Lead–Lag Effects in Australian Industry Portfolios

Haque, Tariq H.

doi:10.1007/s10690-010-9125-1

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…He argues that gradual information diffusion mainly exists in intra-industry rather than cross-industry or outside-industry. Haque [20] further support the hypotheses of Hou [4] with data in Australia. However, they only focus on the internal impact factors of intra-industry information diffusion.…”

Section: Introductionsupporting

confidence: 76%

“…Not only that, the number of firms in each portfolio should be enough for analysis. Examining information diffusion within an industry in the Australian market, Haque [20] chooses industries that have five to 26 firms. Thus, the number of firms in each portfolio is only two to seven.…”

Section: Datamentioning

confidence: 99%

“…This method of calculating weekly returns is commonly used in the literature, see, for example, Hameed and Kusnadi [3], and Hou [4]. Nevertheless, Chordia and Swaminathan [10] and Haque [20] employ both the weekly data and daily data to run the Vector Auto-regression (VAR) model. They find that the result of weekly data and the result of daily data are similar.…”

Section: Datamentioning

confidence: 99%

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The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China

2019

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: Through an investigation into seven major industries in China’s stock market from 2002 to 2013, this study focuses on two main external determinants: market condition and policy change on intra-industry information diffusion. We employ both time-series and panel Vector Auto-regression (VAR) methods on a sample data of 1175 firms for the analysis. The investigation reveals that market conditions and policy changes affect the process of intra-industry information diffusion in China. The speed of intra-industry information diffusion in a down-market state is slower than an upmarket, especially when the evidence is more significant in the longer horizon of the market condition. Policy changes, especially the split-share structure reform, impede the process of intra-industry information diffusion. The investigation outcome also reveals that there is an increasing delay in intra-industry information diffusion over time in China’s stock market after 2005. However, because of the decreasing information volatility of intra-industry information diffusion, policy changes are useful to a certain extent.

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Section: Introductionsupporting

confidence: 76%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China

2019

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“…After this, we compute the alphas of these strategies using as the market portfolio the MSCI World Total Return Index 3 and as the risk-free rate the 4-week T-bill rate as the risk-free rate. Other trading strategies based on lead-lag information have also been presented in the literature, with positive results (see, for instance, Copeland and Copeland 1998 ; Haque 2011 ; Haque 2011 ; Stübinger 2019 , and most notably, Li et al 2022 ).…”

Section: Methodsmentioning

confidence: 98%

“…Lead-lags may arise from information frictions (Aye et al 2017 ), the differential response of some stocks to newly released information (Lo and MacKinlay 1990b ), or asymmetry in trade and liquidity of assets and markets. Additionally, they may be driven by a behavioral trend-chasing strategy whereby lead-lag effects are a function of the degree to which investors are familiar with the stocks in question (Haque 2011 ), different paces of adjustment to the various phases of the business cycle (Kanas and Kouretas 2005 ), different reactions of assets to industry factors (Hou 2007 ), stocks having time-varying and different sensitivities to common fundamental risk factors (Conrad and Kaul 1988 ), market microstructure frictions (Boudoukh et al 1994a ), different monetary and fiscal policies (Caporale et al 2016 ), or behavioral factors such as irrationality, herding, and gaming behavior (Li et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Industry return lead-lag relationships between the US and other major countries

2023

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In this study, we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021. We identify the leading role played by the US internationally by showing that the weekly returns of US industries, especially the US basic materials and energy industries, significantly Granger cause the returns of most other countries’ industries, suggesting that non-US industries react with some delay to new information. This delayed reaction is even more noticeable during periods of recession in the US when cross-country correlations are higher. This implies that the ability of the lagged returns of US industries to predict industries’ returns from other countries is even more pronounced when the US experienced an economic recession. A similar asymmetric relationship is found between the volatility of US industries and that of industries in other markets. The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries, particularly in the presence of extreme negative shocks. Finally, we demonstrate that our predictions are valuable to real-world investors. Long-short strategies generate sizable and statistically significant alphas, and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

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