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      Evidence on rationality and behavioural biases in investment decision making

      ,
      Qualitative Research in Financial Markets
      Emerald

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          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.

          Related collections

          Most cited references47

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          On the evaluation of structural equation models

            Bookmark
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            Prospect Theory: An Analysis of Decision under Risk

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              Comparative fit indexes in structural models.

              P. Bentler (1990)
              Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that they estimate no known population parameters. A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models. Two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes. CFI avoids the underestimation of fit often noted in small samples for Bentler and Bonett's (1980) normed fit index (NFI). FI is a linear function of Bentler and Bonett's non-normed fit index (NNFI) that avoids the extreme underestimation and overestimation often found in NNFI. Asymptotically, CFI, FI, NFI, and a new index developed by Bollen are equivalent measures of comparative fit, whereas NNFI measures relative fit by comparing noncentrality per degree of freedom. All of the indexes are generalized to permit use of Wald and Lagrange multiplier statistics. An example illustrates the behavior of these indexes under conditions of correct specification and misspecification. The new fit indexes perform very well at all sample sizes.
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                Author and article information

                Journal
                Qualitative Research in Financial Markets
                QRFM
                Emerald
                1755-4179
                November 07 2016
                November 07 2016
                : 8
                : 4
                : 270-287
                Article
                10.1108/QRFM-05-2016-0016
                887a97bb-580e-4370-a8c0-d4a9ad2a6a4f
                © 2016

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