Financial flexibility at the outset of a downturn. A key for subsequent industry outperformance?

An analysis by reference to the great recession and the information technology industry

Bachelor Thesis 2013 63 Pages

Business economics - Investment and Finance




2.1. Implications of Operational Efficiency
2.2. The Pallet of Downturn Opportunities
2.3. The Hypothesis

3.1. The Sample
3.2. Financial Flexibility Analysis
3.2.1. Metrics in Detail
3.2.2. The Determination of Financial Flexibility
3.3. High Performance Business Analysis
3.3.1. Dimensions and Metrics in Detail
3.3.2. The Determination of Relative Performance
3.4. Hypothesis Testing

4.1. Financial Flexibility Analysis Results
4.2. High Performance Business Analysis Results
4.3. Hypothesis Testing


I. References

II. Appendix

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten


University of Applied Sciences Kufstein

International Business Studies

Abstract of the Bachelors Thesis

Guntram C. Kieferle

The business cycle is a cyclical phenomenon and major concern for all sorts of businesses. The short run alternation between contractions and expansions hurts companies across industries forcing many firms to drastic measures in order to survive. There are companies however that overcome these downturns with a tendency to subsequently outperform their competitors. It is assumed that a good financial positioning of a company right at the outset of a downturn is linked to a higher probability of subsequent industry-peer outperformance. By using industry- level analyses the author tests the hypothesis that IT companies with a high Financial Flexibility - at the outset of the Great Recession - were subsequently able to perform significantly better than their industry peer. Whereas Financial Flexibility refers to a company’s positioning in terms of current liquidity and its ability to secure new cash through operations or leverage. The quantitative approach was based on two ratio based analyses: Financial Flexibility Analysis and High Performance Business Analysis and was applied on a sample of 100 publicly traded IT companies. In a statistical t-test the change in average performance of two company categories - Financial Flexibles and Non Financial Flexibles - was compared. It turned out that companies with a higher Financial Flexibility at the outset of the Great Recession performed significantly better than their industry peer in the top 300 IT companies population


FH Kufstein

International Business Studies

Kurzfassung der Bachelorarbeit

Guntram C. Kieferle

Der Konjunkturzyklus ist ein Phänomen dem sich kein Unternehmen entziehen kann. Der vorübergehende Wechsel von wirtschaftlichem Auf- und Abschwung hat negativen Einfluss auf Unternehmen aller Industrien. In vielen Fällen sind die Unternehmen gezwungen drastische Maßnahmen durchzuführen um ihr Überleben zu sichern. Es gibt jedoch Unternehmen, denen es nicht nur gelingt die Rezession erfolgreich zu überwinden sondern noch darüber hinaus besser als ihre Wettbewerber zu sein. Ausgangspunkt für die hier durchgeführte Untersuchung ist die Annahme, dass ein Unternehmen, welches sich kurz vor Beginn einer Rezession in finanzieller Hinsicht gut positioniert hat mit höherer Wahrscheinlichkeit über die Krise hinaus bessere Ergebnisse erzielen kann als seine Rivalen. Auf Basis einer Analyse auf Industrie-Ebene testet der Autor die Hypothese, ob IT Unternehmen mit einer hohen Financial Flexibility - zu Beginn der Weltwirtschaftskrise 2007 - anschließend in der Lage waren signifikant bessere Ergebnisse zu erzielen als ihre Wettbewerber? Wobei sich der Begriff Financial Flexibility auf die Positionierung eines Unternehmens in Bezug auf seine momentane Liquidität und seine Fähigkeit neue Mittel durch Betriebsaktivitäten oder Kreditfinanzierung zu generieren bezieht. Der vom Autor verfolgte quantitative Ansatz basiert auf zwei kennzahlbasierten Analysen: Financial Flexibility Analysis und High Performance Business Analysis und wird auf Basis einer Stichprobe von 100 gehandelten IT Unternehmen durchgeführt. Im Anschluss daran wurde im Rahmen eines statistischer T-Tests die

Differenz der durchschnittlichen Performance-Änderung für die

Unternehmenskategorien: Financial Flexible und Non Financial Flexible auf Signifikanz getestet. Dabei konnte festgestellt werden, dass in der Population der Top 300 IT Unternehmen diejenigen Unternehmen bessere Ergebnisse erzielen konnten, die kurz vor Beginn der Weltwirtschaftskrise 2007 über eine hohe Financial Flexibility verfügt haben

Table of Figures

Table 1: 2008 commodities price developments

Table 2: FFA ratios weighting

Table 3: FFA periods weighting

Table 4: FFA lower limits and corresponding grades (single period alignment)

Table 5: HPBA dimensions and metrics

Table 6: HPBA dimension weighting

Table 7: HPBA periods weighting

Table 8: FF and NFF average performances and performance score change


The business cycle is a cyclical phenomenon and major concern for all sorts of businesses. The recurring short run alternation between contractions and expansions hurts companies across industries forcing many firms to drastic cut backs in order to adapt to the decrease in demand. The Global Financial Crisis followed by the Great Recession was a major downturn which struck the whole world economy. Even though there were indicators which suggested that there was a possible crisis ahead the Global Financial Crisis which was seemed to have arrived largely unexpected:

IMF, World Economic Outlook April/ 2007:

“ [ … ] this World Economic Outlook sees global economic risks as having declined since our last issue in September 2006. Certainly this is at odds with many recent newspaper headlines and commentary, which have focused on problems related to U.S. mortgages, the potential for “ disorderly ” unwinding of global imbalances, and worries about rising protectionist pressures. Nevertheless, as discussed in Chapters 1 and 2, looking at the big picture, we actually see the continuation of strong global growth as the most likely scenario. “

In 2008 the world GDP growth plummeted and reached a negative annual growth of -2.2% in 2009 (The World Bank 2013). Stock prices crashed and the MSCI World Index1 dropped by 42 percent together with the S&P 500 which went down by 38 percent. Whole industries were churned during the recession and lots of companies suffered from dramatic losses in revenues. In many cases the survival of a company depends on the management’s ability to properly react on the decrease in demand. A common way to do so is to formulate comprehensive measures in order to secure financial fundamentals, protect profitability and maximize valuation to avoid hostile takeovers (Rhodes and Stelter 2009, p.3f). Panasonic the Japan-based consumer electronics and technology producer faced loss forecasts of USD 4.3 bn for its fiscal year ending in March 2009. This led Panasonic’s management to a rigorous decision in order to save the company:

New York Times, Feb 4th 2009:

“ Panasonic said it would shut 13 manufacturing sites in Japan and 14 abroad by the end of March. It also plans to lay off about 15,000 workers, or 5 percent of its work force, by March 2010. Half of the cuts will be made in Japan. “ (Wassener 2009)

A comprehensive study conducted by Gulati et al. (2010) at the Harvard Business School found that 17 percent of the companies studied did not survive a recession. The study analyzed three global recessions: the 1980, 1990 and the 2000 downturn taking into account the three years before, the three years after and the recession years themselves examining a total of 4,700 public companies. The vast majority of survivors (about 80 percent) were not able to regain prerecession growth rates of sales and profits 3 years after a downturn. There are companies however that overcome recessions with a tendency to subsequently outperform their competitors beyond the midst of crisis. The author noticed that explicit literature on the topic is scarce and primarily limited to general management magazines’ articles, were findings often seem to derive from the experiences of individuals rather than from research. However the study of Gulati et al. (2010) also revealed a cross-industry correlation between operational efficiency, and well balanced offensive measures and the probability to outperform competitors after a recession. Building on these findings the thesis looks at the topic from a slightly different perspective. The focus was not set on the specific measures that need to be undertaken the focus was set on the positioning needed to pursue these measures. More specifically: The relationship of a company’s profitability and liquidity - right at the outset of a downturn - and the firm’s chances to perform significantly better than its industry rivals beyond the recession. To establish the relationship the author chose an approach which is based on industry-level positioning- and performance-analysis. Because a cross-industry analysis would have gone beyond the scope of this bachelor thesis an industry specific focus was set on the information technology (IT) industry. The author hypothesizes that IT companies with a high Financial Flexibility - at the outset of the Great Recession - were subsequently able to perform significantly better than their industry peer. The experiment designed to test the hypothesis included the analysis of a total of 100 publicly traded IT companies examining their performance before and after the Great Recession under consideration of their Financial Flexibility. Complex models were used, taking multiple measures into account to paint a comprising picture of financial positioning and performance change over several years. This was taken at the expense of a broader industry focus and the analysis of multiple economic downturns. The results from the analyses were finally used to conduct statistical hypothesis testing in order to compare the significance of the difference of the change in average performance of Financial Flexible and Non Financial Flexible companies before and after the recession.


For the past global recessions - the 1980 crisis, the 1990 slowdown and the 2000 bust - Gulati et al. (2010) revealed a pattern in terms of company performance and specific styles of downturn navigation. The study concluded that companies which cut costs faster and deeper or boldly invest more than their rivals are least likely to flourish after a recession. In fact both company categories turned out to have the lowest probabilities (21 and 26 percent) of significantly (by ≥ 10 percent) pulling ahead in terms of sales and profit growth. Instead a focus on operational efficiency and the exploitation of opportunities deriving from the crisis turned out to be the most successful strategy. 37 percent of these companies were able to outperform their rivals in terms of top- and bottom line growth. Companies which are well positioned in terms of profitability and liquidity at the outset of a recession are therefore considered as financially well prepared to affront the dangers and exploit the advantages of an imminent downturn.

2.1. Implications of Operational Efficiency

Operational efficiency is strongly driven by low costs and high margins. Rhodes and Stelter (2009, p.2f) emphasize the importance of adequate cash flow in times of crisis as it reflects the ability of a firm to make new investments that generate new cash. High margins and a solid position in terms of cash and short term investments also help companies to meet their daily business obligations and to remain more agile in the short-term. Beyond that, high margins combined with low debt help to get access to new capital as banks appreciate borrowers with a solid capability to generate profits and a good liquidity (Kaiser et al. 2009, p.6). Maintaining the access to leverage is particularly difficult in times of recession where the credit markets tighten as banks are confronted with higher default risks. Solid operational efficiency also positively influences a business’s intrinsic value and may hence affect its stock price (Kollner et al. 2010, p.17f). Furthermore stock markets seem to reward low debt levels and secured access to capital. This is of great importance as a lowering market capitalization would make a company more vulnerable in terms of hostile takeovers. Whereas most firms will suffer from declining stock prices throughout a recession a company that restrains the downward momentum of its stock price relative to its competitors runs a lower risk of getting a takeover target for rivals or hedge funds (Stelter et al. 2009, p.6).

2.2. The Pallet of Downturn Opportunities

With many companies hibernating during the recession investments in research and development (R&D) have the potential to create an advantage that will be of use when the demand eventually rebounds. Beinhocker et al. (2009, p.6) claim that even if the commercialization of innovation slows down during an economic contraction, innovation still remains a key factor of success. Apple for instance made its R&D investments for the iPod during the downturn era of the dot com bubble - between 2001 and 2003 - despite suffering from sharp sales declines. These investments eventually resulted in a game-changing product allowing Apple to repositioning itself as an innovator leaving competitors behind. In fact there is McKinsey and Company research which indicates “ [ … ] that companies investing counter-cyclically in R&D during downturns tend to outpace their competitors on the upswing. ” (Beinhocker et al. 2009, p.6).

Another opportunity derives from mergers and acquisitions (M&A). With crippled companies being traded at lower share prices acquisitions can be executed at much lower costs in times of crisis. If successfully accomplished, M&A can provide market share and leverage synergy effects in terms of economies of scale, economies of vertical integration, or access to complementary resources (refers to the success ingredients smaller acquired firms can provide to the larger ones e.g. an innovative product). Mergers may also be an investment opportunity for cash-rich firms which face a shortage of good investment options (Brealey et al. 2006, p.873f). Beyond that a company which invests in M&A in times of crisis might benefit from increased value generation. Rhodes and Stelter (2009, p.6) claim that in terms of value creation downturn mergers exceed boom time mergers by generating 15 percent more value on average. It needs to be said however that M&A are not necessarily a guarantor for performance gains. Faulkler et al. (2012, p.31) note that a large number of mergers - particularly when conducted in M&A waves - tend to create no benefits for bidders. Nevertheless thoughtfully executed M&A can be used as a growth vehicle if enough funding is available. The impact of the payment method in terms of cash is still a matter of debate. Abhyankar et al. (2005) analyzed the relation between the form of payment and merger performance. Their study concluded that UK acquiring firms ended up with a better performance in acquisitions funded with cash. Prior research by King et al. (2004) found no significant impact of payment methods on acquiring firm’s performance.

The overall production decline creates another possibility for some businesses. In times of a recession a stricken demand at the commodities markets results in key commodities being offered at cheap prices. The crisis in 2008 led to the highest decrease of prices for 5 decades. The Reuters/ Jefferies CRB Index of 19 raw materials experienced a historical decline and fell by 36 percent to a 2002 low (Pham-Duy 2009).

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Table 1: 2008 commodities price developments; Source: Bloomberg (Pham-Duy 2009)

Table 1 depicts the price developments for each of the 19 CRB commodities for 2008. Whereas gold, cocoa, sugar and hog gained, the vast majority of CBRs dropped led by key commodities like metals and fossil fuels. Firms with consolidated fundamentals can use the situation to strategically resupply their stocks. Depressed prices can further be exploited in terms of property, plants and equipment. With their asset costs being lower compared to non-investing competitors’ investing companies may realize relatively higher earnings (Gulati et al. 2012, p.7). Further opportunities derive from the labor market where competition for top employees slackens and investments in high quality staff will correspondingly be cheaper (Rhodes and Stelter 2009, p.7).

Depending on the company’s competitive advantage another scenario would involve discounting to increase market share at the expense of competitors (Wreden 2002, p3). This option is particularly attractive when a recession leads to an increase of price elasticity in the market (Beveja et al. 2002, p.3). Cossin (2005) refers to a case where the computer company Dell took advantage of the strong margin it created during the boom years of the tech bubble (11.2% margin in 1998 versus Compaq’s 4.5%). When the downturn hit, Dell decreased prices by about the margin of its competitors’ margins which resulted competitors’ sales to fall by 30% on average. In 2001 Dell had flat sales and a market leader position while others were facing declines in terms of sales, income and profit. Although this option might always exist for strong companies its impact is much stronger during a downturn with many competitors being distracted or hibernating (Baveja et al. 2002, p.3).

2.3. The Hypothesis

All these opportunities have in common that they create vital advantages for companies in terms of downturn navigation, but they also require a certain positioning in order to be pursued. This not only comprises sufficient funding, it also the ability to secure new cash through operations and leverage. The ability to do so was condensed in the term: Financial Flexibility. With a growing level of Financial Flexibility the amount of options to choose from in order to respond to the downturn increases. Huedig (2009, p.27) notes that for instance: “ [ … ] cash rich companies [ … ] may be better positioned to make large-scale acquisitions than [ … ] competitors with leaner balance sheets. Companies with high levels of free cash flow, market capitalization and reserved assets [ … ] can use deep discounting to increase market share or acquire struggling competitors, or fend off a hostile takeover. ” Furthermore the right timing of the positioning is imperative. Developing strong financials in the midst of crisis is unlikely. This is why Financial Flexibility at the outset of a downturn is considered as the key. According to Gulati et al. (2010, p.8) pursuing downturn opportunities and focusing on operational efficiency may not only increase a firm’s robustness and combat a crisis; it can also build the foundation for continued success after economic contraction. Therefore it is assumed that a high Financial Flexibility at the outset of a crisis increases companies’ chances to perform significantly better than industry rivals. The author transferred this assumption to the IT industry and the Great Recession2 and hypothesized that IT companies with a high Financial Flexibility - at the outset of the Great Recession - were subsequently able to perform significantly better than their industry peer.


To examine the effect of Financial Flexibility on subsequent performance two different types of ratio based analyses were conducted in order to prepare the data for hypothesis testing: Financial Flexibility and High Performance Business Analysis3. It is important to mention that both analyses types do not yield absolute results. They are designed to compare companies in terms of relative positioning or performance. In other words companies were scored based on how each ratio performed in comparison to the ratios of the other companies. In order to avoid an exchange rate related distortion of results the financial ratios were based on local currencies. The data points for analysis were being retrieved from Standard & Poor’s Capital IQ4 whereas data processing was executed in Excel. Statistical hypothesis testing was conducted in SPSS Statistics.

To test the hypothesis the following experiment was designed:

i. Definition of the population and drawing a random sample
ii. Financial Flexibility Analysis to determine companies which had a high/ low Financial Flexibility at the outset of the Great Recession at 12/31/2007
iii. High Performance Business Analysis on the complete sample with 12/31/2007 and 12/31/2012 data
iv. Statistical t-testing in terms of the difference in the change in average performance of the companies with high/ low Financial Flexibility

In both analyses there are parts which rely on an efficient market. It should be noted however that securities prices may not always be based on rational assumptions. Burton (2012) and Shiller (2000) illustrate that the market is characterized by recurring and systematic mispricing. Kahneman (2011) describes systematic irrationality in human behavior which may considerably affect investor’s decision making. The author tried to minimize a negative influence on results via specific weighting adjustments in the analyses (see chapters 3.2 and 3.3.).

3.1. The Sample

The random sample of 100 firms was drawn from a list of the 2007 top 300 publicly traded IT companies (see A18)5. In order to capture the positioning of the IT companies at the moment of the imminent recession a ranking based on the latest market capitalization numbers available at 12/31/2007 was chosen. The single- industry focus is vital as High Performance and Financial Flexibility Analysis should only be determined for businesses within the same industry. The financial ratios used in both models have industry specific characteristics which is why a cross-industry comparison of firms would yield to meaningless results. Therefore the author decided to set the focus of the analysis on the IT industry. In the analyses only operating companies were examined. Not included were operating firm’s corresponding subsidiaries and acquired companies. The limitation to the top 300 companies was a result of previous, unsuccessful trials to cover a larger population/ sample. This limitation is partly related to the fact that both models originally were not designed for large sized sample analyses. Beyond that it became apparent during the analyzing process that many smaller publicly traded companies have a lack of complete data streams which is critical because the scores awarded are interdependent. Especially companies traded at over-the-counter (OTC) exchanges like OTCPK (Pink Sheets) and OTCBB (Bulletin Board) turned out to be problematic in terms of data reporting. The majority of these issuers does not file periodic reports or audited financial statements with the Securities Exchange Commission (SEC) and does not meet the minimum listing requirements for trading on a national stock exchange (SEC 2013). These circumstances led to the exclusion of OTCPK and OTCBB traded companies. A coverage of multiple economic downturns to improve the validity of results was considered but eventually had to be discarded for similar reasons. Both models require a comprehensive and complete stream of data that covers up to 5 years. The fact that High Performance Business Analysis had to be conducted twice in order to detect changes of company performance further extended the time of years captured to 10. This comprehensive approach led to complications regarding the availability of data. For instance: Companies happened be too young to provide older data points which are required to conduct the analyses. The expansion of the amount of crises examined would have correspondingly resulted in an even larger amount of companies with data lacks. Furthermore limited computing capacities6 led to a reduction of the feasible sample size. A planned set of 500 IT companies was discarded and had to be cut back in order to enable an adequate data processing time7. The author notes that these restrictions may have negative effects on the validity of the inferences made.

3.2. Financial Flexibility Analysis

The idea of the Financial Flexibility Analysis8 (FFA) is to create a comprehensive snapshot of a company’s positioning in terms of liquidity and its ability to secure new funding through operations or leverage in relation to its peer. Each ratio is based on the latest company data available at 12/31/2007.

3.2.1. Metrics in Detail

Six financial ratios were incorporated: EBITDA9 Margin, Net Debt Ratio, Cash & Short Term Investments Ratio, Levered Free Cash Flow Ratio, Revenue Growth and Change in Market Capitalization. As the model originally was developed for quickly identifying client needs and consulting areas most measures are simplified revenue based ratios. Although common ratios may have yielded to a more comprehensive view these ratios were regarded comprehensive enough to allow inter-industry company comparison.

The EBITDA Margin was used to reflect a company’s profitability of operations.

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To get a sense of the ability to secure new debt the Net Debt Ratio was incorporated. A low net debt position represents a high reliability which increases chances for the company to access adequate leveraged funding during the recession. This becomes of additional importance as bank loans are sparsely given in the midst of a crisis.

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Cash Ratio was added to the model because it requires a certain level of cash for business operations like investing in a plant or paying bills as they fall (Penman 2013, p.294). It can also play a role as a form of payment in M&A plans (Faulkner et al. 2012, p.177).

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Levered Free Cash Flow Ratio was used to identify companies’ ability to generate cash from operations net of cash investment (Penman 2013, p.242).

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Revenue Growth is a performance indicator and was incorporated to reflect a company’s positioning in terms of growth.

Year over year Change of Market Capitalization is part of the analysis in order to capture the markets view about the company’s ability to maintain its performance and profitability.

Each financial measure was given a separate weighting. To avoid a mispricing related company ranking, change in market capitalization was given a lower weight. Revenue growth was as given the same low weight as well as the author wanted to set the focus on companies’ operational efficiency and their positioning in terms of cash and debt.

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Table 2: FFA ratios weighting; Source: Own table based on FFA configurations.

In order to paint a more comprehensive picture the analysis covers ratios referring to multiple time periods. Each period of time was given a subjective weighting as well. Here the author set focus on the trailing twelve month period. Latest quarter and latest fiscal year were almost equally weighted.

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Table 3: FFA periods weighting; Source: Own table based on FFM configurations.

3.2.2. The Determination of Financial Flexibility

Every company received a score for each of its measures based on the distance in terms of standard deviations (SD) from the average measure value of the sample. Finally the weighted average score of all measures of a company was created yielding an overall company score/ letter grade (see Table 4).

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Table 4: FFA lower limits and corresponding grades (single-period assignment)11 ; Source: Own table based on FFA.

Every score was further partitioned in 3 sub-segments to allow a more detailed differentiation. Only top companies with total letter grades of A+, A, A- or B+ were then assigned to the FF category.

3.3. High Performance Business Analysis

After the creation of the company categories FF and NFF performance analysis was conducted. Here again a model based on Excel and Capital IQ namely High Performance Business Model 2.0 was used. Its underlying principles and parts of the model are described go back to Nunes and Breene (2011), Jumpin the S-Curve.

The general idea of the High Performance Business Analysis (HPBA) is to use different performance indicators to compare companies based on a score similar to the Financial Flexibility Analysis. Therefore the HPBA looks at five dimensions each derived from the average score of several metrics. Businesses were looked at from two angles: On the one hand business execution in terms of growth, profitability and consistency. On the other hand it measures and assesses the expectations of investors in terms of total return to shareholder (TRS) and future value.

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Table 5: HPBA dimensions and metrics; Source: Own table based on Nunes and Breene 2011, p.215.

Again the author considers the inclusion of the market based metrics TRS and future value as critical. By looking at a time horizon of 5 years across all metrics, including TRS and future value the risk of short-term mispricing related bias however was reduced. To further minimize the risk lower weightings were given to future value based metrics. Instead the author wanted to focus on the core performance indicators profitability and growth which is why they were given higher weightings.

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Table 6: HPBA dimension weighting; Source: Own table based on HPBA configurations.

The analysis looks at continuous performance which is why measures were assessed for periods of 3 and 5 years. Each period was given the same weighting to equally consider companies’ short term course and sustained performance.

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Table 7: HPBA periods weighting; Source: Own table based on HPBA configurations.

3.3.1. Dimensions and Metrics in Detail

A key dimension of the HPB Analysis is profitability. It can be measured by the spread of the rate of return of the invested capital and the corresponding cost of capital. To arrive at the spread for each company the following equations were used:

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Where EV is the enterprise value of the company (debt + equity), Kd is the cost of debt, Tr is the marginal tax rate (determined by country of operations), and Ke is the cost of a company’s equity. Ke was determined by calculating the expected return of the company’s security by using the Capital Asset Pricing Model (CAPM):

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The risk free rate - return on a riskless investment such as a T-bill - anchors the relation of risk and corresponding expected return while the market risk rate is the expected return on the stock market as a whole. The beta functions as a standard measure of systematic risk. In other words beta reflects each company’s stock tendency to move in parallel with the stock market as a whole. A security with an average level of risk has a beta of 1.0. This means that the stock rises and falls the same percentage as a broad market index like the S&P’s 500-stock index. A beta greater than 1 reflects a more sensitive reaction to market swings and a stock with a beta smaller than 1 is less sensitive to the market movements. The beta applied in the model to determine each company’s Ke is industry- rather than company-specific beta and was derived from the average beta of the experiment’s population.


1The MSCI World Index is a free float-adjusted market capitalization weighted index that is designed to measure the equity market performance of [24] developed markets. ” (MSCI 2013)

2 Limiting the analysis to the Great Recession and the IT industry was necessary in order to engage in the industry-level analyses applied. For a more detailed reasoning refer to chapter 3.1.

3 Financial Flexibility Analysis and the High Performance Business method for past performance determination were both developed by Accenture Research.

4 Capital IQ is a provider of multi-asset class and company real time data, research and analytics.

5 To randomize the companies the =RAND() Excel function was used.

6 Intel(R) Core(TM) i5 CPU M 520 @ 2.40 GHz; 4 GB RAM

7 Under circumstances given the data retrieval and data processing for 100 IT companies took 9 hours.

8 The Financial Flexibility model was developed by Huedig and team (Accenture Research).

9 Earnings Before Interest Taxes Depreciation and Amortization

10 The reverse order of grades in brackets was assigned to the net debt ratio where a below average performance is considered optimum.

11 These assignment criteria were applied in the first step of scoring, e.g. scoring the trailing twelve month EBITDA margin. In a second step the scores for each period based metric were averaged, e.g. the average of the trailing twelve month, latest fiscal year and latest quarter EBITDA margin. To categorize companies in this second step the multi-period assignment table (see A17) was used.

12 Compound Annual Growth Rate


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University of Applied Sciences Kufstein Tirol
Finance Great Depression Downturn Industry Analysis Industry Peer Finanzierung Finanzkrise 2008 Business Cycle IT Ratio Analysis Kennzahlenanalyse ratios




Title: Financial flexibility at the outset of a downturn. A key for subsequent industry outperformance?