Abstract

PurposeThis paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds.Design/methodology/approachThis paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship.FindingsThis paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications.Research limitations/implicationsThis paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF).Practical implicationsIn light of the existing literature and to the best of the researchers’ knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as “grey”.Social implicationsThe research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML.Originality/valueThis research is extremely novel to the best of the researcher's knowledge.

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