Is value riskier than growth?

Petkova, Ralitsa; Zhang, Lu

doi:10.1016/j.jfineco.2004.12.001

Cited by 533 publications

(144 citation statements)

References 29 publications

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“…2 2 However, Petkova and Zhang (2003) show, using the longer sample from 1927 to 2001 than the short sample from 1963 to 1991 used by Fama and French (1992), that the unconditional market beta spread between value and growth is 0.41, much higher than the effective zero reported by Fama and French. …”

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confidence: 85%

“…Lakonishok et al (1994, p. 1569) contend that “performance in extreme bad states is often the last refuge of those claiming that a high return strategy must be riskier, even when conventional measures of risk such as beta and standard deviation do not show it” (original emphasis). However, Petkova and Zhang (2003) show that they define good and bad times by sorting on the ex post realized market excess returns, as opposed to the more theoretically justifiable expected market risk premium. As a result, their procedure biases the estimates of business cycle sensitivities of value and growth betas toward zero.…”

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confidence: 97%

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The Value Premium

Zhang

2005

The Journal of Finance

1,212

613

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The value anomaly arises naturally in the neoclassical framework with rational expectations. Costly reversibility and countercyclical price of risk cause assets in place to be harder to reduce, and hence are riskier than growth options especially in bad times when the price of risk is high. By linking risk and expected returns to economic primitives, such as tastes and technology, my model generates many empirical regularities in the cross-section of returns; it also yields an array of new refutable hypotheses providing fresh directions for future empirical research.

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confidence: 85%

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confidence: 97%

The Value Premium

Zhang

2005

The Journal of Finance

1,212

613

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“…Specifically, we split the months in our sample into states based on the realized level of RRD during the month. Following Petkova and Zhang (2005) we define four states of the world: State 1 (Low) corresponds to the 10% lowest observations for RRD; State 2 corresponds to below-average RRD, excluding the 10% lowest observations; State 3 corresponds to above-average RRD excluding the 10% highest observations; and State 4 (High) corresponds to the 10% highest observations for RRD. We then document raw and risk-adjusted returns of the accrual and investment hedge portfolios in each state, and we test whether the difference in performance between high and low RRD states is statistically significant.…”

Section: Return Dispersion and Variation In The Profitability Of Accrmentioning

confidence: 99%

“…The results show that innovations in RD (RRD) are positively priced in the cross section of individual stock returns and that low-accrual and low-investment firms We sort calendar months into groups based on the relative return dispersion (RRD) level realized during each month. Following Petkova and Zhang (2005) we define four states of the world: State 1 (Low) corresponds to the 10% lowest observations for RRD; State 2 corresponds to below-average RRD, excluding the 10% lowest observations; State 3 corresponds to above-average RRD excluding the 10% highest observations; and State 4 (High) corresponds to the 10% highest observations for RRD. Panel A reports the average monthly accrual and investment hedge raw returns (percent) for each state.…”

Section: Robustness Checksmentioning

confidence: 99%

Does return dispersion explain the accrual and investment anomalies?

Chichernea

Holder

Petkevich

2015

Journal of Accounting and Economics

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“…In the literature, there are a number of studies that model time-varying risk premium (Ferson and Harvey 1991;Gagliardini, Ossalo, Scaillet 2011;Lettau and Ludvigson 2001) and some other studies that model beta as a linear function of conditioning variables (Ferson and Harvey 1999;Ferson and Schadt 1996). However, Petkova and Zhang (2005) and Ferson and Harvey (1993) estimate both expected market risk premium and conditional betas separately as a linear function of conditioning variables. Furthermore, there are studies that find time-varying sensitivities to size, book-to-market ratio and momentum payoffs that are related to the business cycle and provide explanation to time-varying expected returns (Chordia and Shivakumar 2002;Fama and French 1997;Liew and Vassalou 2000).…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Size, value and momentum in stock returns: evidence from India

Das

Barai

2016

Macroeconomics and Finance in Emerging Market Economies

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This paper examines the effects of size, value and momentum on the cross-sectional relation between expected returns and risk in the Indian stock market. We find that the conditional Carhart four-factor model empirically describes the variation of crosssection of return better than the unconditional model. When size, book-to-market and momentum effects are controlled in the conditional model, the positive relation of market beta, book-to-market and momentum with expected returns remains economically and statistically significant. However, this evidence is found to be subject to characteristics of test portfolios. The expected returns are sensitive to changes in predictive macroeconomic variables. IntroductionThe Capital Asset Pricing Model (CAPM) developed by Sharpe and Lintner has become an important tool in finance for the assessment of cost of capital, valuing investments, portfolio performance measurement and diversification among others. However, recent empirical research in finance focused on firm-specific characteristic or anomalies that explain the cross-section of expected return better than the traditional asset pricing models. Fama and French (1993) propose a three-factor asset pricing model to include small-minus-big size (SMB) and high-minus-low book-to-market (HML) risk factors with the traditional CAPM to capture size and value effect, respectively. Motivated by the Fama-French three-factor model, Carhart (1997) further improved the model by including winners-minus-losers momentum (WML) factor to capture momentum effect with the three-factor model. In light of the recent developments and issues on asset pricing, there are many empirical researches on size, value and momentum effects in the developed stock market especially in the US. Despite the facts that emerging stock markets constitute a significant share of the world equity market, it has received less attention in this regard. There is a discrepancy in the domestic investor's participation rate, equity premium and risk in emerging markets against developed market like the US (Bekaert and Harvey 2013; Giannetti and Koskinen 2010). As the capital market of these markets has become more integrated with regional and world markets, it is necessary to provide academics and practitioners a better understanding on the cross-sectional behaviour of stock returns in these markets. This study is an attempt to address this issue in the Indian context. Indian markets have experienced a phenomenal growth in the last two decades. The consequence

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Is value riskier than growth?

Cited by 533 publications

References 29 publications

The Value Premium

The Value Premium

Does return dispersion explain the accrual and investment anomalies?

Size, value and momentum in stock returns: evidence from India

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