Objective: This study empirically investigates herding bias in six key Asian countries—Indonesia, Singapore, Taiwan, China, Hong Kong, and India—across different periods (pre-, during, and ...post-COVID-19). It analyzes herding behaviour during COVID and non-COVID periods, exploring its impact on volatility and examining asymmetry during bearish and bullish market conditions. Design/Methods/Approach: The investigation employs the Cross-Sectional Absolute Deviation (CSAD) model with a polynomial regression to scrutinize herding behaviour. A GARCH (1,1) volatility model is also established to assess the relationship between herding and volatility. The sample includes daily stock returns from the mentioned countries from January 2, 2019, to September 30, 2023. Findings: The study reveals the presence of herding behaviour in China and Singapore. In Indonesia and China, herding is evident, specifically during and after the COVID period. The research confirms that herding influences volatility and exhibits asymmetry. Herding is more pronounced during bearish market conditions in China, Indonesia, and Taiwan. Originality/Value: This study contributes to the existing literature by providing empirical insights into herding behaviour comparing in Asian markets, while others research usually only focus on one country. This study further distinguishes itself by examining post-pandemic periods, a unique aspect as most studies typically focus only on pre- and during-COVID periods. Including volatility and asymmetry aspects enriches understanding the nuanced relationship between herding and market conditions. Practical/Policy implication: Investors should remain cautious of short-term herding-induced volatility, leveraging stability for consistent profits. Recognizing limited diversification during market losses is crucial. Additionally, governments and regulators should focus on enhancing market transparency and investor education, investing in robust market infrastructure to mitigate the impact of excessive herding.
This review paper delves into the intricate interplay between capital markets, investor protection, portfolio strategies, and behavioural aspects in investments. The VOSviewer 1.6.19 software is ...utilised to perform a bibliometric analysis and a exhaustive systematic literature review on a sample of 248 papers published in journals in Web of Science databases. Our comprehensive analysis reveals the emergence of key themes, shedding light on the critical role of behavioural finance in shaping investment choices and outcomes. We explore how investor behaviour often deviates from traditional models of market efficiency and how these deviations impact portfolio construction and investment strategies. Our paper contributes to a deeper comprehension of the complexities that drive investment decisions and helps academics, society, investors, and regulators by providing a structured analysis of literature strands. Builds a basis for better regulation and protection of investors in the capital markets, with relevant information for future studies on investor behaviour.
We contribute to the literature on herding in the cryptocurrency market by using a unique data set of trader transactions. Using popular metrics, we find significant evidence of herding, which is ...primarily driven by individuals mimicking their own past trades, given the sporadic nature of information as well as the ambiguity and anonymity inherent in this market. Herding is higher during bearish periods as traders react more similarly to negative news. We find evidence of intentional herding due to informational cascades in less liquid cryptocurrencies, where significant price movements may be interpreted as valuable information. Traders with larger accounts tend to mimic their own past trades. Mature traders trade similarly due to their lower tolerance for risk and experimentation. We find herding differentials among traders that arise due to the environment governing the local financial system in which they are located. Moreover, persistence in herding is lower compared to what has been reported in other markets due to the higher degree of ambiguity of cryptocurrencies and the individuals trading them. Finally, market factors such as volatility, have a significant effect on herding. Our results shed light on how trader characteristics and market factors impact an individual’s propensity to herd.
•Herding among cryptocurrency traders is lower compared to equity traders.•Herding arises due to informational cascades in less liquid cryptocurrencies.•Large accounts exhibit coordinated trading.•Mature traders trade similarly due to their lower tolerance for risk.•Market factors, such as volatility, have a significant impact on herding.
The Investment CAPM Zhang, Lu
European financial management : the journal of the European Financial Management Association,
September 2017, Letnik:
23, Številka:
4
Journal Article
Recenzirano
A new class of Capital Asset Pricing Models (CAPM) arises from the first principle of real investment for individual firms. Conceptually as ‘causal’ as the consumption CAPM, yet empirically more ...tractable, the investment CAPM emerges as a leading asset pricing paradigm. Firms do a good job in aligning investment policies with costs of capital, and this alignment drives many empirical patterns that are anomalous in the consumption CAPM. Most important, integrating the anomalies literature in finance and accounting with neoclassical economics, the investment CAPM has succeeded in mounting an efficient markets counterrevolution to behavioural finance over the past 15 years.
The aim of the research was to find out the opinions of stockbrokers working at the Warsaw Stock Exchange regarding the behaviour of Polish investors in the face of the coronavirus (COVID-19) ...pandemic. The research was carried out among 51 stockbrokers representing brokerage houses with a long history of operations. It has been found that psychological conditions of people and stock market sentiments play an important role in the decision-making process, and irrational investor behaviours, including largely herd effects, are particularly evident during the pandemic. The research shows that the occurrence of the coronavirus has not reduced the activity of Polish investors. Thus, significantly growing interest in shares of companies listed on the Warsaw Stock Exchange has been noted. The behaviour and attitude of market participants towards risk were volatile during the developing pandemic, which manifested itself in rapid buying of overvalued assets or rapid selling of assets.
Motivated by the growing convergence between news media and social media as dominant sources of information dissemination, this study examines the connection between textual sentiment and stock ...returns. Previous studies have examined the effect of sentiment extracted from these two sources on stock returns independently, without modelling how one source can confound the relationship between stock returns and the other source. We investigate this using data from four markets (USA, UK, South Africa and Brazil) and a sample period stretching from January 2016 to April 2023. Employing a suite of methods that encompass both simple parametric techniques and complex models designed to address nonlinearity, chaos and deviations from normality, the analysis uncovers a pronounced impact of social media sentiment on stock returns in the United States. This influence overshadows the effect of news media sentiment across the employed methods. Interestingly, in other markets, news media exhibits a greater effect on stock returns compared to social media sentiment. By emphasising the convergence of news media and social media, the study highlights the important interplay between these sources, offering valuable insights into understanding the complex dynamics of modern financial markets.
The authors examined whether stocks with higher left-tail risk measures earn higher or lower futures returns. Specifically, the authors estimate the cross-sectional principal component of a battery ...of left-tail risk measures and analyze future returns on stocks with high principal component values. In contrast to finance theories on the risk-return trade-off relationship, the study results show that high left-tail risk stocks have lower future returns. This finding is robust to various left-tail risk measures and controls for other risk factors. Moreover, the negative relationship between the left-tail risk and returns is more pronounced for stocks that are actively traded by retail investors. This empirical result is consistent with behavioral theory that when investors make decisions based on experience, they tend to underweight the likelihood of rare events.
The role of public sentiment in traders’ decision-making is potentially more pronounced in crypto-asset markets, given a lack of quantifiable financial fundamental information and historical ...precedent for pricing behaviour. Using a data set of over two million transactions executed on a cryptocurrency exchange, we test the extent to which sentiment conveyed within cryptocurrency communities on Reddit impacts upon the performance, deposit and withdrawal behaviour, and position exposure of cryptocurrency traders. Our evidence supports the notion that sentiment plays a role in the investment decision-making process. Traders tend to realise positive returns when sentiment is bullish. Moreover, positive changes in the level of bullishness lead to traders executing larger trades, and a higher probability of depositing and withdrawing funds. Measures such as the degree of consensus within the online crowd, readership size and contributor reputation produce less compelling results, but offer some insights into Reddit community dynamics.
•Over two million crypto trades used to measure sentiment effects on trading behaviour•Sentiment detected in Reddit discussions is associated with traders’ decision-making•Cryptocurrency traders tend to realise positive returns when sentiment is bullish in nature•Positive sentiment change associated with increase in trade size and deposit activity
Practice and empirical observations prove that achieving above-average returns on the stock market is possible. It is possible to achieve both higher and lower returns than those resulting from the ...fundamental value of the companies being valued. This condition is affected by anomalies that make the market ineffective. Numerous studies in behavioural finance show that the causes of market inefficiency are to be found in the incomplete rationality of investors. Numerous deviations of investor behaviour from the homo economicus model result from their cognitive and motivational limitations. Sometimes the mistakes of an individual investor are systematic – such systematic and massive errors take the form of heuristics that can influence the magnitude of market anomalies, including the occurrence of calendar effects. One of the best-known calendar anomalies is the January Effect. The January Effect is characterised by an increase in stock prices in January, andthe occurrence of the January Effect is expressed by the fact that the returns in January are the highest of the entire year. The research conducted on the Warsaw Stock Exchange confirmed the presence of the January Effect in small- and medium-sized companies. During the research, the presence of other calendar effects (related to the months of June and October) was also diagnosed.
•Savings adequacy is best predicted by the decision tree model with 96.1% accuracy.•Mental accounting categories have predictive power on savings adequacy.•Luxury items and current asset amount were ...most important to savings adequacy.
This paper proposes a machine-learning-based method that can predict individuals’ savings adequacy in the presence of mental accounting. The proposed predictive model perceives wealth and consumption, each of which is being divided into three non-fungible distinct classes. The predictive model has found that the mental accounting categories have predictive power on savings adequacy, whereby the emphasis is that the expenditure on luxury items is followed by the total current asset. Savings adequacy is best predicted by the decision tree model based on the Malaysian Ageing and Retirement (MARS) survey data. Surprisingly, it was found that future income and necessities had a lower predictive power on savings adequacy. The findings suggests that individuals, financial professionals, and policymakers should be cognizant that higher likelihood of achieving savings adequacy can be achieved by focusing on accumulation of current asset while lowering expenditure on luxury items.
