There has been a lot of hype lately around fixed-income algorithmic trading. Now, I love hype, but I don't see high-speed direct market access/algorithmic trading technologies - which have become so hot in the U.S. equities markets - migrating into the fixed-income markets anytime soon.
Advanced equity trading takes three major forms: black-box modeling, algorithmic trading and direct market access (DMA), all of which are technologies that leverage hyper-fast, real-time data. Black-box models analyze data to determine liquidity and momentum, enabling firms to spot and capture trading opportunities; algorithms break large orders into small pieces, trading them in line with a specific trading strategy; and DMA enables data aggregation from multiple markets, thus facilitating execution across fragmented marketplaces.
But structural impediments block fixed-income markets from supporting any of these execution models. First, market data is at the heart of real-time electronic trading, but it's not ubiquitous. While U.S. Treasury real-time data is prevalent, and corporate data is creeping toward real time through NASD's Trace, real-time data outside of these markets, even for institutional investors, is spotty at best. Without real-time data, how does one develop real-time execution models?
Liquidity is the second challenge. Finding continuous markets in a wide breadth of securities is near impossible. There are only approximately 6,000 NYSE- and Nasdaq-listed U.S. equities, compared to more than 2.7 million fixed-income securities (not including money markets and municipals). Finding an active bid or offer on anything outside of the most-liquid U.S. Treasuries and newly issued securities is virtually impossible.
Hand in hand with the number of products is product complexity. Stocks are simple; bonds are not. A stock represents fractional ownership of a company. A fixed-income security at its simplest is a claim on future cash flows, and it gets a lot more complex. Issue dates, maturity dates, credit ratings, payment structures and other features not only differentiate the various bonds, but make them nonfungible and often difficult to value.
Finally, the fixed-income market structure is very different from U.S. equities. U.S. equities trade in an open exchange or electronic communications network (ECN), where investors pay commissions to have their orders represented in the market. Fixed-income securities trade in a bifurcated over-the-counter market. Dealers trade with other dealers through interdealer brokers, and clients trade only with dealers, either directly or through request-for-quote distribution platforms such as TradeWeb or MarketAxess. Investors generally do not have interdealer platform access, and, through the current distribution platforms, investors cannot provide liquidity. Both of these features are needed for anonymous equity-style advanced trading.
With all of these impediments, developing real-time fixed-income trading algorithms won't happen soon; but, who said fixed-income algorithmic trading had to look like black-box trading on the equities market? While we won't see high-speed, fixed-income electronic trading, as the market participants get smarter about valuation, product insight, hedging, risk strategies and synthetic securities arbitrage, we'll see more analytically proficient traders migrate to the fixed-income markets. And while they won't be plugging their models directly into a Bond Exchange or ECNs anytime soon, there will be no lack of analytics, modeling, analysis and complex math targeted at trying to better value fixed-income products in search of opportunity. Execution just won't be at light speed.
Larry Tabb is founder and CEO of Westborough, Mass.-based TABB Group, a financial markets strategic advisory firm. [email protected]