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Motivated by the ongoing debate on the existence and magnitude of herding in financial markets, the purpose of this paper is to examine Eurozone stock markets for herding behavior. In the context of the present study, the authors seek for herding behavior of stock markets as a whole as opposed to previous studies that examine herding on stock level.

To this end, the authors employ data on benchmark stock market indices for a long sample starting from 2000 through 2016. The testing procedure entails the standard Capital Asset Pricing Model-based procedure along with an advanced econometric method allowing the coefficients of the model to vary over time.

Results provide evidence in favor of negative herding behavior (anti-herding) for the Eurozone as a whole with noteworthy transitions. Further analysis reveals that stock markets of the periphery exhibit scarce evidence of herding, whereas continental countries are mainly characterized by negative herding behavior.

The present study’s main contribution is twofold. First, herding is examined not in sector or stock level as previous studies but at market level. Second, the testing methodology entails a pure time-varying regression model with stochastic volatility proposed by Nakajima (2011) that has not been previously employed in stock market herding. The results entail significant implications for investors seeking for diversification across Eurozone stock markets.

This study aims to test the presence of herding behavior in commodity markets, including energy, metals and agriculture. Additionally, the authors investigate the possible asymmetric effect of oil price changes on the herding behavior in these markets.

The authors examine herding based on the cross-sectional absolute deviation (CSAD) model in a static and time-varying perspective.

By using daily data over the period 2003–2017, the authors’ findings firstly support the dynamic nature of investor behavior in commodity markets, which oscillates between antiherding during the normal period and herding during and after the global financial crisis of 2008. Furthermore, results highlight that the asymmetric impact of oil shocks on herding differs across commodity sectors and periods. Additionally, herding seems to be more pronounced when the oil market declines, which may be due to the pessimistic investors' sentiments.

This study provides insight into what factors influence herd behavior in commodity markets. The understanding of factors driving herding aids investors to avoid the impact of this behavior and its consequences

To the authors’ knowledge, this study is the first to examine whether the level of herding depends on the oil price fluctuations, as well as the asymmetric effect of the oil price on herding behavior in commodity markets.

Our study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market.

We used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc.

Applying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market.

Our results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors.

Our study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.

The purpose of this paper is to: investigate whether the foreign institutional investors in Taiwan herd towards the stocks in the same industry; identify the causes of industrial herding; analyze whether herding behavior impacts future industrial returns; and trace the changing pattern of industrial herding, especially during the 2007‐2008 financial crisis.

This paper applies Sias' herding measure to identify foreign institutional industrial herding behavior. Moreover, to identify the causes and impacts of herding, the authors use regression models to analyze the relationship between foreign institutional demand for stocks in some particular industries and industrial returns, controlling industrial market capitalization, the number of firms in the industry and industrial speculative intensity. The above methods are applied to the full sample period, as well as two sub‐periods, respectively, to trace the time‐varying trading behavior.

First, on average, foreign institutional investors herd in the Taiwan securities market. They follow each other into and out of the same industries. Second, they were momentum traders in the tranquil period from 2002 to 2006 and contrarian traders in the period of 2007‐2008 financial crisis. Third, such herding behavior has positive impacts on future industrial returns both in the tranquil period as well as in turbulent time. The authors thus conclude that foreign institutional investors demonstrated contrarian trading strategies to stabilize future industrial returns in the financial crisis period; they buy past losers to support the prices and sell past winners to suppress the price volatility.

This paper investigates foreign institutional herding behavior in an emerging market, Taiwan on the micro setting of industrial base. It identifies the causes and impacts of foreign institutional industrial herding from the outlook of information‐base versus non‐information‐base trading. It also traces time‐varying herding behavior, especially during the 2007‐2008 financial crisis. This paper provides useful information to investors participating in emerging markets like Taiwan.

This study aims to investigate herding spillover in BRIC (Brazil, Russia, India and China) countries and Turkey under different regimes by using a time-varying approach.

The authors used the structural change model of Bai and Perron (1998).

The results indicate that there is an evidence of herding behaviour in the Chinese stock market in two different regimes. These regimes cover the recent global financial crisis and the period of Hong Kong protests. We also report the evidence of herding behaviour in the Turkish stock market in the regime covering the COVID-19 period. Findings of herding spillover show that there is a two-way herding among Russia and China during crises and high volatile regimes. Similarly, there exists a cross-country herding among Brazil and India during crisis regimes. Also, there is herding spillover from Turkey to Russia, China and Brazil during the global financial crisis, post-European debt crisis and COVID-19 periods respectively. Furthermore, it is also evident that there is a herding spillover from Russia and China to India during the period covering COVID-19.

To the best of the authors' knowledge, this is the first study that uses structural change approach to identify herding behaviour spillovers from the US stock market to BRIC countries and Turkey and to investigate the cross-country herding behaviour among BRIC countries and Turkey.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.

The authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.

The results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.

This paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.

The purpose of this paper is to explore how heuristics are formed and whether herding and prospect theory act as antecedents to heuristics. The relationship is explored specifically for millennials.

The proposed relationship is explored specifically for millennials. Herding and prospect theory are modelled as antecedents to heuristics. The study uses survey data from 923 millennials from India to test the model for two financial products: equity and mutual funds. Regression analysis is used to evaluate the model.

Findings support the role of herding and prospect theory as antecedents to heuristics of millennials although to varying degrees for equity and mutual fund investments. The impact of herding on heuristics is likely to be smaller for equity investments as compared to mutual fund investments.

The findings provide insights into how heuristics are formed for millennials. The findings add to literature by beginning a new line of inquiry on how heuristics are formed. Since the model is tested on a single generation, future research can test the model on other generations. In addition, future research can also add more antecedents to our proposed model.

Findings from this study can provide financial planners and marketers with an understanding of how heuristics are formed for millennials. Financial planners can use these insights while providing financial advice to this generation and marketers can use them to create more relevant outreach.

Financial investments are an important conduit for financial security. By understanding the cognitive processes that influence financial investment decision-making, it is possible for educators to create content appropriately and for financial planners to advise clients accordingly to enable optimal financial decisions that will be wealth-creating.

Existing literature primarily treats heuristics, herding and prospect theory as being independent of each other. The authors take a novel approach to model the antecedents to heuristics to be herding and prospect theory. The model is tested on millennials for two financial products: equity and mutual funds.

The purpose of this paper is to examine the evidence of herding phenomenon, spill-over effects related to herding and whether herding is driven by fundamentals or non-fundamentals for various sub-periods and sub-samples.

The cross-sectional absolute deviation model is applied to China’s A- and B-share markets in combination with fundamental information.

Herding is prevalent on both A- and B-share markets. In detail, investors on A-share market herd for small and growth stock portfolios irrespective of market states while they only herd for large or value stocks in down market, therefore leading the whole herding behaviour to be pronounced in down market. Comparatively, on B-share market, herding is robust for various investment styles (small or large, value or growth) or market situations. Additionally, spill-over effects related to herding do not exist no matter from A-shares to B-shares or from B-shares to A-shares. Moreover, investors on B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis.

Investors on A- and B-share markets tend to herd as the response to non-fundamental information more frequently during financial crisis. Analysing the herding behaviours could be helpful in controlling the financial risk.

The authors aim to assess whether herding in GCC stock markets is more responsive to global dynamics than its response to regional developments. To do so, they use the largest equity market in the region which is Saudi Arabia as the benchmark, and then they examine if herding crosses from this large regional market to the rest of equities in the neighboring markets during various time periods. To compare the importance of global influences on herding, the authors investigate and compare the impact of the information flow from the US equity market on the herding of equities in the GCC markets.

To investigate herding in GCC markets the authors use the relationship between the squared market return and the cross-section absolute deviation that does not covary with market styles and/or fundamentals. In order to do that we follow Galariotis et al. (2015) and account for four styles: market-oriented, small-cap, value and momentum. As these factors have been shown to be associated with the economic fundamentals, filtering the covariance of deviation with these factors is expected to remove the style and the fundamental herding influence from the value of the dispersion.

The results show significant herding behavior that persists across various independent periods. This evidence stands even when the authors control for the well- known factor structures in stock returns. Importantly, the authors find that the few herding crossovers that occurred during the sample period are more likely to originate from the Saudi market rather than from the US. Therefore, the authors conclude that behavioral inefficiencies in the GCC equity markets are likely to be regional and that the sentiment-based trading in the US has essentially a minimal role to play.

The empirical findings are useful for policymakers who aim at preventing market manipulation in order to preserve the integrity of financial markets. Policymakers in the GCC should disclose more information to aid investors so they do not rely on other investors' trades. The portfolio managers should be aware that the correlation of GCC equities can be higher in the short term due to common market herding in these countries. As the US market does not play an important role in triggering behavioral irrationalities in these markets, investing in GCC equities is a good hedge in a US portfolio. Finally, the results have also important implications for active funds that aim to exploit short-term trending in markets in order to enhance performance.

The authors’ contribution in this paper is to investigate herding in GCC markets by using the relationship between the squared market return and the cross-section absolute deviation that does not covary with market styles and/or fundamentals. Another contribution of our paper is to investigate any cross herding from the Saudi market to the rest of the markets in the area. The previous literature on GCC equity market herding is silent on this issue and it is typically restricted to the level of the single market.