Long-term return reversal: Evidence from international market indices

Malin, Mirela; Bornholt, Graham N.

doi:10.1016/j.intfin.2013.01.002

Cited by 51 publications

(33 citation statements)

References 20 publications

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“…The additional benefit of the MSCI indices is that they are calculated in several different ways, including different currencies, controlling for taxes, accounting for dividends, etc. For example, the MSCI indices were used by Dobrynskaya (2015), Clare et al (2016), Fisher et al (2017), Keppler and Encinosa (2011), Richards (1997), Balvers and Wu (2006), Keimling (2016), Keloharju et al (2016), Malin and Bornholt (2013), Ferson and Harvey (1994b), and many others.…”

Section: Asset Universementioning

confidence: 99%

“…Otherwise, the grouping could result in portfolios containing only a few-or even one-markets, hence being susceptible to the noise in returns. The most popular choices include tertiles (e.g., Daniel and Moskowitz 2016;Geczy and Samonov 2017;Asness et al 2013;Atilgan et al 2019), quartiles (Richards 1997;Blitz and van Vliet 2008;Macedo 1995a;Malin and Bornholt 2013;Erb et al 1995), or quintiles (Clare et al 2016). Alternatively, some studies which assume different portfolio formation methodologies, consider only two portfolios-long and short (e.g., Moskowitz et al 2012).…”

Section: Number Of Portfoliosmentioning

confidence: 99%

“…Gharaibeh (2015) corroborated the long-term reversal phenomenon in the Middle East market indices, and Spierdijk et al (2012) confirmed the findings in study periods exceeding a century. The strategy works well for 36-60-month sorting periods, and Malin and Bornholt (2013), who develop so-called "late stage" contrarian strategies, experiment also with skipping the most recent 12 months as in Fama and French (1996). Finally, Smith and Pantilei (2015) develop a simple mean-reversion-based strategy, which they called "Dogs of the Word".…”

Section: Long-run Reversalmentioning

confidence: 99%

“…Indeed, country-level long-term reversal tends to be very volatile and unstable over time, but its robustness over very long periods casts doubt on such an explanation (Spierdijk et al 2012). Furthermore, Malin and Bornholt (2013), who employ longitudinal analysis, argue that the mean-reversion effect is present even in the post-1989 sample despite the absence of visible contrarian profits for the developed markets.…”

Section: Long-run Reversalmentioning

confidence: 99%

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The Cross Section of Country Equity Returns: A Review of Empirical Literature

Zaremba

2019

JRFM

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The last three decades brought mounting evidence regarding the cross-sectional predictability of country equity returns. The studies not only documented country-level counterparts of well-established stock-level anomalies, such as size, value, or momentum, but also demonstrated some unique return-predicting signals such as fund flows or political regimes. Nonetheless, the different studies vary remarkably in terms of their dataset and methods employed. This study aims to provide a comprehensive review of the current literature on the cross-section of country equity returns. We focus on three particular aspects of the asset pricing literature. First, we study the choice of dataset and sample preparation methods. Second, we survey different aspects of the methodological approaches. Last but not least, we review the country-level equity anomalies discovered so far. The discussed cross-sectional return patterns not only provide new insights into international asset pricing but can also be potentially translated into effective country allocation strategies.

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Section: Asset Universementioning

confidence: 99%

Section: Number Of Portfoliosmentioning

confidence: 99%

Section: Long-run Reversalmentioning

confidence: 99%

Section: Long-run Reversalmentioning

confidence: 99%

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The Cross Section of Country Equity Returns: A Review of Empirical Literature

Zaremba

2019

JRFM

View full text Add to dashboard Cite

show abstract

“…Note that there is a kind of investment strategy called contrarian strategy, the essence of which is to selectively buy stocks performing badly and sell stocks performing well with the purpose of taking advantage of return reversals to make profits [11, 13, 21, 22, 27, 49, 50]. By accurately identifying key drivers of return reversal in advance, our research can hopefully help design more profitable contrarian strategies.…”

Section: Introductionmentioning

confidence: 99%

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

et al. 2016

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In the stock market, return reversal occurs when investors sell overbought stocks and buy oversold stocks, reversing the stocks’ price trends. In this paper, we develop a new method to identify key drivers of return reversal by incorporating a comprehensive set of factors derived from different economic theories into one unified dynamical Bayesian factor graph. We then use the model to depict factor relationships and their dynamics, from which we make some interesting discoveries about the mechanism behind return reversals. Through extensive experiments on the US stock market, we conclude that among the various factors, the liquidity factors consistently emerge as key drivers of return reversal, which is in support of the theory of liquidity effect. Specifically, we find that stocks with high turnover rates or high Amihud illiquidity measures have a greater probability of experiencing return reversals. Apart from the consistent drivers, we find other drivers of return reversal that generally change from year to year, and they serve as important characteristics for evaluating the trends of stock returns. Besides, we also identify some seldom discussed yet enlightening inter-factor relationships, one of which shows that stocks in Finance and Insurance industry are more likely to have high Amihud illiquidity measures in comparison with those in other industries. These conclusions are robust for return reversals under different thresholds.

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Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market

Troster

Penalva

Taamouti

et al. 2021

Journal of Forecasting

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This paper shows that lagged information transmission between industry portfolio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period 1963-2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long-term common factor between industry portfolio and market cumulative returns, has strong predictive power for excess industry portfolio returns. In line with gradual information diffusion across connected industries, the equilibrium error proxies for changes in the investment opportunity set that lead to industry return predictability by informed investors. Forecasting models including the equilibrium error have superior forecasting performance relative to models without it, illustrating the importance of cointegration between the industry portfolio and market prices. Overall, our findings have important implications for investment and risk-management decisions, since the out-of-sample explanatory power of the equilibrium error is economically meaningful for making optimal portfolio allocations.

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Long-term return reversal: Evidence from international market indices

Cited by 51 publications

References 20 publications

The Cross Section of Country Equity Returns: A Review of Empirical Literature

The Cross Section of Country Equity Returns: A Review of Empirical Literature

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market

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