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The purpose of this paper is to examine how financial literacy and demographic variables (gender, age, income level, education, occupation, marital status and investment experience) related to behavioral biases.

The study uses one-way analysis of variance (ANOVA), factor analysis and multiple regression analysis to examine survey data from more than 500 individual investors in India.

The results reveal the presence of different behavioral biases including overconfidence and self-attribution, the disposition effect, anchoring bias, representativeness, mental accounting, emotional biases and herding among Indian investors. Hence, the findings support the view that individual investors do not always act rationally. The results also show that financial literacy has a negative association with the disposition effect and herding bias, a positive relation with mental accounting bias, but no significant relation with overconfidence and emotional biases. Age, occupation and investment experience are the most important demographic variables that relate to the behavioral biases of individual investors in the sample. Regarding gender, males are more overconfident than are females about their knowledge of the stock market.

The study does not test for causality, only association between the variables. Thus, the findings in this study should not be interpreted as suggesting causality. The study may have implications for financial educators in promoting the financial awareness programs for individuals. Financial advisors can potentially become more effective by understanding their clients’ decision-making processes.

Despite an extensive literature on behavioral finance, limited academic research attempts to unravel the relation of how financial literacy and demographic variates relate to behavioral biases. This study contributes to this literature by trying to fill this gap.

The purpose of this paper is to obtain a market-oriented approach to segment individual investors in terms of their attitudes and behaviour towards investment. It also attempts to understand the impact of certain demographic variables like gender, age and education on the behaviour of individual investors in the emerging urban Indian market.

A structured questionnaire was used to obtain a total of 340 valid responses which were collected from March 2016 to August 2016. Factor analysis was used to explore the components. Based on these components, cluster analysis was used to identify different subgroups. Statistical techniques, namely, t-test, analysis of variance and Fisher’s least significant difference test were used to examine the impact of demographic variables.

Factor analysis displayed five components, namely, interest in financial matters, anxiety for money, logical decisions, concern for future and spending tendency. Cluster analysis indicates that individuals can be divided into five clusters based on these components. It further substantiates that gender and education have a significant association with each subgroup.

Individual investor segments that were identified and profiled may provide an opportunity for advisors, financial analysts, organisations and investors to improve investment decision making. In this way, financial service firms can identify and provide services based on group-specific needs.

To the best of the authors’ knowledge, this study is the first of its kind to segment Indian investors into different homogeneous groups based on their attitude and behaviour towards financial matters.

This paper examines the relation between the Big Five model of personality traits and behavioral biases (overconfidence, disposition effect, anchoring, representativeness, metal accounting, emotional bias and herding) of Indian individual investors when making investment decisions.

The authors use a structured questionnaire to obtain responses from 515 stock investors in India between August 2016 and January 2017. Based on components identified through factor analysis, the authors use structural equation modeling to examine the effect of specific personality traits.

The findings indicate a significant association between the traits of neuroticism, extroversion and conscientiousness as well as behavioral biases of individual investors. Openness has a significant relation with only mental accounting and the agreeableness trait has no relation with the behavioral biases examined.

The findings imply that understanding investor personality differences and investment psychology can help financial advisors and wealth managers modify products and services to better suit client needs.

To the best of the authors’ knowledge, no previous study has examined the impact of the Big Five model of personality traits on various behavioral biases among Indian investors.

The purpose of this paper is to investigate the relationship between rational decision-making and behavioural biases among individual investors in India, as well as to examine the influence of demographic variables on rational decision-making process and how those differences manifest themselves in the form of behavioural biases.

Using a structured questionnaire, a total of 386 valid responses have been collected from May to October 2015. Statistical techniques like t-test, analysis of variance (ANOVA) and Fisher’s least significant difference (LSD) test have been used in this study. Structural equation modelling (SEM) has been used to analyse the relationship between rational decision-making and behavioural biases.

The findings show that the structural path model closely fits the sample data, indicating investors follow a rational decision-making process while investing. However, behavioural biases also arise in different stages of the decision-making process. It further explores that gender and income have a significant difference with respect to rational decision-making process. Male investors are more prone to overconfidence and herding bias in India.

The findings of the study have significant implication for the individual investors. It is recommended that if individuals are aware about the biases, they may become alert before taking irrational investment decisions.

To best of the authors’ knowledge, the present study is a first of its kind to investigate the relationship between rational decision-making and behavioural biases among individual investors in India.

The purpose of this paper is to systematically review the literature published in past 33 years on behavioural biases in investment decision-making. The paper highlights the major gaps in the existing studies on behavioural biases. It also aims to raise specific questions for future research.

We employ systematic literature review (SLR) method in the present study. The prominence of research is assessed by studying the year of publication, journal of publication, country of study, types of statistical method, citation analysis and content analysis on the literature on behavioural biases. The present study is based on 117 selected articles published in peer- review journals between 1980 and 2013.

Much of the existing literature on behavioural biases indicates the limited research in emerging economies in this area, the dominance of secondary data-based empirical research, the lack of empirical research on individuals who exhibit herd behaviour, the focus on equity in home bias, and indecisive empirical findings on herding bias.

This study focuses on individuals’ behavioural biases in investment decision-making. Our aim is to analyse the impact of cognitive biases on trading behaviour, volatility, market returns and portfolio selection.

The paper covers a considerable period of time (1980-2013). To the best of authors’ knowledge, this study is the first using systematic literature review method in the area of behavioural finance and also the first to examine a combination of four different biases involved in investment decision-making. This paper will be useful to researchers, academicians and those working in the area of behavioural finance in understanding the impact of behavioural biases on investment decision-making.

This study will help students determine the economic value of a firm particularly in case of a small business. The crux of the case is to help students estimate an enterprise value for a company and figure the actual worth of the company to aid in decision-making.

This case is about a decision dilemma faced by Shashi Hegde, Director, Hycons Renewable Private Ltd, a company ventured into the production of Bio-CNG. It is about a recent proposal received by the firm from APL Ltd for equity investment with 40% stake in the firm. The case reflects the dilemma faced by small businesses to choose between investment or loss of control. Accepting the proposal will bring in additional funds, whereas the Board loss control on the firm. The case revolves around this dilemma. To help Hegde in this task, he seeks advice from his CFO and his confidant Kumar.

This case is most appropriate for a core finance class for both under-graduate and graduate programs.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

The purpose of this study was to ascertain organizational factors that impacted the performance of social enterprises.

For this research study, a structured close-ended survey questionnaire was prepared based upon literature inputs. The data was collected from 370 executives in social enterprises in India. The data was analysed through structural equation modelling.  The data was analysed towards hypothesis development as well as model development explicating the success of social enterprises.

This research study’s findings developed a model towards explicating firm level performance in social enterprises. The antecedent factors were organizational commitment (OC), organizational orientation (OO), employee empowerment (EE) and top management support (TMS). The factor business innovation capability (BIC) was the mediating variable, whereas the firm performance (FP) of social enterprises was the dependent variable. Business innovation creativity had full mediation effect.

In this research study, the variable influencing the performance of social enterprises were ascertained. TMS and EE were independent organizational variables in any social enterprise along with the two organizational factors of OC as well as OO that did matter for enhancement of BIC of social enterprises. BIC had full mediating effect based upon the mentioned factors of OC, organization orientation, EE and TMS, which subsequently manifested in superior social enterprises FP.

Social enterprises had to balance the twin objectives of social good (doing good for society) as well as earning economic benefits for the enterprise.  Given this challenge, social enterprises had to develop an organizational context in which employees were empowered towards undertaking social issues proactively. Furthermore, top management team must provide support for such causes. When this aspect coupled with the presence of OC and OO then in the social enterprise, BIC got developed.  With the presence of BICs, it became easier for social enterprises to undertake innovation that were also socially oriented and led to superior FP.

It has often been observed in developing countries like India that social innovation and entrepreneurial ventures associated with these have become a necessity. However, such ventures often do not to scale up. Hence, its case for business continuance and sustenance have been challenging. This study provided insights regarding the existential aspect of social enterprises in terms of its performance.

This study was one of the first research studies that integrated the factors of OC, OO, EE and TMS in building organizational capability towards innovation in social enterprises. This in turn contributed towards the improvement of FP of social enterprises.

Owing to the dramatic rise of FinTech credit in the financial sector, this study describes its knowledge and intellectual structure and paves the way for future research.

The study employs citation analysis, keyword analysis, co-author analysis, co-citation analysis and bibliographic coupling on 268 peer-reviewed articles published during 2010–2021 and extracted from the Web of Science database.

Research on FinTech credit has picked up momentum from 2016, with majority contributions from China, followed by UK and USA. International Journal of Bank Marketing is found to be the most productive journal. Co-citation analysis reveals that past studies have focused on three dominant themes, viz. (a) factors that influence user intention to adopt technological products and services (b) borrowers' and lenders' characteristics that impact fund-raising in FinTech credit platforms and (c) evolution of FinTech market over the years. Bibliographic coupling reveals that recent trends in FinTech credit include (a) impact of emerging technologies like blockchain, artificial intelligence, big data on financial system, (b) factors that encourage consumers to adopt the FinTech products and services, (c) mechanisms by which FinTechs have transformed formal credit markets, (d) factors that lead to successful fundraising in FinTech platforms and (e) critical perspectives on digital lending platforms.

To the best of the authors' knowledge, this is a pioneering study undertaking an exhaustive analysis of FinTech credit as a research area. The study offers valuable insights on potential topics of research in FinTech credit domain like investigating Balance Sheet Lending Model, investigating the impact of FinTechs on financial system, and new markets by collaborating with scholars of other regions.

Capital structure is an important corporate financing decision, particularly for companies in emerging economies. This paper attempts to understand whether the pandemic had any significant impact on the capital structure of companies in emerging economies. India being a prominent emerging economy is an ideal candidate for the analysis.

The study utilizes three leverage ratios in an extended market index, BSE500, for the period 2015–2021. The ratios considered are short-term leverage ratio (STLR), long-term leverage ratio (LTLR) and total leverage ratio (TLR). A dummy variable differentiates the pre-epidemic (2015–2019) and pandemic (2020–2021) period. Control variables are used to represent firm characteristics such as growth, tangibility, profit, size and liquidity. Dynamic panel data regression is employed to address endogeneity.

The findings point out that Covid-19 has had a significant, negative effect on LTLR, while the impact on STLR and TLR was insignificant. The findings indicate that companies based in a culturally risk-averse environment, such as India, would reduce the long-term debt to avoid bankruptcy in times of uncertainty.

The study covers the impact of the pandemic on Indian companies. Hence, generalization of the findings to global context might not be valid.

To maintain economic growth in the post-crisis period, Indian policymakers should ensure accessibility to low-cost capital. The findings provide impetus to deepen the insignificant corporate bond market in India for future economic revival.

Developing countries are struggling to revive the economies postpandemic. This is particularly true for Asian economies which are heavily reliant on banks for survival. This research finds evidence to utilize bond market as a source of raising capital for economic revival.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.