Search results
1 – 10 of 941Adnan Khan, Rohit Sindhwani, Mohd Atif and Ashish Varma
This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during…
Abstract
Purpose
This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.
Design/methodology/approach
Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.
Findings
The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.
Originality/value
The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.
Details
Keywords
Huasi Xu, Yidi Liu, Bingqing Song, Xueyan Yin and Xin Li
Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion…
Abstract
Purpose
Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.
Design/methodology/approach
The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.
Findings
Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.
Originality/value
The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.
Details
Keywords
Sharmila Devi R., Swamy Perumandla and Som Sekhar Bhattacharyya
The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors…
Abstract
Purpose
The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable.
Design/methodology/approach
A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade.
Findings
This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns.
Research limitations/implications
This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories.
Practical implications
The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics.
Originality/value
Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.
Details
Keywords
Barbara Abou Tanos and Omar Meharzi
The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events…
Abstract
Purpose
The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period.
Design/methodology/approach
The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005).
Findings
Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies.
Practical implications
The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior.
Originality/value
This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.
Details
Keywords
Abderahman Rejeb, Karim Rejeb, Andrea Appolloni and Horst Treiblmaier
Crowdfunding (CF) has become an increasingly popular means of financing for entrepreneurs and has attracted significant attention from both researchers and practitioners in recent…
Abstract
Purpose
Crowdfunding (CF) has become an increasingly popular means of financing for entrepreneurs and has attracted significant attention from both researchers and practitioners in recent years. The purpose of this study is to investigate the core content and knowledge diffusion paths in the CF field. Specifically, we aim to identify the main topics and themes that have emerged in this field and to trace the evolution of CF knowledge over time.
Design/methodology/approach
This study employs co-word clustering and main path analysis (MPA) to examine the historical development of CF research based on 1,528 journal articles retrieved from the Web of Science Core Collection database.
Findings
The results of the analysis reveal that CF research focuses on seven themes: sustainability, entrepreneurial finance, entrepreneurship, fintech, social entrepreneurship, social capital, and microcredits. The analysis of the four main paths reveals that equity CF has been the dominant topic in the past years. Recently, CF research has tended to focus on topics such as fintech, the COVID-19 pandemic, competition, Brexit, and policy response.
Originality/value
To the authors' best knowledge, this is the first attempt to explore knowledge diffusion dynamics in the CF field. Overall, the study offers a structure for analyzing the paths through which knowledge is diffused, enabling scholars to effectively manage a large volume of research papers and gain a deeper understanding of the historical, current, and future trends in the development of CF.
Details
Keywords
Delin Yuan and Yang Li
When emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution…
Abstract
Purpose
When emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution patterns. The purpose of this study is to discover the popularity evolution patterns of social media emergency information and make early predictions.
Design/methodology/approach
We collected the data related to the COVID-19 epidemic on the Sina Weibo platform and applied the K-Shape clustering algorithm to identify five distinct patterns of emergency information popularity evolution patterns. These patterns include strong twin peaks, weak twin peaks, short-lived single peak, slow-to-warm-up single peak and slow-to-decay single peak. Oriented toward early monitoring and warning, we developed a comprehensive characteristic system that incorporates publisher features, information features and early features. In the early features, data measurements are taken within a 1-h time window after the release of emergency information. Considering real-time response and analysis speed, we employed classical machine learning methods to predict the relevant patterns. Multiple classification models were trained and evaluated for this purpose.
Findings
The combined prediction results of the best prediction model and random forest (RF) demonstrate impressive performance, with precision, recall and F1-score reaching 88%. Moreover, the F1 value for each pattern prediction surpasses 87%. The results of the feature importance analysis show that the early features contribute the most to the pattern prediction, followed by the information features and publisher features. Among them, the release time in the information features exhibits the most substantial contribution to the prediction outcome.
Originality/value
This study reveals the phenomena and special patterns of growth and decline, appearance and disappearance of social media emergency information popularity from the time dimension and identifies the patterns of social media emergency information popularity evolution. Meanwhile, early prediction of related patterns is made to explore the role factors behind them. These findings contribute to the formulation of social media emergency information release strategies, online public opinion guidance and risk monitoring.
Details
Keywords
This paper examines the hypothesis of local herding (i.e. own-area effects) by individual investors on a particular stock-month. Using a unique dataset on online and offline…
Abstract
This paper examines the hypothesis of local herding (i.e. own-area effects) by individual investors on a particular stock-month. Using a unique dataset on online and offline individual investors’ trading records in Korea, we analyze buying and selling transactions involving 10,000 accounts from February 1999 to December 2005. We find that both online and offline investors in the same area tend to exhibit stronger local herding compared to investors’ trades who are geographically remote. Interestingly, online investors not only present stronger own-area effects but also exhibit more pronounced other-area effects compared with offline investors. Furthermore, our analysis indicates that gender and religious affiliation are important in investment behavior, with male and non-religious investors displaying a greater stock market participation in contrast to investors who are female and Protestant.
Details
Keywords
Srivatsa Maddodi and Srinivasa Rao Kunte
The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes…
Abstract
Purpose
The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.
Design/methodology/approach
Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.
Findings
Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.
Originality/value
To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.
Details
Keywords
Junyi Chen, Buqing Cao, Zhenlian Peng, Ziming Xie, Shanpeng Liu and Qian Peng
With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application…
Abstract
Purpose
With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.
Design/methodology/approach
In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.
Findings
Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.
Originality/value
In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.
Details
Keywords
Xiao Peng, Hessam Vali, Xixian Peng, Jingjun (David) Xu and Mehmet Bayram Yildirim
The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and…
Abstract
Purpose
The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set.
Design/methodology/approach
A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship.
Findings
Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness.
Originality/value
This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.
Details