Volume 12 Issue 3
Fall 2012
Sector Variations in the Total Asset Turnover/Operating Profit Margin Relationship
Cecilia Wagner Ricci∗
ABSTRACT
The negative correlation between the total asset turnover and the operating profit margin is well
established in the literature, yet one area that requires additional scrutiny is whether the
correlation varies by economic sector. This is the focus of this paper, which uses a subset of the
non-financial firms in the S&P 500 and a time frame of 2001 to 2010 to examine this issue. The
study finds that the extent of the association changes depending on the sector. Given these results,
this paper should be of interest to both marketing and financial analysts, academics teaching
courses in financial statement analysis, and researchers examining the analysis of corporations.
JEL Classification:
Key Words:
INTRODUCTION
Financial statement analysis has been used in a variety of areas in finance, including evaluating
performance (Lintner), valuing stock (Graham and Dodd), predicting bankruptcy (Beaver, Altman),
identifying earnings management (Jansen et al., 2011), and categorizing companies by risk levels (Falk
and Heintz). Two commonly used ratios in financial statement analysis are the total asset turnover and
the operating profit margin. The total asset turnover measures how efficiently a company generates sales
from its asset base, and thus is one of the activity ratios; the operating profit margin measures how
effectively a company manages its operating costs, and is in the profitability category.
This study is the first to examine the total asset turnover/operating profit margin relationship as a
topic unto itself, rather than a byproduct of other research. This subject is important because the
relationship between the ratios has been broadly established, but little is known concerning whether the
correlations vary among industries or sectors. This paper uses the total asset turnovers and operating
profit margins for a subset of the non-financial companies in the S&P 500 from 2001 to 2010 to examine
the relationship between the two ratios. The means, medians, standard deviations, and skewness are
examined for the entire sample and the sample by GIC Economic Sector. Then, partial correlation
coefficients are calculated and evaluated by GIC sector. The study should be useful to financial analysts,
equity analysts and academics teaching financial statement analysis.
The remainder of the paper is arranged as follows: the next section reviews the literature on the
relationship between the total asset turnover and the operating profit margin, followed by information on
the sample and methodology, the results, and finally, conclusions and suggestions for future research.
LITERATURE REVIEW
Finance textbooks espouse the use of financial statement analysis, and include without exception
the total asset turnover and operating profit margin (Brigham and Houston, Gitman and Zutter, Keown et
al., Ross et al., Brealey and Myers, Moyer et al., Van Horne and Wachowicz, Bodie and Merton).
The total asset turnover measures the sales generated per dollar of assets. It is calculated in the
following manner:
Total asset turnover = Total Assets ÷ Sales
(1)
The operating profit margin measures the relationship between earnings before non-operating
expenses (EBIT) and sales. It is calculated as follows:
∗
Ricci is a Professor of Finance at Montclair State University. Her email is [email protected].
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Volume 12 Issue 3
Fall 2012
Operating Profit Margin = Operating Income ÷ Sales
(2)
The majority of the literature on the total asset turnover and operating profit margin consists of
studies for which the relationship is not the focus, but rather a secondary result. For example, using data
from a group of Compustat companies over a period from 1977 to 1986, Selling and Stickney examined
total asset turnovers and operating profit margins as they related to returns on assets. Their sample was
classified into twenty-two industries, and they found negative correlations between the total asset turnover
and operating profit margin ratios in fifteen of them. Reed and Reed used fifty-two companies meeting
certain diversification criteria to study the performances of CEOs vis-à-vis their strategies and
experiences. The total asset turnover and operating profit margin were two of the metrics used to measure
performance, and they found that the two ratios were negatively correlated. Fairfield and Yohn studied
the use of operating profit margins and asset turnovers to forecast future profitability. As part of their
study, they found that the two variables are negatively correlated, and that the correlation is statistically
significant. Finally, Skolnik used the non-financial firms in the S&P 500 and a time frame of 1989
through 1999 to study the relationship among operating returns, operating profit margins and total asset
turnovers. He found that the total asset turnover ratio was decreasing over the study period while the
operating profit margin ratio was increasing. Consequently, he found statistically significant negative
correlations between total asset turnovers and operating profit margins.
It is clear from the previous literature that the total asset turnover and the operating profit margin
are negatively correlated, and that this relationship is statistically significant. However, there is an
additional area that requires investigation. This is discussed in the next section.
SAMPLE AND METHODOLOGY
With the exception of Selling and Stickney, who used data from 1977 to 1986, the previous
literature in this area does not look at the correlations between total asset turnovers and operating profit
margins by industry or sector. This study examines the ratios’ relationship by GIC Economic Sector.
The null hypothesis is:
H0: τ(GIC 10)xy.z = τ(GIC 15)xy.z … τ(GIC 55)xy.z
(3)
where
τ = the partial correlation coefficient
x = the total asset turnover
y = the operating profit margin
z = sales
GIC = economic sector
This sample used here is the S&P 500 for 2001 through 2010. Firms that did not have
information available for all ten years were removed from the sample. Firms involved in the financial
sector (GIC 40) were also eliminated. The final sample contained 328 companies, as may be seen in
Appendix A.
Each firm’s total asset turnovers and net profit margins were calculated on an annual basis for the
period under study, as well as the means, medians, standard deviations, and skewness for each ratio. In
testing for correlation between the total asset turnovers and operating profit margins, sales became a
concern because it is used in both ratio equations. Thus, the use of a partial correlation coefficient was
required.
RESULTS
Tables 1, 2 and 3 contain descriptive information concerning the sample. Table 1 shows the
sample distribution by GIC Economic Sector. As noted previously, financial firms were removed from
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Volume 12 Issue 3
Fall 2012
the sample, and thus GIC 40 is not used. As may be seen in the table, four of the nine sectors each
contributes less than ten percent to the sample: Telecommunication Services (1.83%), Consumer Staples
(8.23%), Materials (8.54%), and Utilities (9.76%). The five remaining sectors each make up at least 12%
of the sample, with the highest being the Industrials (GIC 20) sector with 17.07%.
Table 2 provides means, medians, and standard deviations for the total asset turnovers and
operating profit margins for the entire sample. Both the mean and median for the total asset turnover are
less than one. The operating profit margin mean and median differ greatly, and both ratios exhibit high
standard deviations. The skewness of the distribution was calculated for each ratio and may also be found
in Table 2. The total asset turnover exhibits strongly positive skewness, indicating that the sample tends
toward higher ratios; the operating profit margin indicates strongly negative skewness, indicating a
tendency toward lower ratios.
Table 3 provides the means, medians, standard deviations, and skewness of the total asset
turnovers and operating profit margins by GIC Economic Sector. The total asset turnover exhibits strong
positive skewness for all GIC sectors except Materials (15) and Telecom Services (50), which also exhibit
positive skewness but to a lesser extent. Regarding the means and medians, only two sectors, Consumer
Discretionary (GIC 25) and Consumer Staples (GIC 30), have both greater than one. This may be due to
the fact that both sectors have retail components (GIC 25 includes Internet & Catalog Retail, Multiline
Retail, and Specialty Retail; GIC 30 includes Drug Retail, Food Retail, Hypermarkets and Super Centers),
which tend to have relatively high turnovers.
As for the operating profit margins, only one sector, Consumer Discretionary (GIC 25), has a
negative mean. The others vary from 16% in Consumer Staples (GIC 30) to 42% in Telecom Services
(GIC 50). The medians are also wide ranging, from 15% in Industrials (GIC 20) to 43% in Telecom
Services. Both the total asset turnovers and the operating profit margins have large standard deviations.
The operating profit margins display minor skewness, with the exception of Consumer Discretionary (25),
which shows strongly negative skewness. This may be the result of the sensitivity of the sector to
business cycles. For example, Personal Consumption Expenditures increased in only three of the years
included in the study (2003, 2004, and 2010), were flat in one year (2002), and declined in the remaining
years (2005 to 2009) (NIPA Table). Moreover, the decreases were large, while the increases were, for the
most part, relatively small.
There is some evidence of a negative interconnection between total asset turnovers and operating
margins within sectors. The three sectors with the highest median total asset turnovers (Consumer
Staples, Consumer Discretionary, and Industrials) have the lowest median operating profit margins. And
the sector with the lowest median turnover, Telecom Services, has the highest median operating profit
margin. These linkages are clearer when presented graphically, as may be seen in Chart 1.
To test the significance of this relationship, the partial correlation coefficients were calculated.
Given the previous results, a non-parametric partial correlation coefficient, Kendall’s τ-b, was utilized. It
is employed to determine the relationship between two variables independent of a third variable. It is
used here to determine the relationship between the total asset turnover and the operating profit margin
independently of sales. Since no directions were specified in the hypothesis, the tests were two-tailed.
The test results may be seen in Table 4, which contains the partial correlation coefficients by
GIC. All of the economic sectors show negative correlations between the total asset turnover and
operating profit margins, but only five of the nine economic sectors show statistical significance at the
0.0001 level. Two of sectors lacking significant results, IT (GIC 45) and Telecom Services (GIC 50),
have service components which may dilute the total asset turnover/operating profit margin relationship.
This may also be the case with the third sector, Consumer Discretionary (GIC 25), but this sector also
contains thirty-two sub-industries, each of which may have a different relationship between the ratios, and
thus result in a lack of significance. The fourth sector, Materials (GIC 15), has only twelve subindustries, but they are extremely diverse, (including chemicals, construction materials, packaging, and
mining), which may explain the lack of significance in this sector.
The negative correlations in all GIC sectors are in contrast to Seller and Stickney (1989), who
found positive correlations in seven industries. It is important to note, however, that the results of this
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Volume 12 Issue 3
Fall 2012
study cannot be compared directly to the results of Seller and Stickney (1989) for several reasons. First,
the Global Industrial Classification standard (GIC) used here was not created until 1999, so the groupings
of firms are dissimilar. Second, at the time their research was conducted, there was no information
technology industry (GIC 45), which currently constitutes 15.0% of the S&P 500 and 14.6% of the
current sample. Third, none of Selling and Stickney’s (1989) positive correlations would be statistically
significant in this study because they do not meet the 0.0001 level used in this study
To summarize, the results of the tests of the sample by GIC Economic Sector show that the partial
correlations do not vary by sign (all are negative), but that they do vary by GIC Economic Sector, both in
size and statistical significance. Thus, H0 is rejected.
CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH
This paper adds to the literature on the relationship between the total asset turnover and operating
profit margin ratios by examining whether the correlation varies by GIC Economic Sector. As such, it is
the first study to examine the two ratios by themselves, not as they relate to other variables. The major
limitation to this study is that it is not comparable to the previous research in this area due to firm
groupings. There are many avenues for future research in the relationships among ratios. For example,
this study examines the correlation between ratios; additional research could look at the issue of
causation, that is, is the independent variable the total asset turnover, the operating profit margin, or third
variable, such as sales? The impact, if any, of factors external to the firm could also be investigated. For
example, there may be some economic, accounting or regulatory factors that have an influence on the
relationships between ratios. Future research could also study relationships or lack thereof, among other
commonly used ratios.
Journal of Financial and Economic Practice
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Volume 12 Issue 3
Fall 2012
APPENDIX A. SAMPLE COMPANIES
3M Co
Abbott Laboratories
Adobe Systems Inc
Advanced Micro Devices
Aetna Inc
Agilent Technologies
Air Products & Chemicals
AK Steel Holding Corp
Akamai Technologies
Alcoa Inc
Allegheny Technologies
Allergan
Altera Corp
Altria Group Inc
Amazon.Com Inc
Ameren Corp
American Electric Power
American Tower Corp
Amerisourcebergen
Amgen Inc
Amphenol Corp
Anadarko Petroleum
Analog Devices
Apache Corp
Apollo Group Inc
Apple Inc
Applied Materials Inc
Archer-Daniels-Midland
AT&T Inc
Automatic Data Processing
AutoNation Inc
AutoZone Inc
Avery Dennison Corp
Avon Products
Baker Hughes Inc
Ball Corp
Bard (C.R.) Inc
Baxter International
Becton Dickinson & Co
Bemis Co Inc
Biogen IDEC Inc
Boeing Co
Boston Scientific Corp
Bristol-Myers Squibb Co
Broadcom Corp
C H Robinson Worldwide
Cablevision Sys Corp
Cabot Oil & Gas Corp
Cameron International
Campbell Soup Co
Cardinal Health Inc
Carnival Corp
Caterpillar Inc
CBS Corp
Celgene Corp
Centerpoint Energy Inc
Centurylink Inc
Cephalon Inc
Cerner Corp
Chesapeake Energy Corp
Chevron Corp
Cigna Corp
Cisco Systems Inc
Citrix Systems Inc
Cliffs Natural Resources
Clorox Co/De
CMS Energy Corp
Coach Inc
Coca-Cola Co
Coca-Cola Enterprises Inc
Cognizant Tech Solutions
Colgate-Palmolive Co
ConocoPhillips
Consol Energy Inc
Consolidated Edison Inc
Constellation Energy
Corning Inc
Costco Wholesale Corp
Coventry Health Care
CSX Corp
Cummins Inc
CVS Caremark Corp
Journal of Financial and Economic Practice
D R Horton Inc
Danaher Corp
Davita Inc
Dean Foods Co
Deere & Co
Denbury Resources Inc
Dentsply International
Devon Energy Corp
Devry Inc
Diamond Offshore Drilling
DirecTV
Disney (Walt) Co
Dominion Resources Inc
Donnelley (R R) & Sons
Dover Corp
Dow Chemical
DTE Energy Co
Du Pont De Nemours
Duke Energy Corp
Dun & Bradstreet Corp
Eastman Chemical Co
Eaton Corp
EBay Inc
Ecolab Inc
Edison International
Edwards Lifesciences
El Paso Corp
EMC Corp
Emerson Electric Co
Entergy Corp
EOG Resources Inc
EQT Corp
Equifax Inc
Exelon Corp
Expedia Inc
Expeditors Intl Wash
Express Scripts
Exxon Mobil Corp
F5 Networks Inc
Family Dollar Stores
Fastenal Co
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Volume 12 Issue 3
Firstenergy Corp
Fiserv Inc
FLIR Systems Inc
Flowserve Corp
Fluor Corp
FMC Corp
FMC Technologies Inc
Ford Motor Co
Fortune Brands Inc
Freeport-McMoran
Frontier Communications
Gannett Co
General Dynamics Corp
General Electric Co
Genuine Parts Co
Gilead Sciences Inc
Goodrich Corp
Goodyear Tire & Rubber
Grainger (W W) Inc
Halliburton Co
Harley-Davidson Inc
Harman International
Harris Corp
Hasbro Inc
Helmerich & Payne
Hershey Co
Hess Corp
Hewlett-Packard Co
Honeywell International
Hormel Foods Corp
Humana Inc
Illinois Tool Works
Ingersoll-Rand Plc
Integrys Energy Group Inc
Intel Corp
Interpublic Group
Intl Business Machines
Intl Flavors & Fragrances
Intl Game Technology
Intl Paper Co
Intuit Inc
Intuitive Surgical Inc
Iron Mountain Inc
ITT Corp
Jabil Circuit Inc
Jacobs Engineering
JDS Uniphase Corp
Johnson & Johnson
Johnson Controls Inc
Joy Global Inc
Juniper Networks Inc
Kellogg Co
Kimberly-Clark Corp
KLA-Tencor Corp
Kraft Foods Inc
L-3 Communications
Lab Corp Of America
Lauder (Estee) Cos Inc
Leggett & Platt Inc
Lennar Corp
Lexmark Intl Inc
Life Technologies Corp
Lilly (Eli) & Co
Linear Technology Corp
Lockheed Martin Corp
LSI Corp
Marathon Oil Corp
Marriott Intl Inc
Masco Corp
Massey Energy Co
Mattel Inc
McCormick & Co Inc
McDonald's Corp
McGraw-Hill Companies
Meadwestvaco Corp
Medco Health Solutions
MEMC Electronic Materials
Merck & Co
Micron Technology Inc
Microsoft Corp
Molex Inc
Molson Coors Brewing
Monsanto Co
Monster Worldwide Inc
Journal of Financial and Economic Practice
Fall 2012
Motorola Solutions Inc
Murphy Oil Corp
Mylan Inc
Nabors Industries Ltd
National Oilwell Varco Inc
Netflix Inc
Newell Rubbermaid Inc
Newfield Exploration Co
Newmont Mining Corp
News Corp
Nextera Energy Inc
Nicor Inc
Nisource Inc
Noble Corp
Noble Energy Inc
Norfolk Southern Corp
Northeast Utilities
Northrop Grumman Corp
Novellus Systems Inc
NRG Energy Inc
Nucor Corp
Occidental Petroleum Corp
Omnicom Group
Oneok Inc
O'Reilly Automotive Inc
Owens-Illinois Inc
Paccar Inc
Pall Corp
Parker-Hannifin Corp
Peabody Energy Corp
Pepco Holdings Inc
Pepsico Inc
Perkinelmer Inc
Pfizer Inc
PG&E Corp
Pinnacle West Capital Corp
Pioneer Natural Resources
Pitney Bowes Inc
PPG Industries Inc
PPL Corp
Praxair Inc
Priceline.com Inc
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Volume 12 Issue 3
Procter & Gamble Co
Progress Energy Inc
Public Service Enterprise
QEP Resources Inc
Qualcomm Inc
Quanta Services Inc
Quest Diagnostics Inc
Radioshack Corp
Range Resources Corp
Raytheon Co
Republic Services Inc
Reynolds American Inc
Robert Half Intl Inc
Rockwell Automation
Rockwell Collins Inc
Roper Industries Inc/De
Rowan Cos Inc
Ryder System Inc
Safeway Inc
Sandisk Corp
Sara Lee Corp
Scana Corp
Schlumberger Ltd
Sealed Air Corp
Sempra Energy
Sherwin-Williams Co
Sigma-Aldrich Corp
Snap-On Inc
Southern Co
Southwest Airlines
Southwestern Energy Co
Sprint Nextel Corp
Stericycle Inc
St Jude Medical Inc
Stanley Black & Decker
Starbucks Corp
Starwood Hotels & Resorts
Stryker Corp
Sunoco Inc
Sysco Corp
Teco Energy Inc
Telltabs Inc
Fall 2012
Tenet Healthcare Corp
Teradyne Inc
Tesoro Corp
Texas Instruments Inc
Textron Inc
Thermo Fisher Scientific
Time Warner Inc
Titanium Metals Corp
Total System Services Inc
Tyco International Ltd
Tyson Foods Inc -Cl A
Union Pacific Corp
United Parcel Service Inc
United States Steel Corp
United Technologies Corp
Unitedhealth Group Inc
Valero Energy Corp
Varian Medical Systems
Verisign Inc
Verizon Communications
Vulcan Materials Co
Walgreen Co
Washington Post
Waste Management Inc
Waters Corp
Watson Pharmaceuticals
Wellpoint Inc
Western Digital Corp
Whirlpool Corp
Williams Cos Inc
Wisconsin Energy Corp
Yum Brands Inc
Zimmer Holdings Inc
Wynn Resorts Ltd
Xcel Energy Inc
Xerox Corp
Yahoo Inc
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Table 1 - Sample by GIC Economic Sector.
Frequency
Percent
10
Energy
40
12.20%
15
Materials
28
8.54%
20
Industrials
56
17.07%
25
Consumer Discretionary
46
14.02%
30
Consumer Staples
27
8.23%
35
Health Care
45
13.72%
45
Information Technology
48
14.63%
50
Telecommunication Services
6
1.83%
55
Utilities
32
9.76%
Table 2 - Descriptives, Entire Sample.
Mean
Median
Std. Dev.
Skewness
Total Asset Turnover
0.93
0.76
0.70
2.39
Operating Profit Margin
0.06
0.19
7.42
-56.05
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Table 3 - Descriptives by GIC Economic Sector.
Total Asset Turnover
Operating Profit Margin
Mean
Median
Std. Dev.
Skew.
Mean
Median
Std. Dev.
Skew.
10 Energy
0.79
0.52
0.69
2.04
0.33
0.26
0.24
-0.07
15 Materials
0.89
0.83
0.31
0.74
0.18
0.16
0.10
0.79
20 Industrials
1.06
0.93
0.70
2.47
0.17
0.15
0.08
0.65
25 Consumer
Discretionary
1.10
1.03
0.63
1.25
-0.92
0.16
19.80
-21.00
30 Consumer Staples
1.48
1.23
0.84
1.10
0.16
0.16
0.08
0.31
35 Health Care
1.03
0.75
0.96
2.58
0.22
0.23
0.13
-0.45
45 IT
0.74
0.64
0.41
2.77
0.21
0.21
0.15
-1.27
50 Telecom Services
0.37
0.33
0.15
0.39
0.42
0.43
0.12
0.00
55 Utilities
0.45
0.38
0.21
2.42
0.23
0.24
0.09
-0.24
Table 4 - Kendall’s τ-b Partial Correlation Coefficients
by GIC.
Economic Sector
Coefficient
Significance
10 Energy
-0.48
(0.0000)**
15 Materials
-0.27
(0.0233)
20 Industrials
-0.45
(0.0000)**
25 Consumer Discretionary
-0.30
(0.0018)
30 Consumer Staples
-0.56
(0.0000)**
35 Health Care
-0.37
(0.0000)**
45 IT
-0.16
(0.0495)
50 Telecom Services
-0.40
(0.1314)
55 Utilities
-0.58
(0.0000)**
** statistically significant at α = 0.0001
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Chart 1 - Medians by GIC.
1.40
1.20
1.00
0.80
TATO
0.60
OPM
0.40
0.20
0.00
10
15
20
25
30
35
45
50
55
GIC
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Volume 12 Issue 3
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