Algorithms on Wall Street: High-Frequency Automated Trading
Posted December 21, 2007
Trading in international stock, bond, and currency markets is increasingly carried out by automated, algorithm-driven systems. As both electronic technology and the science of quantitative finance have become more sophisticated, these high-frequency, algorithm-driven trades have also started to shape the way entire markets work, raising new challenges for the humans writing the programs. On November 15, 2007, the Academy's Quantitative Finance Discussion Group invited three experts in computational analysis to discuss the impact of algorithmic trading on three distinct markets: equities, fixed-income, and currency.
Among the points made: While equities markets have adopted algorithmic trading rapidly, currency markets and fixed income markets have trailed slightly behind. High-speed computerized trades have decreased profit margins for many types of traders, and have altered the dynamics of the market. Algorithmic trading can be customized to capture profits from different types of market movements. The need for faster trade execution and adaptation is driving an arms race, with firms constantly seeking faster computer and networking technologies.
Use the tabs above to find a meeting report and multimedia from this event.
The Algorithmic Trading Podcast
An interview series focusing on challenges affecting algorithmic trading environments.
Algorithmic Trading at Wikipedia
An overview of the history of and some current debates concerning algorithmic trading.
A magazine dedicated to automated and algorithmic trading.
Portal to articles about automated trading.
Robert Almgren, PhD
After receiving a PhD in computational and applied mathematics from Princeton University, Robert Almgren embarked on a wide-ranging career in mathematical research, culminating in a tenured associate professorship at the University of Toronto, where he also directed the Masters in Mathematical Finance program. Almgren then moved into the financial industry, and he is now in the electronic trading group at Banc of America Securities.
Kamal Kasera came into the algorithmic trading field with a background in both computer science and business. He holds a Masters degree in computational finance from Carnegie Mellon University, and an MBA and a Masters in computer science from the University of Massachusetts. Currently a director and senior trader at Deutsche Bank specializing in the electronic trading of government bonds, Kasera previously worked as a fixed income strategist at Goldman Sachs.
Mark Mueller, PhD
Originally trained as a physicist, Mark Mueller holds a BS in physics from MIT and a PhD in physics from Stanford University. His career in the finance industry has included stints at Morgan Stanley, where he was a quantitative analyst in equity derivatives, and Goldman Sachs, where he was a member of the fixed income proprietary trading group. In 2003 he cofounded the Algorithmic Trading Division at GMO, where he is director of research.
Alan Dove is a science writer and reporter for Nature Medicine, Nature Biotechnology, and Bioscience Technology. He also teaches at the NYU School of Journalism, and blogs at http://dovdox.com.