The Mispricing Return Premium

Brennan, Michael J.; Wang, Ashley W.

doi:10.1093/rfs/hhq064

Cited by 68 publications

(16 citation statements)

References 59 publications

(36 reference statements)

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“…That is, the effect of implementing the corrections considered is always to reduce the bias attributable to noisy prices. Furthermore, for moderate violations of the independence assumption that are in line with the empirical estimates provided by Brennan and Wang (2010) and Hendershott et al (2011), the remaining bias in the corrected estimates is minimal.…”

mentioning

confidence: 57%

“…Ignoring dividends for simplicity, the true and observed (gross) period t returns are simply

R_{t} = P_{t} / P_{t - 1}

and

R_{t}^{o} = P_{t}^{o} / P_{t - 1}^{o}

, respectively. We follow Brennan and Wang (2010) in relaxing the independence assumption to allow the noise component of the prices, denoted

δ_{t}

, to follow an AR(1) process. In Appendix A, we show that the generalized version of the Blume and Stambaugh (1983) result regarding the expected return to any given security is

E (R_{t}^{o}) ≅ E (R_{t}) (1 + σ^{2} (1 - ρ)),

where

σ^{2}

and

ρ

are the variance and first‐order autocorrelation of

δ_{t}

, respectively.…”

Section: The Economics Of Noisy Pricesmentioning

confidence: 99%

“…Two of the most important parameters in terms of determining the magnitude of the biases attributable to noisy prices and the effectiveness of the proposed correction are the cross‐sectional average noise standard deviation,

σ

, and the magnitude of cross‐sectional variation in mean returns. With regard to the former, we rely on Brennan and Wang (2010), who provide what appears to be the most relevant evidence available to date. They study monthly returns to CRSP common stocks, and estimate

σ = 0.06

(Brennan and Wang (2010, table 2)).…”

Section: Return Estimators In the Presence Of Noisy Pricesmentioning

confidence: 99%

“…They study monthly returns to CRSP common stocks, and estimate

σ = 0.06

(Brennan and Wang (2010, table 2)). We assign a

σ_{n}

to each stock from a uniform distribution on

[0, 0.12]

13 We also accommodate dependence between

σ_{n}

and regression explanatory variables, again relying on estimates provided by Brennan and Wang. In particular, we choose parameters such that the correlation between

σ_{n}

and firm value is −0.235 14…”

Section: Return Estimators In the Presence Of Noisy Pricesmentioning

confidence: 99%

“…The available empirical evidence indicates that the degree of persistence in the noise in prices is modest. Evaluating monthly returns to CRSP common stocks, Brennan and Wang (2010) report a cross‐sectional mean

ρ

estimate equal to 0.07, while Hendershott et al (2011) study monthly returns to NYSE stocks and report a mean estimate of 0.15. Given

c = 0

, the biases in the corrected estimates are small, and are very close to zero for

ρ

in this range.…”

Section: Return Estimators In the Presence Of Noisy Pricesmentioning

confidence: 99%

See 4 more Smart Citations

Noisy Prices and Inference Regarding Returns

Asparouhova¹,

Bessembinder²,

Kalcheva

2013

The Journal of Finance

224

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Temporary deviations of trade prices from fundamental values impart bias to estimates of mean returns to individual securities, to differences in mean returns across portfolios, and to parameters estimated in return regressions. We consider a number of corrections, and show them to be effective under reasonable assumptions. In an application to the Center for Research in Security Prices monthly returns, the corrections indicate significant biases in uncorrected return premium estimates associated with an array of firm characteristics. The bias can be large in economic terms, for example, equal to 50% or more of the corrected estimate for firm size and share price.

show abstract

mentioning

confidence: 57%

“…Ignoring dividends for simplicity, the true and observed (gross) period t returns are simply

R_{t} = P_{t} / P_{t - 1}

and

R_{t}^{o} = P_{t}^{o} / P_{t - 1}^{o}

, respectively. We follow Brennan and Wang (2010) in relaxing the independence assumption to allow the noise component of the prices, denoted

δ_{t}

, to follow an AR(1) process. In Appendix A, we show that the generalized version of the Blume and Stambaugh (1983) result regarding the expected return to any given security is

E (R_{t}^{o}) ≅ E (R_{t}) (1 + σ^{2} (1 - ρ)),

where

σ^{2}

and

ρ

are the variance and first‐order autocorrelation of

δ_{t}

, respectively.…”

Section: The Economics Of Noisy Pricesmentioning

confidence: 99%

σ

σ = 0.06

(Brennan and Wang (2010, table 2)).…”

Section: Return Estimators In the Presence Of Noisy Pricesmentioning

confidence: 99%

“…They study monthly returns to CRSP common stocks, and estimate

σ = 0.06

(Brennan and Wang (2010, table 2)). We assign a

σ_{n}

to each stock from a uniform distribution on

[0, 0.12]

13 We also accommodate dependence between

σ_{n}

and regression explanatory variables, again relying on estimates provided by Brennan and Wang. In particular, we choose parameters such that the correlation between