The IUP Journal of Applied Economics
Modeling the Barriers of Algorithmic Trading in India Using ISM and MICMAC Analysis†

Article Details
Pub. Date : January, 2021
Product Name : The IUP Journal of Applied Economics
Product Type : Article
Product Code : IJAE10121
Author Name : Jyotiranjan Hota, Chandrabhanu Das and Manoj Kumar Jena
Availability : YES
Subject/Domain : Economics
Download Format : PDF Format
No. of Pages : 22



Algorithmic trading has made a paradigm shift in Indian stock market. Popularity of algorithmic trading is gaining momentum among Indian traders and investors due to technological advancement. The objective of this paper is to apply Interpretive Structural Modeling (ISM) to develop a hierarchical structure among the key barriers of algorithmic trading in India. 11 barriers have been identified through the literature review which are then validated for significance, using a structured questionnaire, from the experts. ISM approach has been utilized to rank the barriers and analyze their mutual interactions. Subsequently, MICMAC analysis was conducted to reveal dependence and driving power of these barriers. MICMAC analysis also elicits the relative importance and interdependence between these barriers from the Indian context. A list of relevant barriers significantly helps the practitioners to take right decision while adopting algorithmic trading. The study has importance in the Indian context due to scarcity of research in this area.


In the past couple of decades, technology has played a major role in automating a significant part of human actions. In financial markets, people used to trade over exchanges by submitting buy/sell orders for various financial instruments such as equity, derivatives, commodities, currencies, and exchange traded funds. Over the years, trading of these instruments by human decisions has gradually been taken over by computers, thus giving birth to the term 'Algorithmic trading (Algo trades)'. Using simple coded instructions, a computer program

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