Determinants of Profitability

Determinants of Profitability: What factors play a role
when assessing a firm’s return on assets?
Neil Nagy
Senior Project
Dr. Newman
Dr. Nelson
The University of Akron
This paper looks at financial statement variables that likely have an impact on firm
profitability. I collect firm financial data from recent years and test for factors
affecting a company’s return on assets (ROA). I find evidence that a firm’s sector,
sales, level of acquisition activity, reinvestment rate, prior year’s net profit margin & 3year return are all significant in determining ROA. Also debt-to-equity level, beta, and
dollar value of capital expenditures were also relatively significant. Furthermore, I
find evidence that a firm’s level of free cash flow, taxes, advertising expense, and
inventory turnover do not seem to have an impact on firm ROA.
Note: This paper has been expanded since my Econometrics class. Major
changes are as follows: 1) updated time period of data, 2) ran single
regressions on all years rather than regressions on each individual year, 3)
ran a one and two-way fixed effects model, 4) added new variables, 5)
extended the literature review, and 6) gathered data on solely S&P 500
Introduction, Hypothesis, and Motivation
There has been a growing number of papers recently that test for measures and
determinants of firm profitability in the market place. This paper focuses on one of these
measures: return on assets (ROA) and the factors that influence it.
This is an important topic of interest, as stocks trade in part on news of
profitability. A firm that displays solid operating fundamentals and generates high
returns on its assets is sure to see that success translate into its stock price. Fundamental
profitability analysis is objective and a true indication of how a company is performing.
Stock prices, on the other hand, are subject to speculative swings that can make it
difficult to identify the actual value of a firm. With the current emphasis on personal
retirement plans and the grim outlook for social security, it is becoming more and more
necessary for individuals to understand what drives firm profitability so that they may
recognize these factors and make good investment decisions.
In this study, I will investigate the effects of a number of factors including
research and development (R&D) expenditures, sales, debt-to-equity ratios, capital
expenditures, and the sector the firm is classified in on ROA. This study tests if financial
statement data can be used to determine a firm’s ROA. This will be accomplished by
using multivariate regressions in order to gauge the importance of these factors.
The following portion of this paper is a literary overview of research applicable to
this discussion. I will then proceed into the other sections of the paper: my model setup,
data sources and descriptions, my model estimation, and my findings. To close out I will
briefly summarize and conclude with an economic interpretation of the results.
Literature Review
This section provides a review of some of the relevant literature that has been
done on firm profitability and performance in the market place.
Hansen and Wernerfelt (1989) integrated two sample models of firm
performance, one which used economic factors and one which used organizational
factors. The economic factor model is based primarily on economic tradition,
emphasizing the importance of external market factors in determining firm success. The
other model, organizational, is built on the behavioral and sociological paradigm and sees
organizational factors and their fit with the environment as the major determinants of
success. Their results confirm the importance and independence of both sets of factors in
explaining performance, but they also find that organizational factors explain roughly
twice as much variance in firm profit rates as economic factors.
Hirschey and Wichern (1984) analyze the consistency, determinants, and uses of
accounting and market-value measures of profitability. They find that differences
between accounting and market measures of profitability suggest the validity of
cautioning remarks concerning the use of accounting data as it has a primarily historical
interpretation unlike market-value measures of profitability which are expectational or
forward looking. In addition, they find that there exists a significant explanatory role for
R&D intensity, TV advertising, leverage, and industry growth as determinants of
Kessides (1990) estimated a specified model of oligopoly. Kessides finds that the
existence of firm effects implies inter-firm differences in internal efficiency, and also that
such firm-specific efficiency characteristics persist across industries (i.e. if a firm is
relatively efficient in market A, it is also likely to be relatively efficient in a randomly
selected market B). The author also finds that the presence of industry effects signifies
cross-industry differences in the height of effective entry barriers, the net advantage of
size, and various elasticities. Overall, the study clarifies the relationship between market
share and profitability.
Brush, Bromiley, and Hendrickx (1999) find that both corporation and industry
influence business unit profitability but corporation has the larger influence. The authors
use a continuous variable model, as an alternative to the more conventional ANOVA or
VCA. This approach estimates the coefficients of corporation and industry effects on
business segment returns while explicitly removing the simultaneous effects that might
cause inconsistent estimates. In the end, they find a sizable corporate effect on business
segment performance, one which appears to be greater than the industry effect. Brush
and Bromiley’s findings contradict Rumelt's (1991) widely cited paper, in which Rumelt
finds that corporations explain almost none of the variability in business unit profitability.
Lenz (1981) provided an interdisciplinary review and evaluation of empirical
studies on the performance of whole enterprises. Lenz summarizes and comments on
identifying factors that influence organizational performance and also reviews
environmental factors affecting firm profitability. Importantly, organizational factors are
influenced entirely by human decision making which varies substantially. Also most
substantial environmental changes (ex: consumer demand, inflation) will not likely have
a uniform impact across companies.
Levy (1997) conducts an investment experiment, in which a real monetary profit
or loss can occur, to test the Capital Asset Pricing Model (CAPM). He finds that risk and
return are strongly associated. While in most cases the Generalized CAPM (alternative
risk return model developed by Levy (1978), Merton (1987) and Markowitz (1990)) beta
provides the best results, the CAPM beta reveals a strong positive association with mean
returns. Levy’s results along with the risk-return equilibrium model provide grounds
enough to incorporate beta as a variable associated with company profitability.
Powell (1996) looks to answer the question: how much does industry matter in
explaining firm performance? His findings support those reported in earlier studies (that
industry membership explains roughly 20% of financial performance), but he addresses
shortcomings in previous methodologies including the incorporation of personal
interviews with CEOs, a sample composed of undiversified firms competing in a wide
variety of industry sectors, and analyses of specific industry factors. Powell concludes
that not all of the 80% of unexplained performance variance is attributable to firmspecific resources since some will also be attributable to shared generic strategies,
strategic group membership, other shared resources, or chance.
Roquebert et al (1996) addresses the issue of the relative degree of variance in
ROA accounted for by industry, corporate, and strategic business unit (SBU) effects
while controlling for the business cycle and the interaction between the business cycle
and industry. Their findings provide evidence that strategic management theory has an
important role to play in SBU profitability.
In Campbell’s (1996) paper he uses an equilibrium multifactor model to interpret
the cross-sectional pattern of postwar U.S. stock and bond returns. He uses revisions in
forecasts of future labor income growth as proxies for the return on human capital.
Campbell finds that aggregate stock market risk is the main factor determining excess
returns; but in the presence of human capital, the coefficient of firm risk aversion relative
to non-human capital intensive firms is much higher.
Thomadikis (1977) finds that current market structure appears to imply an ability
of firms to maintain and extend their current advantages into the future. Furthermore, the
author finds support that industry concentration plays a role in the determination of both
excess profits expected from currently held assets and those expected from the firm’s
investment options.
Past literature in relation to firm profitability is extensive and over the course of
time has addressed several missing components as well as crucial flaws and holes in
previous models and methodology. Still today economists acknowledge that there is
plenty of room for further research in this area. For instance, a more in depth look at the
organizational factors referred to Lenz’s (1981) study is still needed as there is no precise
way to account for a company’s management, core business practices, or strategic
outlook within the world of modeling.
Unfortunately I too ran into the same problem as past researchers when trying to
account for the organizational impacts on firm profitability, which is that data for such
factors is both subjective and not easily attainable. Below I will discuss my contributions
to past studies with the motivation of drawing conclusions and discussing the
implications of my findings toward the end of the paper. First, I test for new variables
indicating a firm’s reinvestment rate, prior year’s net profit margin & 3-year return as
well as financial statement data such as free cash flow, taxes, & inventory turnover. My
motivation for testing these new variables is simply the fact that they’ve not been tested
previously as regressors to ROA. I believe drawing conclusions on the implications and
effects of these variables will provide further insight into the relationship between
financial statement data and profitability. Secondly, I look at a more updated time
horizon (2003-2007) than in past studies. These years mark the first period following the
Sarbanes Oxley Act which was put into effect in 2002 and establishes new or enhanced
standards for all U.S. public company boards, management, and public accounting firms.
With this Act accounting standards reached a new level of financial statement clarity and
it has effectively put an end to frauds similar to those created by Enron, Tyco
International, Adelphia, Peregrine Systems and WorldCom.1 Third, in reference to
Brush, Bromiley, and Hendrickx (1999) I decide to test if a firm’s GICS market sector
has an impact on firm performance. I do this with market sector binaries and a two-way
fixed effects model controlling for the company. I also use a variable indicating the
number of industries in which a firm conducts business. Such information will allow me
to investigate whether or not the broader market in which a firm competes has an effect
on profitability. Fourth, I test to see if acquisition activity within a firm has an impact on
the bottom line as management teams conduct acquisitions in expectation that it will be
accretive to the company’s earnings. Finally, I also include a human capital measure to
see if human capital can be shown to influence profitability; I found forecasts of future
labor income growth difficult to attain so instead I use R&D as a proxy for return on
human capital because it is an easily quantifiable indicator of human capital intensity
across companies as it is measured in dollars invested.
Model Setup
Information on Sarbanes Oxley disclosures can be found at The intention of
this paper is not to draw conclusions about the effects of Sarbanes Oxley on firm financial statement data
but only to note that the period tested represents the first years following its passage.
The importance of measuring firm profitability is the result of a number of
underlying economic principles. First, from a labor economics standpoint, in the model I
will be testing the effects of R&D on ROA. Traditionally, R&D has been recognized as a
form of human capital investment (Campbel (1996), Eberhart, Maxwell, & Siddique (2004)),
so results of this model will provide evidence as to how beneficial R&D is to a firm.
Secondly, over the past 15 years the U.S. economy has been driven by debt, due
to the surge of low-rate loans, rapid growth in credit cards, and easy-to-attain mortgages;
Such bad lending which has led to the current credit crunch. (The Economist March 22,
2008 pg79-89) Now with the current deleveraging taking place within the credit markets
the economy has recently been under strain causing lending to slow which has caused
some panic amongst consumers. This could indicate a secular shift from spending to
saving which, according to economic principle, would lower an individuals’ marginal
propensity to consume. Soaring worries concerning the build up of debt and the future of
social security could spark a transition from consumption to savings. With a broader
demand for financial security, the ability to make wise investment choices is an
underlying reason why judging firm profitability is crucial.
Third, by testing the correlation between risk and return I can test the economic
and financial concept of the efficient frontier. The efficient frontier is a line created from
the risk-reward graph, comprised of optimal portfolios. The line includes the optimal
portfolios plotted along the curve with the highest expected return possible for the given
amount of risk. This concept is illustrated below.
Thus a positive correlation between Beta and lnROA will support the economic theory of
the efficient frontier where as a negative correlation would yield contradictory evidence
to the theory.
Finally, further evidence about what components drive company returns will help
generate competition in the market place, as firms strive for efficiency and competitive
advantage. This is important as competitive markets promote low prices, productivity,
new products, and innovation.
The end models used in this analysis is stated below in Equation (1), (2), and (3):
(1) lnROA = β0 + β1RDInt + β2Sales + β3DE + β4Acq + β5CurrR + β6Capx +
β7ThreeYrRt + β8 BusSeg+ β9QualRank + β10NPM + β11ReinR + β12Year +
β13ConsStap + β14Finan + β15HlthCare + β16Tech + β17Industr + β18Telecom +
β19ConsDiscr + β20Energy + β21Mater + ε
(2) lnROA = β0 + β1RDInt + β2Sales + β3DE + β4Acq + β5CurrR + β6Capx +
β7ThreeYrRt + β8 BusSeg+ β9QualRank + β10NPM + β11ReinR + ε
(3) lnROA = β0 + β1RDInt + β2Sales + β3DE + β4Acq + β5CurrR + β6Capx +
β7ThreeYrRt + β8 BusSeg+ β9QualRank + β10NPM + β11ReinR + ε
In my original model I had tested Beta as an independent variable as well,
however, when setting up Beta against lnROA I found that an endogeneity problem
existed as Beta and lnROA had a strong positive correlation. This is consistent with Levy
(1997) findings. In order to maintain the accuracy of the model I removed Beta as it
certainly skews my model results with an artificially high R-Squared. The high
correlation between Beta and lnROA lends support to the financial and economic
principle of the efficient frontier described earlier in this section.
In addition, I also removed variables indicating a firm’s level of free cash flow,
taxes, advertising expense, and inventory turnover as they were not significant in
impacting firm ROA; thus these variables are not accounted for in the final model.
Despite the fact that none of the past literature tests these variables I decided I would try
running them out of curiosity. I expected these to have only small impacts on the
dependent, however, when tested none were significant at any commonly accepted
confidence levels.
My final models combine both elements of past models and the addition of new
regressors which are described in the Literature Review section (Pgs 2-6) and in further
detail below. In addition I use a firm’s GICS sector code and variables for past returns
and acquisition activity which have not been used previously.
The following is a description of some of the financial variables which will be
used in the model, and the signs that they are expected to hold when regressing ROA.
To begin, the dependent variable, ROA, is a ratio of a firm’s net income divided
by its total assets. ROA gives an idea as to how efficient management is using its assets
to generate earnings. The assets of a company are comprised of debt and equity which
are both used to fund the operations of the company. So ROA gives investors an idea of
how effectively the company is converting the money it has to invest into net income.
RDInt represents a firm’s R&D intensity which is defined in two ways: a firm’s
R&D expense divided by its total sales and a firm’s R&D expense divided by its total
assets. This method is consistent with the method used by Chan et al (2001) for defining
R&D intensity. Basically, R&D intensity gives an idea of how much a given firm is
investing in R&D relative to the firm’s size (which is controlled for with total sales and
total assets). I use R&D intensity as a proxy for human capital because the nature of
research and development is idea driven and it is a variable that can be tracked across
time and companies. R&D represents intangible information, meaning investors have no
way of valuing its benefit and thus must speculate as to the return R&D expense will
create and how long it will take to create that return. In my model, I test if R&D intensity
will be at all beneficial to ROA with in the current year. This is a different test than the
one conducted by Eberhart et al (2004), who used lags and concluded that returns from
R&D tend to be fully recognized 5 years later. I estimate that a firm with high R&D
concentration (proxy for high utilization of human capital) will be more profitable than a
firm with lower R&D. I used 3 years for two reasons: first, a 3-year lag has never been
tested, and second, I did not have adequate data for a 5-year lag.
Sales represent a company’s net sales, which are the amount of sales generated by
a company after the deduction of returns, allowances for damaged or missing goods and
any discounts allowed. The sales number reported on a company's financial statements is
a net sales number. Deductions from the gross sales are represented in the net sales
figure. Net sales give a more accurate picture of the actual sales generated by the
company. A company will book its revenue once the good or service is delivered or
performed for the customer. This is an obvious candidate for a variable that would
determine a firm’s profitability. Since it is in a company’s best interest to sell their goods
in the market, one must expect that higher Sales should yield a positive boost in return
relative to the company’s assets as compared to a lower level of sales.
Capx represents capital expenditures (CAPEX). These are funds used by a
company to acquire or upgrade physical assets such as property, industrial buildings or
equipment. This type of outlay is made by companies to maintain or increase the scope
of their operations. These expenditures can include everything from repairing a roof to
building a brand new factory. In terms of accounting, an expense is considered to be a
capital expenditure when the asset is a newly purchased capital asset or an investment
that improves the useful life of an existing capital asset. If an expense is a capital
expenditure, it needs to be capitalized; this requires the company to spread the cost of the
expenditure over the useful life of the asset. Though CAPEX should drive returns in the
long-run, it goes as an expense to the firm in the short run. In my model I use the current
year’s CAPEX so I expect that it will have a negative impact on profitability.
BusSeg is the number of market segments in which a company conducts business.
A well-diversified firm will often achieve its diversification by conducting business in
different geographical areas or in different industries. I unfortunately was not able to
attain sufficient data for the geographical regions in which S&P companies do business
but I was able to find statistics as to how many industries a firm operates within. Since
diversification spreads environmental and business risk it is expected that the number of
business segments in which a company operates would have a positive influence on
return on assets.
ReinR is a firm’s reinvestment rate. The reinvestment rate is defined as the rate of
return for the firm's investments on average. A firm will reinvest income into new or
current investments in order to capture interest rate opportunity. This is similar to
CAPEX but different in the sense that the company is not purchasing or upgrading
physical property but rather buying securities such as stock and bonds. Security
purchases can be conservative or aggressive when taking on risk and seeking return. If
the economy is in a growth cycle (as it was in the time period analyzed), and the firm is
not taking on excessive amounts of debt while being able to attain good short-term
financing, then it is beneficial for it to invest rather than hold cash. Management that
reinvests its earnings should recognize increased efficiency and a greater source of funds
for the future by utilizing a rate of return therefore boosting ROA.
DE is the firm’s debt-to-equity ratio, calculated as total liabilities divided by
shareholders’ equity. This is a measure of a company’s financial leverage. A high debtto-equity ratio tends to mean that a company has been aggressive in financing its growth
with debt. This can create volatile earnings, and puts a company’s stock more at risk, as
it is not a conservative investment. It is a basic principle in finance that greater risk
equals greater potential return so one might expect a high debt-to-equity ratio to generate
a higher ROA, however, like R&D, the effects debt-to-equity are not tangible and risky so
is not likely to create short term returns. Theoretically the debt-to-equity ratio should be
negatively correlated with ROA in the current year.
NPM stands for net profit margin, which is calculated by taking a firm’s net
income divided by revenues. It measures how much out of every dollar of sales a
company keeps in earnings. So for instance, if a company has a net profit margin of 20%
then this means that the company has a net income of $0.20 for each dollar of sales. Net
profit margin is useful when comparing similar companies because a higher profit margin
indicates a more profitable company that has better control over its costs compared to its
competitors. Thus a company which maintains a higher spread over its costs is likely to
recognize higher returns on its assets.
CurrR stands for current ratio, also known as the “liquidity ratio,” which is
calculated by taking a firm’s current assets and dividing them by a firm’s current
liabilities. The current ratio is mainly used to give an idea of the company's ability to pay
back its short-term liabilities (debt and payables) with its short-term assets (cash,
inventory, receivables). The higher the current ratio, the more capable the company is of
paying its obligations. A ratio under 1 suggests that the company would be unable to pay
off its obligations if they came due at that point. This shows the company is not in good
financial health. The current ratio can give a sense of the efficiency of a company's
operating cycle or its ability to turn its product into cash. Companies that have trouble
getting paid on their receivables or have long inventory turnover can run into liquidity
problems because they are unable to alleviate their obligations. A firm with a higher
current ratio will often be able to attain financing at a better rate thus reducing interest
expense and generating higher ROA.
Acq is a variable accounting for a firm’s acquisition activity in a given year. An
acquisition is a corporate action in which a company buys most, if not all, of the target
company's ownership stakes in order to assume control of the target firm. Acquisitions
are often made as part of a company's growth strategy whereby it is more beneficial to
take over an existing firm's operations and niche compared to expanding on its own. The
acquiring company often offers a premium on the market price of the target company's
shares in order to entice shareholders to sell. For example, News Corp.'s bid to acquire
Dow Jones was equal to a 65% premium over the stock's market price. Firms invest in
acquisitions because they believe that they will drive revenue growth down the road,
however, in the short-term it is only likely to hurt a firm’s ROA, as money is used. This
principle would dictate that an increase in Acq will contribute positively to future ROA
but negatively to current ROA therefore Acq should have a negative coefficient.
ThreeYrRt is the 3-year stock return of a company. In other words, this tells how
much a stock appreciates or depreciates in a three year period. This is related to ROA
because a firm with strong historical performance is more-often-than-not likely to
outperform a firm with poor historical performance. A 3-year return measure is an
indication of management and investor confidence. This is why most investment
textbooks (such as 24 Essential Lessons to Investment Success by William J. O’Neil) and
online professional advisement tools (such as Morningstar) will urge investors to check a
stock or mutual fund’s past performance before committing money. I lag this variable
one year in my model as an indicator of how the stock performed in the recent past. If
the company has a high 3-year stock return then one would expect the current year’s ROA
to be higher. Thus a higher ThreeYrRt should yield a stronger ROA.
QualRank is the Standard and Poors quality ranking. Standard and Poors assigns
a ranking to individual company stocks. Over the long term stocks of companies with a
high S&P Quality Ranking, which measures consistency of earnings and dividends
growth over the last 10 years, outperform both low-quality ranking stocks and the S&P
500 Index. I use data for the S&P Quality Ranking to measure company consistency and
stability across years. S&P’s High Quality Ranked stocks have been cited as
outperforming the broader market in articles such as BusinessWeek’s: “Stocks: Stability
is Sexy Again by Karyn McCormack.” To test such statements I incorporate these
rankings and see if they have an impact on ROA. If articles are correct then I’d expect a
negative coefficient on QualRank as lower numbers are equivalent to higher rankings;
thus a company stock ranked: ‘1’ is the highest.
Finally, the remaining variables used are binaries created to reflect the sector a
company is classified in, using the General Industry Classification System (GICS).
Powell (1996) finds strong evidence that industry accounts for approximately 20% of
firm financial performance. Thus it is considered crucial to have an industry variable
within the model. In this analysis, all sectors are tested against ROA, leaving Utilities as
the control group, as it is expected to be the least volatile from year to year. Given the
time periods of the regressions, every sector except Financials should have a positive
correlation with ROA. Financials is not expected to be positively correlated with ROA, as
firms struggled to generate capital initially after 9/11, and then became overvalued in the
following years as speculators drove up prices.
Data Sources and Description
In this analysis, a variety of variables will be investigated that from a theoretical
perspective might logically affect ROA. Table 1 below provides descriptions for each
independent variable within the final regression models.
Std Dev
The independent variables: Sales, Capx, and Acq are recorded in thousands of
dollars and all other variables are reported as a percentage except for QualRank and
BusSeg which are reported at their actual value and the sector binaries which coordinate
to a given GICS sector identification number. The data used in this paper came solely
from Research Insight (previously called COMPUSTAT). Research Insight is a
subscription-needed statistical database, housing U.S. company financial statement and
market data for every year since 1952.
I decided to collect data on the Standard and Poors (S&P) 500 Index as it
represents the premiere 500 companies in the United States in which data are most easily
attainable for and in which individual and institutional investors are most likely to be
attracted to invest in. Because some entries were missing variables, I was only able to
use data from the years 2003-2007. From here, new variables were created using SAS
that were not provided through Research Insight. These variables are described in the
above ‘Model Setup’ section and were used to represent R&D intensity (R&D
expense/Total Assets, R&D expense/Sales) as well as changes in level which are
accounted for by the binary variables for sector and market value.
All preferred stock entries which arose in the Research Insight query results were
excluded from the downloaded dataset because preferred stock does not represent the
issuing company but is rather just a type of stock issued by a company and includes a
different ticker symbol then the company itself. In addition, all exchange-traded funds
(ETFs) which were downloaded into the dataset have been excluded because ETFs are
merely an index of companies in a given industry and therefore, like the preferred stock,
do not actually have data for the variables. My final models used 2076 out of 2393 read
observations because 317 of the observations were missing for several companies in the
Research Insight data for reasons unknown.
In order to create the variable RDInt, R&D expense was divided by total assets. In
addition, R&D expense was divided by total sales. Therefore, all companies were
excluded that had a value of zero for either total assets or total sales, in order to
accomplish this division. Also excluded is any observation where ROA was less than or
equal to 0 because the final model uses semi-log regressions, in which case ROA must
have a positive value. The final model is a semi-log regression because it tested out
better than the linear and double-log regressions.
Table 2 below provides summary statistics for the mean of each of the final
variables. One table is appropriate, as the variables do not change substantially in the
different observation years. The numbers of observations displayed in the table are those
that remained after exclusions.
Model Estimation
There were no relevant models in the reviewed literature that regressed ROA as
the dependent variable, so measurement principles and evidence of variable relationships
from Roquebert et al(1996) and Hirschey and Wichern (1984), specifically measures of
industry (sector binaries, BusSeg) and leverage (DE), are used in order to formulate a
logical equation that theoretically should work to a reasonable extent in modeling ROA.
This approach took a number of tests in which several regressions were run, employing
an assortment of different variables. Linear, semi-log, and double-log models were tested
to determine which approach most closely resembled the data as well as a one-way
(Equation (2)), and two-way fixed (Equation (3)) effects regression controlling for year
and company.
All variables were then removed that were insignificant in every model type
tested. These omitted variables included variables for tax level, advertising expense,
inventory turnover, and free cash flow. I also attempted using 1-year lagged variables for
RDInt, ReinR, and Capx to see if these would drive returns in future periods. The lag
flipped the sign of the parameter estimate for Capx while RDInt and ReinR maintained
the same sign. This was expected, however, every variable I tried to lag became less
significant. This would imply that past performance measurements are less relevant to
current profitability measures and that higher significance is found amongst the
independent variables when taken in the same time period as the dependant. This makes
sense for two reasons. 1) every observation I lagged was a company specific variable
meaning that it varies in its scope and its effectiveness across companies and 2) my
dataset spans only across a relatively short time horizon so I was unable to lag my
variables to test for recurring long-term effects. Take, for instance, R&D intensity. This
variable will always be more significant when observing its effects on profitability in the
same time period as opposed to a future time period. This is because no matter which
company is investing in R&D, the company will always book it as an expense in the
current time period and thus the investment will deduct from total return. However,
when looking at a future time period this will not always be the case as a given company
‘A’ might recognize return on its investment after a year but a second company ‘B’ won’t
recognize any returns for 7 years. This might not be the case when looking over a longer
time horizon or when considering a universal variable such as inflation in which the same
rate would apply to all companies thus creating a more predictable effect in the future.
In the end, the models I decided to use were the Ordinary Least Squares Semi-log
form, the One-way Fixed Effects, and the Two-way Fixed Effects. I went with the Semilog form as it explained more of the variance than that of the ordinary least squares and
double-log models and was also able to account and draw conclusions for my industry
sector binaries. I chose to stick with the one and two-way fixed effects models for three
reasons: 1) to draw conclusions when controlling for the company and the year, 2) to
account for the sector binaries since they won’t run in the fixed effects models, and 3)
though independent variable significance was similar across models there were some
discrepancies. Equation (1) represents the Semi-log model while Equation (2) and
Equation (3) were used for the One-way and Two-way Fixed Effects models respectively.
The major limitations of the models include the inability to account for variables
that have been found to be significant in prior studies. Hanson (1989) finds that both
organizational and economic factors drive firm financial performance. This model is
very organizationally driven, but does not focus much on economic factors except
accounting for firm sector. I chose to ignore inflation rates in the model due to the fact
that I could not regress across years and therefore could not see the affects of changing
inflation within the period. Also past studies such as Lenz (1981) concluded that
inflation did not have a significant impact on firm profitability, as inflationary pressures
tend to be weighted evenly across sectors.
In addition, another limitation that arises is that certain determinants have been
left out of the models due to lack of data and/or hard to quantify values. For instance, it
would have been preferred to have statistics on the efficiency of firm management which
Roquebert et al (1996) suggested may be important in determining company
performance. But this information is subject to highly subjective interpretation and
represents data that is not quantifiable. Similarly, it would have also been nice to have
recorded historical data on the number of geographical areas in which an S&P 500
company operates as a means of measuring geographic diversification which has been
shown to spread and/or minimize a firm’s beta while maintaining better returns.
The results of the various models are listed in Table 2 and Table 3 below. Table 2
reports the relevant variables and how they perform in each model, in addition to the
parameter estimates for the final variables along with their standard errors, and level of
significance. In general, most variables were found to be significant but with low
parameter estimates, meaning that there is strong evidence that the variables are correctly
effecting ROA but to a minimal extent.
Ordinary Least Squares
One-Way Fixed Effects
Two-Way Fixed Effects
-59.332 13.22703 ***
0.0728 *** 1.896134
0.2627 ***
0.43356 ***
0.415 ***
1.0834 ***
0.00660 ***
0.00001 7.651E-7 ***
7.89E-7 *** 2.967E-7 1.872E-6
0.01157 ***
0.00277 ***
0.00284 *** -0.00235
0.00201 ***
0.00211 *** 0.064131
0.00238 ***
0.0475 ***
0.0562 *** -0.20635
0.0438 ***
0.00013 ***
0.000562 0.000135 *** 0.000228
0.0001 **
-0.0001 0.000013 ***
-0.00011 0.000013 ***
0.00812 0.000688 ***
0.008215 0.000847 *** 0.007161
0.00073 ***
0.00720 *** -0.0212
0.00324 ***
0.00367 *** -0.00021
0.06669 ***
0.11796 ***
0.05157 ***
0.05938 ***
0.06487 ***
0.06464 ***
0.06002 ***
0.05312 ***
0.0484 ***
0.0318 ***
0.0469 **
0.0300 ***
0.0468 ***
0.0294 ***
Note: Asterisks indicate significance:*** 1% Level, **5% Level, *10% Level
In addition to the final variables, and after running multiple tests, variables that
were not significant were removed from the model equations. These included variables
representing a firm’s free cash flow, tax level, inventory turnover, and advertising
RDInt is significant at the 1% confidence interval in determining ROA in years
2003 through 2007 in every model. This would imply that the investment in human
capital was very significant and is beneficial to profitability within the same year. This
result varies from what I expected. I predicted that RDInt would have a positive impact
on ROA but I was expecting that contributions to profitability would be seen down the
road and not within the same year as the investment took place. So in summary
according to the Semi-log model, a one unit increase in RDInt caused ROA to rise
2.10047%. In the One-way Fixed Effects model, a one unit increase in RDInt caused
ROA to rise 2.345286%. In the Two-way Fixed Effects model, one unit increase in RDInt
caused ROA to rise 3.78386%.
Year was only used in the Semi-log model as seen in the equations above. Year
accounts for the change in time and is significant at the 1% confidence interval.
According to the Semi-log model, ROA increases 0.02871% for each year that passes.
This makes sense because on average firm profitability has been shown to appreciate
over time which can be seen by looking at any long-term stock index chart as these are
measures of average market performance. Below is a chart from MSN Money showing
the price appreciation of the S&P 500 over the time period in the model.
Sales was significant in both the Semi-log and One-way Fixed Effects models at
the 1% confidence interval. Given the time period in the Semi-log model, for every
$1000 increase in Sales, ROA rose 0.00001%. In the One-way Fixed Effects model, a
$1000 rise in Sales caused ROA to increase 7.833E-6%. This shows that the impact of
sales on profitability is minuscule but positive.
The parameter estimates for CurrR proved significant only in the Semi-log model.
It was significant at the 1% confidence interval but held a negative sign which was not
anticipated. The effect on ROA though negative was small and since it was insignificant
in the other two models I don’t put much weight on the result of the Semi-log model.
The parameter estimate of CurrR in the Semi-log model implies that liquidity has a
negative impact on firm profitability. For every one unit increase in a firm’s current
ratio, ROA dropped 0.031%.
DE in this time period had a slight negative effect on ROA and is significant at the
1% confidence interval in the Semi-log and One-way Fixed Effects models. This means
that in general less leveraged firms benefited more than those maintaining higher debt
levels. This is not surprising as debt financing leads to elevated risk in the near-term
before yielding potentially higher returns down the road. Also higher debt can lead to
bankruptcy if a firm is not careful about managing interest rate risk and cashflow. Given
the time period in the Semi-log model, for every one unit increase in the debt-to-equity
ratio ROA drops 0.0096%. In the One-way Fixed Effects model, a one unit increase in
DE caused ROA to fall 2.345286%.
NPM was significant at the 1% confidence interval across models and had a
positive outcome to ROA. This was expected and signifies that from 2003-2007
companies with a greater profitability margin or spread over their costs tended to see
higher returns on their assets. In the Semi-log model, a one unit increase in a firm’s NPM
yielded a positive impact on ROA of 0.05462%. In the One-way Fixed Effects model, a
one unit increase in NPM caused ROA to increase 0.04839%. In the Two-way Fixed
Effects model, a one unit increase in NPM caused ROA to rise 0.064131%.
Continuing, Acq was also significant at the 1% confidence interval in every model
and the theoretical sign expectations held as Acq had a negative impact on ROA.
Acquisitions are large investments and require large outlays of cash that hurt profitability
in the near-term. In the Semi-log model, a $1000 increase in a firm’s acquisition activity
yielded a negative impact on ROA of 0.2197%. In the One-way Fixed Effects model, a
$1000 increase in Acq caused ROA to decrease 0.27755%. In the Two-way Fixed Effects
model, a $1000 increase in Acq caused ROA to fall 0.20635%.
ReinR was significant in determining ROA at the 1% confidence interval in the
Semi-log and One-way Fixed Effects models and significant at the 5% confidence
interval in the Two-way Fixed Effects model. These results match my initial
expectations as ReinR had a positive influence on ROA. This indicates that companies
that reinvest more of their earnings into securities tend to recognize higher returns in the
near-term. In the Semi-log model, a one unit increase in a firm’s ReinR produced a
positive impact on ROA of 0.00058%. In the One-way Fixed Effects model, a one unit
increase in ReinR caused ROA to rise 0.000562%. In the Two-way Fixed Effects model,
a one unit increase in ReinR caused ROA to rise 0.000228%.
Capx had a slightly negative influence on ROA in every model and was
significant at the 1% confidence level in the Semi-log and One-way Fixed Effects
models. This was anticipated and means that, like acquisition activity, the initial
investment in physical capital at a firm tends to detract from profitability in the first year.
In the Semi-log model, a $1000 increase in a firm’s Capx created a negative impact on
ROA of 0.0001%. In the One-way Fixed Effects model, a $1000 increase in Capx caused
ROA to decrease 0.00011%.
ThreeYrRt was significant at the 1% confidence interval in each of the final
models and always led to higher ROA. This was as predicted and lends support to the
idea that historical performance is useful when generating future return forecasts. In the
Semi-log model, a one unit increase in a firm’s ThreeYrRt produced a positive impact on
ROA of 0.00812%. In the One-way Fixed Effects model, a one unit increase in
ThreeYrRt caused ROA to rise 0.008215%. In the Two-way Fixed Effects model, a one
unit increase in ThreeYrRt caused ROA to increase 0.007161%.
BusSeg was significant at the 1% confidence interval in the One-way Fixed
Effects model and the Two-way Fixed Effects model was significant at the 10%
confidence level. The number of business segments actually had a slight negative effect
on ROA in all three models. This means that diversification across industry segments
was not helpful to attaining stronger profitability. This comes as a surprise as
diversification is a way of diversifying risk and creating safer returns. In the One-way
Fixed Effects model, operating in one additional business segment decreases ROA by
0.0215%. In the Two-way Fixed Effects model, an additional business segment led to a
0.0212% decrease in ROA.
QualRank was beneficial to ROA in all three models and significant at the 1%
confidence level in both the Semi-log and One-way Fixed Effects models. Though the
parameter estimates are negative the S&P quality ranks were actually helpful to
profitability because a rank of 1 is better than a rank of 10. In the Semi-log model, a one
rank increase in the firm’s QualRank detracts from ROA by 0.0419%. In the One-way
Fixed Effects model, a one rank increase in the firm’s QualRank detracts from ROA by
The remaining final variables are binary and represent a firm’s GICS sector
classification. The parameter estimates for every sector variable had a positive impact on
ROA and demonstrated significance at the 1% confidence level in the time period except
for Finan. Finan was not significant in determining profitability in the model. ConsStap
clearly displayed the most relevance, improving ROA by 0.84078%. Companies in the
Telecom and Financial sector benefited the least as Telecom only improved ROA by
0.35872% and Finan benefited ROA by only 0.18875%. In other words, firms in sectors
such as Consumer Staples and Consumer Discretionary tended to see higher returns and
companies in the Financial and Telecom sector benefited the least.
For the fixed effects models independent variables were generated for each year
with 2003 acting as the base year. 2004, 2005, and 2006 were all significant at the 1%
confidence level and contributed to ROA growth by a range of 0.11049% to 0.19042%.
2007 was not significant in determining ROA.
Table 3 provides a summary of the statistical strength of each model. Every
model had a significant F value, meaning that all were valid. The Adj. R2 of the Semi-log
model was 0.5242. For the One-way Fixed Effects it was 0.4642 and for the Two-way
Fixed Effects it was 0.8566. The RMSE was similar in the Semi-log and One-way Fixed
Effects models while varying significantly in the Two-way Fixed Effects model.
OLS Semi-log
One-way Fixed
Two-way Fixed
Adj R2
F Value
This paper sought to answer the question: What factors determine firm
profitability? Or more specifically, what factors affect a firm’s ROA? To conclude, it
was found that a number of financial statement variables and ratios can be used to gauge
ROA. For instance this paper lends support that financial statement data such as: sales,
current ratio, debt-to-equity ratio, and net profit margin are significant when determining
profitability. Also significant were proxies for human capital investment, historical
performance, and industry diversification.
Also of importance, this paper finds evidence that tax levels, advertising costs,
inventory turnover, and free cash flow did not appear to have statistically significant
impacts on a firm’s ROA in any of the models tested.
It was found that the broader market sector in which a firm competes is highly
significant in determining profitability. Of the industry sectors tested, it was found that
companies in Consumer Staples and Consumer Discretionary were the most helpful to
returns. Furthermore, the Financial sector, though beneficial to ROA, was the least
helpful. This is likely due to the raising of interest rates by the Federal Reserve in the
early 2000s, the impact 9/11 had on lending institutions, and possibly the accordance
with the accounting rules dictated by the Sarbanes Oxley Act.
The results leave several implications for investors and businesses. Viewing
evidence about what drives company profitability will help businesses understand which
economic and financial factors are critical to track and analyze in order to attain
operational success. Importantly, if businesses know which factors are likely to boost
performance, then this should create increased competition in the marketplace.
Economically, this would aid in keeping prices low, providing new substitute and
complementary goods and creating jobs.
For individuals, the fear of not having money after retirement provides incentive
for people to setup personal investment plans. Http:// says this
about social security “It is true that Social Security benefits are experiencing financing
problems, and while this will not affect today's retirees and near-retirees, the problems
are very serious. People live longer, the first baby boomers are five years from
retirement, and the birth rate is low. This results in a large drop in the worker-tobeneficiary ratio… At this rate of decline there will not be enough workers to pay
scheduled benefits at current tax rates. Without a large infusion of additional revenue,
Social Security benefits are not sustainable over the long term. There will be a massive
and growing shortfall over the next 75 years.”
Despite the bleak and uncertain outlook on Social Security people can use this
knowledge and personally start taking care of their retirement through equity investing.
Recognizing the drivers of ROA will help new investors analyze financial statements and
make informed equity investment decisions. It is important that individuals recognize the
urgency of investing for their future and that stocks historically have appreciated over
time leading to substantial long-term gains in the market.
Implications for public policy include further education about personal finance in
regards to investment analysis. With a debt driven economy, the need to save and
guarantee future financial security is becoming evermore apparent. A society that
understands financial measurements and their implications on company operating
performance is more capable of making good decisions that can help drive economic
prosperity and growth.
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