Proton Advisors

I am proud to share that, after completing the course work and passing the exams, I am honored to join the distinguished and diverse group of NACD Certified Directors, committed to advancing the highest standards of boardroom excellence.

#NACD

Brennan Carley was quoted in this recent article by Max Bowie at Waters Magazine, on how the Credit Suisse acquisition by UBS will affect their systems and data spend: https://www.waterstechnology.com/data-management/7950801/big-bank-mergers-big-cuts-in-data-spend-not-so-fast-experts-say

Brennan Carley spoke to Waters magazine recently on the people impact of the acquisition of Credit Suisee by UBS:

https://www.waterstechnology.com/data-management/7950766/should-i-stay-or-should-i-go-what-data-execs-can-expect-from-the-ubs-credit-suisse-merger

For Waters 2022 wrap up, Brennan Carley was interviewed on his thoughts regarding the Microsoft / LSEG deal: https://www.waterstechnology.com/emerging-technologies/7950447/waters-wrap-big-tech-exchanges-and-a-rapidly-evolving-market

Brennan Carley was recently interviewed by Anthony Malakian from Waters

https://www.waterstechnology.com/emerging-technologies/7950308/waterswrap-asxs-chess-dlt-meets-calamitous-fate-what-can-be-learned

Following the acquisition of Refinitiv by the London Stock Exchange Group, I will be leaving Refinitiv where I have spent the last ten years. For most of that time I ran the Enterprise Data Solutions business, and two years ago I took on a new challenge and created an entirely new function: the Consulting & Implementation Team which works with Refinitiv’s most strategic customers. I have had many great experiences at Refinitiv, and worked with some amazing people, and now it is time for a new chapter.

Beginning in 2022 I will once again be providing advisory services through Proton Advisors.

Check out Stephanie Hammer’s book, “Architects of Electronic Trading: Technology Leaders Who Are Shaping Today’s Financial Markets.”  I had the opportunity to contribute a chapter (Chapter 14, on FIX.)

In October of 1851, Julius Reuter used carrier pigeons between Brussels and Aachen, closing the gap in telegraph lines that connected Berlin and Paris.  This gave his customers a latency advantage, enabling traders in Paris to learn of news from Germany ahead of their competitors.

Since then, and especially in the last few years, many millions have been spent, and we are now measuring trading delays in microseconds instead of hours. Much has been written on the topic of reducing latency in trading systems, which begs the question: When it comes to trading, how fast is fast enough, and where will it end?

A recent survey concluded that:

• 71.6% of respondents rated latency as crucially important
• Of which 13.8% need the lowest possible latency
• The other 57.8% indicated they don’t necessarily need to be the very fastest, but that being slower does impact negatively on trading profits.

So why the difference, and is it as simple as “need to be the fastest” or “fast is good but it doesn’t need to be the best”?

Let’s analyze this.

• Different firms or trading desks have different strategies.  Some are engaged in pure latency arbitrage (when you see a price divergence of the same instrument traded in two markets, buy the cheaper one and sell the pricier one.)  Others have market making strategies, statistical arbitrage strategies, news-based strategies, and so on.

• For any strategy, there is a signal that is an input to the strategy, from which the strategy ultimately makes a buy or sell (or do nothing) decision.  A signal could range from a price move on a market to a bit of news on a news feed, to a research report published by an analyst.  The trading decision could be fully automated in a computer or it could be made by a human being, the principal is the same.

• While different traders pursue different strategies, they are hardly all unique.  So it should be no surprise that when a signal is generated, there are multiple traders with strategies that will read that signal, make a trading decision, and generate orders into the market.  As those orders flow into the market, they will push the price of the security towards a new equilibrium point, until either the signal has been fully “priced in” to the security being traded, or until a newer signal is created in the market.  The first to trade on that signal will capture most of the “alpha” from the signal, and over time the alpha will decay.

• Take for example a simple pairs trade, i.e. Assume that there is a strong price correlation security A and security B.  If the price of A moves up and B moves down, traders will buy B and sell A, pushing up the price of B and pushing down the price of A, until the prices come back into alignment or until some other event occurs.

• So if your strategy arbitrages A and B, you are competing in the latency game with everyone else that trades that arbitrage.  But suppose also that while there is a correlation between A and B, there is also a correlation between B and C, and therefore between A and C.  You are now not only in a latency race with other traders who are trading A and B, but with those who are trading the arbitrage between B and C and those trading the arbitrage between A and C.  Essentially you are in a race with a set of strategies that are triggered by the same (or correlated) signals.

• Once a signal occurs, the race begins, and the traders with the fastest systems will be the first to trade and capture the maximum possible alpha.  They, and other traders, will continue to trade until the alpha has decayed.

So how fast is fast enough?  Very simply, to capture the maximum value of the trade, you need to be as fast as the fastest of the other traders who have comparable strategies, i.e. strategies that trade off the same or correlated signals.

What if you are not as fast as the fastest of your competitors?  As long as the alpha has not decayed completely, there will be opportunities for slower traders to pick up some of the remaining alpha.  Which begs the question, how quickly does alpha decay?

The answer to that question depends on two things:

• How clear and unambiguous the signal is, which determines how long it takes for the market to digest, analyze, and process the signal.  In the case of a pure latency arbitrage strategy, the signal is the movement in the price of a common security on two venues, which is very clear and will immediately attract traders who will quickly (i.e. in microseconds) arbitrage away the price discrepancy.  At the other end of the spectrum, if the signal is an analysts report, investors will differ in their assessment of true value of the security, and it will take longer (hours, days, perhaps weeks) before they have fully appreciated the impact of the report and it is reflected in the price of the security.

• How many firms are trading on that signal and how fast they are.  The more firms that recognize and trade on a signal, and the quicker they are to send orders into the market, the faster the prices will converge and the alpha will decay.

So how fast is fast enough?  It depends on your strategy.  You need to be as fast as the fastest competitors who are trading “equivalent” strategies (i.e. strategies that are based on the same signals or on signals with strong correlation).  If you are not the fastest, you may still be able to capture some value.  In general, the more your strategy depends on clear and unambiguous trading signals, the more rapidly alpha will decay and therefore the more important it is to be at the very front of the pack.

As the fastest traders continue to invest in infrastructure to reduce latency, the rest of the players need to either step up to the new higher bar, or trade different strategies, typically those where the correlations are less obvious or weaker.  Even within those strategies however, a competitor with an equivalent strategy and faster infrastructure will always gain a greater share of the profits.  150 years ago that advantage was measured in hours.  Now it is measured in microseconds, and firms at the leading edge are measuring in nanoseconds.  As long as someone is able to squeeze some more latency out of their system, the race will continue.

Caveat:  These comments relate exclusively to the ability to capture alpha from trading.  Investors who are looking to enter or exit a position (either long or short) trade with the objective of reducing market impact, which is a quite separate discussion.

On Thursday, October 20, I will be speaking at the Financial Information Forum Market Data Capacity Working Group.  I started the working group in 1997, so it will be fun to go back after all these years!

I am excited to announce that I will be teaching this fall at my alma mater, New York University.  I will be teaching a class in Management Strategy in Technology Sectors:

“This course provides an overview of the process of implementing a successful management strategy in an information-, technology- and knowledge-intensive environment. Fundamental topics include the development of strategic vision, objectives and plans; implementation of strategy and the evaluation of performance; industry and competitive analysis; SWOT analysis and competitive advantage and sustained advantage. Advanced concepts include strategic positioning in global markets, Internet strategy, strategy in diversified firms, and interactions between organizational structure and strategy and between ethics and strategy.”