Outreach Blog

Monday, November 6, 2023

Predictive AI in Securities Finance: Step One

How to Develop an Efficient, Legally-defensible Machine Learning Infrastructure


Author: Ed Blount

 

by Ed Blount and Dan Hammond

 

On April 2nd, 2026, an effusion of data from a daily trove of U.S. regulatory filings will create resources to drive many new use cases for artificial intelligence in capital markets. A clear opportunity exists in securities finance, where practitioners have repeatedly stated that major IT investments will be needed to comply with the many new regulatory mandates. “Black box” AI platforms may seem a ready solution but can also create nightmares for client reviews and lawsuits.[1]

In our opinion, public data can clarify the rational limits of influence for predictive artificial intelligence. The best courtroom-ready models will display an audittrail based on the replication of critical decision parameters and vectors from past markets. Vendor data in securities finance may be more timely and deeper than the public releases but, for judicial purposes, the public data will provide foundational evidence for the “bounded rationality” of decision-makers, as defined by the late Herbert Simon, Nobel Laureate and the father of Artificial Intelligence.

Maintaining a Training Dataset as an Audit Trail for the Courtroom

As we have recently explained, forecasts of financing fees and inventory buffers can be made using regulatory filings to gauge the parameters that have guided or influenced the past actions of traders, head traders, and business heads in capital markets divisions. To embed their insights, AI models must be trained with sequences of concurrent time series for each market participant.

To demonstrate, we have adapted a very large dataset from Tidal Markets LLC and created a prototype AI model for securities finance. Our prototype can be used to customize models for participants to rely on predictions of fees for stock loans and buffers for lendable inventories, in the following fashion:

  • Traders' past insights are defined as 10- and 20-day rolling averages of key metrics, such as fees, tickets, and amounts, and factored as indexes of current financing trends for an individual security issue, sector or market. For example, the range of borrowers' fees are considered to trend as negotiated spreads to adjusted balances posted by FINRA under Rule 10c-1 (read more here) on trading days over the last two to four weeks;
  • The Head traders' predictive dataset compares indexed trends of key traders' metrics to those of the prior quarter to derive the weights of tensors that connect the parameters, based partly on implied gains and losses. The prototype is used to document predictions of the degree to which the desk is likely to meet its annual forecast in business plans for risk-adjusted returns on capital ("raroc");
  • The Management dataset in our prototype considers conditions year-to-year in allocating capital and adjusting raroc the desk’s performance for seasonality, pandemics, and corporate actions, among key benchmarking factors.

For any given market condition, every model in securities finance should be trained with layers of economic capital allocations and predicted regulatory claims on capital. Our prototype predictiveAI model analyzes the current flows of market capital in the context of prior allocation decisions made at each level of trading operations. Fees and new loans are two measures of experience in the market, but inventory buffers and economic capital are the primary limiting factors. If head traders do not allocate enough of their scarce capital resources to traders, then opportunities, no matter how attractive to traders, will be allowed to slip by.[2]

Predictive AI is essentially a sentiment model which tries to replicate lenders' and borrowers' decisions in context of their insights and outcomes, by measuring the loans of securities against collateral. On the assumption that most borrowers are short sellers, our prototype assumes that each level of trading operation “satisfices,” in Simon’s term, its allocated capital for the most efficient, regulatory-compliant opportunities.

Using Regulatory Capital as the Final Constraint on Trading Operations

In global banks whose primary supervisors have adopted Section MAR12.4 of the Basel capital regulations, each business head is defined as an account with claims on a proportional amount of capital from the firm's balance sheet to be used to conduct trading operations. From a regulatory standpoint, the trading book has a different set of restrictions from the banking book. Each head trader in the group is allocated part of that capital for management of a group of traders.

Each head traders' desk is capitalized for operations involving a set of similar securities. The head trader generally decides which securities are traded by the individual traders. Each trader considers the prices and fees of assigned securities in comparison to a rolling average of recent market factors, as described in the bullets above.

Predictions will be accurate to the extent that parameters of other issues change in similar degree to other stressed securities on trader watch lists. In our prototype, the slope and standard deviation of their indexed values is one test we are using to identify issues of significant interest. To illustrate, we have again selected the CBS Viacom (VIAC) manipulation by Archegos of September 2020 through March 2021 that depleted the capital of Credit Suisse Bank. We have used details from the SEC’s complaint against Archegos to guide our modeling.

“MARKET POSSE” ACTION PERIODS:
ACCUMULATION, SURVEILLANCE, AND OPPOSITION

In the Archegos manipulation, short sellers realized that the VIAC stock price was out of control early, but potential profits were too low to commit much capital as shown by 'new loan units'. In each of the three action periods, as shown in our September 28 article, the time-series horizon of the human decision-makers is a variable marked by parametric thresholds. For example, a rising slope of the 20-day rolling average for VIAC vectors of share prices, fees and new loans was the marker for the transition from a watch list entry for short sellers to a target issue. We defined that as the Accumulation period when capital was being allocated to traders by short sellers in order to accumulate borrowed shares in the expectation of a price drop.

Each of the parameters can change in significance from time to time. As a result, depending on circumstances, there are different weights for each of the periods, i.e., Accumulation, Surveillance and Opposition. The predictiveAI model derives its tensor strengths as weighting factors using the training dataset. Each backpropagation in the recursive regression analysis of the training program results in changes for the weights of each parameter. Therein lies the essential value of every generative AI model, i.e., its ability to re-weight its parameters as accurately as possible so as to minimize the "loss function" of its predictions at every step of the time series.

Slopes and StdDevs TableTraining datasets should be used to condition the AI model to a multi-dimensional matrix of decision factors -- in the best case using fees and new loans made in past markets by each human decision-maker -- in order to predict the optimal allocation of capital. Inventory buffers are a derivative of those decisions, but fees are both an input to and output from the model. That's because human decisions are not made in a vacuum. There are other influences on the market conditions within which the trained model will predict future market conditions and recommend reactions. Deeper levels of learning in securities finance, including predictions of fees, will require ticket level data for the training datasets.

 

 

[1] Holistic views of decisions made at all management levels were at the center of many lawsuits that followed the 2007-2008 Great Financial Crisis. Fiduciaries had to prove their actions were reasonable and served their clients’ best interests in context of their contractual policies and procedures.

[2] That was the situation on Black Monday 1987 when stock index futures traded off basis to the cash markets for an extended period. Traders simply did not have enough capital to bring the markets back into sync even though they saw the arbitrage trade of a lifetime slipping away.

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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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