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UMR Extension: A Roadmap For The Buy-Side

Placing technology spend on platforms that allow unbundling of liquidity from credit restrictions could weather the initial margin storm, argues Vikas Srivastava, Chief Revenue Officer at Integral.

Vikas Srivastava, Integral

On the surface, the news that BCBS and IOSCO have granted an extension to the final phase of the Uncleared Margin Rules (UMR) is likely to be welcomed by asset managers currently trading uncleared derivatives with a notional between $8 billion and $50 billion. The extension pushes the initial margin compliance date out by exactly one year to September 2021 for an estimated 8,000 firms. However, this additional year does not apply to the 1,000+ firms that have a notional threshold between $50 billion and $750 billion. And to be honest, the truth is that much elbow grease is still needed in the coming months to prepare for what is essentially a major structural change, regardless of whether an asset manager falls under Phase 5 or Phase 6.

In addition to new documentation requirements, affected firms will also need to gain a new understanding of collateral optimisation, wherein each additional counterparty adds to the level of complexity and exacerbates the inability to realize netting benefits. Since FX is overwhelmingly an OTC market based on bilateral relationships, the challenges of collateral optimisation that large and medium-scale asset managers face across multiple counterparties is not an easy one to tackle. In addition, because the new UMR rules will result in much more exchange of margin than previously experienced, it will lead to more monitoring, reporting, reconciliation and operational burdens.

 

What asset managers are left with is a realization that they must now weigh the liquidity benefits of having several counterparties versus the costs of exchanging margin with each and every one on a bilateral basis. More and more asset managers will begin to consider trading technologies that help unbundle liquidity benefits from credit restraints. Not only can separating liquidity from credit help solve the primary issue of reducing administrative burden, but it actually adds secondary benefits. These benefits include new access to all forms of liquidity previously out of reach including non-bank liquidity, as well as client-to-client matching models.

 

Asset managers pulled into the final two phases should have a roadmap for their respective deadlines. From shifting towards clearing certain instruments, to considering the use of prime brokerage, there are many things that the buy-side should think about and consider. As the burden of managing many more counterparties grows, we should expect to see a movement towards credit intermediation models and toward technology vendors that have a strong understanding and deep experience in facilitating credit intermediated trading. Those who place their technology spend on platforms that allow unbundling of liquidity from credit restrictions will undoubtably be better placed to weather the initial margin storm.

QUICK TAKE: FNMA And FHLMC Conservatorship Near End

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The government agencies Fannie Mae and Freddie Mac might regain some of their autonomy.

The Trump Administration has announced its first formal plan to separate the two agencies from government control and influence. Part of the plan would include allowing the two to keep more capital on hand.

Today, CNBC reported that Treasury Secretary Steven Mnuchin said President Donald Trump “has approved” the Treasury’s plan to reform mortgage giants Fannie Mae and Freddie Mac. Mnuchin appeared before the Senate Banking Committee Tuesday and refused to confirm that when asked then, but said he had since confirmed it.

Secretary Mnuchin also said that negotiations were underway to end the Treasury sweep of Fannie Mae and Freddie Mac’s profits. This reclaimed money would be used to recapitalize the two.

On this news, Cowen Washington Research Group put out a note to clients saying the so-called “profit sweep,” where all of the GSEs’ profits are transferred to the Treasury each quarter, may end as early as this month.

“We expect a deal prior to Sept. 30 in which Fannie and Freddie will stop paying a quarterly dividend to Treasury,” Jaret Seiberg, Cowen’s managing director, wrote in the note. “Instead, they will pay a commitment fee for the outstanding preferred capital line. This means they can retain the rest of their profits in order to rebuild capital.”

The two housing giants have been under conservatorship for the last 11 years since the 2006 housing crisis. Combined the two private companies back approximately $5 trillion of mortgage loans.

The objectives of the administration’s plan are to create a limited role for the federal government in the housing finance system, enhance taxpayer protections and increase the role of private sector competition.

The objectives of the administration’s plan, according to Treasury officials, are to create a limited role for the federal government in the housing finance system, enhance taxpayer protections, and increase the role of private sector competition. Accomplishing all of this will take legislative and administrative action.

“This plan addresses this last unfinished business of the financial crisis in a way that preserves what works in the current system, protects taxpayers, and reduces the influence of the Federal Government in the housing finance system,” the report says.

Inverting The Alternative Data Pyramid

Ed Chidsey, Managing Director and Head of Pricing, Valuations and Reference Data at IHS Markit, looks at how information is turned into insight.

Ed Chidsey, IHS Markit

Integrating alternative data into the investment process is the future of portfolio management, yet the practice of identifying valuable, non-traditional data sets is age old. Some call it fundamental analysis, like interviewing customers about a company’s product.  Others call it plain smarts, like using satellite imagery to measure crop yields or revenue at the mall.  For all the attention it gets, alternative data remains remarkably undefined — and that’s ok.  What qualifies as alternative data is not the important question.  The real question is how do you turn such information into insight?

Technology, of course, is the key to finding insight and the value of such insights in the investment process will only improve as tools to aggregate and interpret data, especially unstructured data, improve.  Similarly, backtesting new datasets is becoming faster and cheaper and is a critical component in validating the value derived from any alternative data set.

The Demystifying Alternative Data study by Greenwich Associates found that 71% of asset managers believe that using alternative data gives them an investing edge over competitors. Around half of the investment managers surveyed by Greenwich are currently using alternative data, with another quarter planning to do so within a year.  In short, alternative data is big and it’s not going away.

The survey found that the most commonly used forms of alternative data include web-scraped data, social media sentiment, web traffic and search trends.  Certainly, there is a lot of value in those areas.  Our testing of our social media sentiment signal shows significant alpha in analyzing Twitter activity.

Yet, web-sourced data are just the starting point on the alternative data spectrum.  Since this information is natively digital, it’s easier to aggregate, analyze and pipe into algorithms.  We are still in the early stages of a profound shift in the types of data investment firms consume and how they consume it.

The most sophisticated asset managers are beginning to test more direct and more granular indicators of supply and demand.  Examples include bills of lading data from cargo ships, energy production and distribution, automobile registrations, technology component cost, political risk and ESG metrics.  The list goes on and on.

There is no doubt this information is typically harder to normalize and analyze, but it’s definitely worth the effort.  According to Greenwich, 42% of all asset managers believe the alpha edge they achieve by using alternative data lasts for at least four years.  That edge is probably even more pronounced for supply chain metrics and our research shows that information like import volumes have strong predictive power [add link to maritime case study].

At the same time, we should realize that many types of capital markets data can be alternative to the equity investor.  The CDS market, for example, can be a leading indicator for equities markets.  Short squeezes are very clearly linked to movement in equity.  A strong ESG focus has also been correlated to a company’s stock outperformance.  Ultimately, investors are likely to use a combination of alternative data factors and even blend prepackaged models with proprietary analytics.

When it comes to alternative data, it is important to understand that data alone are not the answer for most firms.  83% of asset managers in the Greenwich survey want some assistance in understanding alternative data, ingesting it and processing it.  We believe a collaborative service model is essential to bring the benefits of alternative data to the majority of investors.  Of course, the big quants have the horsepower to find hidden value in the raw data, but most firms need supplemental expertise —and we have experts at IHS Markit who can help.  We can consult with firms about our data sources, methodologies and prepackaged analytics for a growing number of alternative data factors.  We also have a growing, world class data science team that partners with clients to design and test advanced data models.

Making it easy for firms to access alternative data spanning the real economy and the derived economy was a major driver of the merger between IHS and Markit in 2016.  Not only is the firm unique for the scope of information we can provide (we are sitting on 25 petabytes of data at our last count), but very few can see the world through a lens like ours.  We are an information company at our core.  We understand the nuances of ingesting data, packaging data for customers and deriving new insights from multiple data sources.

What is alternative data? The better question might be what isn’t alternative data?  Don’t be bound by where the crowd is focusing—imagery, web data, geo location and credit card transactions.  Those are important, but a more interesting challenge, and perhaps a greater source of alpha, is out there for people and firms that are willing to look beyond the herd.  Be creative and get excited.  Engage with your data partners.  If you have a hypothesis, there’s a high likelihood the data exist and experts are there to help you test and discover new relationships between supply, demand, price and profit.

OPINION: AI Ethics Are Not Optional

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Legal liability forces the technology to colour within the lines.

Ethical artificial intelligence and machine learning may sound like an undergraduate elective, but it is a topic that financial institutions need to address urgently.

Firms are exposing themselves to a new type of risk as they either develop AI and machine-learning models or rely on the growing number of third-party model providers.

Do these new models harm a specific subset of the population or unintentionally use practices that market regulators have deemed illegal?

It can be hard to tell since AI and machine learning engines are good at dealing with black and white, but are horrible when it comes to shades of grey.

These engines are only as good as the data that feeds them.

Most of the data sets used to train instances of AI and machine learning are so incredibly large that individuals cannot comprehend everything that might be in those data sets. If some or all of the training data is the result of previously biased behaviour, it shouldn’t be surprising that the resulting models include a portion of that biased behaviour.

However, making sure that AI and machine learning engines colour within the ethical lines is exceedingly tricky when developers have to hardcode an abstract concept of “fairness” in precise mathematical terms.

When working on a paper regarding this topic, Natalia Bailey, associate policy advisor, digital finance at the Institute of International Finance, found approximately 50 definitions for fairness, she said during a recent AI summit in Midtown Manhattan.

Firms may think they have some time to sort this out as they did with data privacy issues before various states enacting their data-privacy regimes and the EU rolling out it General Data Privacy Regulation, they do not.

As Emma Maconick, a partner at the law practice of Shearman & Sterling and who spoke on the same panel noted, the law is ahead of the game respecting the liability a firm faces from a misbehaving AI. The well-trodden laws that address misbehaving children or employees, known as vicarious liability, also cover supervised and non-supervised AI engines.

If financial institutions have not incorporated an ethical analysis as part of their AI development process, there is no time to wait to do so.

SEC Proposes Transparency And Financial Accountability Amendments To The CAT NMS Plan

The Securities and Exchange Commission voted to propose amendments to the national market system plan governing the Consolidated Audit Trail (the “CAT NMS Plan”).

The proposed amendments to the CAT NMS Plan would require self-regulatory organizations that are participants to the CAT NMS Plan (the “Participants”) to file with the Commission and publish a complete implementation plan for the Consolidated Audit Trail (“CAT”) and quarterly progress reports, each of which must be approved by the Operating Committee established by the CAT NMS Plan and submitted to the CEO, President, or equivalently situated senior officer at each Participant. In addition, the proposed amendments would include financial accountability provisions that establish target deadlines for four implementation milestones and reduce the amount of fee recovery available to the Participants if those target deadlines are missed.

“CAT needs to be implemented without further delays,” said SEC Chairman Jay Clayton. “The proposed amendments are designed to bring greater transparency and accountability to the implementation of the CAT.”

The public comment period will remain open for 45 days following publication of the proposing release in the Federal Register.

***

Fact Sheet

Action

Today, the Commission voted to propose amendments to the CAT NMS Plan that are designed to decrease the likelihood of additional delays to CAT implementation by increasing operational transparency and attaching financial accountability to the Participants’ regulatory obligation to implement the CAT in an efficient and expeditious manner.

Operational Transparency Amendments

  • The Participants must file with the Commission, and make publicly available, a detailed implementation plan and ongoing quarterly progress reports.
  • Each document must be submitted to the CEO, President, or an equivalently situated senior officer at each Participant and then approved by a supermajority vote of the Operating Committee.
  • To the extent that any document is not approved by a unanimous vote of the Operating Committee, each Participant whose Operating Committee member did not vote to approve the document must separately file with the Commission, and make publicly available, a statement identifying itself and explaining why it did not vote to approve the document in question.
  • Financial Accountability Amendments

    The proposed amendments establish target deadlines for four critical implementation milestones defined in the proposal, based largely on dates previously published by the SROs:

  • April 30, 2020: Initial Industry Member Core Equity Reporting
  • December 31, 2020: Full Implementation of Core Equity Reporting Requirements
  • December 31, 2021: Full Availability and Regulatory Utilization of Transactional Database Functionality
  • December 31, 2022: Full Implementation of CAT NMS Plan Requirements
  • If the Participants do not meet these target deadlines, the amount of CAT funding that they can recover from Industry Members will be reduced at regular intervals.
    What’s next?

    The Commission will seek public comment on the proposed amendments for 45 days following publication of the proposing release in the Federal Register.

    Buyer’s Guide: Sellside Fixed Income E-trading Solutions 2019

    Buyer’s Guide: Sellside Fixed Income E-trading Solutions 2019

    This report reviews e-trading solutions of nine fixed income trading technology vendors competing for investment bank market share in 2019. Specifically, this report – which details the findings of a GreySpark survey of each of the nine vendors’ offerings – examines the state of play in the sellside fixed income e-trading industry in 2019 from a business perspective and from a functional capabilities perspective.

    https://greyspark.com/report/buyers-guide-sellside-fixed-income-e-trading-solutions-2019/

     

    Buyer’s Guide: Sellside Client Onboarding & KYC Solutions 2019

    Buyer’s Guide: Sellside Client Onboarding & KYC Solutions 2019

    This report reviews the functional capabilities of three sellside client onboarding and KYC solutions vendor offerings. Since 2015, there has been an identifiable shift from a business perspective in the profile of the leading technology vendors in the client onboarding and KYC solutions space away from wide-ranging providers of CIB data management operational support technology designed to interface with existing in-house built front-, middle- and back-office business and trading systems.

    https://greyspark.com/report/buyers-guide-sellside-client-onboarding-kyc-solutions-2019/

    Hong Kong Stock Exchange Offers Surprise $37 Billion Bid for LSEG

    Here’s something straight out of left field.

    The Hong Kong Stock Exchange (HKEx) has entered the fray to buy rival London Stock Exchange for almost $37 billion, as first reported by the Wall Street Journal and confirmed by Markets Media.

    However, there is one caveat – the deal is subject to the falling through of the LSEG’s previously announced deal to purchase data provider Refinitiv. That deal aimed to bring the exchange into the market data business and help it compete with other data providers such as Bloomberg.

    hkex_market_data
    Laura Cha, chairman of the Hong Kong exchange, said the proposal was “a highly compelling strategic opportunity to create a global market infrastructure group, bringing together the largest and most significant financial centres in Asia and Europe.”

    The London Stock Exchange said it “will consider this proposal and will make a further announcement in due course”. It called the proposal “unsolicited, preliminary and highly conditional.”

    Octavio Marenzi, CEO of capital markets management consultancy Opimas, has this to say on Hong Kong Exchanges and Clearing’s bid to acquire the London Stock Exchange Group:

    “This is, first and foremost, a data play, and a way for HKEX to snap up not only another exchange and get greater economies of scale, but also a way of massively expanding HKEX’s revenues from data. Data at exchanges, in general, has been extraordinarily profitable, but at HKEX, the operating margins on market data are a staggering 90%. It is easy to understand HKEX’s strategy on this front.”

    HKEX said the deal would be financed through a combination of existing cash resources and new credit facilities.

    Cha said the HKEX had been in contact with the London Stock Exchange and said it looked forward to “working in detail with the LSEG board to demonstrate that this transaction is in the best interests of all stakeholders, investors and both businesses.”

    Video Interview: Data’s Impact on Trading

    Refinitiv’s Phil DeFrancesco discusses data’s impact on trading.

    State Street Optimizes Securities Lending With AI

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    Identifying hard-to-borrow securities just became easier.

    State Street and its research arm State Street Associates have collaborated to optimize their securities lending business through the deployment of an internally developed machine learning model.

    The project’s goal was to identify when general collateral securities would transform into hard-to-borrow securities, which have greater demands and earn higher fees.

    The events and few are far between, according to Yasser El Hamoumi, assistant vice president, trading and algorithmic strategist (securities finance) at State Street and who presented at the AI in Finance Summit in Midtown Manhattan.

    However, lending hard-to-borrow securities represent 60% of the revenue SSA earns from securities lending while making up 20% of its loans by volume, added co-presenter Travis Whitmore, a quantitative researcher at SSA.

    The firms initially used an Ordinary Least Squares model to see if it could predict a hard-to-borrow event better than a coin flip.

    Although the model did well at categorizing which securities would become hard-to-trade, it did a poor job of identifying when such an event would take place. It had a 4% hit rate using data from January 2012 to February 2019 with a 260-day rolling look back as well as false positive and false negative rates of 27% and 97% respectively.

    The team took the six parameters that it used in its OLS model, return-percent, market capitalization, three-day average fees, utilization, HTB Score, and the Herfindahl-Hirschman Index, and deployed them into a K Nearest Neighbor framework.

    “We stopped with those six variables,” said El Hamoumi. “They were all additive, and they had the best performance together.”

    Adding additional variable would lead down a dangerous path where the model’s result could fool people into thinking it was operating better than it was.”

    The KNN model increased the hit rate to 68% while reducing the false positive rate to 10%. The false-negative rate remained unaffected.

    “We thought to prioritize false positives over false negatives since we would rather be in a high-value trade and miss a low-value trade rather than vice versa,” said El Hamoumi.

    Building out the hard-to-borrow framework has helped the firms build out other frameworks, which is happening more often as the industry increasingly digitalizes, noted Whitmore.

    “As we build tools that are more autonomous, we want to make sure that we have the right frameworks in mind so that we are not releasing the robots without a leash,” added El Hamoumi.

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