There has been substantial increase in algorithmic and/or high-frequency trading in recent years across developed equity, foreign exchange, bond, and derivative markets. More and more emerging markets are also catching up with the same trend. Given its importance, both for market participants and regulators, it is imperative to understand the basic details of the same. In this talk, we propose to present an overview on various forms of algorithmic trading, its underlying concepts, its application to various product markets, and public policy questions related to its impact on market structure and systemic risk.
Gangadhar Darbha holds Post-doc in Finance from The Wharton School, University of Pennsylvania and PhD in Economics from IGIDR, Mumbai. He obtained MA in Economics from Gokhale Inst of Politics and Economics, Pune, and BA from Andhra Loyola College, Vijayawada. Gangadhar Darbha has more than fifteen years of research and investment banking experience. His areas of professional expertise include fixed income analytics, algorithmic trading and high-frequency quantitative trading strategies, quantitative risk capital allocation strategies, and macro-economic modeling. Gangadhar Darbha is currently an advisor to the RBI. Recently, he was with Nomura securities as Head of Algorithmic Trading Strategies and Execution Services. Previously, he was employed in Royal Bank of Scotland, ABN AMRO and Morgan Stanley in London; ISB Hyderabad, NSE India Ltd.; National Institute of Public Finance and Policy, New Delhi; University of Pretoria, South Africa; and Free University, Amsterdam. He was a member of Committee on Financial Benchmarks and Urjit Patel committee on Monetary Policy framework set up by the RBI. He is a member of the academic council of BSE Institute Limited.