Individual Investors Research Articles

Non-controlling shareholders' network and excess goodwill: Evidence from listed companies in China

This study investigates the impact of non-controlling shareholders' network on corporate excess goodwill using Chinese publicly listed companies from 2007 to 2020. We find that a stronger centrality of non-controlling shareholders' network leads to a significant decrease in excess goodwill resulting from mergers and acquisitions. This implies that the non-controlling shareholders' network has a significant inhibitory effect on the occurrence of goodwill bubbles. Mechanism analysis finds that non-controlling shareholders' network can inhibit excess goodwill thorough information effect, resource effect, and governance effect. Furthermore, this inhibitory effect is attributed to pressure-resistant institutional investors and individual investors, and is more pronounced in firms located in less developed intermediary market and legal system environment, as well as firms with lower audit quality. In summary, the non-controlling shareholders' network plays a positive role in curbing excess goodwill in listed companies.

Egg Size Scales Negatively With System Size in a Periodic Fish Species.

Optimal egg size theory implies that female organisms balance between fecundity and individual offspring investment according to their environment. Past interspecific studies suggest that fishes in large marine systems generally produce smaller eggs than those in small freshwater systems. We tested whether intraspecific egg size variation reflected a similar pattern by comparing egg size among yellow perch (Perca flavescens) populations inhabiting a range of system sizes. In 2018, 2019, and 2023, we collected yellow perch egg samples from 12 locations in systems ranging in surface area from 37 to 5,390,492 ha. First, we found that egg diameter significantly increased with maternal total length in five of eight individually tested populations. After accounting for these maternal effects, we found a significant interaction, where females inhabiting larger lakes, such as the main basins of Lakes Erie and Michigan, produced smaller eggs than those in smaller inland lakes, and the greatest differences were demonstrated among females of greater total length. This egg size variation in the largest females is consistent with interspecific egg size comparisons between marine and freshwater fishes. However, by examining a single species across vastly different environments, we were able to support theoretical expectations that maternal investment in offspring should vary with environmental conditions controlling early-life resource acquisition and competition.

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

I feel and I invest: Social emotions and sustainable investing in Germany and Japan

AbstractThis research focuses on the increasing but unexplored role of individual investors in sustained sustainable investment. Sustainable investment with pecuniary motives tends to be fractured by economic downturns. Drawing on research on social emotions shaping decision‐making, this study explores how non‐pecuniary motives and emotions—empathy and self‐esteem—affect the attention to sustainability ratings by individual investors. We administered the original survey‐embedded experiments to nationals of two countries in the Global North, Germany, and Japan. We found that evoking empathy can lead experienced Japanese investors to focus on sustainability ratings while German investors are unaffected. On the other hand, self‐esteem stimuli are effective for German potential investors who have expressed an interest in investing but not for Japanese investors. These results underline the potential of emotional stimuli in promoting sustainable investment and highlight the importance of tailoring such stimuli to different cultural contexts and groups of people.

Influence and Inspiration of Asymmetric Information on Individual Investor Behavior

Information asymmetry is a pervasive phenomenon in the financial market. For individual investors, this imbalance usually results in making them vulnerable in the investment decision-making processes, thus raising the challenges as well as risks that characterize their participation in the market. Unlike financial institutions that have access to extensive resources and research capabilities, the lack of reliable sources of data and knowledge about how to interpret and analyze available information add layer of complexity for individual investors to acquire full and sufficiently accurate information, which increases risks for them during investment activities compared with financial institutions. Without proper understanding of financial concepts and analysis techniques, they may struggle to make informed decisions in an environment characterized by asymmetric information. Thus, according to the current literature on information asymmetry and individual investor behavior, this paper sorts out and summarizes the viewpoints and conclusions of each paper. On this basis, this paper provides a series of suggestions for individual investors to have better insight of this financial phenomenon and make reasonable investment in the financial market environment with asymmetric information.

Analysis of Information Sources for Investment Decision in Stock Market by Individual Investors – Evidence from Odisha

Analysis of Information Sources for Investment Decision in Stock Market by Individual Investors – Evidence from Odisha

Leveraging corporate governance characteristics for stock crash risk assessment

Causing considerable losses for individual investors, stock crashes are also potential triggers for wider financial crises. In contrast to existing research that primarily focuses on specific corporate governance characteristics in relation to stock crash risk, in this study, we construct a set of corporate governance attributes and employ machine learning techniques for a comprehensive assessment of stock crash risk. Through empirical analysis in the Chinese stock market, we find that our corporate governance attributes are effective indicators of stock crash risk, yielding statistically significant and economically meaningful crash risk assessment. Additionally, our heterogeneity tests suggest that firms with less concentrated shareholding result in superior assessment accuracy. This observation is consistent with prior findings that increased retail investor participation enhances corporate governance transparency, thereby contributing to better assessment performance. Industry-wise, the performance of crash risk assessment for real estate companies is higher than that for cultural, sports, and entertainment sectors. We speculate that these variations stem from industry-specific factors, such as business logic, operational models, and profit mechanisms. Our results also suggest that characteristics related to shareholder meetings are pivotal in assessing crash risks for manufacturing companies, while board-related features are essential for real estate firms. Overall, this study contributes to current literature by validating the feasibility of employing corporate governance characteristics to assess crash risk and by exploring the economic value of crash risk assessment. Furthermore, we discuss several valuable insights into the Chinese stock market through heterogeneity analysis.

Multidimensional social influence drives leadership and composition-dependent success in octopus-fish hunting groups.

Collective behaviour, social interactions and leadership in animal groups are often driven by individual differences. However, most studies focus on same-species groups, in which individual variation is relatively low. Multispecies groups, however, entail interactions among highly divergent phenotypes, ranging from simple exploitative actions to complex coordinated networks. Here we studied hunting groups of otherwise-solitary Octopus cyanea and multiple fish species, to unravel hidden mechanisms of leadership and associated dynamics in functional nature and complexity, when divergence is maximized. Using three-dimensional field-based tracking and field experiments, we found that these groups exhibit complex functional dynamics and composition-dependent properties. Social influence is hierarchically distributed over multiscale dimensions representing role specializations: fish (particularly goatfish) drive environmental exploration, deciding where, while the octopus decides if, and when, the group moves. Thus, 'classical leadership' can be insufficient to describe complex heterogeneous systems, in which leadership instead can be driven by both stimulating and inhibiting movement. Furthermore, group composition altered individual investment and collective action, triggering partner control mechanisms (that is, punching) and benefits for the de facto leader, the octopus. This seemingly non-social invertebrate flexibly adapts to heterospecific actions, showing hallmarks of social competence and cognition. These findings expand our current understanding of what leadership is and what sociality is.

Shipping Market Forecasting Considering the Individual Investor Emotions: A Novel Twofold Partial Swarm Optimization Based Stacked Long Short-Term Memory Model

ABSTRACT Purpose The general purpose of shipping market prediction is to help shipping analysts design a strategy to make shipping strategies. Previous studies have pointed out human emotions has an obvious impact on markets, however, few research focus on utilizing human emotions to improve the shipping market forecasting issue. This paper fills the gap in the literature of considering industrial individual emotions to shipping market prediction issue Methodology We proposed a conceptual framework of an emotion-based shipping market prediction (ESMP) system focused on considering the multidimensional emotions of individual investors. To make it precisely, a two-fold Particle Swarm Optimization (TFPSO) algorithm is proposed conduct the shipping forecasting under the framework of ESMP, with advantages of automatically finding the architecture and hyperparameters of the Deep Long Short-Term Memory (DLSTM) simultaneously in the shipping market forecasting the values of the Baltic Dry, Dirty Tanker and Container indices of ocean transportation in the world ocean during the crisis period 2010–2022 Findings The prediction accuracy of the ESMP was higher than that of the models using the conventional factors in all learning periods of the study, and it was determined that the results complement the sentiment indicator employed to predict the shipping indices. Additionally, the accuracy of the proposed method is superior to conventional neural network models in all used error metrics. Additional Mann–Whitney U test on MSE difference between TFPSO-DLSTM and compared models demonstrates that the significant advantages boosted by TFPSO-DLSTM Originality Shipping sentiment has provided further proof that shipping decisions are significantly driven by emotions. our study shows that individual shipping investors’ sentiment are able to improve shipping rate forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of view Research implications This study undertook the initiative to procure BDI, BDTI, BCTI, COVID-19 data and shipping sentiment index data through in-depth interviews to provide first-hand perspectives into whether shipping sentiments impact the shipping market trend. It builds upon existing literature on the present stance of deep learning models, which largely relies non-sentiment factors. The study also extends prior literature on hyperparameter searching methods by highlighting the structure of searching method. Besides this, the contribution of this study also aligns with the prior discussions on evolving methodologies in temporal forecasting research Practical implications This study implies that shipping sentiments enables to make better shipping marketing trends, and foster sustainable growth. Shipping fear index serves for measuring investors’ attitude and mood toward shipping markets in terms of the general and certain sectors or assets, possibly promoting the movement of price and providing long-term investors and active traders with trading or arbitrage opportunities. Due to the complexities and regulations of the shipping market, it is essential to collaborate exclusively with shipping sentiment indices with artificial intelligent model in shipping market forecasting research

Litigation Risk, Ownership Structure and Payout Policy: An Agency Theory Perspective

The study aims to investigate the relationship between litigation risk, ownership structure and payout policy in the light of agency theory. The study analyzed a data set of 690 non-financial Chinese firms spanning 2013–2021, by employing two-stage dynamic panel data methodology. The results reveal that litigation risk adversely impacts dividend payouts, while managerial ownership has a similar negative effect. Conversely, institutional and concentrated ownership exhibit positive associations with dividend yield. Furthermore, managerial and institutional ownership moderates the link between litigation risk and dividend payout ratio, while concentrated ownership strengthens this link. These findings confirm the presence of agency theory dynamics in emerging economies, providing valuable insights for corporate decision-makers, regulators and investors. By considering the moderating role of ownership structure, this research enhances our understanding of the multifaceted relationship between litigation risk and payout policy, facilitating better-informed strategic decisions and policy formulation. The study provides valuable implications for corporate managers and individual investors for constructing a diversified portfolio in the context of emerging economies.

LOSS AVERSION: THE UNSEEN FORCE SHAPING INVESTMENT DECISIONS

This research paper aims to investigate the profound impact of loss aversion on individual investment decision-making, with a specific focus on its influence on risk preferences. Loss aversion, a key concept in behavioral economics, posits that individuals experience a greater psychological impact from losses than equivalent gains, thereby shaping their decision-making processes. The study employs an empirical approach, utilizing a diverse sample of individual investors to analyze how loss aversion interacts with risk preferences and influences investment choices. The research methodology involves a combination of surveys and data analysis to explore the intricate relationship between loss aversion and risk preferences. By examining real-world investment decisions in various market conditions, the study seeks to identify patterns and behavioral tendencies that emerge when individuals confront potential losses in their investment portfolios. The research design also considers demographic factors, financial literacy, and other relevant variables to provide a comprehensive understanding of the factors influencing investment choices. Ultimately, this study aims to enhance our comprehension of the psychological factors driving individual investment decisions and to inform the development of strategies that mitigate the negative impacts of loss aversion on financial outcomes.

Clearing Up Confusion: The Effect of Outlier Similarity on IPO Underpricing

Prior research demonstrates that audiences tend to converge in their valuations of firms similar to preexisting category prototypes or exemplars. Much less is known of the influence of salient outliers, specific firms that receive market-wide attention due to their extreme, ambiguous performance, on audiences’ valuations. We argue that outlier similarity, by contrast with prototype similarity, leads to divergent valuations among individual investors. We explore this insight in the context of initial public offerings (IPOs). In this context, converging valuations among investors lead to limited information asymmetry concerns and hence reduced underpricing on the first day of trading of an issuing firm. Hence, we expect that prototype similarity leads to lower underpricing while outlier similarity leads to higher underpricing. We test our hypotheses using a sample of 2,488 U.S. IPOs from 1996 to 2015, measuring prototype and outlier similarity through a natural language processing technique applied to nearly 160,000 financial documents. We find that in low-tech industries, where prototypes are informative about category members, prototype similarity reduces underpricing, but not in high-tech industries. Additionally, we find that outlier similarity increases underpricing, especially for more recent outliers. This paper contributes to the literature on market valuation and market categories, and advances research on meaning and culture using new text-based computational methods.

Taking matters into their own hands: How Investors' stock preferences affect mutual fund flows in China

We examine the impact of investors' stock preferences in mutual funds' portfolios on fund flows in China. Our results indicate that mutual funds with an investment style consistent with individual investors' stock preferences have higher fund flow. We find that it affects only individual investors' fund flow, not institutional investors' fund flow. This impact is greater during periods of low economic policy uncertainty or high investor sentiment. Moreover, this impact is reduced with larger fund size or longer fund age. The reasonable explanation is that investors infer fund managers' skills by analyzing the stocks in the fund's portfolio. This explanation is further validated by the results using index funds as the control group. Our findings challenge the assumption that mutual fund investors fully delegate decisions to professionals. Further analysis reveals there is no significant correlation between these preferences and future returns. Our findings contribute to the study of mutual fund investor behavior from a fresh perspective.

Using machine learning to predict investors’ switching behaviour

Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.

Understanding Mutual Funds Performance with mediation of Human Capital and GDP-Growth in Pakistan Mutual Funds: Demonstration of Structural Equation Model

This work sheds light on the complex interplay between mutual fund performance, using human capital, and GDP growth as mediating variables in Pakistan through the lens of Structural Equation Modeling. By employing this sophisticated analytical approach, the findings uncovered nuanced nexus that traditional methods might have overlooked. The findings underscore the critical role of GDP-Growth and human capital as a mediating factor, suggesting that investments in mutual funds could have far-reaching effects on the financial performance on individual investors. Moreover, the demonstrated link between GDP-growth and mutual fund outcomes highlights the intricate web of economic factors influencing investment landscapes. Our findings thus reveal that the impact of the market factor and GDP growth on portfolio returns shows statistically significant estimates both mediation models. However, size-factor shows two portfolios significant while two portfolios insignificant results using both mediating variables. However, the value factor shows highly significant in both mediating models but reveal insignificant nexus with HC-Growth as mediating variable using only FF3FM. Moreover, momentum factor shows insignificant results for both mediating models. As Pakistan continues to develop its mutual fund market, policymakers and fund managers alike would do well to consider these interconnections in their decision-making processes.

Enhancing the sustainable development of investment funds: a study of the stock market in Vietnam

Stock investment funds play an important role in the development of the stock market by creating demand for the market, while increasing the professionalism of market activities. With expertise, the ability to access and process information effectively, investment funds will bring benefits to all participants. The role of stock investment funds becomes even more important in developing countries where market information is often imperfect and the state's warning systems and control capabilities are limited. In Vietnam, the strong growth of the stock market over the past 10 years has been the foundation for the formation and development of stock investment funds. However, Vietnam's fund industry has only started to take shape in the last 5 years. Up to now, there are only 24 domestic closed-end stock investment funds operating on the Vietnamese stock market, with limited size and diversity of fund products compared to the market potential. At the same time, the stock investment funds themselves have not yet promoted their role in attracting investment capital, leading individual investors, and contributing to the stability of the stock market during their existence. Keywords: Investment Fund, Stocks, Vietnam.

Effect of Behavioural Biases on Investment Decisions of Individual Investors in India

The current study "effect of behavioural biases on investment decisions of individual investors in India" aims to uncover and quantify the influence of behavioural biases on individual investors' investment decisions. The study's goal is to examine the influence of behavioural biases on individual investors' investing decisions. It is discovered that five components are formed from the thirteen items as a consequence of heuristics factor analysis, namely Availability, Representativeness, Overconfidence, Anchoring, and Gambler's Fallacy. The prospective factor analysis also reveals that three components are retrieved and loaded from the eleven items, namely Loss Aversion, Regret Aversion, and Mental Accounting. According to the findings of this study, behavioural biases affect individual investors while making investment decisions in the Indian securities market. Investor behaviour in making investment decisions is influenced to a larger extent by availability, representativeness, overconfidence, anchoring, and loss aversion. To be successful, an investor must first understand his or her own investment behaviour, which begins with recognising and avoiding behavioural biases from their own experiences, followed by setting realistic and achievable goals through a diversifiable portfolio, and taking into account all financial market mechanisms.

“A Comprehensive Review on the Psychological Underpinnings of Investment Decisions among Chinese Investors"

Through a systematic literature review, this paper provides insights into the psychological and behavioural factors that influence Chinese investors' investment decisions. It is found that Chinese investors' behaviours are influenced by a complex interaction of cultural values (e.g. Confucianism), economic factors and the regulatory environment. Common psychological biases such as overconfidence, loss aversion and herding behaviour are prevalent among Chinese investors but are often moderated by cultural factors. Influenced by cultural norms, Chinese investors generally exhibit a long-term investment orientation and high savings rates. The study also found that the fast-growing technology sector and the property market are notable areas of investment activity in China, demonstrating both successes and failures. In addition, regulatory changes and government policies play an important role in shaping investor sentiment and decision-making. However, existing research remains deficient, particularly with regard to the interaction between cultural factors and individual investor characteristics, as well as institutional investor behaviour. This study provides insights into the unique aspects of Chinese investment psychology and behaviour for regulators, investors and financial institutions, contributing to a better understanding and characterisation of the Chinese investment market.

Information asymmetry, policy shocks, and international equity investment: Evidence from the anti‐corruption campaign in China

AbstractInformation plays a critical role in financial transactions and markets, especially in international investment where foreign investors are at an informational disadvantage relative to domestic investors. Prior studies show a positive reaction of the Chinese capital market to China's anti‐corruption campaign, which is also consistent with improved performance of Chinese listed firms following the campaign. We document a contrasting reaction of less‐informed foreign investors. Exploiting staggered anti‐corruption investigations across provinces as plausibly exogenous shocks, we find that foreign institutional investors increase their equity investments in Chinese listed firms headquartered in affected provinces following the anti‐corruption campaign; foreign individual investors, by contrast, reduce their equity investments after the campaign, which is driven by those with non‐Chinese ethnicity. Further analyses show that the increased equity investments of foreign investors stem primarily from those residing in jurisdictions sharing a similar cultural tradition with China. Our study has significant implications for foreign investors in the context of the increasingly intense relationship between China and the West.

Investigating the role of Fintech innovations and green finance toward sustainable economic development: a bibliometric analysis

PurposeThe purpose of this study is to present a bibliometric and network analysis that uses the Scopus and Dimension databases to provide new insights into the progression toward the study of sustainable economic development.Design/methodology/approachThis analysis has been drawn on 665 papers published between 2015 and 2023. Bibliometric analysis characterizes a research topic by identifying leading nations, the most significant authors and expressive publications. Network analysis revealed keyword evolution over time, co-citation patterns and study grouping. Content analysis was used to identify major topic in the discipline, with a focus on their interrelationships. Each publication in the data set is briefly described, along with its methodological approach.FindingsThe results of this study show that green finance plays a major role in long-term economic growth, having a significant influence on the preservation of environmental quality, economic efficacy and a more comprehensive economic system. Financial technology also accelerates the transition to a carbon-neutral economy by enhancing the beneficial effects of green finance on aspects of the economic system and environmental conservation.Research limitations/implicationsThe investigation is based only on Scopus and Dimensions-indexed journal articles. However, additional studies should incorporate publications from other reputable databases, such as Web of Science, PubMed and Science Direct, for the bibliometric analysis, so that the findings of the model analysis become more reliable and valid with examination of more documents. The visualization of similarity viewer was used for data analysis in the study, there is a scope for using other tools such as Biblioshiney and CitNet Explorer.Practical implicationsTo support long-term economic growth, authorities should encourage Fintech companies to actively participate in various green finance initiatives and environmental conservation businesses. Financial managers should facilitate the integration of technology and green finance for financial services. It is important to encourage institutional and individual investors alike to look into more environmentally friendly ways to invest and save money. Policymakers should provide a platform for global awareness and government agencies should enhance their recommendations to state governments to increase the efficacy of green finance.Originality/valueThis study contributes to the literature by investigating the relationship between Fintech and green financing. This study holds significance for financial intermediaries, industrialists, investors and policymakers by providing insights into the integration of Fintech with green finance for sustainable development. These findings affirm the pivotal role of Fintech and green finance in fostering sustainable economic development. The novelty of the topic and the variety of publications in which it has been published demonstrate that sustainable economic development has piqued the interest of a wide range of areas.