the high returns to low volatility stocks are actually a premium on high quality firms

Review of Financial Economics xxx (2013) xxx–xxx

REVFIN-00309; No of Pages 7

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Review of Financial Economics

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The high returns to low volatility stocks are actually a premium on highquality firms

Christian Walkshäusl ⁎University of Regensburg, Center of Finance, Universitätsstraße 31, 93053 Regensburg, Germany

⁎ Tel.: +49 941 943 2729.

1 See, for instance, Ang et al. (2006, 2009), Clarke, deand van Vliet (2007), Baker et al. (2011), and Baker andand Cakici (2008) find for the U.S. that the volatility efportfolios are equal-weighted and when the volatilitmonthly instead of daily data.

1058-3300/$ – see front matter © 2013 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.rfe.2013.06.001

Please cite this article as:Walkshäusl, C., TheEconomics (2013), http://dx.doi.org/10.1016

a b s t r a c t
a r t i c l e i n f o
Article history:Received 19 April 2013Received in revised form 14 June 2013Accepted 18 June 2013Available online xxxx

JEL classification:G11G12G15

Keywords:Volatility effectQuality investingAsset pricingInternational markets

Recent empirical research shows that low volatility stocks outperform high volatility stocks around the world.This study documents that the volatility effect is associated with the quality of the firm using a large sample ofinternational stocks. First, adding a quality factor to the Fama–French model contributes to the explanation ofthe volatility effect. Furthermore, the negative volatility–return relation is shown to be stronger and significantonly among high quality firms which are profitable and have stable cash flows. Second, a fundamental invest-ment strategy that goes long high quality firms and short low quality firms performs like a volatility strategyand cannot be explained by common asset pricing models. However, a low–high volatility factor adds to the ex-planation of the return difference between high and low quality stocks as volatility and quality strategies have acommon component.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

A large number of recent empirical research document that low vol-atility stocks have higher average returns than high volatility stocksaround the world.1 The outperformance of low volatility stocks overhigh volatility stocks is economically exceptionally large, amountingon average to 12% per year. Baker, Bradley, and Wurgler (2011, p. 43)therefore argue that “the outperformance of low-risk portfolios is per-haps the greatest anomaly in finance”.

Risk-based explanations have problems in describing the observedreturn pattern, as the return difference between low and high riskstocks cannot be captured by common asset pricing models. This ismainly due to the fact that low volatility stocks have typically lowmarket betas, whereas high volatility stocks exhibit high marketbetas. Blitz and van Vliet (2007) therefore argue that low risk stocksshould be considered as a distinct asset class in the strategic asset al-location process.

Ang, Hodrick, Xing, and Zhang (2006, 2009) rule out a large num-ber of possible explanations for the observed volatility effect in U.S.

Silva, and Thorley (2006), BlitzHaugen (2012). However, Balifect is weaker when volatilityy variable is estimated using

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high returns to low volatility/j.rfe.2013.06.001

and international returns. They provide evidence that explanationsbased on aggregatemarket volatility risk, microstructuremeasures, dis-persion in analysts' forecasts, costs of trading, and information dissem-ination cannot explain the negative volatility–return relation aroundthe world. Baker et al. (2011) offer behavioral explanations for thisanomaly. They argue that the volatility effect may be partly explainedby the irrational preference for high volatility stocks by individual in-vestors and the institutional investor's mandate to beat a given bench-mark which limits investments in low volatility stocks.

In this paper, we examine a large sample of international firms withtwo goals. First, we document that the volatility effect, the empirical ev-idence of high returns to low volatility stocks, is associated with thequality of thefirm asmeasured by profitability and cashflowvariability.Second, we propose a fundamental investment strategy based on thequality of the firm that performs like a volatility strategy and presentevidence that volatility and quality strategies have a common compo-nent in international markets.

In thefirst part of the paper,we show that the high returns to lowvol-atility stocks are associatedwith the quality of the firm in financial terms.After having established the puzzling negative volatility–return relationin international markets, we create at first a quality factor based on prof-itability or cash flow variability that we use as the fourth factor to theFama–French model, extending it to a quality-enhanced four-factormodel for explaining the return behavior of volatility portfolios. In linewith Huang (2009), we use cash flow from operations as a proxy forthe firm's economic earnings as accounting earnings may underestimate

stocks are actually a premiumon high quality firms, Review of Financial

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2 C. Walkshäusl / Review of Financial Economics xxx (2013) xxx–xxx

the variability in operational profit due to earnings smoothing. McGuire,Schneeweis, and Branch (1990) document that profitability and operat-ing income growth are important determinants for investors' perceptionof firmquality. Moreover, it is well documented that the stockmarket re-sponses favorably to firms with high profitability and low cash flow var-iability. Famaand French (2006) show thatfirmswithhigher profitabilityearn higher future returns. Using a variety of alternative profitabilitymeasures, Bali, Demirtas, and Tehranian (2008) document in a broaderstudy likewise a strong time-series and cross-sectional return predict-ability of profitability. Allayannis, Rountree, and Weston (2005) showthat cash flow variability has a negative impact on firm value.

The regression results based on the quality-enhanced four-factormodel reveal that quality adds significantly to the explanation ofthe volatility effect. The loadings on the quality factor decrease mono-tonically from low to high volatility portfolios and the risk-adjustedoutperformance of low volatility stocks over high volatility stocks isreduced in magnitude and statistical significance compared to theFama–French model.

To further examine the relation between quality and the volatilityeffect in international markets, we separate firms into low and highquality firms according to their profitability and cash flow variability.We document that firms with low return volatilities earn high andsignificant risk-adjusted returns among high quality firms but closeto zero returns among low quality firms. Consequently, the volatilityeffect comes out to be strong among high quality firms but weakamong low quality firms.

Our international evidence is related to previous U.S. findings.Huang (2009) shows that average returns decrease with higher levelsof cash flow variability and that cash flow variability is closely relatedto the idiosyncratic volatility in average returns. Irvine and Pontiff(2009) find that the increase in idiosyncratic volatility over time(Campbell, Lettau, Malkiel, & Xu, 2001) is associated with an increasein the cash flow variability and that this increase is largely attributableto the intensifiedmarket competition.Wei and Zhang (2006) documentthat stock return volatility is negatively related to the profitability of thefirm. While the relation of idiosyncratic volatility with profitability andcashflow variability has been documented for the United States, this re-lation is virtually unknown for other countries. Using a large sample ofinternational equity markets, we thus contribute to the literature bydocumenting how the volatility anomaly is affected when the firm'sprofitability and cash flow variability is taken into account which haslikewise not been directly investigated by the previous U.S. literature.

Though financial leverage is also well-known to influence investors'perception offirmquality (McGuire et al., 1990),we do not usefinancialleverage as a quality characteristic in this study due to the fact that Anget al. (2006, 2009) find that controlling for leverage cannot explain theobserved volatility effect in U.S. and non-U.S. equity markets.

In the secondpart of the paper,we show that afinancial quality or lowfundamental risk strategy performs like a return volatility strategy. Basedon our international findings and the previous U.S. evidence, we proposean easily implementable yearly-rebalanced strategy that goes long highquality stocks, i.e., firms with high profitability and low cash flow vari-ability and short low quality stocks, i.e., firms with low profitability andhigh cash flow variability. We find that the return difference associatedwith quality is large and similar to the one documented for a low–highvolatility strategy. In particular, the risk-adjusted outperformance ofhigh quality stocks over low quality stocks amounts to more than 0.91%per month and cannot be explained by common asset pricing models.Furthermore, assessing the performance over longer horizons showsthat the outperformance lasts for up to three years after portfolio forma-tion, making the quality strategy exceptionally promising for long-terminvestors. Though the return difference between high quality stocks andlow quality stocks cannot be attributed to conventional measures ofrisk, we present evidence that the quality strategy and the volatility strat-egy have a common component as a low–high volatility factor adds sig-nificantly to the explanation of the return behavior of the quality strategy.

Please cite this article as:Walkshäusl, C., The high returns to low volatilityEconomics (2013), http://dx.doi.org/10.1016/j.rfe.2013.06.001

In this study, we define quality solely in terms of financial strengthusing measures based on profitability and cash flow variability whichaim to reflect the quality of the firm. Profitable firms with stable cashflows have assumingly solid business models and sustainable com-petitive advantages in the market and should therefore be regardedas firms of high quality. However, we are well aware that firm qualitycan be defined in other ways. For instance, Anderson and Smith(2006) and Anginer and Statman (2010) use the survey of companyreputation published by the Fortune magazine to separate firms intogroups of admired (high quality) firms and spurned (low quality)firms. Hence, our findings may be potentially related to the strandof literature examining the relation between the firm's perceived rep-utation and subsequent returns due to the fact that the firm's reputa-tion is highly correlated with the firm's financial strength as pointedout by McGuire et al. (1990).

The remainder of the paper is organized as follows. Section 2 de-scribes the international data and variables. Section 3 establishesthe puzzling negative relation between volatility and average returnsin international markets. In Section 4, we show that the volatility ef-fect is related to the financial quality of the firm. Section 5 investi-gates the performance of a quality strategy based on profitabilityand cash flow variability, and shows that volatility and quality strate-gies have a common component. Finally, Section 6 concludes thepaper.

2. Data and variables

The international sample in this study consists of firms from 22non-U.S. developed markets. The selection of countries resembles thecountries classified as developed markets according to Morgan StanleyCapital International (MSCI). We obtain total return data on commonstocks from Datastream and accounting data (e.g., the book value of eq-uity) fromWorldscope. All data are denominated in U.S. dollars and theone-month U.S. Treasury bill rate is used as the risk-free rate. We followAng et al. (2009) and exclude very small firms by eliminating the 5% offirms with the lowest market capitalization in each country. The sampleperiod is July 1985 to December 2011. However, we use for the construc-tion of variables accounting data going back to 1980 (the initial year ofavailable accounting information in Worldscope).

Panel A in Table 1 shows summary statistics for the countries includ-ed in the international sample. Except for five markets, the majority ofcountries in the sample have return data available from the beginningof the sample period. The two largest markets are Japan and the UnitedKingdom. Japan accounts on average for 2469 firms and 31.2% of thesample's total market capitalization, whereas the United Kingdom con-stitutes 1215 firms and 13.5% of total market capitalization. Theremaining countries are smaller in terms of sample firms and total mar-ket capitalization.

The variables used in this study are defined as follows. A firm's sizeis its market capitalization, i.e., its stock price multiplied by the num-ber of shares outstanding. Book-to-market is the ratio of book value ofequity to the market value of equity at the fiscal year end. Volatility isthe idiosyncratic volatility relative to the Fama–French model usingdaily returns over the previous month (Ang et al., 2006, 2009). Prof-itability is equity income (income before extraordinary items) divid-ed by lagged book equity. Cash flow variability is the standarddeviation of cash flow from operations scaled by the number of sharesoutstanding measured over a five-year period.

The construction of the Fama–Frenchmodel follows Fama and French(1993, 1998). The market factor (MKT) is the value-weighted return ofall stocks in excess of the risk-free rate. The size factor (SMB, smallminus big) is the return difference between a portfolio of small stocksand a portfolio of big stocks. The value factor (HML, high minus low) isthe return difference between a portfolio of high book-to-market stocksand a portfolio of low book-to-market stocks.



Table 1Summary statistics.This table shows summary statistics for the countries included in the internationalsample and the variables used in this study. Panel A reports the start year of returns,the average number of firms in each country, and the country's average percentageweight in terms of total market capitalization. Panel B reports the mean, 25th percen-tile, median, and the 75th percentile of the variables. Size is market capitalization(stock price multiplied by the number of shares outstanding) in million U.S. dollars.Book-to-market is the ratio of book value of equity to the market value of equity atthe fiscal year end. Volatility is the idiosyncratic volatility relative to the Fama–Frenchmodel using daily returns over the previous month (reported in annualized terms).Profitability is equity income (income before extraordinary items) divided by laggedbook equity. Cash flow variability is the standard deviation of cash flow from opera-tions scaled by the number of shares outstanding measured over a five-year period.The sample period is July 1985 to December 2011.

Panel A: sample countries

Country Start year Firms Weight (in %)

Australia 1985 580 4.4Austria 1985 70 0.6Belgium 1985 94 1.2Canada 1985 605 7.3Denmark 1985 143 0.9Finland 1987 81 0.9France 1985 531 8.2Germany 1985 483 7.0Greece 1988 154 0.8Hong Kong 1985 436 4.5Ireland 1985 45 0.6Italy 1985 206 3.7Japan 1985 2469 31.2Netherlands 1985 134 2.8New Zealand 1986 59 0.3Norway 1985 127 0.9Portugal 1988 49 0.6Singapore 1985 276 2.0Spain 1987 111 3.1Sweden 1985 204 2.2Switzerland 1985 179 3.3United Kingdom 1985 1215 13.5

Panel B: variables

Variable Mean 25th Median 75th

Size 941 52 167 581Book-to-market 0.84 0.39 0.66 1.08Volatility 0.38 0.23 0.33 0.47Profitability 0.06 0.00 0.07 0.16Cash flow variability 0.97 0.08 0.24 0.84

0.99 0.98

0.870.82

0.29

0.70

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Low 2 3 4 High Low–High

Ave

rag

e M

on

thly

Ret

urn

(in

%)

Volatility Portfolio

Fig. 1. Volatility portfolio returns. This figure shows average monthly returns (in %) forportfolios sorted on volatility. The low–high portfolio takes a long position in the lowportfolio and a short position in the high portfolio, representing the return differencebetween low and high volatility stocks.

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The breakpoints for the sorts on size and book-to-market are the30th and 70th percentiles, respectively. For perspective, the averagemarket premium during the sample period is 0.58% per month(t = 2.01), and the average SMB and HML returns are 0.23% permonth (t = 1.31) and 0.36% per month (t = 2.34), respectively.Hence, there exists a significant value premium in internationalreturns during the sample period, whereas the evidence for a reliablesize premium is statistically weak.

Panel B in Table 1 shows summary statistics for the variables usedin this study. A typical firm in the sample has a size of $941 million interms of market capitalization and a book-to-market ratio of 0.84. Theaverage (idiosyncratic) volatility is 38% per year relative to the Fama–French model. The mean (median) profitability is 6% (7%) and themean (median) cash flow variability is 97% (24%) among internation-al firms.

3. The negative volatility–return relation

We begin our analysis by replicating the finding that low volatilitystocks have higher average returns than high volatility stocks, culminat-ing in the puzzling volatility anomaly. In particular, to examine the vola-tility–return relation, we create portfolios sorted on volatility that areformed as follows. Each month, all stocks in the sample are allocated to


five portfolios based on the quintile breakpoints for the volatility variableand value-weighted returns on the portfolios are calculated for the subse-quent month and the portfolios are rebalanced eachmonth. The low vol-atility portfolio contains the stocks that have the lowest (idiosyncratic)return volatility, whereas the high volatility portfolio contains the stocksthat have the highest (idiosyncratic) return volatility. The low–high port-folio takes a long position in the low volatility portfolio and a short posi-tion in the high volatility portfolio, representing the return differencebetween low and high volatility stocks.

Fig. 1 shows average monthly returns on the volatility portfolios.We observe a clear negative volatility–return relation. Averagereturns constantly decrease with higher levels of volatility. The port-folio of stocks with the lowest volatilities produces on average 0.99%per month, whereas the portfolio of stocks with the highest volatil-ities yields only 0.29% per month, resulting in a sizeable return differ-ence of 0.70% per month or 8.40% per year. This indicates that lowvolatility stocks outperform high volatility stocks. Can the observedreturn behavior be explained by common asset pricing models?

Table 2 shows the risk-adjusted returns of volatility portfoliosusing the CAPM and Fama–French model. The results make clearthat the observed return pattern cannot be accounted for by risk ad-justment. The CAPM alpha for the low–high portfolio is 1.18% permonth and the Fama–French alpha is 0.98% per month, and both arestatistically highly significant. The high returns to low volatility stockscannot be attributed to higher systematic risk, as low volatility stocksare in general low beta stocks. Furthermore, we confirm that low vol-atility stocks tend to be large value stocks with high book-to-marketratios, whereas high volatility stocks tend to be small growth stockswith low book-to-market ratios, as indicated by the respective factorloadings on SMB and HML.

In summary, our findings are in line with the previous evidence inAng et al. (2006, 2009) and others, confirming the existence of astrong volatility effect in average returns around the world.

Table 3 shows the characteristics of volatility portfolios. Expectedly,the average annualized volatility increases from low to high portfolios.The average volatility is 15% per year for the low volatility portfolioand 74% per year for the high volatility portfolio. As variations inmarketbeta, size, and book-to-market cannot capture the return difference be-tween portfolios of low and high volatilities, we concentrate in the fol-lowing on quality characteristics based on profitability and cash flowvariability to shed light on the volatility anomaly.

Similar to the previous U.S. evidence (Wei & Zhang, 2006; Huang,2009; Irvine & Pontiff, 2009), there seems to be a negative relation be-tween volatility and profitability, and a positive relation between vola-tility and cash flow variability in international markets. Low volatility


image of Fig.�1


Table 2Risk-adjusted volatility portfolio returns.This table shows the risk-adjusted returns of volatility portfolios. The table reports re-sults from regressing the monthly excess returns of volatility portfolios on the explan-atory factors of the CAPM and the Fama–French model. α, the alpha estimate, is therisk-adjusted return according to the considered model. MKT is the market factor(the value-weighted return of all stocks in excess of the risk-free rate), SMB is thesize factor (the return difference between small and big stocks), and HML is thevalue factor (the return difference between high and low book-to-market stocks). Ro-bust Newey–West t-statistics are reported in parentheses.

Portfolio CAPM Fama–French model

α MKT α MKT SMB HML

Low 0.30 0.63 0.18 0.68 0.03 0.24(2.68) (18.93) (1.61) (20.60) (0.53) (6.30)

2 0.18 0.84 0.09 0.88 −0.03 0.20(2.53) (36.86) (1.34) (47.79) (−0.74) (6.25)

3 −0.05 1.06 −0.07 1.06 −0.00 0.04(−0.90) (76.65) (−1.13) (85.53) (−0.12) (1.56)

4 −0.24 1.28 −0.15 1.25 0.13 −0.27(−1.74) (42.84) (−1.20) (35.16) (2.19) (−4.85)

High −0.88 1.49 −0.80 1.47 0.40 −0.46(−3.59) (25.30) (−3.37) (23.03) (3.07) (−3.55)

Low–high 1.18 −0.86 0.98 −0.79 −0.37 0.70(3.61) (−10.31) (3.06) (−8.62) (−2.16) (4.53)

Table 4Risk-adjusted volatility portfolio returns based on a quality-enhanced four-factormodel.This table shows the risk-adjusted returns of volatility portfolios based on a quality-enhancedfour-factor model. The table reports results from regressing the monthly excess returns ofvolatility portfolios on the explanatory factors of the Fama–Frenchmodel and a quality factor.α, the alpha estimate, is the risk-adjusted return according to the model. MKT is the marketfactor (the value-weighted return of all stocks in excess of the risk-free rate), SMB is the sizefactor (the return difference between small and big stocks), and HML is the value factor (thereturn difference between high and low book-to-market stocks). QPRO, in Panel A, is thequality factor based on profitability (the return difference between high and low profitabilitystocks) and QCFV, in Panel B, is the quality factor based on cash flow variability (the returndifference between low and high cash flow variability stocks). Robust Newey–Westt-statistics are reported in parentheses.

Panel A: quality factor based on profitability

Portfolio α MKT SMB HML QPRO

Low 0.05 0.73 0.08 0.17 0.25(0.54) (21.61) (1.21) (5.06) (7.29)

2 −0.01 0.91 0.02 0.15 0.18(−0.08) (45.68) (0.61) (4.80) (7.18)

3 −0.07 1.05 0.02 0.05 −0.06(−1.13) (90.01) (1.02) (2.17) (−2.52)

4 −0.05 1.20 0.13 −0.20 −0.28(−0.46) (36.50) (2.28) (−3.01) (−3.44)

High −0.66 1.38 0.39 −0.35 −0.37(−2.76) (20.64) (2.95) (−2.32) (−3.79)

Low–high 0.71 −0.65 −0.31 0.52 0.62(2.37) (−6.88) (−1.84) (2.92) (5.46)

Panel B: quality factor based on cash flow variability

Portfolio α MKT SMB HML QCFV


stocks tend to be profitable firmswith low cash flow variability, where-as high volatility stocks tend to be unprofitable firms with high cashflow variability. Consequently, a factor based on quality characteristicsmay contribute to the explanation of the return difference betweenlow and high volatility stocks. Furthermore, the volatility effect shouldbe stronger for high quality firms which are profitable and have stablecash flows.

4. Quality and the volatility–return relation

To investigate the relation between quality and the volatility effectin international markets, we begin our analysis by creating a qualityfactor as the fourth factor to the Fama–French model, extending itto a quality-enhanced four-factor model. We are interested in wheth-er a quality factor is able to further reduce the puzzling return differ-ence between low and high volatility stocks.

The quality factor is formed like the value factor. As we have twoquality characteristics, we create two quality factors and use them se-quentially as the fourth factor in the analysis. When quality is definedby profitability, the corresponding quality factor (QPRO) is the returndifference between a portfolio of high profitability stocks and a port-folio of low profitability stocks. When quality is defined by cash flowvariability, the corresponding quality factor (QCFV) is the return dif-ference between a portfolio of low cash flow variability stocks and aportfolio of high cash flow variability stocks. The average quality pre-miums during the sample period are 0.40% per month (t = 2.43)based on profitability (QPRO) and 0.33% per month (t = 2.52)based on cash flow variability (QCFV).

Table 4 shows the risk-adjusted returns of volatility portfoliosbased on a quality-enhanced four-factor model. Regardless of the ap-plied quality factor, we observe a clear pattern that portfolios sortedon volatility are strongly related to the quality of the firm as mea-sured by profitability and cash flow variability. The loadings on the

Table 3Characteristics of volatility portfolios.This table shows the characteristics of volatility portfolios. The table reports the aver-age (idiosyncratic) volatility (in annualized terms), profitability, and cash flowvariability.

Characteristic Low 2 3 4 High

Volatility 0.15 0.25 0.34 0.46 0.74Profitability 0.11 0.10 0.08 0.04 −0.03Cash flow variability 0.78 0.80 0.91 1.07 1.31


quality factor decrease monotonically from low to high volatilityportfolios. Low volatility stocks load significantly positive on quality,whereas high volatility stocks load significantly negative on quality.

Augmenting the Fama–French model with a quality factor thereforecontributes to the explanation of the volatility effect. The quality-enhanced four-factor model is able to further reduce the puzzlingrisk-adjusted outperformance of lowvolatility stocks over high volatilitystocks from 0.98% in the Fama–French model to 0.73% or 0.71% permonth. However, though the t-statistics of the alpha estimates are re-duced as well, the outperformance still remains significant.

To shed further light on the relation between quality and the vol-atility effect in international markets, we form double-sorted portfo-lios on profitability or cash flow variability and volatility. Then, weexamine the strength and significance of the risk-adjusted return dif-ference between low and high volatility stocks in the two groups offirms.

By way of illustration, the profitability–volatility portfolios areformed as follows. Each month, all stocks in the sample are allocatedto two profitability groups using the median as breakpoint and to fivevolatility groups based on the quintile breakpoints for the volatility var-iable. Value-weighted returns on the portfolios are calculated for thesubsequentmonth and theportfolios are rebalanced eachmonth. To en-sure that the accounting data used for the construction of profitability ispublicly available at the time of portfolio formation, wematch portfolioreturns before July in calendar year t with accounting data for the fiscalyear ending in calendar year t-2, and portfolio returns from July

Low 0.08 0.70 0.07 0.22 0.20(0.79) (22.97) (1.16) (6.22) (4.12)

2 0.02 0.89 0.02 0.18 0.12(0.34) (50.34) (0.45) (6.45) (2.95)

3 −0.07 1.05 0.02 0.03 −0.05(−1.05) (95.08) (0.98) (1.47) (−1.78)

4 −0.07 1.22 0.13 −0.26 −0.24(−0.62) (37.00) (2.13) (−4.45) (−3.69)

High −0.65 1.42 0.38 −0.45 −0.41(−2.81) (22.32) (2.74) (−3.20) (−3.63)

Low–high 0.73 −0.72 −0.31 0.67 0.61(2.42) (−8.12) (−1.68) (4.15) (4.25)



Table 6Risk-adjusted volatility portfolio returns with control for cash flow variability.This table shows the risk-adjusted returns of volatility portfolios for firms with lowcash flow variability and for firms with high cash flow variability using the median asbreakpoint for placing firms to the low or high cash flow variability group. The table re-ports the alpha estimates from regressing the monthly excess returns of volatility port-folios on the explanatory factors of the Fama–French model. Robust Newey–Westt-statistics are reported in parentheses.

Portfolio Low CF variability High CF variability

Low 0.32 0.07(2.53) (0.59)

2 0.19 0.07(1.69) (0.79)

3 −0.05 0.01(−0.47) (0.12)

4 0.00 −0.18(0.05) (−1.52)

High −0.33 −0.35(−1.40) (−1.53)

Low–high 0.65 0.42(2.28) (1.36)


onwards in calendar year t with accounting data for the fiscal year end-ing in calendar year t-1. The lag is consistent with Fama and French(1993) and others. The cash flow variability–volatility portfolios areformed in analogous manner.

Table 5 shows the risk-adjusted returns of volatility portfolioswith control for profitability based on the Fama–French model.Firms with high profitability are in general perceived as high qualityfirms and vice versa.

Controlling for profitability produces similarly to Table 2 portfolioreturns that decrease from low to high volatilities. However, low vol-atility stocks significantly outperform high volatility stocks onlyamong profitable firms. The risk-adjusted return on the low–highportfolio among firms with high profitability is large with 0.71% permonth and significant, whereas the risk-adjusted return differenceamong firms with low profitability has about half the magnitude(0.39% per month) and is statistically no longer distinguishable fromzero.

Interestingly, while the source of the positive Fama–French alphaof the unrestricted volatility strategy in Table 2 comes largely fromthe short position, i.e., the significant underperformance of high vola-tility stocks, the alpha source of the volatility strategy among profit-able firms comes apparently from the long position and is thereforeactually due to the significant outperformance of low volatility stocks.

Table 6 shows the risk-adjusted returns of volatility portfolioswith control for cash flow variability based on the Fama–Frenchmodel. Firms with low cash flow variability are in general perceivedas high quality firms and vice versa.

Like profitability, cash flow variability substantially affects the vola-tility–return relation. Though average returns decrease with higherlevels of volatility in both groups, significant risk-adjusted returns onthe low–high portfolio are only detected among firms with low cashflow variability. The magnitude of the Fama–French alpha with 0.65%per month is comparable to the one in Table 5 among profitable firms.Furthermore, similar to the profitability-controlled volatility portfolios,the alpha source comes againmainly from the long position, i.e., the sig-nificant outperformance of low volatility stocks among firms with lowcash flow variability.

5. The quality strategy

After having established in the previous section that the volatilityeffect is in general related to quality and stronger among high qualityfirms, we show in this section that a quality or low fundamental riskstrategy performs like a return volatility strategy and that quality andvolatility strategies are two sides of the same coin as they have a com-mon component in international markets.

Table 5Risk-adjusted volatility portfolio returns with control for profitability.This table shows the risk-adjusted returns of volatility portfolios for firms with lowprofitability and for firms with high profitability using the median as breakpoint forplacing firms to the low or high profitability group. The table reports the alpha esti-mates from regressing the monthly excess returns of volatility portfolios on the explan-atory factors of the Fama–French model. Robust Newey–West t-statistics are reportedin parentheses.

Portfolio Low profitability High profitability

Low −0.06 0.25(−0.45) (2.32)

2 −0.13 0.25(−1.02) (2.64)

3 −0.10 0.11(−0.74) (1.30)

4 −0.15 0.10(−0.92) (0.97)

High −0.45 −0.46(−1.78) (−1.86)

Low–high 0.39 0.71(1.22) (2.38)


Based on our former empirical findings and the previous U.S. evi-dence, we propose an easily implementable strategy that goes longhigh quality stocks, i.e., firms with high profitability and low cashflow variability and short low quality stocks, i.e., firms with low prof-itability and high cash flow variability. In particular, each June, we al-locate all stocks in the sample to five profitability groups and to fivecash flow variability groups based on the quintile breakpoints forthe variables. Value-weighted returns on the portfolios are calculatedfor the subsequent twelve months and the portfolios are rebalancedeach year. The low quality portfolio contains the stocks that havethe lowest profitability and the highest cash flow variability, whereasthe high quality portfolio contains the stocks that have the highestprofitability and the lowest cash flow variability. The high–low port-folio takes a long position in the high quality portfolio and a short po-sition in the low quality portfolio, representing the return differencebetween high and low quality stocks.

Fig. 2 shows average monthly returns on the quality portfolios.The portfolio of high quality stocks generates on average 1.35% permonth, whereas the portfolio of low quality stocks exhibits only0.67% per month, resulting in a return premium of 0.68% per monthor 8.16% per year for high quality stocks over low quality stocks inthe high–low portfolio. The return difference is largely similar to thespread observed in Fig. 1 between low and high volatility stocks.

Fig. 3 illustrates the cumulative payoff of a $1 investment in theportfolio of low quality stocks (dashed line) and the portfolio ofhigh quality stocks (solid line) over the sample period. For compari-son, a similar investment in the market portfolio (dotted line) is in-cluded. The figure clearly demonstrates that an investment in highquality stocks rewards large payoffs. The $1 investment in the portfo-lio of high quality stocks grows over time to $40.95, whereas a similarinvestment in the portfolio of low quality stocks yields only to $4.01at the end of the sample period. This is considerably less than a like-wise investment in the market portfolio which produces a cumulativepayoff of $11.23.

Table 7 shows the risk-adjusted returns of quality portfolios usingthe CAPM and Fama–French model. The results reveal that low qualitystocks tend to be penalizedwith negative risk-adjusted returns (thoughnot statistically significant), whereas high quality stocks are rewardedwith positive and significant risk-adjusted returns. The high–low port-folio demonstrates that high quality stocks significantly outperformlow quality stocks by a sizeable risk-adjusted return. The CAPM alphais 0.79% per month and the Fama–French alpha amounts to 0.91% permonth which is comparable in magnitude to the risk-adjusted spreadreturn of the volatility portfolios in Table 2.

High quality stocks tend to be low beta stocks, similar to low vol-atility stocks, whereas low quality stocks tend to be high beta stocks,



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Fig. 2. Quality portfolio returns. Thisfigure shows averagemonthly returns (in %) for port-folios of low quality firms and high quality firms. The high–low portfolio takes a long po-sition in the high portfolio and a short position in the low portfolio, representing thereturn difference between high and low quality stocks.

Table 7Risk-adjusted quality portfolio returns.This table shows the risk-adjusted returns of quality portfolios. The table reports re-sults from regressing the monthly excess returns of quality portfolios on the explana-tory factors of the CAPM and the Fama–French model. α, the alpha estimate, is therisk-adjusted return according to the considered model. MKT is the market factor(the value-weighted return of all stocks in excess of the risk-free rate), SMB is thesize factor (the return difference between small and big stocks), and HML is thevalue factor (the return difference between high and low book-to-market stocks). Ro-bust Newey–West t-statistics are reported in parentheses.

Portfolio CAPM Fama–French model

α MKT α MKT SMB HML

Low −0.28 1.06 −0.52 1.15 0.20 0.41(−0.80) (13.38) (−1.62) (21.65) (2.04) (2.82)

High 0.51 0.87 0.38 0.95 0.14 0.12(2.91) (13.78) (2.48) (15.68) (1.47) (1.11)

High–low 0.79 −0.19 0.91 −0.20 −0.06 −0.29(2.77) (−2.06) (3.15) (−2.32) (−0.34) (−1.81)


similar to high volatility stocks. However, the spread in the marketbeta is smaller in comparison to the volatility sorts. The portfolio oflow quality stocks exhibits significantly positive exposures to thesize and value factors. Thus, low quality stocks have the characteris-tics of small value stocks with high book-to-market ratios. However,the value characteristic cannot be thought of as a promising underval-uation in the sense of value investing in this case but rather has to beunderstood as a signal of financial distress. Fama and French (1995)show that small firms and firms with high book-to-market ratiosare associated with low profitability. As low quality stocks have lowprofitability measures, the factor loadings are in line with the litera-ture. The results of the high quality portfolio reveal that high qualitystocks have neither a significant exposure to the size factor nor a sig-nificant exposure to the value factor. Thus, the stocks in the high qual-ity portfolio seem to be firms with average market capitalizations andaverage book-to-market ratios.

How persistent is the performance of the quality strategy overlonger holding periods? Fig. 4 shows longer horizon risk-adjustedreturns of high–low quality portfolios in the second, third, and fourthyear after portfolio formation based on the Fama–French model. Thequality strategy seems to be strongly persistent over longer holdingperiods. More precisely, the results suggest that high quality stockssignificantly outperform low quality stocks for at least three yearsafter portfolio formation, making the quality strategy exceptionally

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Fig. 3. Cumulative payoff of low and high quality stocks. This figure illustrates the cu-mulative payoff of a $1 investment in the portfolio of low quality stocks (dashed line)and the portfolio of high quality stocks (solid line) over the sample period. For compar-ison, a similar investment in the market portfolio (dotted line) is included.


promising for long-term investors. The risk-adjusted returns in thesecond and third year are large with 0.77% per month (t = 2.87)and 0.74% per month (t = 2.72), and both are statistically significant.However, the risk-adjusted return in the fourth year is considerablysmaller (0.37% per month, t = 1.47) and no longer statistically distin-guishable from zero, thus, indicating that the return difference be-tween high and low quality stocks becomes weak after this time.Though high quality firms tend to maintain their high quality charac-teristic for more than three years after portfolio formation, low qual-ity firms undergo a quicker reversion to the mean, i.e., their values forprofitability and cash flow variability move towards the sample'smean. As higher profitability and lower cash flow variability is associ-ated with higher subsequent stock returns, the average return on thelow quality portfolio increases therefore over time and thus rendersthe return spread between high and low quality stocks insignificant.

Based on our previous findings, we suggest that the volatilitystrategy and the quality strategy might have a common component.Following the approach of Rouwenhorst (1998), we evaluate this as-sumption by estimating a regression of the high–low quality portfolioreturns on low–high volatility portfolio returns (see Fig. 1) to test forpotential co-movement between the two strategies. We include inthis regression the controls of the Fama–French model, so that thespecification can be thought of as a volatility-enhanced four-factormodel in analogy to Table 4. If the quality strategy is related to thevolatility strategy in international markets, then there should be a sig-nificant loading on the volatility factor.

Table 8 shows the risk-adjusted return of the quality strategy con-ditional on the volatility strategy and the co-movement between the

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Fig. 4. Longer horizon risk-adjusted high–low quality returns. This figure shows therisk-adjusted monthly returns (in %) of high–low quality portfolios in the second,third, and fourth year after portfolio formation based on the Fama–French model.


image of Fig.�2

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Table 8Co-movement between quality and volatility strategies.This table shows the risk-adjusted return of the quality strategy conditional on the vol-atility strategy and the co-movement between the strategies. The table reports resultsfrom regressing the monthly high–low quality portfolio returns on the low–high vola-tility portfolio returns (see Fig. 1) with control for the explanatory factors of the Fama–French model. Robust Newey–West t-statistics are reported in parentheses.

α MKT SMB HML VOL

0.69 −0.08 0.05 −0.43 0.24(2.27) (−0.79) (0.14) (−2.96) (3.71)


strategies. The regression results reveal that the quality strategy issignificantly positively related to the return behavior associatedwith the volatility strategy as indicated by the respective factor load-ing. Thus, we present evidence that volatility and quality strategieshave a common component in international markets. Furthermore,the volatility factor adds to the explanation of the return differencebetween high and low quality stocks. In comparison to the results ofthe Fama–French model in Table 7, the risk-adjusted return of thequality strategy is further reduced in terms of magnitude and corre-sponding t-statistic. Though the outperformance remains significant,the level is almost identical to the one of the volatility strategy inthe quality-enhanced four-factor model, which underscores that thetwo strategies seem to be two sides of the same coin.

6. Conclusions

Using a large sample of international equitymarkets, this paper stud-ies how the volatility effect, the empirical evidence of high returns tolow volatility stocks, is associated with the quality of the firm aroundthe world.

First, adding a quality factor based on profitability or cash flow vari-ability to the Fama–French model contributes to the explanation of thevolatility effect. Lowvolatility stocks load significantly positive on quality,whereas high volatility stocks load significantly negative on quality, thus,reducing the puzzling return difference between low and high volatilitystocks. Furthermore, after separating firms into low and high qualityfirms according to their profitability and cash flow variability, we docu-ment that low volatility stocks earn high and significant risk-adjustedreturns only among firms with high profitability and among firms withlow cash flow variability. Hence, the negative volatility–return relationis shown to be stronger for high quality firms.

Second, as low volatility stocks exhibit characteristics of high qualityfirms, we propose an easily implementable yearly-rebalanced invest-ment strategy that goes long high quality stocks and short low qualitystocks. We find that the quality strategy yields risk-adjusted returnssimilar to a return volatility strategy and that its outperformance lastsfor up to three years after portfolio formation, making it exceptionallypromising for long-term investors. Though the return difference be-tween high and low quality stocks cannot be explained by common


asset pricing models, a low–high volatility factor adds significantly tothe explanation of the return behavior of the quality strategy as volatil-ity and quality strategies have a common component and thereforeseem to be two sides of the same coin.

In a recent study, Bali, Cakici, and Whitelaw (2011) find a strongrelation between the maximum daily return over the past month(MAX) and the idiosyncratic volatility. Controlling for MAX reversesthe puzzling negative volatility–return relation in the U.S. equity mar-ket. Looking at the relation between MAX and quality in internationalmarkets is beyond the scope of this paper but promises to be an inter-esting avenue for future research.

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the high returns to low volatility stocks are actually a premium on high quality firms

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