Chapter 11 - Electronic and Algorithmic Trading for Different Asset Classes

Abstract

Electronic trading in the United States and European markets has continued to develop and evolve, with trading platforms developing value-added services such as historical pricing data, confirmation, allocation services, order management systems, and electronic research delivery. Equity markets are the best known and most widely studied examples of electronic trading. The move to electronic trading in fixed-income markets has however been slower than for equities. Algorithmic trading clearly suits the equity and foreign exchange markets for the time being. There are fewer instruments, prices are transparent, liquidity is concentrated, and the availability of multiple execution channels allows savvy investors to exploit inefficiencies. The bond market, however, is dominated by a handful of large brokers such as eSpeed and BrokerTec, most of which is centered on actively traded markets such as U.S. Treasury bonds—the only current fixed-income market that is able to provide the liquidity needed for algorithmic trading to be effective. Thinly traded sectors such as credit markets do not offer consistent pricing in sufficient size to fit an algorithmic trading model. The implementation of Trade Reporting and Compliance Engine (TRACE) reporting, however, is beginning to bridge that gap and provide more transparency in credit markets. Most traditional investment managers trade electronically by submitting a request for quote (RFQ) through an electronic communication network (ECN) to dealers. The growth of algorithmic trading would depend on the growth of trading multiassets globally. As the barriers among markets fall, technology becomes more sophisticated, and the marketplace begins to offer trading on a single platform in all asset classes. Thus, the growth of algorithmic trading is linked to how quickly the barriers among markets fall as trading across asset classes becomes more prominent globally.