Redefining the US Consolidated Tape

Larry Tabb TABB Group – “with a new governance structure, a new and better consolidated tape could tackle some major issues: … Reducing the latency associated with how data is aggregated, normalized, and redistributed. … Increase the depth-of-book levels incorporated in the consolidated tape … Fix or revise the professional versus non-professional definitions that determine how much a firm needs to pay the SIP per user. … Putting odd-lot quotes into the consolidated quote. “

The Problem with Algo Wheels

Michael Mollemans – TABB Group “The problem with algo wheels, however, is that they tend not to account for the risk factors underlying the fat-tail distribution of algo returns, and algo wheel selection processes often turn into a game of guessing the finer points in the algo wheel’s logic. The better the guess, the better the fit to the algo wheel, the better the ranking.

All in all, algo wheels need to go beyond gross median cost calculations to include standard deviation risk factors, as well as netting out market and sector betas, or the end result likely will be broker algo rankings that just oscillate between best to worst and then from worst to best along a mean reverting system over time.”

Click here for other articles on the wheel.

Academic Research Takes Center Stage at IEX

Elaine Wah – “After opening the call for papers in early August, we received nearly 60 submissions — addressing a wide range of modern market structure topics. We ultimately selected eight papers, some of which focus on the most important issues being debated today, others on those which will likely be discussed in years to come. For those who were not able to attend, we are providing brief summaries of the eight papers in this blog post. Of course, as we cannot do full justice to the papers here, we encourage you to read the papers, which are hosted on the conference website.

Routing 201: Some of the Choices an Algo Makes in the Life of an Order

Routing 101: Identifying the Cost of Routing Decisions


AI-Washing: Is It Machine Learning … Or Worse?

Dr. Bimal Roy Bhanu AiXPRT – “In terms of regulatory compliance in financial services – for example, automating the KYC processes for AML and CTF – the utopia is an AI solution system that harnesses machine learning and natural language algorithms. The AI engine should not be static; rather, it should be trainable to understand any regulation regardless of geography. Also, once the system has learned a regulation, it should be simple – using a straightforward text input interface – to teach the engine to understand any differences or changes in regulation. It might take a person weeks to understand and be trained for changing regulations, whereas the AI solution can do it in a matter of hours.

Given that it can take months to manually complete compliance assurance processes, the business case for embracing the automated efficiencies, cost savings and analytics delivered by AI compliance solutions is undeniable. Such platforms are available and are infinitely superior to the illusory masquerade of the AI-washing brigade.”

State Street Announces Strategic Partnership with FactSet to Distribute MediaStats

BUSINESS WIRE – State Street MediaStats scours more than 100,000 media sources as well as other big data sources to estimate future price changes and risks pertaining to individual equities, country equity indexes, and foreign exchange rates. The indicators use natural language processing and machine learning algorithms to account for biases and helps filter out noise in the media based on an extensive body of founding partners’ research.

How Machine Learning Pushes Us to Define Fairness

  • David Weinberger – As we look at higher levels of abstraction — from using sliders to adjust the mixes in the bins, to questions about optimizing possibly inconsistent values — ML is teaching us that fairness is not simple but complex, and that it is not an absolute but a matter of trade-offs. 
  • FinServ Ramps Up Machine Learning

    Shanny Basar – The study said firms were using machine learning in cases ranging from equity trading to optimize order-routing and deal execution to anti-money laundering where the technology is used to analyse millions of documents for ‘know-your-customer’ checks. Insurance and banking had the most live cases in the sample with the median bank having 5.5 machine learning applications.

    What’s the Difference Between AI, ML, Deep Learning, and Active Learning?

    Kiran VajapeyToday, the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably. While the terms are related, they mean different things. We map out how they all relate to one another, so your team can find the best candidates, best approaches and best frameworks as you embark upon your AI journey.