Impact of Anchoring, Herding and Loss-Aversion on Working Women’s Investment Decision-Making

This study investigates how overconfidence, loss aversion, and perceptions of risk affect investment decisions in the Nepal Stock Exchange. Making investment decisions is a complicated process that is influenced by several psychological elements. Using structured questionnaires, data was collected from individuals actively involved in stock trading. Employing a quantitative approach, the research utilizes a descriptive research design and conducts multiple regression analyses. Findings reveal that risk perception significantly impacts investment decisions, with individuals perceiving higher risks displaying a greater propensity to invest in high-risk assets. Additionally, overconfidence bias positively influences investment decisions, indicating that individuals with higher confidence levels tend to favour riskier investments. Loss aversion bias plays a significant role, as individuals averse to losses prefer investments that minimize potential losses. These results underscore the substantial impact of behavioural biases on investment decision-making, with overconfidence bias exhibiting the most significant influence, followed by risk perception and loss aversion bias. The findings emphasize the importance of psychological biases in understanding investment behaviour. Investors, financial advisors, and policymakers can all benefit from understanding how risk perception, overconfidence, and loss aversion affect investment decisions. Investors can improve portfolio performance, lessen the chance of financial crises, and make more informed decisions by identifying and correcting these biases. Therefore, to encourage more effective and efficient investment decision-making processes, it is critical to increase awareness of these biases and develop measures to mitigate their negative consequences. Conducting more studies to examine these biases’ additional dimensions and how they affect investment decisions is advisable.

PurposeInnovation is the way of life and we see various innovative techniques and methods being introduced in our daily life. This study aims to focus on digital innovation in the wealth management domain. This study examines the effect of usage of robo-advisory services in investment decision-making and behavioural biases, i.e. overconfidence and loss aversion. Such studies are more pronounced in developed countries and little has been studied about investor behaviour in association with advisory services in developing countries such as India.Design/methodology/approachOverconfidence and loss-aversion biases, investment decision-making and advisory services questions are measured using a five-point Likert scale. The number of respondents was 172 investors. A purposive sampling is used for gathering responses from investors. Structural equation modeling model was run using AMOS 22 version software package.FindingsThe authors found that behavioural biases positively and significantly influence the irrationalities of investment decision-making. The findings of this study also provide empirical evidence that the usage of robo-advisory services, by individual investors, is still incapable of mitigating behavioural biases, such as overconfidence bias and loss-aversion bias.Research limitations/implicationsThe sample size of this study could be a limiting factor. This study is limited only to two biases, while other behavioural biases affect the investment decision-making of the investors, which can be considered for future research along with the impact of robo-advisory services in different socio-cultural backgrounds.Practical implicationsThis study will assist fintech start-ups, banks, architecture of robo advisors, product owners and wealth management service providers improvise their products, platforms and offerings of these automated advisory services. This could help individual investors to mitigate their behavioural biases in investment decision-making.Social implicationsThis study is useful to society as the awareness of robo-advisory services is very less, at present, and there is a need to increase the usage of these services to extend the benefit of this to the lower stratum of society. These services would be useful to all investors who find it difficult to afford financial advisors and help them mitigate their behavioural biases for investment decision-making.Originality/valueThis study is the first of its type that establishes the linkage between behavioural biases, digital innovation in fintech, i.e. robo-advisory services and individual investor’s investment decision-making in individual investor of the Indian stock market.

In the process of making investment decisions, investors are required to consider several factors in order to avoid loss. The irrational decisions often made by investors have been associated with the influence of some psychological and emotional factors. Therefore, this research aimed to analyze the influence of overconfidence and loss aversion on investment decisions as well as the moderating effect of reference groups. The sample was 102 State University students in Malang City, Indonesia, and data obtained were analyzed using the Partial Least Square method. The results showed that overconfidence and loss aversion had a negative and significant influence on investment decisions. Moreover, the reference group moderated the influence of overconfidence and loss aversion bias on investment decisions. Young investors tend to have behavioral biases when investing, and these biases are exacerbated when they join a community. Early investment education is needed for young investors to improve investment literacy and improve their investment decision-making

Background: Behavioral bias factors influence individual decision-making. Technological innovations in the financial services industry have introduced automated financial advisors, or robo-advisors, to assist in mutual fund investment decisions and reduce behavioral biases. Purpose: This study aims to prove the influence of overconfidence and loss aversion behavior bias on mutual fund investment decisions by using robo-advisors as moderator variables.Design/methodology/approach: The research sample was 100 respondents with the criteria of young investors in the age range of 18 to 25 who invested in mutual funds for the last five years and were officially registered with the Financial Services Authority. The data processing method uses multiple linear analysis with moderation dummy variable, using a robo-advisor or not.Finding/Result: The results indicate that overconfidence and loss aversion biases significantly impact mutual fund investment decisions positively. Apart from that, the results also show that robo-advisors succeed in weakening the relationship between overconfidence bias and mutual fund investment decisions. Meanwhile, robo-advisors show results that cannot moderate the relationship between loss aversion and mutual fund investment decisions.Conclusion: Robo-advisors moderate the relationship between overconfidence bias and investment decisions but do not moderate the relationship between loss aversion and mutual fund investment decisions. The high overconfidence is caused by the ease of access to information related to investment assets that is widely spread through social media. Young investors are expected to be able to screen all information related to investment knowledge to reduce loss aversion from young investors. It can help investors make more rational decisions.Originality/value (State of the art):This research is unique because it examines the behavioral biases associated with robo-advisors on investment decisions, especially investments in mutual funds. This research is novel and includes artificial intelligence technology developing in finance using robo-advisor and mutual fund investment. These have managerial implications, such as the high overconfidence in the younger generation due to easy access to information related to investment assets, which is widely spread via social media. Knowledge related to finance is considered capable of reducing loss aversion from young investors to help them make more rational and better decisions. Robo-advisor technology has reduced the irrationality of mutual fund investors' investment decisions. The research results show that overconfidence and loss aversion bias positively and significantly influence investment decisions. Apart from that, the results also show that robo-advisors succeed in weakening the relationship between overconfidence bias and investment decisions. Meanwhile, robo-advisors show results that cannot moderate the relationship between loss aversion and investment decisions. Keywords: Robo-advisor, behavioral bias, overconfidence, loss aversion, mutual fund investment decision

Discussion on the innovation of enterprise investment management

The aim of the study, Investment choices that have historically been made using rational economic models are coming under increasing pressure from behavioral finance theories that take psychological factors into account. “This study examines how individual equity investors' investment decisions are influenced by behavioral biases that are both cognitive and emotional. The research examines how biases like overconfidence, anchoring, representativeness, loss aversion, herding behavior, and regret aversion affect investor behavior and frequently result in less-than-ideal outcomes, drawing on theories like Prospect Theory and Heuristics and Biases. Using structured surveys, a quantitative, cross-sectional research design was used to gather information from 200 individual stock market participants. The associations between behavioral biases and investment decision-making were examined using SmartPLS and structural equation modeling (SEM). All of the cognitive and emotional biases that were examined had a statistically significant effect on investment decisions, according to the results, with financial literacy, herding behavior, and representativeness bias showing the biggest effects. High factor loadings, composite reliability, and average variance extracted (AVE) values all showed that the measurement model was highly valid and reliable. The Heterotrait-Monotrait ratio and the Fornell-Larcker criterion were used to verify discriminant validity. The results emphasize how important it is to raise awareness of these biases and develop mitigation techniques, particularly for individual investors. This study highlights the value of incorporating psychological insights into financial education, policy-making, and advisory services while also adding to the expanding corpus of knowledge in behavioral finance. By addressing behavioral distortions, it also provides policymakers and financial advisors with useful implications for improving investor decision-making and advancing market efficiency”.

The purpose of this study was to examine the impact of behavioral biases, such as overconfidence, disposition effect, herding, risk aversion, and financial literacy, on investment decision making. The sample was collected using a convenient method, and 338 respondents participated in the study. The study utilized descriptive statistics, ANOVA, independent sample t-tests, correlation, and linear regression analysis to analyze the data. The findings of the study suggest that overconfidence, disposition effect, and risk aversion have a significant positive impact on investment decision making, while herding does not have a significant effect. Furthermore, the results indicate that financial literacy moderate’s overconfidence, disposition effect, risk aversion, and herding negatively. This implies that higher financial literacy levels can help mitigate the impact of these biases on investment decisions. The study provides valuable insights for policymakers, stakeholders, and financial institutions to develop policies and strategies aimed at improving financial literacy. It can be useful for researchers and the general public as well, as it provides a deeper understanding of the behaviour bias and investment decision. Future research can broaden the scope of the study by including new independent variables, such as loss aversion and confirmation bias. Additionally, future research can explore the moderating effect of other factors, such as age and gender, on the relationship between behavioral biases and investment decision-making. Overall, the study highlights the importance of understanding and managing behavioral biases in investment decision making, and suggests that increasing financial literacy can help individuals make more informed investment decisions.

Research background There is no research found till date that investigated the behavioural biases (“disposition effect, herding behaviour and blue-chip stocks”) and “risk perception on investment decision making” in the Indian stock market (National Stock Exchange) by applying Structured Equation Modelling. Purpose The present study examines the connections between behavioural biases, investment decision making, and individual perspectives on risk (risk perception) in the Indian stock market. There are two exchanges in India but the National Stock Exchange (NSE) is used for the study. Research methodology In the study, the target population are the investors of the National Stock Exchange in India. The sample size of the study is 380 for the final analysis. Structured Equation Modelling (SEM) is applied to construct the model and provide standard estimates for the study by using AMOS software version 23. Results The findings show that risk perception partially mediates the relationship between blue-chip stock preference and investment decisions, indicating that blue-chip investments may lower perceived risk and influence investor behaviour accordingly. Herding behaviour, the disposition effect, and investment decision making are all unrelated to risk perception. Novelty To begin with, the given study develops a mediating framework, elucidating the risk perception as a confounder between behavioural biases (overconfidence and herding) and investment decision-making which has been insufficiently explored in Indian empirical contexts. The available literature in the most part looks at these variables as secluded entities or as point predictors, not considering the inherent mentalizing frameworks that inform decisions. Secondly, the study draws on a unique and diverse sample of individual retail investors across multiple Indian cities, incorporating both Tier I and Tier II urban centres. This geographical diversity adds robustness and generalizability to the findings, which is often missing in studies limited to metro-focused or demographically narrow samples. Thirdly, the study presents a fine methodological design, where the Structural Equation Modelling (SEM) was utilized to ascertain the direct and indirect effects in a single continuum, thus providing a more subtilised picture of the relationships between behavioural constructs.

The objective of this study is to examine the impact of loss aversion, Herding behavioral and Cognitive dissonance on an individual investor’s investment decisions making in Pakistan. The study used the moderating role of financial literacy with behavioral and cognitive biases to influence on investment decisions. The study is quantitative in nature and used major behavioral and psychological biases that impact investors’ investment decisions making in Pakistan. The data is collected by using questionnaires. The population of the study consists of investors in the twin cities of Islamabad and Rawalpindi. The sample size included study was about 251 respondents. The study used SPSS and Smart PLS software to analyze the data. The study used a convenient sampling technique. The major estimation tools used in the study are the alpha correlation matrix, descriptive summary, regression, etc. The findings of the study reveal that there is a significant impact of loss aversion, and herding on investment no significant relationship exists between cognitive dissonance and investment decision-making. The study is limited to developing market individual investors of Islamabad Pakistan. This study is implacable for students, researchers, and investors to take certain decisions and control biases. In the future, the study can be extended to some institutional investors’ biases, and other behavioral and cognitive biases can be added to the study. The study can be extended by using the mediating role of human moderators as well.

Investment decision making is a critical aspect of financial planning. It involves allocating the financial resources to various investment avenues with an objective of generating future returns. Behavioral finance provides a theoretical framework for understanding psychological biases investment decision making which changes the assumptions of traditional finance. This study examines five important behavioral biases such as heuristics, prospect theory, emotions, market impact, and herding behavior on investment decision-making and portfolio management by considering investor experience and financial literacy as moderating factors. Primary data was collected from individual investors in the BSE and NSE using a structured questionnaire administered through a purposive random sampling. Resulting in 151 complete responses were obtained and were considered valid for the purpose of our study. PLS-SEM was used to test the proposed hypotheses as well as the moderating effect. Study finding indicates that heuristics have a positive and statistically significant effect on investment decisions, while prospect theory, emotions, market impact, and herding behavior showed no significant direct influence. The moderation analysis reveals that both investor experience and financial literacy significantly moderate the effects of emotions and market impact on investment decisions. However, their influence on heuristics, prospect theory, and herding behavior was statistically insignificant. The results of this study lead to several conclusions. In this present study only one behavioral bias heuristics (HU) demonstrated a significant direct influence on investment decisions. In contrast, other behavioral biases such as prospect theory (P), emotions (E), market impact (M), and herding behavior (HB) do not significantly affect investment decisions (p > 0.05). The moderating effect of investors' experience and financial literacy investor behavior are minimal. The result underscores that improving financial education, skills, knowledge and gaining experience may help investors regulate emotional and trend-based decisions but may not be sufficient to address more instinctive cognitive biases. The significance of the study provides important implications for financial educators, advisors, policymakers and stock market authorities regarding the need for behaviorally informed investor training, decision-support systems, and informed advisory services to promote rational investment behavior.

Background Investment decision making is a critical aspect of financial planning. It involves allocating the financial resources to various investment avenues with an objective of generating future returns. Behavioral finance provides a theoretical framework for understanding psychological biases investment decision making which changes the assumptions of traditional finance. This study examines five important behavioral biases such as heuristics, prospect theory, emotions, market impact, and herding behavior on investment decision-making and portfolio management by considering investor experience and financial literacy as moderating factors. Methods Primary data was collected from individual investors in the BSE and NSE using a structured questionnaire administered through a purposive random sampling. Resulting in 151 complete responses were obtained and were considered valid for the purpose of our study. PLS-SEM was used to test the proposed hypotheses as well as the moderating effect Results Study finding indicates that heuristics have a positive and statistically significant effect on investment decisions, while prospect theory, emotions, market impact, and herding behavior showed no significant direct influence. The moderation analysis reveals that both investor experience and financial literacy significantly moderate the effects of emotions and market impact on investment decisions. However, their influence on heuristics, prospect theory, and herding behavior was statistically insignificant. Conclusions The results of this study lead to several conclusions. In this present study only one behavioral bias heuristics (HU) demonstrated a significant direct influence on investment decisions. In contrast, other behavioral biases such as prospect theory (P), emotions (E), market impact (M), and herding behavior (HB) do not significantly affect investment decisions (p > 0.05). The moderating effect of investors’ experience and financial literacy investor behavior are minimal. The result underscores that improving financial education, skills, knowledge and gaining experience may help investors regulate emotional and trend-based decisions but may not be sufficient to address more instinctive cognitive biases. The significance of the study provides important implications for financial educators, advisors, policymakers and stock market authorities regarding the need for behaviorally informed investor training, decision-support systems, and informed advisory services to promote rational investment behavior.

PurposeThe purpose of this paper is to study and describe several biases in investment decision-making through the review of research articles in the area of behavioral finance. It also includes some of the analytical and foundational work and how this has progressed over the years to make behavioral finance an established and specific area of study. The study includes behavioral patterns of individual investors, institutional investors and financial advisors.Design/methodology/approachThe research papers are analyzed on the basis of searching the keywords related to behavioral finance on various published journals, conference proceedings, working papers and some other published books. These papers are collected over a period of year’s right from the time when the most introductory paper was published (1979) that contributed this area a basic foundation till the most recent papers (2016). These articles are segregated into biases wise, year-wise, country-wise and author wise. All research tools that have been used by authors related to primary and secondary data have also been included into our table.FindingsA new era of understanding of human emotions, behavior and sentiments has been started which was earlier dominated by the study of financial markets. Moreover, this area is not only attracting the, attention of academicians but also of the various corporates, financial intermediaries and entrepreneurs thus adding to its importance. The study is more inclined toward the study of individual and institutional investors and financial advisors’ investors but the behavior of intermediaries through which some of them invest should be focused upon, narrowing down population into various variables, targeting the expanding economies to reap some unexplained theories. This study has identified 17 different types of biases and also summarized in the form of tables.Research limitations/implicationsThe study is based on some of the most recent findings to have a quick overview of the latest work carried out in this area. So far very few extensive review papers have been published to highlight the research work in the area of behavioral finance. This study will be helpful for new researches in this field and to identify the areas where possible work can be done.Practical implicationsPractical implication of the research is that companies, policymakers and issuers of securities can watch out of investors’ interest before issuing securities into the market.Social implicationsUnder the Social Implication, investors can recognize several behavioral biases, take sound investment decisions and can also minimize their risk.Originality/valueThe essence of this paper is the identification of 17 types of biases and the literature related to them. The study is based on both, the literature on investment decisions and the biases in investment decision-making. Such study is less prevalent in the developing country like India. This paper does not only focus on the basic principles of behavioral finance but also explain some emerging concepts and theories of behavioral finance. Thus, the paper generates interest in the readers to find the solutions to minimize the effect of biases in decision-making.

Cryptocurrencies have gained dominance in the digital currency market as the number of investors is increasing day by day. Cryptocurrencies have emerged as highly sought-after assets among investors worldwide. Forbes reports that in 2024, the entire market capitalization of the cryptocurrency market has surpassed USD 3.64 trillion. The role of behavioral biases in investment decision-making within this area remains unexplored. This paper aims to fulfill this gap by addressing a literature review on the topic of behavioral biases and their impact on cryptocurrencies. It specifically emphasizes how behavioral biases, like herding behavior, overconfidence, loss aversion, fear of missing out, and several other biases affect investing decision-making in cryptocurrencies. This study has been done through an extensive literature review by synthesizing various research articles. To identify the relevant studies, databases such as Google Scholar, Scopus, and Web of Science have been used by employing different keywords like “behavioral finance,” “behavioral biases,” “cryptocurrency,” “investment,” etc. These studies have been reviewed and critically analyzed to identify the major biases, obstacles, and future research directions. This paper has shown that behavioral biases significantly influence investment decisions among cryptocurrencies. For instance, cryptocurrency does contribute to the herd mentality of the investors. Likewise, loss aversion bias significantly impacts investment decisions, suggesting that individuals fearful of financial loss tend to favor lower-risk ventures. Despite these insights, limited empirical studies focus specifically on cryptocurrency, highlighting a research gap that needs to be addressed. Future research will include the need for longitudinal studies to examine the importance of behavioral biases in cryptocurrency markets and the identification of regulatory interventions to mitigate irrational behavior. This study contributes to enhancing the understanding of cryptocurrency investment and provides fruitful insights to the researchers, practitioners, and policymakers by shedding light on the interaction of behavioral biases and cryptocurrency investment decision-making.

PurposeThe purpose of this paper is to determine whether individual investor sentiment and its factors influence investment decision-making behavior in the Indian stock market. The study contributes to the novel conceptual framework that integrates the impact of investor sentiment and outlines the role of its factors (herding, media factor, advocate recommendation and social interaction) during the investment decision-making process.Design/methodology/approachIn this paper, data were collected using a structured questionnaire survey from Indian individual investors. It uses self-reported sources of information collected via a survey of individual investors and estimated the linkage via path modeling. The collected data were analyzed using partial least square structural equation modeling to examine the relationship between the construct, namely, herding, media, advocate recommendation and social interaction with investor sentiment and investment decision-making.FindingsThe study shows that herding, media factor, advocate recommendation and social interaction significantly and positively influence the investor sentiment. Among all the factors, social interaction has the lowest influence on investor sentiment. The study also reveals that investor sentiment has a positive impact on investment decision-making.Practical implicationsThe study provides valuable insights for the individual investors, financial advisors, policymakers and other stakeholders. Knowledge of behavioral finance would enhance the decision-making capabilities of individual investors in the stock market. Thus, the study calls for the need to increase awareness among Indian investors about behavioral finance and its usefulness in investment decision-making. The paper also sheds light upon the influence of investor sentiment and its antecedents on investment decision-making. The study confirms that the investor relies on their sentiment while making investment decisions. Hence, the stakeholders in the stock market should focus on investor sentiment and other psychological aspects of individual investors as well.Originality/valueThere are very few studies that deal with the behavioral aspects of individual investors in an emerging market context. The study mainly focuses on the antecedent of investor sentiment and its influence on investment decision-making in the Indian stock market. To the best of authors’ knowledge, the present study unique nature that examines the impact of the antecedent of investor sentiment which was not explored in the Indian context and investment decision-making of individual investors.

Behavioral finance has emerged as a significant field of study that challenges the traditional assumptions of rationality in financial decision-making. In contrast to classical theories which presume investors are fully rational and always seek to maximize utility, behavioral finance explores how psychological biases, emotions, and cognitive errors influence investment choices. This study investigates the impact of behavioral finance factors on investment decision-making in the context of the Indian stock market—a market characterized by growing participation from retail investors, high volatility, and increasing digital access. The research focuses on key behavioral biases such as overconfidence, herd behavior, loss aversion, anchoring, and mental accounting, and examines how these biases shape the decision-making process of Indian investors. A mixed-method approach is adopted, combining survey data from individual investors with statistical analysis to understand the extent and influence of these biases. Findings indicate that despite access to financial information and analytical tools, investor decisions are often influenced by irrational and emotional factors, leading to suboptimal investment outcomes. The study highlights that demographic factors such as age, income level, and investment experience also play a role in susceptibility to behavioral biases. Understanding these tendencies is crucial for financial advisors, policymakers, and investors themselves, as it can lead to more informed strategies and improved financial literacy. The paper concludes by recommending behavioral interventions and investor education programs aimed at minimizing the adverse effects of cognitive biases in the Indian stock market.