Behavioural Biases Scale for Retail Investors' Trading Behaviour

Gabriel V. Komba

doi:10.51867/ajernet.5.4.168

Autores

Gabriel V. Komba School of Business, Mzumbe University, Tanzania https://orcid.org/0000-0001-5741-6478

DOI:

https://doi.org/10.51867/ajernet.5.4.168

Resumo

The purpose of this paper was to describe the development of a survey instrument designed to directly measure the effects of the underlying psychological constructs on the behaviour of retail-investors engaging in stock trading at the Dar es Salaam stock exchange. The paper adopts a survey research approach to outline the process of developing, validating and testing as survey instrument to help researchers better understand how retail investors make decisions when trading stocks. This study is guided by six behavioural theories, namely availability bias, representativeness bias, overconfidence bias, ambiguity aversion, regret aversion, and loss aversion. Empirical data for these variables were collected through a questionnaire administered to a sample of 280 respondents. Convenience and snowball sampling techniques were employed to recruit participants. To ensure reliability, the study employed the Cronbach’s alpha (α) coefficient, while Principal Component Analysis (PCA) was performed to assess the construct validity of various behavioural biases. The results indicate that the final survey instrument comprises 49 items developed from eight behavioural constructs, organised into seventeen scales, all of which demonstrate acceptable levels of content validity, reliability and construct validity. The paper concludes that the developed instrument is valuable for both academic and practitioner communities, particularly interested in studying trading behaviour of retail stock investors. The primary contribution of this study is the development of a reliable instrument for measuring retail investor trading behaviour in frontier markets. Therefore, the study recommends that researchers, professionals, policy makers and other stakeholders to utilise these constructs in future investigations of investors trading behaviour and into educational programmes to mitigate the negative impacts of biases on decision-making.

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