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The impact of the great recession on S&P 500 firms and their marketing efforts: an empirical analysis.

INTRODUCTION

The Great Recession of 2007-2009 unearthed massive changes in the industrial landscape. Wikipedia maintains a list of companies bankrupted during this time, which illuminates the period and notably includes defunct Lehman Brothers, Circuit City, Washington Mutual, and the currently resurrected Chrysler and General Motors ("List of companies bankrupted during the Great Recession," n.d.). While many businesses collapsed, others were given a chance to clean up inefficiencies as was the case with the government bailout of General Motors. Some companies naturally fare well during recessionary periods due to structural issues related to the industries themselves. So-called "recession proof' businesses include discount retailers that leverage economies of scale to deliver cheap goods to consumers' during hard economic times. These industries also include "sin industries", like Beattie notes, especially retailers offering smaller pleasures such as cigarettes or wine, which help consumers escape the anxiety of the times (Beattie, n.d.).

Other businesses thrive during recessions because of calculated strategic moves. In 2009, pizza chain Domino's, for example, invested millions of dollars in advertising after its stock price hit a record low of $2.83 per share (Jackson & Feld, 2011). Domino's now infamous brutally honesty campaign, in part funded by an obligatory 5% of all profits dedicated to advertising expenditures imposed on franchisees (Ramnarayan, 2009), included an on-air admission that some consumers think Domino's pizza "sucks" (Jackson & Feld, 2011). Domino's campaign, which included a revamping of its pizza product, had sales at U.S. locations increase 14% by the first quarter of 2010. By the third quarter of 2011, Domino's stock had gained 233% since it announced its plan in 2009 (Jackson & Feld, 2011). Advertising experts hailed Domino's strategy as brilliant for capturing the Zeitgeist of low consumer trust (Parekh, 2010).

Domino's is but one example of how a firm's strategic actions related to marketing (here advertising and building brand equity) helped it to weather a recessionary period. This study examines financial performance ratios during pre- and post-financial crisis years with known relationships to the marketing function of the firm including Tobin's q, intangible assets to total assets (IntanTA), and cash flow to sales (CFsales). Other variables considered in this study include advertising to sales ratio, auditor opinion (AUOP), and capital expenditure to property, plant and equipment ratio (CapInt). Years analyzed were 2006 (pre-financial crisis) and 2010 (post-financial crisis) for the S&P 500 firms. S&P 500 firms are large US companies from a diverse range of prominent industries and their stocks are widely held by stockholders. These 500 companies represent approximately 70 percent of equity market capitalization in the US ("S&P 500 Index," n.d.) and hence are interesting to examine how they responded to the great recession from the marketing and financial perspectives.

This study investigates the following research questions: Were there any differences in intangible assets and Tobin's q between pre- and post-crisis periods? Did the cash flows improve significantly in the post-crisis period? Did the advertising expenditures spike significantly? Did the capital expenditures increase significantly in the post-crisis period?

BACKGROUND OF THIS STUDY Intangible Assets to Total Assets

An intangible asset "is a non-physical asset having a useful life greater than one year" ("What are examples of intangible assets?," n.d.). Many examples of intangibles interface with the marketing function of the firm. These include trademarks, noncompetition agreements, customer lists, and licensing agreements among others (Mohr & Batsakis, 2014). Estimates for some highly developed countries such as the U.S. and the United Kingdom suggest that investments in intangible assets are greater than for tangible assets (Corrado & Hao, 2013). With respect to intangible investment, the largest component is "economic competencies", which includes brand equity (Corrado & Hao, 2013). Brand equity in turn refers to the economic value derived from having a well-established brand relative to an unknown brand. Consider, for example, a tablet computer. Consider a technologically-sophisticated product like a tablet computer. Most consumers are incapable of repairing tablets themselves, and therefore feel more confident in purchasing Apple or Samsung tablets relative to "no-name" brands due to the perceived risk of product failure of unknown brands (cf. Nepomuceno, Laroche, & Richard, 2014). This is because Apple and Samsung have built reputations for creating high quality computer products.

However, an argument could be made that firms have a disincentive to invest in brands during recessionary periods as consumers switch to lower-priced alternatives. Nevertheless, as Haigh (2009) points out over-hyped and over-priced brands are those most likely to suffer during recessions. Haigh describes these brands as having been "swimming naked" for years, and he includes many of the banks, insurance companies and ratings agencies that were hardest hit during the Great Recession. Indeed, Haigh predicted that the brands that would emerge unscathed from the recession would be those that continued to maintain their brand investments while realigning their brands to reflect the economic realities of the recession. According to Haigh, this means a balancing of functional, image and conduct attributes where functional aspects of brands such as price come to the fore, image attributes become relatively less important, and conduct attributes are balanced with respect to cost.

Moreover, Haigh says that firms need to hold fast to their values with respect to their customers and staff. In support of this view, Haigh points to a 1998 study put out by the Strategic Planning Institute from their profit impact of market strategy (PIMS) database indicating that the short-term profit gain from slashing marketing budgets during the 1992-1993 recession led to a long-term loss of market share. Thus, the authors predict that firms emerging successfully through the 2007-2009 recession will be those that continued to invest in intangibles, in particular brand equity, while concurrently trimming the fat, so to speak, relative to other firm assets (e.g., buying new equipment).

Tobin's q

Tobin's q is the ratio of the total market value of a firm to its total asset value. A q less than 1 implies an undervalued stock whereas a q greater than 1 implies an overvalued stock. Prior research (e.g. Anderson, Fomell, & Mazvancheryl, 2004) suggests that Tobin's q is related to customer satisfaction. As Haigh noticed (2009, p. 44), many of the companies that failed during the recession were those evidencing a "cynical disregard for their customers and staff'. When consumers are more satisfied, the benefits of customer loyalty (e.g., customer retention, revenue growth, etc ...) increase (Williams & Naumann, 2011). Williams and Naumann tracked a large Fortune 100 company with a strategic goal of improved customer satisfaction over 19 quarters and found a positive relationship between consumer satisfaction and Tobin's q.

Consider also that greater than a third of the S&P 500 are family-owned firms (Anderson & Reeb, 2003). This is important because family-led firms seem to consider longer investment horizons than do non-family firms (Kashmiri & Mahajan, 2014). As Kashmiri and Mahajan demonstrated, during recessions family-led firms exhibit higher levels of proactive marketing while maintaining strong emphases on corporate social responsibility. Kashmiri and Mahajan also found that during the 2007-2009 recession, family-led firms displayed higher levels of advertising intensity and continued to introduce new products, which they argue led to smaller drops in Tobin's q compared to non-family firms during those years.

Nevertheless, proactive marketing and a continued commitment to corporate social responsibility seems only to stave off declines in Tobin's q in a relative sense rather than total inoculation. Although we predict positive relationships between q and financial performance measures largely related to marketing, others have suggested that the relationship between q and firm investment may be small (Blundell, Bond, Devereux, & Schiantarelli, 1992) or at least challenging to measure (Erickson & Whited, 2000).

Advertising to Sales Ratio

On the other hand, positive growth in the advertising expense to sales ratio (AdvSales) is expected. The authors contend that firms will have surmised the lessons of the 1992-1993 recession, notably from the PIMS data, that customer-based brand equity leads to a retention of market share. Relatedly, Kashmiri and Mahajan (2014) claimed that advertising intensity is related to q. Moreover, as has been repeatedly shown in the marketing literature, advertising intensity is crucial for building consumer-based brand equity. In their study of store brands, which are typically under advertised, Levy and Gendel-Guterman (2012) found that advertising intensity increased consumers' purchase intentions via perceived quality, a proxy for brand equity. The application of advertising intensity to store brand equity is important because national brands are so heavily advertised, and as Levy and Gendel-Guterman illustrate advertising intensity plays a large role in national brand equity. Thus, that firms will increase their advertising to sales ratio is expected during the 2007-2009 recession so as to positively affect brand equity and retain market share.

Cash flow to Sales

Cash flows provided by a firm's operations relative to its sales are indicative of a firm's ability to convert sales into cash. Cash flow is affected both by cash inflows such as sales to customers, equity purchases, and lending sources such as loans from banks ("Cash Flow", n.d.) and by outflows such as payments to employees, suppliers, and creditors. Firms that invest in marketing-related activities that lead to customer satisfaction enjoy the benefits of reliable, future cash flows (Gruca & Rego, 2005).

Concomitantly, as Gruca and Rego demonstrate this increases shareholder value and thus stock price because the results of investing in actions that generate customer satisfaction lead to growth and stability, which markets reward. Moreover, corporate cultures associated with delivering strong customer satisfaction or "customer citizenship behavior" foster environments that lead to strong, positive employee performance and commitment resulting in decreased turnover (Yi, Nataraajan, & Gong, 2011). As such, firms that seek to maintain high levels of customer satisfaction irrespective of recessionary pressures may reap not only rewards associated with increased cash flows, but also benefit through reduced cash outflows due to factors such as employee replacement costs.

Auditor Opinion

An auditor opinion, is "a certification that accompanies financial statements and is provided by the independent accountants who audit a company's books and records and help produce the financial statements" ("Auditor Opinion," n.d.). Audit opinion could be viewed as a proxy for the corporate governance mechanism. For example, Li, Song, and Wong (2004) found that firms' higher stock market returns were correlated with cleaner (unqualified) audit opinions.

Of the four kinds of auditor's reports issued, the highest with respect to quality is the clean or unqualified report. A clean report is issued only when the auditor determines that a firm's financial records are free of misrepresentations and in accord with Generally Accepted Accounting Principles or GAAP (Henderson, n.d.). Ironically, during the height of the economic recession in 2007, the auditing process broke down despite an abundance of clean reports from the most damaged firms. As McKenna poignantly explains, "When each of the notorious "financial crisis" institutions collapsed, were bailed out/nationalized by their governments or were acquired/rescued by "healthier" institutions, they were all carrying in their wallets nonqualified, clean opinions on their financial statements from their auditors." Some of these unqualified opinions, however, had modifications (explanatory paragraphs) attached to them to indicate accounting method changes.

The result of these mistakes led to several bouts of litigation, which typically provides a check on audit quality (Ghosh & Tang, 2015). As a recent example, Ernst & Young LLP agreed to settle a case originating in 2010 with the New York Attorney General's office for $10 million dollars (Freifield, 2015). Although technically admitting no wrong doing, the settlement was in addition to $99 million Ernst & Young agreed to pay in a class action lawsuit settled with Lehman investors last year, which when combined virtually wiped out earnings from fees that the firm took in from Lehman between 2001 and 2008 (Freifield, 2015).

Capital Intensity

The capital expenditure to property, plant, and equipment ratio, also known as tangible fixed assets, reflects the relationship between a firm's physical assets, minus depreciation and amortization (a kind of debt repayment) for the year, to its sales revenue. It is well known that corporations withheld investments in property, plant, and equipment during the global financial crisis. Although corporations have been accused of "hoarding cash" and holding back job creation, it appears more likely a consequence of the global financial crisis than a cause (Gruber & Kamin, 2015). However, these economic discussions are beyond the scope of the present study. Notwithstanding, we expect a decline in the capital intensity ratio in line with conventional wisdom.

STUDY OBJECTIVES AND HYPOTHESES

The objective of this study is investigate and answer the following research questions: Were there any differences in intangible assets and Tobin's q between pre- and post-crisis periods? Did the cash flows improve significantly in the post crisis period? Did the advertising expenditures spike significantly? Did the capital expenditures increase significantly in the post-crisis period? The Logit results of this study suggest that auditor opinion (a governance proxy), intangible assets to total assets ratio (brand equity), cash flow to sales ratio, capital intensity, and Tobin's q (growth proxy) are significantly different between pre- and post-crisis periods.

Based on these questions and the components of the background of this study, the following hypotheses have been developed and tested:

[H.sub.1]: There is no statistically significant difference in Tobin's q ratios between "pre-crisis" and "post-crisis " years.

[H.sub.2]: There is no statistically significant difference in intangible asset intensity ratios between "precrisis" and "post-crisis" years.

[H.sub.3]: There is no statistically significant difference in the cash flow to sales ratios between "precrisis" and "post-crisis" years.

[H.sub.4]: There is no statistically significant difference in the audit opinions between "pre-crisis" and "post-crisis" years.

[H.sub.5]: There is no statistically significant difference in capital intensity ratios between "pre-crisis " and "post-crisis" years.

METHODOLOGY

Sample and Data Collection

Data for S&P 500 companies were obtained from the COMPUSTAT (Research Insight) database. The authors of this study analyzed data for the year 2006 (pre-crisis) and for the year 2010 (post-crisis). The datasets used in this study consisted of six attributes for each firm. These attributes were: Tobin's q ratio, intangible assets to total assets ratio (IntanTA), cash flow to sales ratio (CFsales), auditor opinion (AUOP), capital expenditure to property, plant & equipment ratio (CapInt), and advertising expense to sales ratio (AdvSale). Support for using these specific variables is found in prior research described in the preceding research section. The dependent variable Y is a dichotomous (0, 1) variable representing the two groups, pre-crisis period (Y=0) and post-crisis (Y=1) period.

RESULTS OF THIS STUDY

Descriptive Statistics

Table 1 provides a summary of descriptive statistics for the variables used in this study. This table separately reports the mean, the standard deviation, and T-statistics for variables used in this study, for pre-crisis (2006) and post-crisis (2010) years. Mean values indicate that the postcrisis period has higher intangible assets to total assets ratio, cleaner auditor opinions, and most importantly, higher cash flow to sales ratio. However, the pre-crisis period has higher averages for Tobin's q, higher averages for capital intensity and advertising intensity ratios. T-tests for mean differences indicate that capital intensity ratios, auditor opinion, and Tobin's q are significantly different between the two periods.

Tobin's q = Tobin's q ratio

IntanTA = Intangible assets to Total Assets ratio

CFsales = Cash flow to Sales ratio

AUOP = Auditor opinion

CapInt = Capital Expenditure to Total Assets ratio

AdvSales = Advertising Expense to Sales ratio

Pearson correlation coefficients for the explanatory variables used in this study are provided in Table 2. Correlations among the explanatory variables are not very strong. Capital intensity and advertising intensity are strongly and positively correlated with Tobin's q. The cash flow to sales ratio, intangible assets to total assets, and auditor opinion are negatively correlated with Tobin's q. Advertising intensity is positively correlated with intangible assets to total assets ratio. Capital intensity and cash flow to sales ratio have a negative relationship. Auditor opinion is positively correlated with capital intensity.

Advertising intensity has a strong positive relationship with capital intensity. Even though a few of these relationships among independent variables are significant at conventional levels, none of the correlations are greater than 0.427. Only one correlation (out of 15) is greater than 0.4.

Judge et al. (1985) suggest that multicollinearity problems arise only when the correlations among explanatory variables are higher than 0.8. Hence, the degree of collinearity present among independent variables appears to be too small to invalidate estimation results. We also computed the VIF values and all six of them are less than 1.269 and this also suggests that multicollinearity is not an issue. According to Gatignon (2013), only if a VIF value exceeds 10, multicollinearity is a concern.

Tobin's q = Tobin's q ratio

IntanTA = Intangible assets to Total Assets ratio

CFsales = Cash flow to Sales ratio

AUOP = Auditor opinion

CapInt = Capital Expenditure to Total Assets ratio

AdvSales = Advertising Expense to Sales ratio

MULTIVARIATE TESTS--LOGIT MODEL & RESULTS

Univariate tests may not produce robust results if independent variables are correlated. Using the independent variables in a multivariate context, however, allows us to examine their relative explanatory power. Multivariate tests can lead to better predictions since the information contained in the cross-correlations among independent variables is utilized. A primary objective of many multivariate statistical techniques is to classify entries correctly into mutually exclusive groups. Logistic regression (Logit), Multiple discriminant analysis, and PROBIT are some of the multivariate models. In this study, the following logistic regression (LOGIT) model is proposed:

Pr(Y=1|X) = F ([[beta].sub.0] + [[beta].sub.1] [x.sub.1] + [[beta].sub.2] [x.sub.2] + ..... + [[beta].sub.K] [x.sub.K])

The dependent variable Y is a dichotomous (0, 1) variable representing the two groups, precrisis group (Y=0) and post-crisis group (Y=1) firms. The independent variables [X.sub.1], [X.sub.2], .... [X.sub.K] include

Tobin's q, Intangible assets to Total Assets ratio, Cash flow to Sales ratio, auditor opinion, Capital Expenditure to Total Assets ratio, and Advertising Expense to Sales ratio. These independent variables are described in the prior research section. Specifically, these explanatory variables are:

Tobin's q = Tobin's q ratio

IntanTA = Intangible assets to Total Assets ratio

CFsales = Cash flow to Sales ratio

AUOP = Auditor opinion

CapInt = Capital Expenditure to Total Assets ratio

AdvSales = Advertising Expense to Sales ratio

It is assumed that no exact linear dependencies exist among X's across k, and that the relationship between Y's and X's are non-linear or logistic (i.e., P(Y =1|X) = exp ([SIGMA][[beta].sub.K][X.sub.K]) / [1 + exp ([SIGMA][[BETA].sub.K][X.sub.K])].)

Empirical Results

Data analysis in Table 3 shows that the coefficient estimate for Tobin's q is -0.214 and is statistically significant at the 1 percent level. This suggests that Tobin's q is different between the two periods. Thus the first hypothesis is rejected. Pre-crisis period had larger Tobin's q ratios than the post-crisis period. Tobin's q has been used in prior research as a proxy for growth and firm value (Francis, Hasan, & Wu, 2015). It could be that the firm valuations have not caught up to the pre-crisis levels in 2010.

The coefficient estimate for the intangible asset to total asset ratio is 0.888 and is statistically significant at the 0.05 level. This suggests intangible asset intensity ratios are different between the two periods. Thus, the second hypothesis is rejected. Post-crisis period had larger intangible asset ratios than the pre-crisis period. This is quite intuitive and conforms to our predictions. S&P 500 companies that have successfully emerged from the 2007-2009 recession appear to have continued to invest in intangibles, including brand equity.

The coefficient estimate for the cash flow variable is 1.793 and is statistically significant at the conventional levels. This suggests that the cash flow to sales ratio measure is different between the two periods. Hence, the third hypothesis is rejected. Cash flow to sales ratios are larger, on average, during the post-crisis period. This is an interesting result. This could indicate that firms have shed waste during the crisis period and have become leaner and more efficient in the post-crisis era.

The coefficient estimate for audit opinions is -0.867 and is statistically significant at the 1 percent level. This suggests that audit opinions, on average, are different between the two periods. Hence, the fourth hypothesis is rejected. Audit opinion is a proxy for the corporate governance mechanism. Li, Song, and Wong (2004) found that firms with higher stock market returns were correlated with cleaner (unqualified) audit opinions. S&P 500 firms have been receiving, on average, more unqualified opinions (without modifications) in the post-crisis period. The coefficient estimate for the capital intensity variable is -3.920 and it is statistically significant at the 1 percent level. Thus, the fifth hypothesis is rejected. This suggests that capital expenditures (as a percentage of sales) have not caught up to the pre-crisis levels. This is an important finding.

Tobin's q = Tobin's q ratio

IntanTA = Intangible assets to Total Assets ratio

CFsales = Cash flow to Sales ratio

AUOP = Auditor opinion

CapInt = Capital Expenditure to Total Assets ratio

AdvSales = Advertising Expense to Sales ratio

The average advertising expense during pre-crisis period is $471 million (for 191 firms in 2006) while the average for the post-crisis period is $505 million (for 196 firms in 2010). While average advertising expense has increased from pre-crisis to post-crisis period, the mean advertising intensity (advertising expense to sales ratio) has decreased from 0.0133 to 0.0128. For the majority of firms (309 in 2006 and 304 in 2010), the advertising expense is coded as missing in the Compustat data base. Following Lev, Petrovits, and Radhakrishnan (2010), the advertising expense is set to zero if it is missing in the Compustat data base, while running the Logit model II.

Robustness tests were performed by adding advertising expense to sales ratio as an additional independent variable in Logit model II. The model I results are confirmed and four of the five explanatory variables as in model I are statistically significant in model II as well (see Table 3). H6 (null) suggests that there is no statistically significant difference in the advertising expense to sales ratios between "pre-crisis" and "post-crisis" years. The coefficient estimate for the advertising intensity ratio is 4.351 and is not significant at the conventional levels. Advertising expense to sales ratio is statistically significant only at the 11.5 percent level. This result could be due to the fact that advertising expense to sales ratio is significantly correlated with intangible assets to total assets ratio. Another explanation is even though advertising expense is increasing in the post-crisis period, as a percentage of sales it is still slightly below the pre-crisis levels. As an additional robustness test, we compared pre-crisis 2006 data with post-crisis 2011 data using the same variables in our logit model and the results were qualitatively similar.

IMPLICATIONS

The recession of 2007-2009 was the largest for the U.S. economy since World War 2. In the modern era, the recessions of the early 1990s and 2001 foreshadowed a new logic for how firms should market during recessionary periods. As noted above, findings from the Strategic Planning Institute's Profit Impact of Market Strategy showed that any shortcuts to hitting financial targets from reducing marketing budgets during the recession of the early 1990s was shortsighted and led to overall reduced market share.

The results of our analysis of financial performance ratios demonstrated that firms have heeded this advice. Average spending data demonstrates that firms continued to invest in advertising during the recession. Advertising is a key driver of consumer-based brand equity and firms seem to recognize the importance of maintaining brand equity. Although our prediction regarding advertising as a function of sales (AdvSales) was not significant, factors related to brand equity nevertheless improved out of the recessionary period.

Unlike past recessions, the Federal Reserve kept interest rates low while the federal government intervened to salvage companies even up to the Fortune 100. These actions by federal policymakers may have emboldened corporate decision makers to feel confident to pursue strategic decisions that previously would have been considered risky. Although not a Fortune 100 company itself, Domino's advertising strategy underscores the gutsy strategic moves undertaken by firms that left the recession better than when it started.

CONCLUSION

In general, firm growth and value (Tobin's q) stagnated as a result of the recession; unqualified (clean) opinions (as measured by AOUP) grew, likely due to pressures to "clean up the books"; and, firms held back on capital improvements, which was widely reported throughout the recessionary news cycle. But, firms continued to invest in intangible assets, of which a key component is brand equity, while cash flows also improved. One interpretation of these results is that firms focused on taking care of their customers. While it's true that consumers adapt spending patterns during recessions, only 10% of consumers are substantially motivated by price sensitivity (Hollis, 2008). Instead, many consumers seek value offerings. This requires identifying or maintaining your brand's value proposition and regularly communicating that to your customers (Keller, 2010). Whether advertising budgets in and of themselves are necessary to achieve these means is an open question. The emergent service-dominant logic infiltrating marketing and industry (Vargo & Lusch, 2016) suggests that these effects may be captured elsewhere on the balance sheet.

A noteworthy result of this study is the juxtaposition of relatively stable, but partially increased ad spending without a significant difference in the logistic regression model of AdvSales from 2006 to 2010. Although it is presumed that the impact of increased ad spending is captured by IntanTA, one wonders why spending has not been as robust as allowed for by market conditions. As Hollis (2008) notes, recessions offer dominant firms an opportunity to strike while the "iron is hot", so to speak, because advertising costs decrease concurrently with advertising overcrowding. In other words, it becomes both cheaper to advertise and advertising clutter is reduced (Hollis, 2008). This study offers a starting point to explore how successful firms that emerged unscathed from the 2007-2009 recession achieved their means. Unanswered questions include: If firms built brand equity during the recession in ways beyond advertising, what did they do? And; how were enhanced cash flows benefitted from firms' marketing actions specifically? Future research should also seek ways to integrate factors outside of the balance sheet into models predicting differences between pre and post recessionary time windows.

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William J. Jones

Srinivasan Ragothaman

The University of South Dakota

About the Authors:

William Jones is an Assistant Professor of Marketing at the University of South Dakota. Previously, Dr. Jones was Visiting Assistant Professor of Marketing at Wayne State University, Detroit, MI. Dr. Jones has published multiple articles in refereed journals such as Biological Psychology and Journal for Advancement of Marketing Education in addition to published proceedings and presentations at various international marketing conferences. Dr. Jones holds a Ph.D. in Marketing from the University of Kentucky.

Srinivasan Ragothaman received his Ph.D. from the University of Kansas in 1991 and is a Professor of Accounting at the University of South Dakota. He has published more than thirty articles in various refereed journals over the years including Journal of Emerging Technologies in Accounting, Issues in Accounting Education, International Journal of Business, Accounting & Finance, Expert Systems with Applications, Advances in Accounting, Journal of Accounting & Finance, Information Systems Frontiers, Quarterly Journal of Business & Economics, Journal of Forensic Accounting, and others.
Table 1
Descriptive Statistics and t-test

            Firm                    Std.
Variables   Code    N     Mean    Deviation    T-stat

Tobin's q    1     496   1.838      1.110
             0     478   2.266      1.468     5.125 **
IntanTA      1     489   0.218      0.209
             0     479   0.198      0.199      -1.531
CFsales      1     479   0.167      0.106
             0     469   -0.017     3.817      -1.053
AUOP         1     500   1.560      1.173
             0     491   3.410      1.191     24.625 **
CapInt       1     458   0.214      0.122
             0     447   0.248      0.160     3.631 **
AdvSales     1     500   0.0133     0.029
             0     500   0.0128     0.031       0.265

Firm code: 0 = 2006; 1 = 2010

* Statistically significant at 10% level;

** Statistically significant at 5% level

Table 2
Pearson Correlations

             Tobin's                       Auditor
                q      IntanTA   CfSales   Opinion   CapInt   AdvSales

Tobin's q     1.000
IntanTA       -.104     1.000
CfSales       -.064      .023     1.000
Auditor       -.002      .022     -.030     1.000
  Opinion
CapInt         .427      .024     -.140      .041    1.000
AdvToSales     .149      .195      .015      .031     .176      1.000

Table 3
Logit analysis: Test for Differences in S&P 500 Financial
Charachteristics--Pre-Crisis vs Post Crisis

                            I               II

Tobin's q                 -0.214          -0.233
                       (7.110) ***      (8.279) ***

IntanTA                   0.888            0.747
                        (4.535) **       (3.075) *

CFsales                   1.793            1.825
                        (5.784) **      (5.926) **

AUOP                      -0.867          -0.874
                      (198.148) ***    (198.684) ***

CapInt                    -3.920          -4.058
                      (26.761) * **    (28.958) ***

AdvSales                    --             4.351
                                          (2.479)

CONSTANT                  3.308            3.359
                      (102.992) * **   (104.104) ***

-2 Log Likelihood         876.64          874.24

Nagelkerke R-square       0.408            0.411

(a) The dependent variable is dichotomous: 0 = 2006; 1 = 2010

* Statistically significant at 10% level;

** Statistically significant at 5% level

*** Statistically significant at 1% level
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Author:Jones, William J.; Ragothaman, Srinivasan
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Article Type:Report
Geographic Code:1USA
Date:Mar 22, 2016
Words:5703
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