Outreach Blog

Friday, October 27, 2023

Beyond Benchmarking: The Race to Predictive Analytics in Securities Finance

10C-1 public data can reveal Watch Lists, but vendor data can predict market leverage and fees


Author: Ed Blount

 

Data engineering by Dan Hammond

When, on October 13, 2023, the Securities and Exchange Commission released its long-awaited final 10c-1 rule on reporting and public disclosure of securities loans (explained here), the most important passage, at least to the commercial data vendors who support the securities finance community, stated that, "the final rule could render existing securities lending data services less valuable, potentially leading to less revenue for the firms currently compiling and distributing these data for a fee."[1] But is that true? Are bonuses and careers really at risk?? As shown in the table below, there is hope for vendors because the public data release will either omit or delay several data elements that are crucial to many important vendor applications today.

Table 1 Data Elements available to securities finance analysts, now and as mandated.

Notes: New rule 10c-1 requires daily submission of confidential loan data to the SEC, as well as partial fail data
if shares are borrowed to avoid Reg SHO buy-ins. Public data disclosures will be available from FINRA no later
than next morning (X) after each loan is effected or delayed 20 days (Y). For vendor availability, please refer to
data dictionaries and subscriber restrictions.

Modern benchmarking of performance as a securities lender requires comparison to the results of similar funds that, since neither lenders nor borrowers will be disclosed to the public, will be impossible with public data alone. Benchmarking vendors today use fund type codes from service providers to create peer reviews. Without adjustments for the lender’s portfolio composition or investment style, it will not be possible to group similar lenders. However, the vendor advantage may be short lived if large competitors can use public data to extrapolate or validate their B2B datasets. Lending agents who are now benchmarking customers of vendors may elect to use the tens of millions of loan data records and modifications that will be released each month as a resource to train their own generative AI models to predict borrower demand and fees, inventory fails and other key performance metrics.

Accurate market sizing will enable database engineers to calibrate the statistical significance of smaller sample sizes when publishing benchmarks or indexes.  As the SEC pointed out, the public market data will raise the competitive bar by enabling new services[2] to complement opportunities in advanced analytics.[3] Fintechs will see product opportunities in the more than one million records submitted daily from service providers on behalf of market participants.

We can use a well-known episode in recent market history to illustrate how public and vendor securities lending data can be combined to better understand how securities finance imposes credit constraints that limit the excesses in trading markets. In March 2021, Archegos, a family office with experienced hedge fund managers and a multi-billion dollar portfolio, leveraged single name swaps to rig the market for CBS Viacom (VIAC) shares and a dozen other issues (read more here).

As shown in Figure 1, a simple comparison of new loans to the stock price would have revealed the activity of short sellers to the Archegos pump-and-dump scheme. New loan data will be available in the 10c-1 release. However, vendor data would be needed to advance the analytics. With enrichment of public datasets by benchmarkers, e.g., by adding aggregate loan size, tenure of returns, and units outstanding, even the most basic GenAI models could have estimated the unrealized gains/losses of strategic short sellers, as their models reacted to the leverage of Archegos. More advanced GenAI models, enriched by details at the trade ticket level, could also have generated recommendations for inventory buffers and smart recalls to comply with voting stewardship reports.

Trading Analytics at the Next Level

Outstanding units on loan is one of the simplest and still best indicators of market leverage.[4] As shown in Figures 2 and 3, conditions in the securities funding market for VIAC shares during the Archegos manipulation were much tighter than in the prior quarter.

These are useful values in predictive analytics but only available to vendors’ customers. As used in Figure 3, the aging of returned loans can be used to infer the strategies being followed by borrowers. (For example, long term loans being returned implies the buy-in of short positions by directional hedge funds.) If borrowers can be analyzed as a basket of loans, then any change in that basket’s aging can signal a shift in dominant style, both for markets and individual counterparties.

Product managers at fintechs will use the public data reports to create the Market Posse chart shown in Figure 4, which shows the inflection point at which the bubble was burst by tactical short sellers. Transaction-level data is needed for semi-supervised learning in generative ai, starting with peer clustering. Models of risk tolerances, preferences, and other factors can be used to create a world within the real world. However, the total of loaned volume in each synthetic model must be constrained to the disclosed metrics. Within those limits, dispersion models of synthetic borrowers can be generated with risk profiles for a large number of security baskets. Securities lenders could then match the risk profiles of their actual borrowers to the synthetic profiles on a real-time basis.

A more advanced data set can be used to train loan risk classification models for use by an enabling platform to predict and avoid stock loan defaults by borrowers. Pooled ticket level data from the beneficial owners of a data trust can provide that more granular dataset to improve their ability to optimize lendable portfolio inventories. (Read more here.)

Counterparties approaching their risk tolerance limits can be prioritized by lending agents as targets for smart recalls. GenAI models with an enabling platform can trim, at the very least, their lenders' marginal periods of risk and potential exposure at default. That can spell major savings in economic capital charges.

New Opportunities for Data Vendors

Through a variety of methods, lending agents monitor those of their counterparties who most closely fit the riskiest profiles they see on the market surface. By embedding that expertise in genAI models, synthetic counterparties can be created for lending agents from enriched loan data to train deep learning models. Agents will want the most reliable datasets for training their distribution queues and prime brokers will want vendors to enrich their dynamic margining algorithms for selected asset classes or issues. These are quite literally golden opportunities for vendors.

The best data products will help to reduce the costs of fails after the T+1 settlement upgrade in the U.S. and may argue for lowering the higher risk-weighted asset (RWA) capital charges for borrower default indemnification under Basel III+. Lending agents for U. S. mutual funds will offer analytics that predict fails and avoid N-PX penalties. The most successful vendors will offer upgraded analytic platforms, such as FISʼ Lending Pit, that can turn raw data into the parameters needed by generativeAI models. Shared ledgers will use plug-in APIs to mix the predictive analytics with their customersʼ own risk management platforms.

Predictive analytics based on enriched loan filings can help with dynamic management of buffers in a straight through processing environment to minimize settlement delays. Costly inventory fails-to- deliver and CSDR-like penalties from their global custodians are not the only costs; fails may also be leading indicators of default for overleveraged counterparties, such as in the Archegos scheme.

 

[1] Securities and Exchange Commission, Release No. 34-98737; File No. S7-18-21, page 178

[2] “The use of third-party vendors by covered persons could enable the innovation and development of novel reporting services, such as the data trusts described by one commenter.  See Advanced Securities Consulting Letter.” Ibid, footnote 190, page 45

[3] “The Commission believes that a potential mitigating factor that may reduce or even offset the severity of this loss in revenue will be that commercial data vendors may offset some of the impact of lowered demand for their data by enhancing their related data analytics businesses using Rule 10c-1a data.” Ibid, page 275; “To the extent that customers value the availability of information about securities available to lend and utilization rates, the fact that commercial data vendors will continue to have superior access to this information will likely mitigate or even offset a decrease in demand, as investors would continue to purchase commercial datasets in order to gain access to this information.”

[4] In the final rule, the proposed obligation to report outstanding and available loan position data was eliminated. In effect, the SEC carved out a base upon which data vendors might advance their business models with a mix of existing and enriched datasets.

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The CSFME’s Regulatory Outreach Programs

Regulatory reform has become a collaborative process. Where once market supervisors promulgated rules without regard for input from practitioners, today’s reform process has evolved into a dialogue of mutual respect for the opinions of all stakeholders in the capital markets. The process of regulatory outreach has become embodied in virtually every developed markets in the world.

The CSFME has adopted a role of facilitating this collaborative dialogue at all stages of the professional contribution process. Starting with students’ contributions to published commentary letters, through panel presentation and webinars, right up to trade association initiatives, the CSFME provides assistance through education, data compilation, analysis and commentary for some of the most pressing issues in contemporary markets.

DLT and Preferred Securities Financing

We believe the widespread use of encrypted third-party ledgers, blockchains, and smart contracts (i.e., DLT) is inevitable in securities finance, and that those technologies will permit lending agents to offer new revenue opportunities to their clients. Among these, we believe that certain agents will use DLT to help their lenders expand their loan books by opening their lendable portfolios on a preferential basis to the hedge funds in which they've already invested, as well as to other trusted counterparties, a concept we have dubbed, “Preferred Securities Financing.”  

CSFME is openly soliciting participation in a research initiative to assess the potential benefits to securities lenders from the use of DLT and data sourced from new regulatory disclosures. Specifically, our research will focus on how DLT, blockchain, and smart contracts can facilitate Preferred Securities Financing.  Learn More about our DLT Securities Finance Initiative

Research and Analysis of the Effects of Financial Regulatory Reforms

Given the sweeping changes in financial market regulation following the financial crisis, CSFME has turned its focus to questions relating to to how these changes are affecting the risks and economics of bank activities. The purpose of the Center’s research in this area is to foster sound policymaking and effective regulation with minimal adverse and unintended consequences. CSFME studies supervision and regulation of global financial institutions, the effects of reregulation on the global financial industry, optimal roles and methods of regulation in securities markets, corporate governance at financial institutions, and the most effective metrics and methods of data collection for understanding and measuring the effects of regulations on the global financial landscape. 

Lately, in response to a call from the FDIC for research on financial sector policy and regulation, the Center submitted a paper modeling the indirect costs to markets of bank regulatory reform.  The paper critiques regulators’ models for assessing these costs, and provides empirically-based suggestions for a more complete dynamic model of the long-term effect of bank capital reform.  Mindful of the Basel Committee's ongoing reviews of modeling tools, i.e., May 2012 and March 2016, the Center's critique is intended as a constructive addition to the holistic conceptual base of the regulatory reforms.

The Center also continues to provide input on regulatory proposals.

In March of 2016, CSFME submitted a comment letter to the Bank for International Settlement's (BIS) December 2015 consultative document regarding step in risk.  While supporting generally the goals of the Basel Committee to minimize the potential systemic implications resulting from situations where banks may choose to provide financial support during periods of financial stress to entities beyond or in the absence of any contractual obligations, the Center expressed some concerns and offered some suggestions regarding the approach taken by the Consultation. Drawing on practical experience, the Center offered an example from the trade finance sector supporting its belief that the nature of step-in risk may be one example of an acceptable, non-diversifiable exposure, given the potential positives for the economy at large.

In February 2015, CSFME submitted a comment letter in response to the Financial Stability Board’s November 2014 consultative document, Standards and Processes for Global Securities Financing Data Collection and Aggregation. In its letter, the Center identified additional metrics that may be necessary to assess properly the risk of collateral fire sales associated with securities lending transactions.  In particular, CSFME asserted that FSB and sovereign regulators must expand the data initiative beyond position aggregates, to include risk mitigation resources as well as termination activity.

Students Learn to Evaluate and Contribute to the Reform Process

As the level of intensity surrounding the reform process continued to build in 2013, the CSFME began to bring a fresh perspective to the reform process. By working with finance students and the US regulatory agencies, CSFME hoped to challenge the settled views of stakeholder by introducing the views of those whose careers would be shaped by the outcome of the reforms.

In the spring of 2013, a select group of Fordham University economics students met in Washington with officials at the U.S. Treasury, Office of Management and Budget, Federal Reserve Board, and the Securities and Exchange Commission. The CSFME helped arrange the meetings and funded the logistics. By all accounts, the experience was very positive for students and regulators alike.

Buidling upon the success of the 2013 pilot program, in 2014, both Fordham and the CSFME decided to expand the outreach program and formalized the Regulatory Outreach for Student Education program as the ROSE program. Honor students in finance and economics were selected by the deans of four schools within the university: the Graduate School of Business Administration, Fordham College at Lincoln Center, the Gabelli School of Business, and Fordham College at Rose Hill. The students were organized into four teams representing their schools. The CSFME selected a contemporary issue of career significance, the Financial Stability Board’s Consultative Document on G-SIFI designation of non-bank, non-insurer financial institutions. Each team was charged with studying the issues in debate, then presenting their opinions in the manner of a formal comment letter to the FSB. Over four months, the students reviewed earlier opinion pieces, met with practitioners and regulators, and then submitted their opinions. Without influencing their opinions, the CSFME arranged access to research materials and opinion leaders, then reviewed their letters and, as appropriate, recommended submission on university letterhead. In April, 2014, the four teams’ letters were published by the FSB on its website. In recent memory, no university had ever had one letter, much less four, published on a regulatory website. To finalize the 2014 ROSE program, the CSFME arranged for all four teams to present their opinions to the key regulators at the Federal Reserve Board and the SEC in Washington, D.C. The day of meetings ended with regulators’ praise at the degree to which the students had understood the issues and presented their opinions clearly.

One student team even offered suggestions that regulators had not previously considered and praised for their creativity. “We always know what the trade groups will say, but you brought a fresh perspective.” That team, Fordham College at Lincoln Center, was awarded the 2014 ROSE Award for Analytic Excellence. In retrospect. each student completed the program with a credit that will not only endure on their resumes but also contribute to the evolution of the financial markets through the Twenty First Century.

In 2015 and 2016, Fordham formalized the ROSE Program as a for-credit course in their curriculum. The focus of the 2016 ROSE Program was the Bank for International Settlement's December 2015 consultative document proposing a preliminary framework for identifying, assessing and addressing step-in risk potentially embedded in banks' relationships with shadow banking entities.  Five teams of graduate and undergraduate students in economics, finance, accounting, management, and law researched and drafted comment letters on the consultation and submitted their letters to a panel of distinguished industry judges.  After reviewing each excellent submission, the judges then one winning letter to be presented at a visit to the Federal Reserve Bank on April 27, 2016. The winning team's letter was submitted in full to the BIS, along with a summary of the key ideas from the letters from each of the other four teams, and the submission was published on the organization's website with those of the consultation's other commenters.   All five teams of Fordham Scholars visited Washington, DC on April 27, 2016 and met with officials at the Fed, Treasury Department, and FINRA.  

Institutional Securities Lenders respond to Academic Criticisms

In 2006 the Center was created, initially for the purpose of testing academic criticisms of the securities lending markets. With funding and data support from the Risk Management Association, CSFME found “no strong evidence to conclude that securities lending programs have been used to any great extent to manipulate proxy votes or exercise undue influence on Corporate Governance issues.” Our study also found that “broker borrowbacks” had contributed to spikes in lending activity around record date – the same phenomenon that the academics had misinterpreted as evidence of hedge fund manipulation – due to the efforts of brokers to meet recall notices from securities lenders. In effect, the brokers were scrambling to acquire votes for their customers, not building positions to swing corporate elections. The academics had fatally misinterpreted their findings!

Ed Blount of CSFME testified at the SEC’s Roundtable on the results of the research in September, 2009. Then, the CSFME white paper, published in 2010, was submitted to the SEC as an attachment in response to a consultative document on the “Proxy Plumbing” process. As a result of the Center’s contribution to the collaborative process, the misguided call for reform of securities lending began to subside. Once again, securities borrowers were fairly recognized to be honest brokers in the corporate governance arena.

Securities Lenders consider new means to retain their Voting Rights

In a follow-up to the Empty Voting project (“Borrowed Proxy Abuse” as it came to be known), the CSFME responded in 2011 to requests by the participating securities lenders, by turning its attention to ways in which those lenders might be able to retain their corporate governance rights, while still benefiting from the income attributable to their securities loans. After all, as many studies have found, securities lending contributes significantly to the efficiency of market operations. Why should lenders be forced to choose between their loan fees and fiduciary duties to vote their shares, especially if they are contributing to market efficiency?? With independent funding, the CSFME retained attorneys from two prestigious Washington D.C. law firms, Stradley Ronon and Sidley Austin, to investigate the legal underpinnings to market practices which force pensions, mutual funds, insurers and other institutional securities lenders to give up their voting rights when they lend portfolio securities. In practice, margin customers of brokers also lend their securities, yet they usually retain voting rights -- and most of them aren’t even long-term beneficial owners. Both groups of beneficial owners retain dividend rights, so why, institutional investors asked, shouldn’t institutions also keep their voting rights? With the benefit of exhaustive legal research, CSFME filed a petition with the Securities & Exchange Commission to initiate a pilot program to test new market procedures by which recently-introduced efficiencies in market operations might permit lender to retain votes.  Learn more about Paradoxical Erosion of Corporate Governance

In 2013, the SEC approved that pilot program, largely in response to the encouraging recommendations of the International Corporate Governance Association, as well as the California State Teachers Retirement System and the Florida State Board of Administration.

That pilot was initiated in 2014. Simultaneously, the CSFME began to apply the results to new initiatives in Canada and Switzerland, where the pressure to meet fiduciary voting obligations was intensifying.  More about Full Entitlement Voting



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