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Article
Publication date: 17 December 2018

H. Kent Baker, Satish Kumar, Nisha Goyal and Vidhu Gaur

The purpose of this paper is to examine how financial literacy and demographic variables (gender, age, income level, education, occupation, marital status and investment…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Managerial Finance, vol. 45 no. 1
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 8 May 2018

Satish Kumar, Nisha Goyal and Rituparna Basu

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

International Journal of Bank Marketing, vol. 36 no. 3
Type: Research Article
ISSN: 0265-2323

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Article
Publication date: 23 May 2019

H. Kent Baker, Satish Kumar and Nisha Goyal

This paper examines the relation between the Big Five model of personality traits and behavioral biases (overconfidence, disposition effect, anchoring, representativeness…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Review of Behavioral Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1940-5979

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Article
Publication date: 7 November 2016

Satish Kumar and Nisha Goyal

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Qualitative Research in Financial Markets, vol. 8 no. 4
Type: Research Article
ISSN: 1755-4179

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Article
Publication date: 2 February 2015

Satish Kumar and Nisha Goyal

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Qualitative Research in Financial Markets, vol. 7 no. 1
Type: Research Article
ISSN: 1755-4179

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Book part
Publication date: 30 September 2020

B. G. Deepa and S. Senthil

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…

Abstract

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.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

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Article
Publication date: 16 February 2021

Zhongjun Tang, Tingting Wang, Junfu Cui, Zhongya Han and Bo He

Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the…

Abstract

Purpose

Because of short life cycle and fluctuating greatly in total sales volumes (TSV), it is difficult to accumulate enough sales data and mine an attribute set reflecting the common needs of all consumers for a kind of experiential product with short life cycle (EPSLC). Methods for predicting TSV of long-life-cycle products may not be suitable for EPSLC. Furthermore, point prediction cannot obtain satisfactory prediction results because information available before production is inadequate. Thus, this paper aims at proposing and verifying a novel interval prediction method (IPM).

Design/methodology/approach

Because interval prediction may satisfy requirements of preproduction investment decision-making, interval prediction was adopted, and then the prediction difficult was converted into a classification problem. The classification was designed by comparing similarities in attribute relationship patterns between a new EPSLC and existing product groups. The product introduction may be written or obtained before production and thus was designed as primary source information. IPM was verified by using data of crime movies released in China from 2013 to 2017.

Findings

The IPM is valid, which uses product introduction as input, classifies existing products into three groups with different TSV intervals, mines attribute relationship patterns using content and association analyses and compares similarities in attribute relationship patterns – to predict TSV interval of a new EPSLC before production.

Originality/value

Different from other studies, the IPM uses product introduction to mine attribute relationship patterns and compares similarities in attribute relationship patterns to predict the interval values. It has a strong applicability in data content and structure and may realize rolling prediction.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 12 June 2020

Ana Colovic and Sonia Mehrotra

The purpose of this paper is to investigate how a local trade union improves living conditions for women entrepreneurs in India and how its activities have evolved over time.

Abstract

Purpose

The purpose of this paper is to investigate how a local trade union improves living conditions for women entrepreneurs in India and how its activities have evolved over time.

Design/methodology/approach

The authors conducted a longitudinal case study of the self-employed women’s association (SEWA) in India. Founded in 1972, this organization fosters and supports women’s entrepreneurship. The approach of this study combines qualitative face-to-face interviews and secondary data analysis.

Findings

The findings highlight the fact that SEWA, which combines the features of a trade union and a social movement, improves women’s conditions in several different ways. The study shows that the organization’s main role has evolved from creating a community to expanding it and finally to becoming an agent of societal change.

Originality/value

The study contributes to the literature by analyzing how locally grown organizations fight social exclusion and improve the conditions of deprived groups in emerging economies.

Details

European Business Review, vol. 32 no. 5
Type: Research Article
ISSN: 0955-534X

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Article
Publication date: 10 August 2020

Abhilasha Meena, Sanjay Dhir and Sushil

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the…

Abstract

Purpose

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the mutual interactions between the factors, their dependence and their driving power.

Design/methodology/approach

This study first identifies the growth-accelerating factors and then uses the modified total interpretive structural modeling (m-TISM) framework, which is an extended version of TISM. It further uses MICMAC analysis to analyze the mutual interrelation between the identified factors.

Findings

This study highlights the interrelation amongst the factors using m-TISM model. A hierarchical model shows the level of autonomous, dependence, linkage and independent factors considering the Indian automotive industry. This study also provides the understanding related to the interdependence of growth-accelerating factors.

Research limitations/implications

The government and practitioners could evaluate the growth-accelerating factors which have higher driving power for implementing efficient policies and strategy formulation. By implementing m-TISM model in the Indian automotive industry, auto manufacturers can become more productive and profitable. Future studies could use other methods such as expert opinion to derive the factors, and further model could be verified using structural equation modeling technique.

Originality/value

This study uses a novel m-TISM framework for the analysis of growth-accelerating factors in the context of the Indian automotive industry. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched growth-accelerating factors.

Details

International Journal of Productivity and Performance Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0401

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Article
Publication date: 31 October 2018

Reza Shayestehfar and Bita Yazdani

The purpose of this paper is to compare the perceptions of Bank Saderat Iran’s (BSI) customers in Isfahan and Dubai to find the probable differences in BSI service quality…

Abstract

Purpose

The purpose of this paper is to compare the perceptions of Bank Saderat Iran’s (BSI) customers in Isfahan and Dubai to find the probable differences in BSI service quality in these cities.

Design/methodology/approach

The required data were collected by adapted Bank Service Quality (BSQ) questionnaire from two samples of BSI customers (300 in Isfahan and 100 in Dubai). In this research, BSQ was measured by seven dimensions, including Bahia and Nantel (2000) BSQ dimensions, and globalization of bank services as the added dimension. The factor analysis was used to analyze the data, independent-samples t-test for comparing the means and Friedman test for ranking of the BSQ dimensions and items.

Findings

The results of this research revealed a relative satisfaction of customers with BSI service quality in both cities; however, the respondents in Dubai perceived a higher service quality. The most important dimensions were access and effectiveness and assurance in Isfahan and reliability and tangibles in Dubai. In addition, although these cities are located in developing countries, the respondents’ perceptions were similar to those in developed countries.

Practical implications

It is proposed that BSI managers should eliminate the barriers to prompt service provision, review service charges, integrate decision-making systems, decrease the bureaucratic factors and provide training programs to increase the personnel’s’ interactive skills in Isfahan.

Originality/value

A few studies have been conducted in the field of BSQ in Iran banking industry, and none has measured BSQ using a cross-country and cross-cultural method. No research has been conducted on BSI service quality in Isfahan and Dubai, and this is the first research in both cities. Furthermore, it is one of the few times that a specific tool is used for measuring BSQ in Iran banking industry.

Details

The TQM Journal, vol. 31 no. 1
Type: Research Article
ISSN: 1754-2731

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