its investments or its operations.  One way to measure financial risk is through activity analysis, specifically focusing on the impact of financing activities on profits.  In this post, we focus on understanding the Degree of Financial Leverage, defined below, as a measure of risk. This is a companion to the post on the Degree of Operating Leverage (DOL).

A different measure of risk, beta, is based on stock returns.  It measures the sensitivity of a stock’s return to the market as a whole.  In its pure form, it does not pay attention to fundamentals.  However, people have developed the concept of a “fundamental beta” which takes into account information such as operating and financial risk.  One argument in favor of this approach is that beta typically is calculated using historical data, while fundamentals provide information about the future performance of a firm. 

Financial Risk

In this post, we consider financial risk estimated from a firm’s fundamentals.  There are two popular approaches adopted in practice.  Both approaches share the objective of measuring financial leverage but one adopts a balance sheet (i.e., stock approach) to the problem and the other adopts an income statement (i.e., flow approach) to the problem.  The stock approach estimates financial leverage directly from the debt to equity ratio and the flow approach estimates financial leverage as an elasticity relative to Earnings Before Interest and Taxes (EBIT).  This latter approach is referred to as Activity Analysis, and here, we describe how you use it to measure financial risk.

Financial risk in “Activity Analysis” is measured by the “Degree of Financial Leverage.”  The ideas underlying this measure can be developed as follows.  From the firm’s financial statements we can first estimate a firm’s Earnings Before Interest and Taxes (EBIT) which provides a measure of the operating performance for a firm.    Similarly, if we subtract away Net Interest Expense from EBIT then this yields Earnings Before Taxes (EBT).   However, interest expense is tax deductible and thus what is important for financial decisions in the real world is after-tax interest expense.  As a result, in practice the Degree of Financial Leverage (DFL) is defined and measured in terms of Earnings per share.  That is, the usual definition is:

Degree of Financial Leverage (DFL) = % Change in EPS/% Change in EBIT

This definition has the additional nice practical property that it is in a form that provides an immediate linkage from Sales Revenue forecasts to analyst earnings’ forecasts via the degree of total leverage.  This is defined as follows:

Degree of Total Leverage (DTL) = % Change in EPS/% Change in Sales = DFL * DOL

In the above the DOL is the Degree of Operating Leverage which was introduced in a previous LESSON in this series.

In the lesson, you will learn how to calculate the DFL for Wal-Mart and Intel.  Then, you will compare them to the betas, and see if a higher DFL is associated with a higher beta.  At the end of the lesson is an exercise that lets you conduct a more systematic analysis of the relationship between financial leverage and beta.

To access the lesson, from the FSA module, simply select it from the Lessons menu:

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Understanding Degree of Operating Leverage

The operating, or “fundamental,” risk of a company is the risk a company faces because of the business it is in.  For example, the profits a firm makes depend on the demand for its goods and the costs of producing the goods, so it inherently faces the risk that costs could increase and/or demand could fall, which would affect the profits of the firm.

Operating risks result from the investment decision of the firm, and are separate from financial risk, which results from the financing decision.   One way to measure operating risk is through activity analysis, which studies how the operating activities of the company generate profits, see below.

Fundamental Risk and Beta

 A different measure of risk, beta, is based on stock returns.  It measures the sensitivity of a stock’s return to the market as a whole.  In its pure form, it does not pay attention to fundamentals.  However, people have developed the concept of a “fundamental beta” which takes into account information such as operating and financial risk.  One argument in favor of this approach is that beta typically is calculated using historical data, while fundamentals provide information about the future performance of a firm. 

So one question you can ask is whether beta reflects fundamental risk.  For example, do firms with high operating risk have high betas, or more broadly, if there is a systematic relationship between beta and fundamentals.

Operating Risk

 Operating risk in “Activity Analysis” is measured by the “Degree of Operating Leverage.”  The ideas underlying this measure can be developed as follows.  From the firm’s financial statements we can first estimate a firm’s Earnings Before Interest and Taxes (EBIT) which provides a measure of the operating income for a firm.  One measure of operating risk is the Degree of Operating Leverage (DOL) defined by

Degree of Operating Leverage (DOL) = % Change EBIT/% Change in sales revenue

This definition is related to the concept of elasticity in economics: it measures the percentage change of one variable due to a percentage change in another.  So here, operating risk is defined as the elasticity of a firm’s EBIT to Sales Revenue. 

Why is this measure of operating risk?  Because it tells you how sensitive profits are to changes in sales.  If they are very sensitive, then it could mean, for example, that the firm has high fixed costs, so it is more exposed to a downturn in sales than a firm with low fixed costs.  Numerically, consider a firm that has a fixed cost of $50m and then a variable cost of $1 per unit.  If it sells 100m units at $2 each, it has a profit of $50m (200m in revenue minus 100m in variable costs minus 50m in fixed costs).  If sales drop by 10% to 90m units, profit drops to 180m – 90m – 50m = 40m, which is a 20% drop in profit.  If the same firm had zero fixed costs, its profit would drop by 10% if sales dropped by 10%.

In the lesson, you will learn how to calculate the DOL for Wal-Mart and Intel.  Then, you will compare them to the betas, and see if a higher DOL is associated with a higher beta.  At the end of the lesson is an exercise that lets you conduct a more systematic analysis of the relationship between operating risk and beta.

To access the lesson, from the FSA module, simply select it from the Lessons menu:

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