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This study investigates how individuals' self-awareness (specifically, private and public self-awareness) and environment-awareness (perceived expertise, similarity and familiarity) shape herd behavior, encompassing discounting one’s information and imitating others. Drawing from latent state-trait theory, this research aims to discern the impact of these factors on purchase intention and behavior.

Longitudinal data from 231 users in Xiaohongshu, China’s leading social commerce platform, were collected to test the proposed model and hypotheses.

The findings from this study show that private self-awareness negatively influences discounting one’s own information and imitating others. Public self-awareness positively affects imitating others, while it does not affect discounting one’s own information. Perceived expertise diminishes discounting one’s own information but does not significantly affect imitating others. Perceived similarity and perceived familiarity are positively related to discounting one’s own information and imitating others. The results confirm different interaction effects between self-awareness and environment-awareness on herd behavior.

First, this contributes back to the latent state-trait theory by expanding the applicability of this theory to explain the phenomenon of herd behavior. Second, this study takes an important step toward theoretical advancement in the extant literature by qualifying that both self- and environment-awareness should be considered to trigger additional effects on herd behavior. Third, this study provides a more enlightened understanding of herd behavior by highlighting the significance of considering the interplay between self- and environment-awareness on herd behavior. Finally, this study also empirically confirms the validity of classifying self-awareness into private and public aspects.

The present study sets out to examine the empirical literature on the behavioural aspects of cryptocurrencies, showing the findings of related studies and discussing the various results. A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effects.

Systematic literature review.

A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment (investor's, market's) and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effect.

The authors' survey paper complements recent papers in the area by offering a systematic account on the influence of behavioural factors on cryptocurrencies. Further, this study's purpose is not just to index the relevant literature, but rather to showcase and pinpoint several research areas that have emerged in the field of behavioural cryptocurrency research. For all these reasons, a systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.

This study aims to examine that personality traits are associated with the investor’s ability to exhibit disposition effect, herding behavior and overconfidence. It also explores how risk-attitude can modify investor behavior by moderating the association between personality traits, disposition effect, herding and overconfidence.

Data were collected from 396 respondents by using personally administrated survey. Confirmatory factor analysis (CFA) was used to confirm the validity and reliability of data. Regression analysis was used to test the proposed hypotheses.

The results supported the proposed hypotheses and showed that extravert investors were more likely to exhibit disposition effect, herding and overconfidence. The conscientiousness trait was associated with disposition effect and overconfidence, while neuroticism was associated with herding behavior. The results confirmed the moderating effect of risk aversion on the association between personality traits, disposition effect, herding and overconfidence.

This study demonstrates how risk aversion modes the strength of association between psychological characteristics (represented by personality traits) and cognitive biases (disposition effect, herding and overconfidence). The results support the “auction” interpretation of investors' behavior by suggesting that personality traits are associated with investment decision-making and that investors are marginal price setters.

This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.

The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.

The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.

These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.

Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

The purpose of this paper is to empirically analyze the effects of stock price synchronicity and herding behavior of qualified foreign institutional investors (QFII) on stock price crash risk, especially the mediating effect of herding behavior of QFII on the relation of stock price synchronicity and stock price crash risk.

Taking China’s A-share listed companies from 2005 to 2014 and QFII holding shares data as the research sample, this study calculates herding effect index, sock price synchronicity index and stock price crash risk index, and perform linear regression.

This study concludes that, either herding behavior of QFII or the stock price synchronicity can increase the stock price crash risk. Further study reveals that, the herding behavior of QFII also improves the effect of stock price synchronicity on stock price crash risk. Namely, herding behavior of QFII acts as the mediating role between stock price synchronicity and stock price crash risk.

This study empirically analyzes and verifies the mediating roles of herding behavior of QFII in affecting the relation of sock price synchronicity and stock price crash risk for the first time. The findings of this study contribute to the study of the role of QFII in stabilizing Chinese security market.

The study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.

The study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.

The results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.

The results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.

Majority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

Investor sentiment (optimism or pessimism) may influence investors to follow others (herding) while taking their investment decisions. Herding may result in bubbles and crashes in the financial markets. The purpose of the study is to examine the presence of herding and the effects of investor sentiment on herding in China and Pakistan.

The investor sentiment is captured by five variables (trading volume, advance/decline ratio, weighted price-to-earnings ratio, relative strength index and interest rates) and a sentiment index developed through principal component analysis (PCA). The study uses daily prices of 2,184 firms from China and 568 firms from Pakistan for the period 2005 to 2018.

The study finds that herding prevails in China while reverse herding prevails in Pakistan. Interestingly, as investors become optimistic, herding in China and reverse herding in Pakistan decrease. This indicates that herding and reverse herding are greater during pessimistic periods. Further, the increase in herding in one market reduces herding in the other market. Moreover, optimistic sentiment in the Chinese market increases herding in the Pakistani market but the reverse is not true.

Considering the greater global financial liberalization, and better opportunities for emotion sharing, this study has important implications for regulators and investors. Market participants need to understand the prevalent irrational behavior before trading in the markets.

Since individual proxies may depict different picture of the relationship between sentiment and herding therefore the study also develops a sentiment index through PCA and incorporates this index in the analysis. Further, this study examines cross-country effects of herding and investor sentiment.

The purpose of this paper is to show that the level of herding in an industry can be the basis for a profitable investment strategy.

The authors apply three different herding measures in the paper, including cross-sectional standard deviation, cross-sectional absolute deviation and non-linear model – state–space model.

The authors find that industries that experience a high level of herding yield higher subsequent returns regardless of their past performance. Consequently, the authors show that a herding-based investment strategy generates significant profits, even after adjusting for risk. The findings also show that the herding effect when combined with past performance as part of a conditional investment strategy yields significant profits regardless of the formation and holding periods. The findings suggest that the level of herding could serve as a systematic driver of returns and could be exploited for profitable investment strategies.

To the best of authors’ knowledge, this is the first study in the literature to show that herding by itself can serve as a determinant of returns regardless of past performance.

The purpose of this paper is to examine herding in four frontier markets in the Balkan region, namely, Bulgaria, Croatia, Romania and Slovenia, from October 2000 to December 2016.

The author employs Chang et al.’s (2000) cross-sectional dispersion approach to capture herding, while also testing for the global financial crisis’ effects and the European Union (EU)/Euro zone accession effects over herding. Potential asymmetric herding effects conditional on market performance, domestic volatility, German and US investor sentiment are also examined. Finally, the cross-market herding dynamics of the region are also explored.

Overall, Romania exhibits the most extensive evidence of herding across various estimations. The empirical results indicate that cross-market herding dynamics within the region generate stronger herding (compared to the herding observed within each stock market individually), suggesting that Balkan stock exchanges’ growing financial integration leads their herding to be “imported”, rather than domestically motivated.

The findings provide useful insights for regulators in frontier markets, considering the destabilising potential of herding; they are also of particular interest to the investment community for reasons of international asset allocation, diversification and hedging strategies.

This study contributes to the limited herding literature regarding frontier markets and provides novel findings regarding the herding dynamics in the Balkan region, the EU/Euro zone accession’s effect and global factors’ impact on herding estimations.