Product Market Concentration, Financing Constraints,
and Firms’ Business Cycle Sensitivity
Peter Pontuch∗ This version: January 2011
Abstract
We study the effects of product market concentration and financing con-straints, separately and jointly, on the business cycle sensitivity of firms’ opera-tions. We confirm that the sensitivity is higher for firms in industries with both a low concentration and a relatively even distribution of market power (truly competitive industries) and lower for firms in concentrated industries. Next, we find that financially constrained firms are in general more sensitive to the cy-cle, but they also have a less favorable exposure to leading downturn indicators (CP spread or bond spread). This suggests that constrained firms are the first to be affected by turnarounds in the business cycle, consistent with a financial accelerator effect. However we show that this effect of financing constraints is mostly present in truly competitive industries, and less so in concentrated in-dustries. Lastly we find that financing constraints have a significant impact on competition mechanisms, proxied by the cross-sectional mean-reversion of com-pany profitability.
Keywords: product market competition, financing constraints, business cycle, firm profitability, profitability mean-reversion.
∗Université Paris Dauphine - DRM Finance - Place du Maréchal de Lattre de Tassigny - 75775
Paris cedex 16 - pontuch@gmail.com. I am deeply grateful to Gilles Chemla (my advisor), Gaëlle Le Fol, Jayant Kale, and Gordon Phillips for valuable suggestions. I also thank seminar participants at Université Paris Dauphine and the Universty of Maryland, College Park, for their comments and ideas. All errors are my own. I gratefully acknowledge financial support from the FBF Chaire de Finance d’Entreprise.
1
Introduction
It is widely acknowledged that firms’ operating performance strongly covaries with the business cycle. For all but some particular industries corporate earnings are highly procyclical and more volatile than the overall economy. This characteristic is due to their junior status relative to other claims on the company: Longstaff and Piazzesi (2004) illustrate their analysis by showing that earnings are about 7 times more pro-cyclical than aggregate consumption, with a correlation between the two variables close to 69%. Another example is Lamont (1998) who interprets his finding of a neg-ative correlation between aggregate earnings and expected stock returns as the result of (i) the positive relationship between corporate earnings and business conditions and (ii) the negative relationship between risk premia and current economic activity. Modeling assumptions about profit flows are therefore as important for equity valua-tion as specifying a correct and feasible pricing kernel. In particular, if profitability follows regular patterns related to the business cycle, taking into account these prop-erties is necessary to obtain a relevant valuation. Longstaff and Piazzesi (2004) show that by allowing the aggregate dividend to be more volatile than aggregate consump-tion, one can partially explain the longstanding puzzle first brought to light by Mehra and Prescott (1985).
In this paper we argue that firms’ business cycle exposure, as measured by the sensitivity of their profitability to macroeconomic variables, is affected by various factors at the firm and industry level. We look at two such factors, which have been extensively studied in the literature on the cyclicality of corporate markups1: product
market concentration and financing constraints. Market concentration was studied among others by Rotemberg and Saloner (1986), Domowitz, Hubbard and Petersen (1986), and Rotemberg and Woodford (1991). Financing constraints were formalized in the markup literature by Chevalier and Scharfstein (1996), building on previous works in macroeconomics (see the survey in Bernanke, Gertler and Gilchrist, 1996). It is important to stress that markups are not necessarily in a one-for-one relationship with profits. For example, Hall (1986) finds that a majority of U.S. industries at the two digit level have markups greater than one, but this apparent market power does not seem to be reflected in excess profitability. We show in this paper that the same variables that were discussed in the context of markups play a role in how the business cycle affects firms’ operations.
Both of these factors are market imperfections: one in the product market, the other in the financial market. Another common feature is that both imperfections can only be measured by proxies. We have no single perfect measure of the competi-tion distorcompeti-tions generated by market concentracompeti-tion (see the discussion of two common measures—the Herfindahl-Hirschman index and the concentration ratio—in Martin, 1993). We address the issue of measuring market concentration in a preliminary
anal-1
In microeconomics, the markup is defined as the ratio of price to marginal cost of production. In a perfectly competitive market, markups are equal to one, therefore price equals marginal cost. On the other hand, the higher the markups the higher the likely market power of the producers and, possibly, their profits.
ysis, where we show that product market imperfections should ideally be measured along two dimensions: concentration of market shares (as measured by the HHI) and distribution of firms’ market power (as measured by the dispersion of firm Lerner indexes within the industry). The situation is also complex for financing constraints: attempts at synthetic measures (e.g. Kaplan and Zingales, 1997, Whited and Wu, 2006) have had limited empirical success and fundamental proxies are often preferred (e.g. Almeida, Campello, and Weisbach, 2004). We therefore use several proxies for each of the two factors and check if the patterns in the data match our hypotheses.
Our sample is composed of U.S.-incorporated companies over the period 1977-2009, excluding finance and utility companies. We first confirm that, after controlling for firm-level characteristics, profitability levels have a statistically significant rela-tionship with our macroeconomic variables. We then study the effects of our two factors on this relationship. We find a significant effect of market concentration (MC) only if we use a bi-dimensional measure (fitted Herfindahl-Hirschman index by Hoberg and Phillips, 2010, in combination with a measure of the variability of market power within the 3-digit industry). The sensitivity of profitability to business cycle variables is highest in industries with low market concentration and low inequality of firm mar-ket power, i.e. truly competitive industries. When looking at financing constraints, we show that the sensitivity of profitability to the business cycle is increasing in most measures of financing constraints (FC). In particular, evidence is provided about the different effects of a leading indicator, the CP spread, on financially constrained firms, in line with the flight to quality idea in Bernanke, Gertler and Gilchrist (1996). When analyzing the interaction of the two factors, FC seem to have a significant effect only in truly competitive industries (low concentration and even distribution of market power), while the effect is more ambiguous in industries with high concentration or high inequality of market power.
A set of results is presented about profitability mean-reversion, extending those of Fama and French (2000). First, we show in a simple setting that cross-sectional mean-reversion of firm profitability is weaker in recession years than in expansion years, sug-gesting that competition mechanisms seem to work less well in downturns. This result seems to be in line with Rotemberg and Saloner (1986). We show however that indus-try concentration (as would be consistent with the spirit of the Rotemberg and Saloner article) does not seem to significantly affect this mechanism, whereas an individual measure of market power (Lerner index measured relative to the SIC3 industry aver-age) does. As for financing constraints, our results suggest that constrained firms have a significantly higher mean-reversion coefficient than unconstrained firms. In other words a constrained firm has more difficulties in sustaining abnormally high prof-itability, because it cannot raise enough external finance to fund necessary on-going investments. Conversely, a constrained firm cannot sustain below-average profitabil-ity either (no short-term subsidizing of operations is possible) and therefore has to use other options, e.g. selling off its assets. The “recession effect” (lower mean-reversion in recession years) seems to persist even after controlling for financing constraints.
The paper is organized as follows. In section 2 we present related literature and formulate our research hypotheses. We then present our data in section 3. In section
4 we give some initial observations on aggregate, mean and median profitability in our sample. Our results on the sensitivity of profitability levels are presented in section 5, while mean-reversion is studied in section 6.
2
Background and research questions
The complex relationships between macroeconomic fluctuations, markups and mar-ket structure were studied by Hall (1986), who suggests that a large number of U.S. industries operate above competitive markup levels. This fact has implications for the macroeconomics of productivity shocks: noncompetitive structures could be a source of underutilization of resources and aggregate fluctuations. Other contribu-tions include Domowitz, Hubbard and Petersen (1986), Bils (1987), Haskel, Martin and Small (1995), Rotemberg and Saloner (1986), Rotemberg and Woodford (1991). Oliveira Martins and Scarpetta (2002) give a brief survey of the related theoretical and empirical literature. They note that there is no consensus on whether markups are procyclical or countercyclical, the latter requiring generally some non-competitive el-ements in the model (e.g. monopolistic competition with pro-cyclical product variety, or oligopoly with collusions). For instance, Rotemberg and Saloner (1986) develop a model of countercyclical markups in an oligopolistic market with implicit collusions. The basic idea of the model is that the collusion agreement is much less stable under economic expansions (where demand is high and punishments hurt therefore less), than in economic downturns.
Elements of financing frictions were formalized in the context of markups by Chevalier and Scharfstein (1996) who develop and test a model of countercyclical markups with capital market imperfections where consumers face a switching cost. Fi-nancially constrained firms finance their operations in downturns by increasing prices and preserving short-term profits and sacrificing long-term market shares and profits, hence their markups are counter-cyclical. How should one reconcile this result with a more traditional and intuitive view summarized in Bernanke, Gertler and Gilchrist (1996), namely that constrained firms tend to suffer in recessions earlier and more than unconstrained firms? In fact, we need to point out that Chevalier and Scharfstein present a setting with a limited number of firms and with consumers facing switching costs. These costs generate market power for firms; they can use it to sustain low demand states by temporarily charging relatively higher prices than the competitors, at the expense of their long term market share.
A rich body of literature was born from the longstanding debate opposing Fazzari, Hubbard and Petersen (1988, FHP) and Kaplan and Zingales (1997, KZ) regarding the cash-flow sensitivity of investment. In one of these studies Lamont, Polk and Saá-Requejo (2001) study a common factor in stock returns related to financial constraints (measured by the KZ index based on Kaplan and Zingales, 1997), but two unexpected results show up: this factor earns on average a negative risk premium and the con-straints factor does not seem to respond to macroeconomic variables. Moyen(2004) tries to reconcile FHP and KZ and proposes a model of investment/cash flow
sen-sitivity where two special cases allow consistency with either FHP or KZ. Whited and Wu (2006) propose an alternative index to the KZ index, using a structural model of investment. They find evidence of a common financial constraints factor in stock returns, which earns a positive but statistically not significant premium. In-terestingly, controlling for financing constraints eliminates the size premium. More recently, Gomes, Yaron and Zhang (2006) propose an investment asset pricing model where financing constraints appear as a common factor in the cross-section of stock returs and where the negative consequences of financing constraints are pro-cyclical. Livdan, Sapriza and Zhang (2009) incorporate dynamic debt into a production asset pricing model and provide evidence in simulated data of a priced financing constraints factor, giving a slight superiority to the WW index over the KZ index.
Product market concentration was recently studied in finance in the context of the cross section of stock market returns by Hou and Robinson (2006), who show that competitive firms earn higher returns even after controlling for standard factors. Gaspar and Massa (2006) find that industry competition increases idiosyncratic risk of firms. The authors look at firms’ resistance to individual cost shocks, as opposed to systematic aggregate shocks studied in our paper. Stock market efficiency with respect to private information was analysed by Peress (2010). Lastly, Hoberg and Phillips (2010) analyze industry dynamics related to valuation booms in competitive and noncompetitive industries. They show that competitive pressure in competi-tive industries tends to reduce future profitability, leading to a sharp drop in sector valuation.
The interaction between market competition and financing constraints have re-ceived much less attention in the finance literature. Povel and Raith (2004) propose a model of duopoly with one firm financially constrained and show that the constrained firm tends to have a lower market share due to cost disadvantages, while both firms benefit from a higher price than under competition.
Lastly, the predictability of company earnings and profitability was extensively studied in the accounting literature at least since the early 1970s. In their survey of this body of literature Fama and French (2000) stress the limits of most of these studies: either a lack of formal testing, or a formal testing on individual company time series with low statistical power and possible survivor bias, or a formal testing on larger cross sections but without sufficient treatment of correlation of errors within the cross sections. Fama and French (2000) take as starting point one of the foundations of economic theory that firm profitability (defined here as earnings divided by total firm assets) tends to revert to some common mean due to competition mechanisms. The authors present evidence of mean-reversion towards a firm-specific expected value of profitability as well as towards a common cross-sectional mean, with significant non-linearities (mean-reversion is stronger for negative deviations and large deviations from the mean).
In this paper we address two research questions: (i) how do financing constraints and market concentration affect the sensitivity of companies’ operations to the busi-ness cycle (marginal effects of each, as well as their interaction)?, and (ii) how do these factors affect the mean reversion of profitability in annual cross sections? Based
on related literature and economic reasoning, we formulate the following hypotheses: • H1: Firms in concentrated industries have excess market power and are able to pass on the effects of adverse aggregate shocks to their customers. Their profitability is less exposed to the business cycle, while the profitability of firms in competitive industries is more sensitive to the overall economy.
• H2: Firms subject to financing constraints are more affected by aggregate shocks than unconstrained firms.
• H3: The effect of financing constraints formulated in H2 should be higher in competitive industries than in concentrated industries.
• H4: The cross-sectional mean-reversion of firm profitability is higher for firms active in competitive industries.
• H5: Financially constrained firms’ profitability mean-reverts faster than the profitability of unconstrained firms.
To address these research questions we need to define measures for both factors we analyze, which is not an easy task. In the following section we address issues related to these measures. In particular, we propose an improved measure of market con-centration by integrating information about the intra-industry variability of market power.
3
Data description
3.1 Sample firms
We use all data on U.S. public companies over the period from 1977 to 2009 from the Compustat CCM database. Following Fama and French (2000) we drop financial companies (SIC between 6000 and 6999) and utilities (SIC between 4900 and 4999) due to the likely impact of regulation on profitability levels in these two industries. We drop firm-years with total assets below $10 million or with common equity below $5 million, again following Fama and French (2000). In the appendix we detail required valid Compustat fields.
We define profitability (ROA) as earnings before extraordinary items and interest (EBI) divided by total assets (A). We define the market to book ratio as the market value of equity plus total book assets less book equity divided by total book assets. Book equity is total assets less total liabilities less preferred stock plus balance sheet deferred taxes and tax credits (our definitions follow Fama and French, 2000). We winsorize all ratio variables at the 1% level. We have in total 77,618 firm-years. Our average cross-section is 2,352 observations, the smallest one being 1,340 observations in 1977, the largest one 3,329 in 1998. We present the number of observations in each year in table 9 in the appendix.
We present summary statistics of our sample in table 1. The median asset prof-itability is 6.7%, while the ROE is somewhat higher at 8.4%. The median M/B of assets is 1.279 while real sales growth are a solid 4.9% for the median firm. Median total payout is only 11.7% and median investment rate is at 4.7% of assets.
Table 1: Descriptive statistics .
We present descriptive statistics for our sample of U.S. companies, excluding financial companies and utilities. Market equity is the market value of common equity at the end of the fiscal year, Assets is total book assets, ROA is earnings divided by total assets, ROE is earnings available to common shareholders divided by book equity, M/B assets is the market to book ratio measured at fiscal year end, real sales growth is sales growth adjusted for CPI inflation, P/O ratio is cash common dividends plus stock repurchases divided by earnings, Capex/Assets is capital expenditures divided by total assets. Variables in levels are in $ million. (1) mean sd p25 p50 p75 Market equity 2044.4 12013.0 44.58 170.0 746.2 Assets 1919.2 12673.3 61.83 191.4 770.0 ROA 0.0337 0.142 0.0180 0.0673 0.103 ROE 0.00213 0.334 -0.00156 0.0842 0.145 M/B assets 1.654 1.160 0.989 1.279 1.860
Real sales growth 0.0979 0.334 -0.0469 0.0486 0.169
P/O ratio 0.662 1.380 0 0.117 0.469
Capex/Assets 0.0662 0.0677 0.0239 0.0469 0.0845
Observations 77618
3.2 Business cycle variables
We use the following macroeconomic data from the Federal Reserve2: real annual GDP, Federal funds rate, 3 and 6-month Treasury bill rate, 10-year Treasury note rate, 3-month non-financial Commercial paper rate3, yields on seasoned Aaa and Baa bonds by Moody’s.
Our first business cycle variable is real U.S. GDP growth (denoted GDPGR). Next, we use the average annual spread between the 3-month commercial paper rate and the 3-month Treasury bill rate (CPSPR), because it is considered as a predictor of future economic fluctuations in the sense that downturns are generally preceded by a spike in the CP spread and recoveries by its tightening (see Friedman and Kuttner, 1991). One of the rationales for this property given by Friedman and Kuttner (and also among others Bernanke, Gertler and Gilchrist, 1996), besides default risk and monetary conditions, is companies’ variability in cash needs over the business cycle.
2
Data download facility on the Fed’s web site www.federalreserve.gov/releases.
3
Before 1997 we use the 3-month prime commercial rate which includes financial and non-financial companies.
−.02 0 .02 .04 .06 .08 1970 1980 1990 2000 2010 fyear
Real GDP growth CP spread
Bond spread Short spread
Term spread
Figure 1: Business cycle variables, annual data.
Presented are the annual real U.S. GDP growth, the average spread between the 3-month Commercial paper and the corresponding T-Bill, the average spread between the 10-year T-note rate and the 3-month T-Bill rate, the average spread between seasoned Aaa and Baa bonds by Moody’s, and the spread between the 6-month T-Bill and the Fed Funds rate. Variables were decorrelated in the following order: GDP growth, CP spread, bond spread, term spread, short rate spreas. Source: Federal Reserve, author’s calculations.
The CP spread would be a demand-driven phenomenon, resulting from companies’ operating performance—a causality direction which is less interesting for our study. Operating cash flows and short term financing needs are indeed very likely correlated, but we stop short of declaring a one-way causality between profitability and the paper spread. Spikes in short therm financing costs (related to rising credit risk premiums) are also a signal of a general tightening of credit conditions, which forces companies to restrain some of their operations and therefore reduce their profitability (as noted by Bernanke, Gertler and Gilchrist, 1996). A causality relationship in this direction is more interesting in our context. For similar reasons we include also the bond spread (BSPR) calculated as the average yield differential between seasoned Aaa and Baa bonds by Moody’s.
Our next variable is the spread between the 10-year Treasury note rate and the 3-month Treasury bill rate (TSPR), following studies on the information content of the term spread with respect to future real economic activity (see Estrella and Hardouvelis, 1991, and more recently Hamilton and Kim, 2002).
The last variable is the short rate spread (SSPR) between the 6-month Treasury bill and the Fed Funds rate as a proxy for the current direction of monetary policy (Korajczyk and Levy, 2003).
All spread variables are taken as annual averages of weekly values. GDP growth is calculated as the rate of change of real annual GDP. Given that raw macro vari-ables are highly correlated, we de-correlate them by taking residuals from regressions
on other variables in the following order: GDPGR, CPSPR, BSPR, TSPR, SSPR. Specifically, GDPGR is kept unchanged, CPSPR is the residual from the regression of the CP spread on a constant and GDPGR, BSPR is the residual from the regression of the bond spread on a constant, GDPGR and CPSPR etc.
3.3 Measuring product market imperfections
There are several measures of the degree of product market concentration (PMC). The most commonly used is the Herfindahl-Hirschman index (HHI), somewhat pre-ferred to simple concentration ratios (ratio of x largest firms’ market share to the overall market). There is a wide agreement that the HHI is an imperfect measure of concentration, for it does not take into account regional concentration effects as well as more complex market structures with Stackelberg traits (see the discussion in Martin 1993, p. 167). Intermediate HH index values of about 500 can be associated with very different levels of competitive distortions, as we illustrate below.4
We measure market concentration using the annual fitted Herfindahl-Hirschman index by Hoberg and Phillips (2010) from their Web Data Library.5 These data are constructed by using the Census HHI of manufacturing industries and extending them to all 3-digit industries using the HHI calculated from public firms in Compustat. This dataset is a compromise between using only Census data (having a low 5-year frequency and only manufacturing industries coverage) and calculating HHI from Compustat data only, thus excluding private firms (see critique by Ali, Klasa and Yeung, 2009).
We also use an individual measure of market power as in Peress (2010), the relative Lerner index. The raw Lerner index is defined for each firm as Lernerit = (Salesit− COGSit− SGAit)/Salesit, where COGS is the Cost of goods sold and SGA is Selling,
general and administrative expenses. We truncate the raw Lerner indexes at 0, as negative values of the index have little theoretical foundations and we consider them to imply zero market power. The relative index is obtained by subtracting from the raw value the firm’s SIC3 industry sales-weighted average.
To address the imperfection of the HHI we inspected at the 3-digit SIC level the relationship between the fitted HHI value and the intraindustry dispersion of indi-vidual firm market power, measured by the industry standard deviation of firms’ raw Lerner indexes in a given year. As illustrated in figure 2, there is relatively little dispersion of firm Lerner indexes among the highly concentrated industries. On the other hand, the dispersion of firm Lerner indexes varies a lot at the lower end of the HHI. The table 2 presents for the year 2005 industries with a HHI below 500 and with a dispersion of firm Lerner indexes at the high and low end. We can see that industries with a low dispersion of Lerner indexes (i.e. relatively equal distribution of market power) are mostly standardized products and services, with the exception of
News-4
Note that the Department of Justice considers industries moderately concentrated above the value 1000 and concentrated above 1800. http://www.justice.gov/atr/public/testimony/hhi.htm (ac-cessed January 2010).
Figure 2: Herfindahl-Hirschman index and the industry dispersion of firm Lerner indexes.
We plot the Hoberg-Phillips (2010) fitted HHI data on the horizontal axis and the standard deviation of firm Lerner indexes within the SIC3 industry on the vertical axis. We present plots for three years within our sample period: 1980, 1995 and 2005.
1980, equally-weighted 1980, sales-weighted 0 .1 .2 .3 (sd) lerner 500 1000 1500 2000 (mean) fithhi 0 .05 .1 .15 .2 (sd) lerner 500 1000 1500 2000 (mean) fithhi 1995, equally-weighted 1995, sales-weighted 0 .1 .2 .3 (sd) lerner 500 1000 1500 2000 2500 (mean) fithhi 0 .05 .1 .15 .2 (sd) lerner 500 1000 1500 2000 2500 (mean) fithhi 2005, equally-weighted 2005, sales-weighted 0 .05 .1 .15 .2 .25 (sd) lerner 500 1000 1500 2000 2500 (mean) fithhi 0 .05 .1 .15 .2 (sd) lerner 500 1000 1500 2000 2500 (mean) fithhi
Table 2: US industries with low concentration and high/low dispersion of firm Lerner indexes as of 2005.
We present the standard deviations of individual firms’ Lerner indexes within 3-digit SIC industries as of 2005. We present industries with a fitted HHI below 500, at the high and low end of the distribution of Lerner standard deviations in that year. Standard deviations of Lerner indexes are calculated on our sample of Compustat US firms satisfying our data requirements. Fitted HHI data are from the Hoberg and Phillips Data Library.
Industry name SIC3 Fitted HHI σ(Lerner)
Panel A: High dispersion of firm Lerner indexes
Gold & silver ores (mining) 104 436 0.175
Crude petrol. & Nat. Gas (extraction) 131 399 0.240
Drugs 283 450 0.141
Deep sea foreign trans. of freight 441 500 0.146
Tel. communication 481 499 0.169
Cable & other pay TV services 484 473 0.159
Advertising 731 495 0.133
Misc. equipment rental & leasing 735 388 0.142
Commercial sports 794 481 0.187
Panel B: Low dispersion of firm Lerner indexes
Operative builders 153 355 0.044
Womens’, Misses’, and Juniors’ Outerwear 233 423 0.069
Newspapers 271 491 0.052
Misc. chemical products 289 458 0.049
Misc. plastics products 308 444 0.049
Blast furnace & basic steel products 331 481 0.066
General ind. machinery 356 423 0.064
Machinery, equipment & supplies (wholesale trade) 508 385 0.062
papers. Conversely, industries with dispersed Lerner indexes (uneven distribution of market power) are industries with likely geographical, technological or variety-related concentration effects.
We argue that the HH index of concentration does not sufficiently capture the market structure heterogeneity, especially at intermediate levels. In particular, this index cannot easily distinguish situations such as monopolistic competition, Stackel-bergian structures, regional or vertical concentration, or consolidation.
To address this issue we use a bi-dimensional measure of market concentration based on our graphical analysis of the relationship between industry fitted HHI and the intraindustry standard deviation of firm Lerner indexes. As is obvious from figure 2, highly concentrated industries tend to have a low dispersion of Lerner indexes, whereas medium and low concentration industries can have a high or low dispersion of individual Lerner indexes.
3.4 Measuring financing constraints
We measure financial constraints using the synthetic Whited and Wu (2006) index (WW) and four fundamentals-based proxies following Almeida, Campello and Weis-bach (2004), Faulkender and Wang (2006), and Denis and Sbilikov (2010). The index by Whited and Wu (2006) is defined as WW = −0.091 ∗ CF/TA − 0.062 ∗ Divpos + 0.021 ∗ LTD/TA − 0.044 ∗ LNTA + 0.102 ∗ ISG − 0.035SG where CF is income before extraordinary items plus depreciation expense, TA is total assets, Divpos is a dummy variable for positive cash dividend, LTD is total long term debt, LNTA is the natural logarithm of total assets, ISG is 3-digit level industry sales growth, SG is the indi-vidual firm sales growth. Note that the initial index was estimated using quarterly data.
The fundamentals-based proxies of FC are:
• P/O ratio measured as (Dividends + Repurchases)/Earnings after taxes before interest,
• Size measured as Total assets
• Indicator of whether the firm has a S&P LT issuer rating • Indicator of whether the firm has a S&P ST issuer rating
We note that all five measures are only proxies based on empirically observed char-acteristics of constrained firms. It is therefore not sufficient to analyze the interaction between industry concentration and financing constraints by merely looking at values of FC proxies in concentrated and competitive industries, as we don’t know wheter these proxies have the same effect in industries with different market structures.
−.05
0
.05
.1
1980 1990 2000 2010
Data Year − Fiscal
Aggregate ROA Mean ROA Median ROA
(a) All firms
−.05
0
.05
.1
1980 1990 2000 2010
Data Year − Fiscal
Aggregate mfg. ROA Mean mfg. ROA Median mfg. ROA
(b) Manufacturing firms
Figure 3: Aggregate, mean, and median return on assets of large and mid-sized U.S. companies: (a) all firms excl. financials and utilities and (b) manufacturing firms (SIC between 2000 and 3999).
4
Some observations on profitability
We now turn to our data and provide some quick insights and stylized facts about profitability over our sample period. In figure 3 we present aggregate, average and median ROA for all sample firms and for manufacturing firms only. We define aggre-gate return on assets as the sum of earnings (EBI) of individual firms divided by the corresponding sum of total assets. First, we note that all three series confirm conven-tional wisdom about the strong dependence of profitability on the business cycle. We see significant dips during recession periods.
The second observation is somewhat more surprising. We observe a clear negative trend on the aggregate and median profitability, while average profitability drifts away from the other two measures. This fact is consistent with evidence in Fama and French (2001, fig. 3), suggesting that the distribution of firm profitability is more and more negatively skewed. But besides distributional changes at the tails, one can observe a clear negative trend on the aggregate and median indicators. This is consistent with the findings of an initiative by Deloitte, an accounting firm, called the Shift Index: a steady decline, over the last four decades, of the ROA of US companies which hit the company landscape asymmetrically (a limited number of leaders resisted relatively well, while the majority of companies declined). The study assigns this asymmetric decline to increasing international competition, growing share of revenue remunerating knowledge and talent, and stronger bargaining position of consumers.6
Third, aggregate and median profitability stay relatively close in the full sample. However, in the manufacturing sample a separation occurs in the mid 2000s, sug-gesting that the exceptionally high profitability in that period was asymmetrically enjoyed by large players.
These stylized facts imply that different approaches to studying profitability (us-ing aggregate or sectoral time series versus cross-sectional firm data) may yield com-plementary information. In this paper we will first study panel data of individual profitability levels (analyzing the average profitability level). In the second part we analyze the mean-reversion in the cross-section of individual profitabilities (focusing on the differences between individual and mean profitabilities).
5
Profitability and business cycle variables
5.1 Estimation method
We estimate a simple model in which we regress firms’ profitability (earnings before extraordinary items and interest divided by total assets, ROAt) on our set of
macroe-conomic variables and a set of individual variables. To mitigate endogeneity prob-lems, all individual variables are lagged. Our individual variables include those used by Fama and French (2000) to estimate expected profitability, namely the market-to-book assets ratio (MAt−1/BAt−1), dividends divided by book equity (Dt−1/BEt−1),
6
and a dummy variable indicating nonzero common dividends during the current year (DDUMt−1). We also add a variable measuring capital intensity calculated as
prop-erty, plant and equipment divided by total assets (Kt−1/At−1), and an investment
intensity variable measured by capital expenditures to total assets (It−1/At−1). We
include a linear trend and estimate the following equation using a fixed individual effect estimator with firm-clustered standard errors:
ROAit= α + 5 X j=1 βjMacroVarjt+ 5 X k=1
γkIndVarkit−1+ Trendt+ ǫit (1)
where the MacroVar set includes GDPGR, CPSPR, BSPR, TSPR, and SSPR and the IndVar set includes MA/BA, D/BE, DDUM, K/A and I/A.
This equation is first estimated without further interaction terms, to serve as base-line. Next, we rank every year all observations based on the measure of FC or PMC and we define indicator variables for the bottom and top 30% for each measure. We then interact the macro variables with such indicator variables of financing constraints and market concentration (lagged), which allows us to judge if any subgroups have a significantly different sensitivity than the other subgroups.
5.2 Results on market concentration
The first column of table 3 presents our baseline case. Our business cycle variables’ coefficients are strongly statistically significant except for BSPR. The GDP growth variable has a positive sign, as expected. As a rule of thumb, one additional per-centage point in real GDP growth translates into 0.15 perper-centage points of additional profitability. A more surprising result is the significantly positive coefficient on the CP spread, given that a high CP spread is in general an early sign of a downturn. Similarly, the term spread seems to be correlated negatively, despite the intuition that high term spread is generally observed in early phases of upturns and low or negative term spreads tend to appear at the beginning of downturns. The sign on the SSPR coefficient is positive, consistently with intuition.
Column (2) shows that interacting macro variables with HHI-based indicator vari-ables does not yield any meaningful patterns. Both the top and bottom 30% of firms seem to be less exposed to the GDP growth than the middle 40%. All other interac-tion terms except one are not significant. Column (3) interacts macro variables with firms’ relative Lerner index. At face value the results would suggest that firms with little market power (low relative Lerner index) have a negative exposure to the GDP growth, whereas firms with high market power would be very exposed to this vari-able. Other macro variables don’t show a meaningful pattern either. Both columns’ regressions seem to omit some important factors.
Our analysis presented in section 3.3 shows that concentrated industries tend to have a low dispersion of firm-level Lerner indexes, whereas industries with medium and low HH indexes can have either a high or a low dispersion of Lerner indexes. We argued that industries with a low HH index and with a high inequality of firm Lerner indexes
Table 3: The impact of product market concentration on business cycle sen-sitivity of annual firm profitability.
We regress ROAton five macro variables, a set of lagged firm-level variables, a linear trend and a constant. In columns (2)-(4) we interact the macro variables with a lagged indicator of product market concentration (PMC). Column (2) shows results using the fitted Herfindahl-Hirschman index (HHI) by Hoberg and Phillips (2010). Column (3) uses the relative Lerner index (price-cost margin of the firm minus its SIC3 sector average). Column (4) uses the fitted HHI and an indicator of within industry dispersion of individual Lerner indexes. GDPGR is the annual real U.S. GDP growth, CPSPR is the Commercial Paper spread, BSPR is the bond spread between seasoned Baa and Aaa bonds, TSPR is the term spread between the 10-year and 3-month Treasury rates, SSPR is the short rates spread between the 6-month Treasury bill and the Fed Funds rate. The firm-level variables are: Capex/Total assets; PPE/Total assets; a dummy variable equal to one if common dividends in the current year were non-zero; Dividends+repurchases/Book equity; and the M/B of assets. LoPMC(HiPMC) is an indicator variable for observations in the bottom (top) 30% in the previous year’s cross-section of the measure of product market concentration. Loσ(Lerner)(Hiσ(Lerner)) is an indicator for industries with a dispersion of the firm Lerner indexes within the SIC3 sector below (above) the median of all sectors in the previous year. We use all Compustat CCM firms satisfying our data requirements. The estimation period is from 1977 to 2009. We present t-statistics using firm-clustered standard errors.
(1) (2) (3) (4)
Baseline HH R-Lerner HH and σ(Lerner)
×Loσ(Lerner) ×Hiσ(Lerner)
GDPGR 0.148∗∗∗ 0.215∗∗∗ 0.199∗∗∗ 0.204∗∗∗ (7.02) (7.11) (8.05) (7.17) CPSPR 0.965∗∗∗ 1.024∗∗∗ 1.779∗∗∗ 2.535∗∗∗ (6.31) (4.00) (9.84) (10.81) BSPR −0.0600 −0.363 0.372∗ 0.258 (−0.40) (−1.49) (2.01) (1.04) TSPR −0.449∗∗∗ −0.440∗∗∗ −0.274∗∗∗ −0.249∗∗∗ (−11.01) (−5.89) (−5.25) (−3.43) SSPR 0.910∗∗∗ 1.406∗∗∗ 1.379∗∗∗ 1.819∗∗∗ (7.01) (5.69) (8.11) (7.94) GDPGR×LoPMC −0.100∗ −0.527∗∗∗ −0.0223 −0.193∗∗∗ (−2.53) (−13.31) (−0.52) (−4.03) GDPGR×HiPMC −0.0686∗ 0.320∗∗∗ −0.0654∗ (−2.08) (11.62) (−1.99) CPSPR×LoPMC 0.00536 1.158∗∗ −1.196∗∗ −0.596 (0.01) (3.16) (−3.09) (−1.30) CPSPR×HiPMC 0.169 0.290 −0.423 (0.55) (1.15) (−1.38) BSPR×LoPMC 0.0562 1.098∗∗ −0.508 0.787 (0.16) (2.97) (−1.21) (1.90) BSPR×HiPMC 0.733∗ −0.259 0.772∗ (2.45) (−1.04) (2.50) TSPR×LoPMC 0.0316 0.0164 −0.0489 0.151 (0.27) (0.12) (−0.39) (1.12) TSPR×HiPMC −0.0886 0.0262 −0.124 (−0.94) (0.34) (−1.33) SSPR×LoPMC 0.365 1.020∗ −1.243∗∗ 0.887∗ (0.88) (2.46) (−3.18) (1.98) SSPR×HiPMC −1.376∗∗∗ −0.770∗∗ −1.611∗∗∗ (−4.49) (−3.17) (−5.60) Trend −0.00235∗∗∗ −0.00231∗∗∗ −0.00204∗∗∗ −0.00193∗∗∗ (−24.98) (−22.84) (−20.46) (−18.59)
Firm varst−1 Yes Yes Yes Yes
Constant and FE Yes Yes Yes Yes
could belong into one of the following cases: monopolistic competition, Stackelbergian oligopoly, sector undergoing a technological transformation or a consolidation, etc. In some of these cases it is ambiguous whether such industry should be seen rather as a competitive or a concentrated one, despite having a HH index in the lower 30% of observations. It is therefore useful to distinguish Low HH industries based on their dispersion of the Lerner indexes. Column (4) presents such results. Firms in concentrated industries are less sensitive to the GDP growth than the mid HH firms. They have a higher positive exposure to the bond spread, suggesting that concentrated industries resist better when bond spreads start to spike. On the other hand, firms in competitive industries with low Lerner dispersion have a lower (though positive) coefficient on the CP spread, suggesting that as downturns approach, these firms tend to be affected earlier than other firms. Last, firms in low HH industries with high Lerner dispersion confirm their specific status as their exposure to the GDP growth is the lowest, while having the highest exposure to the short rate spread.
5.3 Results on financial constraints
Table 4 presents results for our proxies of financing constraints (note that for the two ratings proxies there are only dummies for low constrained firms given the binary nature of these proxies). Across all proxies except one we see that unconstrained (constrained) firms are significantly less (more) sensitive to the growth of the over-all economy. This disparity is largest when using the size and the P/O proxy, the difference on the coefficients being 0.4 for the former, 0.28 for the latter.
Low constraints firms have a higher exposure to the CP spread variable for most of our proxies. The Bond spread exposure is mostly positive for unconstrained firms and mostly negative for constrained firms. Exposure differentials on these two leading indicators suggests that constrained and unconstrained firms are differently affected as the economy approaches a downturn. The other two macro variables do not present different sensitivity patterns for constrained and unconstrained firms.
To sum up, using various proxies of financing constraints we conclude that con-strained firms’ operations are more sensitive to the overall business cycle, being af-fected more and earlier by changes in the aggregate economy. Our results are con-sistent with the flight to quality effect of Bernanke, Gertler and Gilchrist (1996), according to which the financially vulnerable firms (mostly small firms) tend to be most affected at the onset of a downturn.
5.4 Interaction of financial constraints and market concentration
In order to keep our analysis feasible and readable, we simplify the framework some-what. We reduce our macro variables to GDPGR as coincident indicator and CPSPR as leading indicator. We use our two-dimensional definition of market concentration as in section 5.2. To pin down the idea of our hypothesis H5, we interact the macro variables with the High FC indicator variable and then with three cases of market con-centration as previously described. Table 5 confirms that firms with low constraints
Table 4: The impact of financing constraints on business cycle sensitivity of annual firm profitability.
We regress ROAton five macro variables, a set of lagged firm-level variables, a linear trend and a constant. In columns (2)-(6) we interact the macro variables with a lagged indicator of financing constraints (FC). Column (2) shows results using the Whited and Wu index, (3) uses the payout ratio, (4) uses total assets, (5) and (6) use the indicator of presence of a long-term (short-term) issuer rating. GDPGR is the annual real U.S. GDP growth, CPSPR is the Commercial Paper spread, BSPR is the bond spread between seasoned Baa and Aaa bonds, TSPR is the term spread between the 10-year and 3-month Treasury rates, SSPR is the short rates spread between the 6-month Treasury bill and the Fed Funds rate. The firm-level variables are: Capex/Total assets; PPE/Total assets; a dummy variable equal to one if common dividends in the current year were non-zero; Dividends+repurchases/Book equity; and the M/B of assets. In columns (1) to (4) LoFC (HiFC) is an indicator variable for the 30% least (most) constrained firms in the previous year’s cross-section. In columns (5) and (6) LoFC is an indicator for firms having a valid non-default rating in the previous year. We use all Compustat CCM firms satisfying our data requirements. The estimation period is from 1977 to 2009 except where ratings data are used (available 1985 onwards). We present t-statistics using firm-clustered standard errors.
(1) (2) (3) (4) (5) (6)
Baseline WW P/O Size LT rating ST rating
GDPGR 0.148∗∗∗ 0.230∗∗∗ 0.274∗∗∗ 0.180∗∗∗ 0.371∗∗∗ 0.376∗∗∗ (7.02) (7.93) (12.40) (6.22) (8.57) (9.46) CPSPR 0.965∗∗∗ 0.739∗∗ 0.602∗∗∗ 0.794∗∗∗ −2.015∗∗∗ −2.154∗∗∗ (6.31) (3.28) (3.57) (3.61) (−5.31) (−6.23) BSPR −0.0600 −0.743∗∗∗ −0.0458 −0.463∗ −0.863∗∗ −0.717∗∗ (−0.40) (−3.34) (−0.29) (−2.11) (−3.02) (−2.89) TSPR −0.449∗∗∗ −0.529∗∗∗ −0.468∗∗∗ −0.477∗∗∗ −0.147∗ −0.190∗∗∗ (−11.01) (−8.14) (−9.71) (−7.42) (−2.57) (−3.83) SSPR 0.910∗∗∗ 1.014∗∗∗ 0.0739 0.841∗∗∗ 2.736∗∗∗ 2.665∗∗∗ (7.01) (4.81) (0.54) (4.09) (8.95) (10.18) GDPGR×LoFC −0.177∗∗∗ −0.325∗∗∗ −0.199∗∗∗ −0.0439 −0.230∗∗∗ (−5.75) (−13.84) (−5.95) (−0.96) (−4.22) GDPGR×HiFC −0.0271 −0.0473 0.198∗∗∗ (−0.59) (−1.35) (4.11) CPSPR×LoFC 0.591∗ 0.560∗ 0.619∗ −0.0142 0.998∗ (2.10) (2.47) (2.28) (−0.03) (2.09) CPSPR×HiFC 0.0455 0.320 −0.327 (0.11) (0.99) (−0.82) BSPR×LoFC 1.463∗∗∗ −0.184 0.855∗∗ 1.353∗∗ 1.706∗∗∗ (5.56) (−0.82) (3.25) (3.26) (4.11) BSPR×HiFC 0.713 −0.128 0.170 (1.81) (−0.41) (0.41) TSPR×LoFC 0.0477 0.0161 −0.102 −0.248∗∗ −0.105 (0.60) (0.21) (−1.29) (−3.12) (−1.25) TSPR×HiFC 0.258 0.0744 0.276∗ (1.94) (0.75) (2.16) SSPR×LoFC 0.256 0.666∗∗ 0.540 −0.470 −0.166 (0.89) (2.79) (1.94) (−1.09) (−0.37) SSPR×HiFC −0.148 1.739∗∗∗ −0.502 (−0.36) (5.25) (−1.29) Trend −0.00235∗∗∗ −0.00223∗∗∗ −0.00229∗∗∗ −0.00224∗∗∗ −0.00206∗∗∗ −0.00206∗∗∗ (−24.98) (−21.69) (−24.27) (−23.59) (−14.25) (−14.31)
Firm varst−1 Yes Yes Yes Yes
Constant and FE Yes Yes Yes Yes
Observations 77621 72216 77617 77621 63704 63704
Adjusted R2 0.067 0.066 0.070 0.069 0.054 0.054
tstatistics in parentheses
Table 5: The interaction of financing constraints and product market con-centration.
We regress ROAt on two macro variables, a set of lagged firm-level variables, a linear trend and a constant. We interact the macro variables with a lagged measure of concentration and lagged measures of financing constraints (one per column). Column (1) shows results for the Whited and Wu index, (3) uses total assets, (4) uses the payout ratio, (5) and (6) use the indicator of presence of a long-term (short-term) issuer rating. GDPGR is the annual real U.S. GDP growth, CPSPR is the Commercial Paper spread. The firm-level variables are: Capex/Total assets; PPE/Total assets; a dummy variable equal to one if common dividends in the current year were non-zero; Dividends+repurchases/Book equity; and the M/B of assets. LoHH (HiHH) is an indicator vari-able for observations in the bottom (top) 30% in the previous year’s cross-section. Loσ(Lerner) (Hiσ(Lerner)) is an indicator for industries with a dispersion of the firm Lerner indexes within the SIC3 sector below (above) the median of all sectors in the previous year. We use all Compustat CCM firms satisfying our data requirements. The estimation period is from 1977 to 2009 except where ratings data are used (available 1985 onwards). We present t-statistics using firm-clustered standard errors.
(1) (2) (3) (4) (5)
WW P/O Size LT rating ST rating
GDPGR 0.292∗∗∗ 0.316∗∗∗ 0.237∗∗∗ 0.501∗∗∗ 0.506∗∗∗ (9.69) (13.55) (7.59) (7.93) (8.49) GDPGR×LoFC −0.202∗∗∗ −0.326∗∗∗ −0.222∗∗∗ −0.232∗∗∗ −0.353∗∗∗ (−6.00) (−12.54) (−5.93) (−3.88) (−5.46) GDPGR×HiFC 0.0419 0.0986 0.282∗∗∗ (0.66) (1.89) (4.11) GDPGR×HiFC ×LoHH×Loσ(Lerner) 0.0265 −0.105 −0.0538 0.00595 0.0173 (0.29) (−1.20) (−0.60) (0.07) (0.23) GDPGR×HiFC ×LoHH×Hiσ(Lerner) −0.0439 −0.204∗ −0.00744 −0.290∗∗∗ −0.313∗∗∗ (−0.44) (−2.49) (−0.07) (−3.48) (−4.10) GDPGR×HiFC ×HiHH 0.00348 −0.113 −0.00564 −0.153∗∗ −0.195∗∗∗ (0.04) (−1.71) (−0.06) (−2.72) (−3.96) CPSPR 2.158∗∗∗ 1.723∗∗∗ 2.289∗∗∗ 2.829∗∗∗ 2.774∗∗∗ (9.51) (10.58) (9.91) (5.07) (5.44) CPSPR×LoFC 0.624∗ 0.643∗∗ 0.534 −0.628 −0.802 (2.05) (2.70) (1.77) (−0.91) (−1.28) CPSPR×HiFC 0.346 2.405∗∗∗ −0.657 (0.55) (4.21) (−1.06) CPSPR×HiFC ×LoHH×Loσ(Lerner) −1.921∗ −3.761∗∗∗ 0.148 −1.499 −2.331∗ (−2.30) (−4.32) (0.19) (−1.47) (−2.47) CPSPR×HiFC ×LoHH×Hiσ(Lerner) −0.774 −1.575 −0.627 −0.694 −0.340 (−0.75) (−1.64) (−0.58) (−0.68) (−0.36) CPSPR×HiFC ×HiHH 0.333 −1.663∗ 0.605 −1.137 −0.990 (0.39) (−2.20) (0.71) (−1.57) (−1.57) Trend −0.00180∗∗∗ −0.00185∗∗∗ −0.00177∗∗∗ −0.00110∗∗∗ −0.00114∗∗∗ (−16.96) (−19.35) (−17.88) (−5.99) (−6.42)
Firm varst−1 Yes Yes Yes Yes Yes
Constant and FE Yes Yes Yes Yes Yes
Observations 60591 63255 63259 50968 50968
Adjusted R2 0.066 0.069 0.068 0.051 0.051
t statistics in parentheses
tend to be less exposed to the GDP growth. High FC firms in low concentration industries with high Lerner inequality seem to be less sensitive to the GDP growth (3 out of 5 cases are significant), suggesting that the FC are less penalizing here. Similarly, firms with a high FC proxy operating in concentrated industries seem to be less affected (interaction coefficient significant in 2 out of 5 cases).
The interaction of the CP spread with FC and PMC variables also yields some insights. High FC firms in competitive industries with low Lerner dispersion are the least positively exposed to the CP spread, again suggesting an earlier impact of an approaching downturn on these firms. High FC firms in low concentration industries with high Lerner inequality also have a mostly negative interaction term, but not significant and of lesser magnitude than in the previous case. High FC and High HH firms have mostly not significant coefficients too.
We provided some evidence in favor of H5: Financing constraints as measured by our proxies seem to have more impact on companies’ operations in the most compet-itive industries with low inequality of market power as measured by the dispersion of firm Lerner indexes. Industries with low concentration and high inequality of mar-ket power, as well as industries with high concentration seem to be less exposed to macroeconomic fluctuations.
6
Profitability mean-reversion
6.1 Mean reversion and the business cycle
We now focus our analysis on the profitability mean-reversion property. Following Fama and French (2000, hereinafter FF) we estimate a simple model of mean-reversion of profitability. In their paper FF propose a two step method. In the first step they estimate on each year’s cross-section the expected profitability for each firm based on three variables: M/B of assets, dividends to book equity and a dummy indicating zero dividends.7 As a second step, the authors estimate a model where the profitability
change for a firm in year t is explained by the lagged profitability change and, more importantly, by the difference between observed and expected profitability in t − 1. But as FF show (and our results in section 5 are consistent with this), profitability is strongly positively related to M/B of assets. We find therefore such a specification somewhat awkward, because it allows the expected profitability in t − 1 to “revert” to effective profitability instead of the inverse. Take the following example: a firm reports a surprisingly high profitability in the first three quarters of the year t − 1. By the end of the fourth quarter, the market will have analyzed the surprise of the first three quarters. If the market considers this surprise temporary, market value of equity (and thus of assets) increases little over the year t − 1, whereas if this surprise is interpreted as a “new normal”, equity value increases significantly over the first three quarters (and so does the M/B of assets). In the former case, the
7
All three are included as individual firm variables in our regressions of profitability levels in section 5, except that our dummy = 1− the FF dummy. Also, we use lagged values, not current ones as FF.
temporarily high profitability will likely revert to a “normal” level in year t if the market analyzed correctly the profitability information in t − 1. In the latter case, the expected profitability driven by a rising M/B increases to a higher level by the end of the year t − 1 and the reversion in year t is mechanically easier, but again only if the market’s interpretation of information is on average correct.
The authors find that the rate of mean reversion in the simplest form of their model is about 38% a year. FF include also a more robust specification in which profitability is assumed to mean-revert to a common cross-sectional mean. In this setting mean reversion is also significant, though lower, at about 30% a year. Under this specification it is certain that it is profitability that reverts to the mean and not the other way round.
We use as our reference model this simpler and more robust specification and we estimate the following equation:
ROAit− ROAit−1 = α + βROAit−1+ γ (ROAit−1− ROAit−2) + ǫit, (2)
or, using the same intuitive notations for the change of profitability between t − 1 and t (denoted CPt) as in FF:
CPit= α + βROAit−1+ γCPit−1+ ǫit. (3)
We estimate this equation following FF, i.e. using the Fama and MacBeth (1973) method. Our results in column (2) are reasonably close to those in column (1) taken from Fama and French (2000), which were estimated on an earlier period partially overlapping ours. Our mean reversion coefficient is 26.8% a year, somewhat smaller than the FF result of 30%. The autoregressive term is stronger in our results and the constant is lower than in the FF results. The differences can probably be attributed to different time periods and sample firms (FF sample ends in 1996, i.e. it mostly excludes the tech boom of the late 1990s and early 2000s).
Note that autocorrelation in the year-by-year slope estimates is not being cor-rected for in our tables, which means that t-statistics are overestimated. If we had sufficiently long time series of these slope estimates, we would be able to estimate reliable autocorrelation estimates and calculate adjusted SEs. Since this is not the case, to infer significance we follow Fama and French (2000) and require somewhat higher t-statistics for significance. FF estimate that with an autocorrelation of 0.5, the SEs should be increased by about 40%, therefore the threshold for t-stats should be at about 2.8 instead of the commonly used 1.96.
We first wish to study the pure effect of business cycle conditions on the mean-reverting property of profitability. Due to the fact that we use the Fama and Mac-Beth approach, it is impossible to include macroeconomic variables in our regressions, as they are constant in a given year for the whole cross-section and therefore non-identifiable by this technique. We therefore have to use a trick and modify our esti-mated equation. We define recession years as years that contain at least 2 recession months according to the NBER business cycle dating. All other years are consid-ered as expansion years. This definition yields seven recession years over our sample
Table 6: Mean-reversion of firm profitability.
We regress the change of firm profitability ∆ROAt on lagged profitability and lagged
change of profitability. Column (1) presents results from Fama and French (2000) on the period 1964-96, with the level of precision as in the published article and the number of observations calculated as the number of years times the reported average cross section. Columns (2) to (4) present means and t-statistics of coefficients from yearly cross-sectional regressions using equation 3. Column (2) uses all sample years, column (3) only expansion years, column (4) only recession years. Column (5) shows the results of an estimation using the Arellano-Bond system GMM estimation on all years with a recession indicator variable DREC interacted with the explanatory variables. The estimation period is from 1977 to 2009. Recession years are defined as years containing at least 2 NBER-declared recession months, namely 1980, 1981, 1982, 1990, 1991, 2001 and 2008.
(1) (2) (3) (4) (5)
F&F(2000) All years Exp. years Rec. years A-B
ROAt−1 −0.30 −0.268 −0.280 −0.230 −0.326 (−14.30) (−15.83) (−15.84) (−5.46) (−68.21) ∆ROAt−1 −0.13 −0.173 −0.158 −0.221 −0.158 (−7.61) (−10.71) (−8.85) (−6.71) (−34.61) ROAt−1×DREC 0.160 (17.94) ∆ROAt−1×DREC −0.195 (−18.23) DREC −0.0278 (−25.84) Constant 0.016 0.00436 0.00862 −0.00897 0.00799 (7.38) (1.20) (3.03) (−0.79) (17.40) Observations 77319 77621 60873 16748 77621 R2 0.18 0.173 0.174 0.169 n/a t statistics in parentheses
period: 1980, 1981, 1982, 1990, 1991, 2001, and 2008. Let the dummy variable Rt
be equal to 1 if t is a recession year and zero otherwise. We assume that all three parameters allow a different value in recession years, the incremental parameter being denoted with a superscript “R”. The modified model is therefore:
CPit= α + RtαR + β + RtβR ROAit−1+ γ + RtγR CPit−1+ ǫit. (4)
This model can be estimated if we separate recession years and expansion years in the Fama MacBeth procedure. Colums (3) and (4) of table 6 present our results. First, we note that the mean-reversion coefficient in recession years is weaker by about βR = −0.230 − (−0.280) = 0.050, which is about one fifth of the “expansion”
coefficient 0.280. It would be interesting to test the significance of βR. Unfortunately
it is impossible in this setting given the inability to estimate the covariance between our estimates of β and βR. The slope on the autoregressive term is stronger by about
0.06.
To validate our estimation technique of the recession effect, we estimate equation 3 with recession indicator variable interactions using the Arellano and Bond system GMM approach. The results in column (5) confirm the existence of a significant recession effect of an even higher magnitude: the mean-reversion is about 50% weaker in recession years.
Our results here have potentially strong implications, as they are in line with Rotemberg and Saloner (1986), who oppose the “industrial organization folklore” ac-cording to which competition is the most fierce during recessions. In a parsimonious model we find that mean reversion of profitability in recession years is significantly weaker than in expansion years. Borrowing the words of Rotemberg and Saloner, “[...] recessions are not only bad because output is low but also because microeco-nomic distortions are greater. This suggests that stabilization of output at a high level is desirable because it reduces these distortions.” (p. 406) To be consistent with Rotemberg and Saloner, our observed recession effect should be strongest in indus-tries that suffer from weak competition. In the next sections we will try to inspect the effect of both market concentration and financing constraints on the mean-reversion of profitability.
6.2 Market concentration
We introduce in the mean-reversion equation additional terms for the differences in in-dustry concentration. We use the same three proxies of market concentration/market power: the fitted Herfindahl-Hirschman index, the relative Lerner index and the fitted HHI interacted with dummies of high/low industry dispersion of firm Lerner indexes. In column (1) of table 7 we present the results for the fitted HHI. The coefficients are neither statistically nor economically significant, in the whole sample as well as in recession and expansion years separately. The simple fitted HHI does not seem to affect significantly the mean-reversion mechanism in profitability
Column (2) shows results using the relative Lerner index as proxy of product mar-ket power. The interaction term for high marmar-ket power is positive and significant for
Table 7: The impact of product market concentration on the mean-reversion of firm profitability.
We regress the change of firm profitability ∆ROAton lagged profitability and lagged change
of profitability. We interact the explanatory variables with a lagged indicator of prod-uct market concentration (PMC). Column (1) shows results using the fitted Herfindahl-Hirschman index (HHI) by Hoberg and Phillips (2010). Column (2) uses the relative Lerner index (price-cost margin of the firm minus its SIC3 sector average). Column (3) uses the fitted HHI and an indicator of within industry dispersion of individual Lerner indexes. Panel A uses all sample years, Panel B only expansion years, Panel C only recession years. LoPMC (HiPMC) is an indicator variable for observations in the bottom (top) 30% in the
previous year’s cross-section of the measure of product market concentration. Loσ(Lerner)
(Hiσ(Lerner)) is an indicator for industries with a dispersion of the firm Lerner indexes
within the SIC3 sector below (above) the median of all sectors in the previous year. The estimation period is from 1977 to 2009. Recession years are defined as years containing at least 2 NBER-declared recession months, namely 1980, 1981, 1982, 1990, 1991, 2001 and 2008.
(1) (2) (3)
HH R-Lerner HH and σ(Lerner)
×Loσ(Lerner) ×Hiσ(Lerner) Panel A: All years (77,621 firm-years)
ROAt−1 −0.271 −0.272 −0.272 (−16.07) (−16.26) (−16.29) ∆ROAt−1 −0.170 −0.162 −0.170 (−10.77) (−9.64) (−10.83) ROAt−1×LoPMC −0.0129 −0.0477 0.00743 −0.00974 (−0.67) (−2.24) (0.38) (−0.45) ROAt−1×HiPMC 0.0210 0.0500 0.0222 (1.44) (4.70) (1.54) Constant 0.00427 0.00340 0.00423 (1.16) (0.94) (1.15)
Panel B: Expansion years (60,873 firm-years)
ROAt−1 −0.280 −0.289 −0.281 (−14.87) (−15.45) (−15.20) ∆ROAt−1 −0.154 −0.149 −0.154 (−8.96) (−7.97) (−9.05) ROAt−1×LoPMC −0.0217 −0.0380 0.0162 −0.0208 (−0.88) (−1.59) (0.63) (−0.76) ROAt−1×HiPMC 0.0158 0.0574 0.0170 (0.93) (4.45) (1.02) Constant 0.00861 0.00758 0.00854 (3.05) (2.58) (3.03)
Panel C: Recession years (16,748 firm-years)
ROAt−1 −0.240 −0.220 −0.241 (−6.50) (−6.85) (−6.45) ∆ROAt−1 −0.221 −0.205 −0.222 (−6.79) (−5.76) (−6.82) ROAt−1×LoPMC 0.0145 −0.0780 −0.0201 0.0249 (0.79) (−1.65) (−2.02) (1.11) ROAt−1×HiPMC 0.0373 0.0268 0.0383 (1.25) (1.68) (1.28) Constant −0.00930 −0.00965 −0.00927 (−0.80) (−0.86) (−0.80)
the whole sample as well as for expansion years. The interaction term for low mar-ket power is negative but not significant. The coefficient pattern suggests that firms enjoying higher market power mean-revert at a rate slower by about 9-10 percentage points per year compared to constrained firms.
We can see in column (3) that interacting the HHI indicator with the indicator of dispersion of firm Lerner indexes does not yield any significant results. We conclude that individual (as opposed to industry) measures of market power are more suitable for analyzing profitability mean-reversion.
6.3 Financing constraints
We study the effect of financing constraints by adding interactions with indicator variables for Low FC and High FC companies for our five proxies. The results in table 8 are consistent across all proxies: firms that were constrained in t − 1 revert to the mean in t at a higher rate than firms that were unconstrained. The effect is the weakest when using the existence of a LT rating as proxy of financing constraints. For the other proxies the rate differential is between 8 and 14 percentage points.
The recession effect (i.e. weaker mean-reversion during downturns) is present in all five cases of the FC proxy. Moreover, the differential in reversion rates between constrained and unconstrained firms is slightly stronger during recession years.
What drives these results ? To answer this question we plotted in figure 4 year-by-year estimates of the overall mean-reversion coefficient as well as the adjusted values for unconstrained and constrained firms based on the WW index. First of all, we see that the overall mean reversion rate effectively tends to decrease during recession years following a higher value in the previous expansion. We also note that the mean-reversion rate for unconstrained firms is systematically lower than that of constrained firms and the gap tends to increase in years of strong expansion. In some years unconstrained firms experienced a “mean-diversion” of profitability, where their mean-reversion coefficient became positive (2001 and 2008). Also, periods of expansion (late 1980s, most of the mid and late 1990s and 2003/04) generally see a high rate of mean-reversion. Lastly, we note that the contribution of 2001 and 2008 to our estimate of lower mean reversion in recession years is clearly very high and that in these two years the overall mean-reversion coefficient was practically zero and only constrained firms were subject to mean reversion.
We conclude that controlling for financing constraints does not eliminate the “re-cession effect” on profitability mean-reversion, but it confirms that this parameter plays an important role in the mechanism of profitability mean-reversion. We show that the recession years of 2001 and 2008 were exceptional compared to other years.
7
Robustness
Our results on financing constraints hold for several proxies commonly used in the literature, four fundamentals-based and one synthetic. Both the sensitivity to the business cycle and the mean-reversion were present on most of our proxies. We
Table 8: The impact of financing constraints on the mean-reversion of firm profitability.
We regress the change of firm profitability ∆ROAt on lagged profitability and lagged
change of profitability. We interact the explanatory variables with a lagged indicator of financing constraints (FC). Column (1) shows results using the Whited and Wu index, (2) uses the payout ratio, (3) uses total assets, (5) and (6) use the indicator of presence of a long-term (short-term) issuer rating. Panel A uses all sample years, Panel B only expansion years, Panel C only recession years. LoFC (HiFC) is an indicator variable for
observations in the bottom (top) 30% in the previous year’s cross-section of the measure of financing constraints. Loσ(Lerner)(Hiσ(Lerner)) is an indicator variable for industries that
have a dispersion of firm Lerner indexes within the SIC3 sector below (above) the median of all sectors in the previous year. The estimation period is from 1977 to 2009. Recession years are defined as years containing at least 2 NBER-declared recession months, namely 1980, 1981, 1982, 1990, 1991, 2001 and 2008.
(1) (2) (3) (4) (5)
WW P/O Size LT rating ST rating
Panel A: All years
ROAt−1 −0.254 −0.215 −0.290 −0.265 −0.267 (−13.72) (−12.25) (−14.75) (−12.15) (−11.92) ∆ROAt−1 −0.167 −0.166 −0.170 −0.209 −0.207 (−10.39) (−10.37) (−10.82) (−14.10) (−13.85) ROAt−1×LoFC 0.0820 −0.0266 0.0668 0.0414 0.153 (7.92) (−1.84) (6.77) (2.29) (11.04) ROAt−1×HiFC −0.0593 −0.0874 0.0129 (−4.47) (−7.03) (0.90) Constant 0.00185 0.00275 0.00385 −0.00258 −0.00291 (0.47) (0.73) (1.02) (−0.61) (−0.70) Observations 77621 77617 77621 63704 63704
Panel B: Expansion years
ROAt−1 −0.269 −0.232 −0.307 −0.275 −0.277 (−15.82) (−14.55) (−16.85) (−12.51) (−12.49) ∆ROAt−1 −0.152 −0.153 −0.156 −0.191 −0.190 (−8.51) (−8.41) (−8.82) (−12.90) (−12.76) ROAt−1×LoFC 0.0784 −0.0219 0.0629 0.0309 0.138 (7.77) (−1.50) (5.84) (1.84) (12.18) ROAt−1×HiFC −0.0535 −0.0819 0.0230 (−3.72) (−5.56) (1.53) Constant 0.00635 0.00722 0.00838 0.00346 0.00308 (2.16) (2.46) (2.98) (1.33) (1.23) Observations 60873 60869 60873 51383 51383
Panel C: Recession years
ROAt−1 −0.208 −0.163 −0.240 −0.226 −0.226 (−3.82) (−3.21) (−4.19) (−3.41) (−3.24) ∆ROAt−1 −0.213 −0.209 −0.216 −0.275 −0.273 (−6.60) (−6.71) (−7.03) (−9.14) (−8.55) ROAt−1×LoFC 0.0932 −0.0413 0.0792 0.0811 0.214 (3.10) (−1.03) (3.33) (1.33) (4.77) ROAt−1×HiFC −0.0774 −0.105 −0.0188 (−2.40) (−4.50) (−0.53) Constant −0.0122 −0.0112 −0.0103 −0.0256 −0.0256 (−0.96) (−0.94) (−0.85) (−1.77) (−1.81) Observations 16748 16748 16748 12321 12321 t statistics in parentheses
−.6 −.4 −.2 0 .2 .4 1970 1980 1990 2000 2010 year
Mean−rev. coef. Mean−rev. coef., high WW Mean−rev. coef., low WW
Figure 4: Mean-reversion coefficient of firm profitability by year.
Presented is the coefficient β of mean-reversion of firm profitability from annual cross-sectional regressions of the change of individual firm profitability (CPit) based on equation
(3) for a given year t. We present the overall coefficient of mean-reversion, as well as the coefficients for firm-years with high and low financing constraints (as measured by the WW index and explained in section 6.) These annual cross-sectional regressions serve for estimating equation 3 with the Fama-MacBeth method, as presented in columns (2) to (4) of table 8. We define recession years as years containing at least two NBER-designated recession months. Recession years are highlighted with a vertical line. The vertical axis uses an inverted scale.
