Herding behavior, market sentiment and volatility: Will the bubble resume?

Bekiros, Stelios; Jlassi, Mouna; Lucey, Brian M.; Naoui, Kamel; Uddin, Gazi Salah

doi:10.1016/j.najef.2017.07.005

Cited by 92 publications

(67 citation statements)

References 75 publications

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“…Results of the study indicated inverse feedback found in Asian financial markets as a consequence of the herding phenomenon (Bekiros, Jlassi, Lucey, Naoui, & Uddin, 2017). In one of the studies herding behavior of Muslims in Islamic banking and finance has been studied.…”

Section: Review Of Literaturementioning

confidence: 94%

An Analysis of Behavioral Biases in Investment Decision-Making

Madaan¹,

Singh²

2019

IJFR

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Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.

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Section: Review Of Literaturementioning

confidence: 94%

An Analysis of Behavioral Biases in Investment Decision-Making

Madaan¹,

Singh²

2019

IJFR

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“…Scholars have also shown that herding is more pronounced in crisis and bullish periods (see Bikhchandani and Sharma 2000; Bowe and Domuta 2004;Hwang and Salmon 2004;Philippas et al, 2013;Yao et al, 2014;Galariotis et al, 2015;Bekiros et al, 2017). As such, dummy variables D bullish and D crisis are introduced, which take value 1 during the period of the bullish market (the largest bullish cluster) from 3/15/2017 to 1/05/2018, and during the period of crisis (the largest crisis cluster) from 1/07/2018 to 12/05/2018, respectively, or otherwise zero.…”

Section: Econometric Frameworkmentioning

confidence: 99%

Herd behaviour in the cryptocurrency market: Fundamental vs. spurious herding

Senarathne¹,

Jiang²

2020

EurJ Appl Econ

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This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.

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“…As the launch of the Shanghai-Hong Kong Stock Connect, its net capital flow has become an important market reference for both institutional and individual investors, affecting investors' sentiment. Existing literature shows that there is asymmetry of herd behavior in stock market (Christie & Huang, 1995;Hwang & Salmon, 2004;Bekiros et al, 2017). The magnitude of herding amplifies generally in bearish market.…”

Section: Modified Cck Modelmentioning

confidence: 99%

Herding Behavior between Chinese and Hong Kong Stock Market—Based on Shanghai-Hong Kong Stock Connect Policy

He¹

2020

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This paper investigates herding behavior between Chinese A-share and HK stock market based on Shanghai-Hong Kong Stock Connect. This research innovatively details the difference in herding behavior between two markets bidirectionally. By applying CCK model and quantile regression, we examine herding behavior between Chinese and HK stock market in asymmetric situations from the perspective of net capital flow of Shanghai-Hong Kong Stock Connect. Our findings suggest that the impact of HK share on A share's herding behavior increases after Shanghai-Hong Kong Stock Connect starts trading. To some extent, the direction of net capital flow of Shanghai-Hong Kong Stock Connect acts as a powerful indicator for investors, which skillfully deflects Chinese stock market. Besides, the way in which HK's stock market influences that of Chinese mainland has undergone a transformation.Herding refers to the tendency of investors' imitating others trading decision. According to the definition of Christie and Huang (1995), herd behavior is a phenomenon that people give up their personal judgment and base their investment decisions on collective behavior in the market, mimicking the investment decisions of others. This can be explained by psychological mechanism. Kameda and Nakanishi (2003) argue that herding behavior depends on the cost of information collection. When the economic cost of information collection and cognition becomes high, investors tend to follow behavior of others in order to save resources. In addition, heuristic is one of the ways in which humans process information (Jiang et al., 2010). Investors treat investment behaviors of others and experts as a source of information. Therefore, when faced with uncertainty, they tend to be consistent with others' trading activity for believing that others' investment information is more efficient and authoritative.After implementation of Shanghai-Hong Kong Stock Connect, China's stock market has become the world's 2rd largest one in terms of market capitalization and trading volume (in a broad sense, including Shanghai, Shenzhen and Hong Kong stock market). However, Chinese's stock market as an emerging one has some defects such as irrational valuations, excessive volatility and imperfect regulations. Among A-share investors, individual investors account for more than a half, indicating that they are still the investment entities with the greatest influence in the market. These problems further increase the irrationality of investment behavior. Therefore, understanding herd behavior of China's capital market is of great significance for improvement of capital market efficiency and the integration of A stock market and HK equity market.This study focuses on bidirectional herd behavior between A-share and HK stock market. Specifically, this paper examines the cross-market contagion of herd behavior between the two stock markets before and after the launch of Shanghai-HK Stock Connect. This study innovatively details the difference of cross-market herding behavior between the ...

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Herding behavior, market sentiment and volatility: Will the bubble resume?

Cited by 92 publications

References 75 publications

An Analysis of Behavioral Biases in Investment Decision-Making

An Analysis of Behavioral Biases in Investment Decision-Making

Herd behaviour in the cryptocurrency market: Fundamental vs. spurious herding

Herding Behavior between Chinese and Hong Kong Stock Market—Based on Shanghai-Hong Kong Stock Connect Policy

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