The post-2008 crash business world has taken a quantitative turn, focused on data driven and analytics led search for uncovering complex feedbacks that operate in the highly networked economy. The modern economy is a network of multiple markets that connects individuals, small businesses, banks, corporations and governments. Understanding the architecture of the network and how feedbacks operate between these entities has become imperative for businesses. Since consumer choice is both informed and shaped by peer networks, understanding how feedbacks operate in this network of opinions and a perception has become quite fundamental for business in this information age.
In a networked economy, with feedbacks running between entities at various levels, the methods and tools of mainstream economics and finance are of little use in understanding the complexities. The business world has recognised this failure and has taken to big data driven analytics in order to thrive in such a complex network.
With more businesses realising the power of analytics, the corporates are investing heavily in big data and driving the ‘Quant revolution’ around the world. New cross-cutting business sectors have evolved. The fintech (financial technology) sector is one example, where it harnesses two domains, finance and information technology. Since the 2008 crash, the finance sector is moving towards new ‘secure’ technologies, such as block chain, for their products. This is the fastest growing sector in the global finance industry and in the context of digitisation and the inevitable financialisation of the Indian economy, the fintech sector is expected to grow here also. Even in the traditional business sectors, like retail and wholesale, transportation, hospitality, etc., there is a move towards big-data and analytics.
In this scenario, there is a greater need for students with strong analytics and computing/IT skills. Science, engineering and technology students, with a post-graduate degree in economics and finance, can excel in this new environment. For instance, in the financial sector, be it in developing programme trading strategies for high-frequency trading, or for back-testing, or in product development, there is a growing demand for students with diverse skill set, such as economics/finance, analytics and computing.
Traditionally in the finance industry, science and tech students have performed quite well, be it in hedge funds or insurance companies or in investment banks. In fact, there exists a community of ‘model vendors’, who supply bespoke models in financial risk measurement and management. These model vendors’ backgrounds are typically in science and engineering/IT with strong coding and computing skills.
Outside of banking and finance, economics students usually make a career in policy think tanks, central banks, and international development agencies, among others. Some of these institutions have realised the shortcomings of the traditional approach to policy analysis and are moving towards more interdisciplinary approaches, which, in turn, have opened up opportunities for the science and tech students with a post-graduate degree in economics/finance.
In terms of post-graduate education, the science and technology students should look for interdisciplinary programmes in the frontier areas of international finance, financial analytics and computing, business analytics, fintech etc. that provide good grounding in economics, finance and the interface between the two.