When are Contrarian Profits Due to Stock Market Overreaction?

Lo, Andrew W.; MacKinlay, A. Craig

doi:10.3386/w2977

Cited by 489 publications

(691 citation statements)

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“…The individual stock returns within each industry are more volatile, and autocorrelations are smaller and generally negative, averaging between 0.0 and -0.14. These return characteristics are consistent with those reported for earlier sample periods (see Lo and MacKinlay (1990), Moskowitz and Grinblatt (1999) and others).…”

Section: Empirical Evidencesupporting

confidence: 91%

Industries and Stock Return Reversals

Hameed¹,

Mian²

2014

J. Financ. Quant. Anal.

108

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This paper documents pervasive evidence of intra-industry reversals in monthly returns.Unlike the conventional reversal strategy based on stock returns relative to the market portfolio, we document intra-industry return reversals that are larger in magnitude, consistently present over time, and prevalent across sub-group of stocks, including large and liquid stocks. These return reversals are driven by order imbalances and non-informational shocks. Consistent with reversals representing compensation for supplying liquidity, intra-industry reversals are stronger following aggregate market declines and volatile times, reflecting binding capital constraints and limited risk bearing capacity of liquidity providers.

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Section: Empirical Evidencesupporting

confidence: 91%

Industries and Stock Return Reversals

Hameed¹,

Mian²

2014

J. Financ. Quant. Anal.

108

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“…Strategies less than 6-6 months are avoided as they require high-frequency price data which may in turn reflect microstructure impacts (see Lo and MacKinlay, 1990).…”

Section: Methodsmentioning

confidence: 99%

Past price changes, trading volume and prediction of portfolio returns

Sehgal

Vasishth

2015

Journal of Advances in Management Research

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2015),"Perishable food supply chain quality (PFSCQ): A structured review and implications for future research"If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to evaluate the profitability of investment strategies based on past price changes and trading volumes. Design/methodology/approach -Data are employed from January 1998 to December 2011 for select emerging markets. Portfolios are formed on the basis of past information on prices and/or volumes. Unrestricted and risk adjusted returns for sample portfolios are analyzed. The risk models employed in study are Capital Asset Pricing Model (CAPM), Fama-French (F-F) Model and Fama-French augmented models. Findings -Price momentum patterns are observed for Brazil, India, South Africa and South Korea, while there are reversals in Indonesia and China. Low-volume stocks outperform high-volume stocks for all sample countries except China. Further, volume and price based bivariate strategies do a better job than univariate strategies in case of India, South Africa and South Korea. The past price and volume patterns in stock returns are not fully explained by CAPM as well as the F-F Model. Price and volume momentum factors do play a role in explaining some of these return patterns. Finally, the unexplained returns seem to be an outcome of investor under or overreaction to past information. The sources of price and volume momentum seem to be partly risk based and partly behavioral. Originality/value -The study analyzes combined role of price and volume in portfolio formation with post holding analysis. The work is useful for global portfolio managers, policy makers, market regulators and the academic community. The study contributes to asset pricing and behavioral finance literature for emerging markets.

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“…Cross-autocorrelations between different stocks or stock portfolios are reported by Lo and MacKinlay [1990]; Conrad, Kaul, and Nimalendran [1991] and Badrinath, Kale, and Noe [1995]; studies which find lead-lag patterns for international stock markets include Eun and Shim [1989]; Arshanapalli and Doukas [1993]; Richards [1995]; Soydemir [2000]; and Masih and Masih [2001]. Lead-lag relations between stock indices and stock index futures are investigated by Kawaller, Koch, and Koch [1987]; Stoll and Whaley [1990]; and Chan [1992].…”

Section: Endnotesmentioning

confidence: 96%

Do Lead-Lag Effects Affect Derivative Pricing?

Korn¹,

Uhrig‐Homburg²

2007

JOD

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The chosen parameter values are σ p 2 = σ s 2 = 0.3, a = 1, and ρ ps is either -1, -0.5, 0, or 0.5.. The current share price is always P(0) = 40, the current index level S(0) is either 40, 45, or 50, and the time to maturity is either one year, five years, or 10 years. Variances and covariance of finite holding-period returns are fixed at Var [∆p * ]/τ = 0.1456, Var [∆s * ]/τ = 0.1456, and Cov[∆p * , ∆s * ]/τ = 0.0728. This exhibit shows parameter estimates resulting from estimator (3), corrected estimates obtained from Equations (12), and the corresponding theoretical prices (in €) of one SAP stock option with 10 years to maturity on December 31, 1999, when the stock price was €161.67. Option prices are given for freeze-out periods of 2, 3, and 4 years.This exhibits shows estimated autocorrelations and cross-autocorrelations based on 250 daily observations from the period preceding December 31, 1999. Autocorrelations are provided for up to 10 lags and cross-autocorrelations for up to five lags. Negative lags for the cross-atutocorrelations refer to lagged returns of the GSTI Software Index and positive lags to lagged returns of SAP stocks. The horizontal lines are the boundaries of a 95% confidence interval. SUMMARY AND CONCLUSIONLo and Wang [1995] have shown that the predictability of an asset's return has an impact on derivative prices since, for a fixed variance of finite-holding period returns, predictability changes the instantaneous variance. We extend Lo and Wang's analysis by considering 46 DO LEAD-LAG EFFECTS AFFECT DERIVATIVE PRICING? FALL 2007 E X H I B I T 5 Parameter Estimates and Quanto Forward Price AdjustmentsThis exhibit shows parameter estimates resulting from estimator (3), the corrected estimates obtained from Equations (12), and the corresponding discount rates from Equation (21). The data consists of daily returns on the BOVESPA Index and the relative changes of the value of U.S. $1 in Brazilian real over the year 2006.This exhibit shows parameter estimates resulting from estimator (3), the corrected estimates obtained from Equations (12), and the present value of the yearly incentive fee charged by the DWS European Stocks Type-O Fund (in percent of the net asset value). The data consists of daily returns of the fund and the MSCI Europe index over the year 2006.

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When are Contrarian Profits Due to Stock Market Overreaction?

Cited by 489 publications

References 13 publications

Industries and Stock Return Reversals

Industries and Stock Return Reversals

Past price changes, trading volume and prediction of portfolio returns

Do Lead-Lag Effects Affect Derivative Pricing?

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