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Data Science and the Trading Desk

Traders Magazine Online News, October 25, 2018

Terry Flanagan

Francis Bacon, René Descartes and Isaac Newton were among pioneers who advanced the idea of making conclusions based on observation and evidence, rather than just reasoning.

Centuries later, institutional brokers are incorporating tenets of the scientific method into their own pursuits of buying and selling blocks of equity.

The nutshell premise is that data and proof walk, conjecture talks. This is especially the case in a rapidly evolving market with a multitude of promising -- but untested -- trading options.

"At UBS in the Americas our view is that the equity ecosystem continues to evolve and become increasingly complex in terms of new order types, new venues and new sources of liquidity,” said Todd Lopez, Head of Americas Cash Equities at investment bank UBS. "There continues to be more competition and diversity in liquidity sources. To effectively navigate this environment we need to understand in forensic detail when and how to access these sources and leverage new order types."

Sell-side trading desks utilizing data isn’t new. What is new is the level of sophistication of buy-side investment managers, who need to see evidence that a methodology works. Brokers need to show, not just tell.

"Our clients are becoming increasingly sophisticated in how they measure results and are pushing us harder to optimize our capabilities to solve their specific use cases," Lopez said. “They require empirical evidence that taking a particular approach will result in lower implementation costs of trading."

‘Significant Differentiator’

“A broker’s client base is diverse and each buy-side customer may have varying order flow and therefore different liquidity needs,” said Curt Engler, Head of Equity Trading, Americas, at J.P. Morgan Asset Management. “The ability to test varying theories and quantify the results, especially client- specific needs, should be a significant differentiator for algorithmic providers.”

In the early 17th century, Galileo Galilei used the scientific method to contradict the long-accepted Aristotelian notion that the rate at which objects fall is proportional to their weight. He did this by dropping two balls of different weights onto ramps, which slowed speeds and enabled more precise time measurement. When the balls reached the ground at the same time, the theory that objects fall with the same acceleration regardless of mass was proven.

In 2018, trading desks are out to prove or disprove their own theories, in complex, high-speed electronic markets rather than backyards. UBS is doing so via a framework which is designed to improve algorithmic performance by allowing for controlled experimentation with different trading hypotheses.

There are plenty of new developments for trading desks to work with. For instance, recently exchange operator Nasdaq launched Midpoint Extended Life Order, which is meant to unite counterparties with longer-term investment horizons. Conceptually, the order type is attractive, as large institutions with buy-and-hold clientele prefer to trade with each other rather than with market participants who make their money moving in and out of markets quickly.

UBS ran an internal pilot program to determine if M-ELO lives up to its promise. “We collect a statistically significant number of observations, which helps us understand where this new order type may or may not make sense,” Lopez said. “We can then work with clients and use this data to further optimize their execution process.”

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