Analysis of intraday herding behavior among the sector ETFs

Gleason, Kimberly C.; Mathur, Ike; Peterson, Mark A.

doi:10.1016/j.jempfin.2003.06.003

Cited by 239 publications

(163 citation statements)

References 38 publications

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“…According to Gleason, Mathur, and Peterson (2004), Demirer and Kutan (2006) and Tan et al (2008) herd behavior can be revealed through market volatility Cross Vol. 7, No.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…According to Gleason, Mathur, and Peterson (2004) and Henker, Henker and Mitsios (2004), dispersion measures the extent to which investors follow market's expectations (degree of herd behavior), and accordingly, if investors adopt herd behavior, we expect return dispersion to be less than the mean.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Experimental studies have focused primarily on the United States and the European and Asian markets. Many researches (Christie & Huang, 1995;Chang et al, 2000;Gleason, Mathur, & Peterson, 2004) has rejected the existence hypothesis of the herd behavior in the US stock market. In contrast, Demirer and Kutan, (2006) concluded that the lack of evidence to herd behavior in the Chinese Stock Market.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Sectional Absolute Deviation Approach for Testing the Herd Behavior Theory: The Case of the ASE Index

Ramadan¹

2015

IJEF

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This study aimed to test whether the herd behavior appears in the Amman Stock Exchange (ASE). Using data on a daily basis for a sample of companies in the Free Float Share Weighted Index during the study period from the beginning of the, 2000 to the end of August 2014 and using the Cross-Sectional Absolute Deviation (CSAD) Approach. The results found that the non-linear relationship between the cross sectional absolute deviation of the stock returns and the return of the market portfolio is an inverse relationship (γ 3 = -0.179), so that the dispersion decreases with the increase in market rate of return, which means that investors during the study period were emulating the performance of the market without paying attention to the stock's characteristics regarding risk and return, which suggests that investors are taking the herd behavior.

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“…According to Gleason, Mathur, and Peterson (2004), Demirer and Kutan (2006) and Tan et al (2008) herd behavior can be revealed through market volatility Cross Vol. 7, No.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cross-Sectional Absolute Deviation Approach for Testing the Herd Behavior Theory: The Case of the ASE Index

Ramadan¹

2015

IJEF

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“…It follows from the first-order condition, (72) that the optimization problem can be reduced to finding the value for α which maximizes the concentrated likelihood…”

Section: Estimating the Parameters Of Stable Paretian Distributionmentioning

confidence: 99%

“…Some examples are risk management (e.g., Beltratti and Morana (1999)), model evaluation (e.g., Gençay et al(2002), and Bollerslev and Zhang (2003)), market efficiency test (e.g., Hotchkiss and Ronen (2002)), electricity price analysis (e.g., Longstaff and Wang (2004)), information shocks to financial market (e.g., Faust et al (2003) and Adams et al (2004)), and financial market anomalies (e.g., Gleason et al (2004)). …”

Section: Introductionmentioning

confidence: 99%

Long-Range Dependence, Fractal Processes, and Intra-Daily Data

Sun

Rachev

Fabozzi

Handbook on Information Technology in Finance

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With the availability of intra-daily price data, researchers have focused more attention on market microstructure issues to understand and help formulate strategies for the timing of trades. The purpose of this article is to provide a brief survey of the research employing intra-daily price data. Specifically, we review stylized facts of intra-daily data, econometric issues of data analysis, application of intra-daily data in volatility and liquidity research, and the applications to market microstructure theory. Long-range dependence is observed in intra-daily data. Because fractal processes or fractional integrated models are usually used to model long-range dependence, we also provide a review of fractal processes and long-range dependence in order to consider them in future research using intra-daily data.

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Overnight returns of industry exchange‐traded funds, investor sentiment, and futures market returns

Lee

Liao

Lee

2022

Journal of Futures Markets

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This study investigates whether investor sentiment estimated by overnight returns of industry exchange‐traded funds (ETFs) affects Volatility Index (VIX) futures and stock index futures returns. Our empirical results indicate that high overnight returns of industry ETFs are associated with sentiment‐based trading. The results also show that investor sentiment, as measured by the relative comovements of overnight returns of industry ETFs, Granger‐causes VIX futures and stock index futures returns, but not vice versa. Finally, investor sentiment, as measured by the relative comovements, displays statistically and economically significant out‐of‐sample predictive power for VIX futures and stock index futures returns.

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Analysis of intraday herding behavior among the sector ETFs

Cited by 239 publications

References 38 publications

Cross-Sectional Absolute Deviation Approach for Testing the Herd Behavior Theory: The Case of the ASE Index

Cross-Sectional Absolute Deviation Approach for Testing the Herd Behavior Theory: The Case of the ASE Index

Long-Range Dependence, Fractal Processes, and Intra-Daily Data

Overnight returns of industry exchange‐traded funds, investor sentiment, and futures market returns

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