放牧
系统回顾
独创性
实证研究
优势(遗传学)
文件夹
精算学
经济
心理学
金融经济学
社会心理学
政治学
哲学
生物化学
化学
梅德林
认识论
创造力
法学
林业
基因
地理
作者
Satish Kumar,Nisha Goyal
标识
DOI:10.1108/qrfm-07-2014-0022
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI