What is LDV ?

Who benefits from LDV?

LDV benefits all participants in the securities finance industry.  Lenders are better able to exercise their corporate governance responsibilities and, since lenders recall fewer loans, overall securities lending volume and revenue increase.  Loan, borrow, and collateral portfolios are more stable, allowing agents and brokers to more effectively manage investment, counterparty, and operational risks.  Corporate issuers receive more proxy votes from long-term investors, allowing them to reach quorum more quickly and at lower cost, and counterbalance votes of short-term activists.  Higher loan volumes also improve financial market liquidity and price discovery.

 

What is Lender-Directed Voting, or LDV?

LDV is a new process that matches securities lenders' loaned shares to broker securities that would otherwise go unvoted, enabling lenders to direct proxies without recalling loans.  It substantially improves existing market practices, which require lenders to recall loan in order to vote proxies.  Recalls are inefficient in that they reduce overall lending and borrowing revenue, and create instability in loan, borrow, and collateral portfolios. 

Why haven't lenders voted on loaned shares in the past?

Historically, institutional securities lenders had to forgo voting rights on loaned shares because there was no mechanism to vote without recalls.  Recent technology and transparency improvements in securities finance markets, however, enable loaned shares to be matched with broker shares that would otherwise go unvoted.  In particular, the Agent Lender Disclosure Initiative made apparent the direct counterparty relationship between lenders and broker-borrowers and provided brokers with detailed loan data necessary to include lenders in their proxy allocation routines.

Are there enough unvoted shares to cover lender voting interest?

Approximately 60 billion U.S. equities go unvoted each year[1], while roughly 15 billion shares are on loan[2], suggesting that sufficient votes could be available to meet lender vote demand.  However, it is unlikely that lender voting interest will be fully covered for all issues, such as those with particularly contentious proxy events or that are hard-to-borrow in securities lending markets. 


[1] www.broadridge.com/investor–communications /us/Broadridge_Proxy_Stats_2010.pdf
[2] Data from RMA securities lending composite, assuming $20 average stock price

Does the broker have the lender’s shares on the proxy record date?

1.  U.S. Federal Reserve Regulation T (“Reg T”) defines the permitted purposes for the extension of credit in the borrowing and lending of securities. In general, all of these purposes involve settling trades through re-delivery of the borrowed securities. Most often, the broker’s need to borrow has arisen after failing to receive securities required for an impending trade settlement, either as the result of an operational breakdown or after a short sale.

2.  Given the broker-borrower’s mandatory compliance with Reg T, it can be argued that borrowed shares, which are re-delivered in the settlement of a trade, are not available on the broker’s books (as a technical matter, the position would be held at DTCC) in order to earn voting rights on the proxy record date. However, this argument would only be true per se if the settlement took place on the proxy record date, because an analysis of the ongoing process reveals that the proxy votes, not just the entitled shares, are properly treated as fully fungible on the broker-borrower’s books.

3.  Reg T does not require that the borrowed shares be returned to the original lender when a subsequent receipt of securities is used to offset the original failure-to-receive. At that point, the borrower can certainly return the securities to the original lender. Yet, an active borrower can also compliantly decide to close a loan of the same securities with a different institutional lender whose terms may have become less attractive or from another broker-dealer lender who may be viewed as more likely to recall shares at an inconvenient time in the future, especially if the shares were borrowed for an ongoing short position. Still another reason may exist to hold the securities if the broker considers the return on its cash collateral, received through a rebate from the lender, to be very attractive compared with other investment options. In all those cases, as well as for actively traded issues where there may be a high risk of ongoing settlement failures, the broker can simply keep the newly-received shares in its inventory, balanced against its obligation to the lender.

4. As a result of efficient management of its settlement obligations, a broker – perhaps all brokers – may well have borrowed positions on their books on proxy record dates. The brokers would have gained the right to assign proxies or even to vote at the next corporate meeting as a direct result of the original loans from institutional lenders. In effect, the proxies are fungible on the brokers’ books, along with the borrowed shares themselves subject, of course, to an equitable assignment of proxy rights in compliance with stock exchange rules. Yet, brokers are not expressly permitted to assign proxies to their institutional lenders. At this point, the Lender Directed Voting (“LDV”) argument gains relevance and substance.

5. As noted, in addition to holding the shares cum voting rights, the broker also retains an obligation to its original lender. Indeed, one could argue that an institutional lender's ownership rights are stronger than those of other “beneficial owners” to whom the broker owes shares in the same securities. That is partly due to the distinction that can be drawn between the institutional lenders, who do not receive proxy assignments, and the broker’s own margin customers and hedge fund clients, who do receive proxy assignments. The distinction resides in the timeline of their property rights: the former owned the shares fully prior to lending them to the broker, while the latter required broker-financing in order to acquire their positions. Although we have seen that the institution’s shares may now be on the broker’s books, it is very likely that the financing customers’ shares are out on loan, i.e., hypothecated as collateral to source the broker’s own funding needs. And, in such cases, those positions are truly not in the brokers’ DTC account, although the brokers may well be assigning proxy rights to their accountholders. One can ably argue that those proxies would more equitably be assigned to the institutional lenders.

How can lenders instruct broker shares?

Brokers administer proxy allocation routines to distribute proxies to their customers.  Since broker shares are held in fungible bulk and lenders have beneficial ownership to loaned shares, brokers can include lenders in their allocation routines.  After brokers allocate proxies to lenders, standard proxy processes are followed to garner and submit voting instructions and submit them to corporate issuers.  For example, proxies are assigned to Broadridge accounts designated for the lenders, then are instructed by lenders or ISS on the lenders' behalf.

Could lenders also instruct custodians' unvoted shares?

Regulatory and operational considerations may pose challenges to matching custodians' unvoted shares with lenders’ loan positions.  In particular, custodian shares are not held in fungible bulk, as are broker shares, which presents difficulties when considering custodial allocation of proxies across lender accounts. Furthermore, custodians are not counterparties on loans, so the lenders are not beneficial owners to any of the custodians’ unvoted shares.

Does LDV contribute to “over-reporting,” since lenders’ shares were delivered to new buyers who now have the associated voting rights?

Existing proxy reconciliation processes are sufficient to address any potential "over-reporting" issues.  For example, brokers already use post-reconciliation processes to mitigate the risk of over-reporting that may arise from assigning proxies to margin customers whose shares may have been loaned or rehypothecated.

How do brokers decide which lender(s) are assigned proxies?

Beneficial owners and regulators have expressed concerns about voting opportunities being directed to preferred lenders or leveraged for beneficial loan terms.  In the same way that agent lending queues are designed so that lenders get equitable access to borrower demand, brokers need pre-defined and algorithmic “proxy queues” to ensure equitable assignment of voting opportunities.  Furthermore, on-going auditing and validation of proxy assignments may be needed to ensure against development of a “market for votes.” 

What if proxies are not available from a lender's borrower, but are from another broker?

Reallocation of the loans to brokers with available proxies would increase overall lender voting opportunities.  However, numerous other loan factors would need to be taken into account, such as counterparty risk assessments and credit limits, loan prices, and collateral types and quantity.  Considering these factors, loan reallocations may not be in the overall best interest of lenders and borrowers, and will have to be considered on a case-by-case basis.

How can lenders know, before record date, how many proxies they will be assigned?

To the extent that lenders receive proxies through LDV, they will not have to recall loans to regain voting rights.  However, broker holdings change daily and varying numbers of investors vote, so the number of proxies that can be assigned to lenders cannot be known with certainty until just before the meeting date, which is typically two months after lenders must make record date recall decisions.   The number of available proxies must therefore be forecasted, taking into account factors such as each broker's customer base, the scarcity of shares in the securities lending market, and the expected materiality of proxy ballot items.

Corporate Governance 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.

Print

Corporate Outreach Milestones

MILESTONES FOR LENDER DIRECTED VOTING

May 8, 2014: Council of Institutional Investors; - CII Elects New Board, Names Jay Chaudhuri Board Chair. http://www.bloomberg.com/news/2014-01-31/north-carolina-treasurer-may-cede-pension-control-5-questions.html )

February 2014:  Swiss Minder Initiative implies the value of LDV. http://www.ipe.com/switzerlands-minder-initiative-will-cripple-securities-lending-experts-warn/10000947.article.

January 2014FL SBA begins their SecLending Auction Program with eSecLending.

November 27, 2013 – CSFME staff call with Glass Lewis Chief Operating Officer. He gave his commitment for cooperation and support for LDV, and most importantly, he suggested that perhaps we should discuss with a Broadridge/State Street/Citi the scenario that permits Citi to forward an “Omnibus Ballot” of proxies to State Street, which State Street would then take and assign the proxies to their pension lenders/LDV participants, which would then be incorporated into a single ballot and sent to Broadridge. This eliminates the secondary ballot issue. While this description is oversimplified, Glass Lewis was fairly certain the parties involved could operationally create such a combined ballot. Responding to the question on cost, the Glass Lewis executive stated that the cost depends on the number of voting policies a fund has. Most funds have one policy; therefore, depending on the client, the cost would be $.75 – $2.00 per ballot.

October 21, 2013 – CSFME staff call with ISS Chief Operations Officer. He committed his cooperation and support to advance LDV’s implementation into the markets. He responded to the question about cost: “It depends on the client and the services they use. $6-7 per ballot on average.”

June 25-28, 2013 – CSFME staff attended ICGN Annual Conference in NY, NY. Spoke with executives of CalSTRS; ICGN Chair and Blackrock about LDV.  We received favorable comments and encouragement from each.

June 6, 2013: CSFME meets with Chief Investment Officer for NYC Pension Funds. While very much in favor of the LDV concept, the comments that the NYC Pension Fund Boards are for the most part followers in new initiatives and would prefer a roll-out by other funds first.

April 5, 2013: ‘SEC gives CSFME limited approval for LDV going forward’ providing brokers assign proxies only from their proprietary shares.

March 26, 2013 – CSFME and its legal team presented the case for LDV to SEC Commissioner Dan Gallagher. Present by phone and speaking on behalf of LDV were representatives of FL SBA who spoke about the difficulty of timely recall of shares on loan following release of record date and issues on agenda; and a representative from CalSTRS who spoke about their recall policy affecting income.

March 13, 2013 – CSFME meet staff of Senator Rob Portman and Congressman Steve Stivers of Ohio. These meetings were for the purpose of lining up political support, should the SEC resist the LDV concept. We also met and spoke with CII Deputy Director Amy Borrus for one hour and 15 minutes for a scheduled 30 minute meeting.  She expressed great interest in the value of LDV to long-term beneficial owners.

January 17, 2013 – CSFME conference call with CoPERA Director of Investments.  Among CoPERA’s concerns were: (1) How are agents/brokers notified re: LDV? (2) Who moves or approaches first lender to agent or agent to lender? CSFME responds  that a side letter is needed between lender, agent and broker.

November 8, 2012 – CSFME conference call with Council of Institutional Investors (CII) detailing LDV. Some in attendance were opposed to securities lending because of their desire to vote 100% of recall. This position would be irrelevant giving CalSTRS’ change to policy on proxy recall.

October 24, 2012, 2PM – CSFME presents LDV to Broadridge Institutional Investor Group. At this meeting, a representative of CalSTRS states: “We would view brokers willing to provide proxies more favorably than those who would not.” We were also informed by CalSTRS that they were looking to change their 100% recall policy. A representative of SWIB led a discussion on International Voting Issues, and apparently was chairing 3 meetings to determine the following: 1. who is voting internationally? 2. What are the issues in the international markets? 3. How do we increase and improve international processes?

October 24, 2012, 11AM – EWB/KT conference call with ICGN.  Executives stated that the argument for LDV may not be as strong in a non-record date market, and asked what would be the cost for LDV.  They further stated that they would like to see the U.S. go with LDV first and would need more information and operational detail.

October 13, 2012 email note from Elizabeth Danese Mozely to Broadridge’s Institutional Investor Working Group: “TerriJo Saarela, State of Wisconsin Investment Board, will provide commentary on their fund’s interest in international voting and an update on her participation in the Council of Institutional Investors’ working group on international voting.  Our discussion will include the differences in process for voting abroad, share blocking, attendance at the meeting via proxy or Power of Attorney (POA), best practices available through the various laws and regulations, etc.”

September 18, 2012: CSFME contacts Blackrock/ICGN Chair for a brief on LDV.

August 13, 2012 – CSFME conference call with OTPP.  Discussion of LDV was not timely in that their SecLending Program stopped lending securities through agents in mid-2006. State Street is their custodian and they were using a tri-party repo through Chase to Lehman, until the Lehman collapse. All the assets sat at Chase. It was not clear who had voting rights. At the time of this discussion in August 2012, OTPP was thinking formulating an SLA because they do not have the capacity to lend securities on their own. We have had no discussion with them since.

August 2, 2012 – CSFME contacts Ontario Teachers’ Pension Plan (OTPP) regarding LDV.

March 19, 2012 – CSFME conference call with executive in charge of securities lending for Franklin Templeton

February 22, 2012ICGN sends LDV letter of support to the SEC, signed by Chairman of the ICGN Board of Governors.

September 30, 2011CalSTRS sends LDV letter of support to the SEC, signed by Director of Corporate Governance Anne Sheehan.

July 18, 2011Florida SBA sends LDV letter of support to the SEC, signed by Executive Director and Chief Investment Officer.

November 2011 – CSFME introduces Council of Institutional Investors editor to LDV.

July 5, 2011 – CSFME sends a Comment Letter to the Securities and Exchange Commission regarding LDV.

October 2010 – CSFME releases report: Borrowed Proxy Abuse: Real or Not? This report and the SEC’s Securities Lending and Short Selling Roundtable prompted the question from beneficial owners and regulators regarding the need to recall shares on loan to vote proxies, why can’t lenders receive proxies for shares on loan when we get the dividends? From this question, the idea for Lender Directed Voting was born.

January 2010 – SEC issues rules that brokers no longer have the discretion to vote their customers’ shares held in companies without receiving voting instructions from those customers about how to vote them in an election of directors. http://www.sec.gov/investor/alerts/votingrules2010.htm. The rule, periodically, contributed to the difficulty of corporate meetings attaining a quorum.

Fall 2009/2010 – Four public pension funds join CSFME in Empty Voting studies/LDV initiative; FL SBA, CalSTRS, SWIB and CoPERA.

September 29-30, 2009 - SEC Announces Panelists for Securities Lending and Short Sale Roundtable; http://www.sec.gov/news/press/2009/2009-207.htm