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Bridging the Capital Markets Digital Divide
Traders Magazine Online News, June 1, 2017
In the first quarter of this year, quantitative hedge funds received $4.6 billion in net new investments. By comparison, the overall hedge fund industry during the quarter, had withdrawals of $5.5 billion. Those figures, were reported in a Wall Street Journal article ominously titled The Quants Run Wall Street Now.
The rise of quants is not just a hedge fund issue. For several years now, there have been debates and discussions about the digital divide that is emerging in capital markets, between an elite group of technology-focused firms that have invested in data and computing resources for spotting alpha in highly automated markets, and a larger group of market participants who are still wrestling with how significantly they should shift from traditional trading models.
What’s more daunting, is that despite the impact that big data and algorithmic trading have already had on markets, the biggest changes may still be yet to come. Industry veteran Michael Spencer, who founded electronic markets and post-trade specialist ICAP, which became NEX Group after selling its hybrid voice broking business Tullett-Prebon last year, predicts a “tectonic change” in the finance industry over the next few years.
“Technology will bring us a new breed of trader, and those trading firms that embrace this change will end up looking more like Silicon Valley than Wall Street,” Spencer wrote in a recent column in Financial News.
For the market players who have not yet crossed the digital divide, the investment required to compete with statistical arbitrage firms who are successfully leveraging highly automated quantitative strategies may seem overwhelming. But for players who have already invested in high-tech trading infrastructures, costs are challenging as well. Guggenheim Partners LLC, which built a supercomputing cluster at Lawrence Berkeley National Laboratory in California, spends $1 million a year just on the electricity to run the cluster, The Wall Street Journal reported. Anecdotally, some firms have told us that the cost of “training algorithms” to leverage data for a trading advantage can be ten times the already pricey data acquisition and management expenses.
A new infrastructure paradigm
Joel Steinmetz, a Fluent Trade LLC managing director and a veteran of Citadel and Citigroup, pointed out in an article in Tabb Forum a few years ago that trading in milliseconds means that there are 23,400,000 possible trade times in each trading day. Microsecond trading means there are 23,400,000,000 potential execution points in a day.
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