In January 2026, when the new regulatory disclosure (SLATE) rules become effective, lending agents will face consultants to Beneficial Owner lenders who will adjust their best execution models to a new reference standard before awarding a contract solicitation. Best practice in performance benchmarking will inevitably evolve to cite best execution as the “mean average error” of an agent’s rebate rates when compared to its peers’ actual rates, all factors having been normalized.
Consultants to securities lenders have taken note of the upcoming public data release into the public domain. Soon, their institutional clients’ selection committee fiduciaries will be able to evaluate the best execution reports of their bidding agents. This will be one material benefit to the industry of SLATE transparency: greater oversight and efficiency of their lending programs for Beneficial Owners.
A. How Predictive Pricing Will Improve Fiduciaries’ Oversight – and Revenues
Boards of directors and their RFP advisors will soon come to expect winning bidders to provide independent, validated proof of precision pricing – and offer a daily exception report. The analysis for that report would not be reliable with today’s sample-based datasets. However, with SLATE, every sample can be extrapolated to the regulatory market census. SLATE data normalizations will benefit agents that train their pricing models with feature sets calibrated and extrapolated with the SLATE market census.
In securities lending, a rate model can be used to monitor for clues to fair pricing and price leadership. For stocks in high demand, the borrowing fees change throughout the trading day. That dispersion of fees can be tracked over time and illustrated with a dynamic histogram.
1. Dynamic Histograms Enable Rebate Tracking
The BYND rebate rate dispersion chart, above, divides each day’s rebate levels into quartile tranches based on share-weighted volume through the month of May. Movement on the x-axis shows the market’s adjustment of rebate rates to satisfy demand from borrowers – more or fewer shares being loaned out at higher or lower rebate rate tranches, reacting first to increasing demand early in the month and then to decreasing demand later.
Considering Beyond Meat Inc., a high profile example of a hot stock, we can see that BYND became a target for short-sellers when two of the early corporate adopters (Del Taco and Carls’ Junior) discontinued offering vegan burgers on their menus. The blue area on the chart signifies high rebates being paid to borrowers prior to the announcement. Initially, all rebates were high/blue. BYND then became exceptionally hot. As days passed, agents lowered rebates shown by rightward movement into the green, orange, and yellow areas signifying lower and lower rebate rate tranches. (In fact, there is an extremely broad rate distribution with over a dozen rate tranches just using 500 bps increments in this case ranging from 5% to more than 100%.)
Lending agents were establishing price discovery over this time, each with various levels of information, technology resources, analytical skills, and, of course, fair pricing strategies. When the competition finally adjusted their own rebates, the rate leaders had already planned the next, more profitable re-rate. Agents who could closely follow the leader benefited the most.
The interquartile range, a statistical measure of dispersion, moves with demand. As illustrated by the red arrow on the chart, if demand is rising, then the Goldilocks (GLr) rate is oriented within the second quartile for each range of rebate rate bucket. This precise rate can be used to optimize algorithms for managing cashflow, return on assets, and counterparty risk.
2. Repricing to the Goldilocks Rate Preserves Margins
In mid-May 2024, the BYND lending market was moving to meet the increasing market demand from short sellers and the rerates of rebate price leaders, who had already captured the revenue opportunity. Using the data on 5/13 as one example, and illustrated by point A on the graph, the average published rebate was -94.4%. ASC’s calculated GLr rate was -98.4%, resulting in a 4% advantage. With $204 million1 of BYND open cash loans that day, that equates to $22 thousand in lost lending revenue that day. As illustrated by points B, C, & D, we see that those differentials change each day. Calculating ASC’s GLr rate comparative advantage each day for May, for just this one example, resulted in $195 thousand in additional revenue.
Building upon the above example, if we extrapolate the BNYD rate dynamics to the entire $138 billion of US equity specials on loan that drew in more than 250 bps of intrinsic revenues in May 2024, we conclude that lenders missed over $128 million in lost revenue from incorrect rerates across the entire market!
3. Specials Forecasting Can Reveal Scarcity Trends
A lending agent with just 5% of that market share could have lost opportunities worth $6.4 million if its loans were just priced at the average rebate rate. That does not even consider the inference that GLr rate leaders should experience fewer returns by existing borrowers, leading to more stable cash balances, and thereby magnifying the strategic benefits of clients’ cash collateral pools.
Of course, the key to this concept is the assumption that the superior lending agent can predict the future direction and magnitude of rates better than competitors, considering the scarcity of the security. When BYND hit a new all-time high for the volume of shares loaned, new loans could only be made with returned shares. That scarcity component must also be considered when forecasting rebate rate values.
4. GC Forecasting Can Widen Tight Spreads
The SLATE disclosures will enable precision pricing based on concurrent deep learning models, setting a standard that is not possible today for securities lending transactions. Fortunately, the securities finance market is a closed system that is bounded by collateral and net capital rules, among other binding constraints. Therefore, with this calibration benefit from the SLATE disclosures, reliable forecasting in securities finance will be possible for the first time, even for bundled pricing of tightly-set securities loans backed by general collateral.
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Our research has found that liquid, easy to borrow securities can capture extra attention from short sellers when untoward events hurt their prices. For instance, borrowing activity for Visa (V) surged when a U.S. Supreme Court ruling questioned a tenet of its basic revenue model. New loans to settle short sales grew to record levels over the week that it took for borrowers to digest the severity of the Court’s ruling. Even one month later, the variability in the distribution of rebates rates were observed in Visa, creating revenue opportunities for an otherwise placid inventory position.
5. Risk Management will Improve with Slate
There are numerous opportunities across GC. There are substantial revenues from rerating “warms” and other low-tier specials for those agents who can forecast the direction, magnitude, and range of tomorrow’s market rate.
With SLATE, regulators will have more data to decompose share aggregates into loans, then loans into borrowers, and then borrowers into counterparty risks. Armed with the same resources, i.e., pooled copies of their own reporting data, lenders can risk-adjust their GLr to a higher level of precision pricing. Regulators should not be the first to see overleveraged borrowers in squeezed markets.
Lenders know that markups-to-market mean nervous borrowers are watching their capital disappear as prices rise on the loaned securities. The potential risk intelligence trove from their own SLATE reports should be available to lender-counterparties. They can analyze that data to exit toxic borrower relationships before regulators – or the bankruptcy courts – force the very public liquidation of positions at fire sale prices or move to place a stay order on the borrower’s collateral.
B. Capital Charges for Exposures will Constrain the Dynamics
Ultimately, lending returns and fees must be weighed against the probability of defaults, exposures at default, and likely period until recovery. The resulting capital charges for implicitly-indemnified loan positions are the final authority for relative availability of stocks; and can be defined as the degree to which lenders’ portfolios can satisfy any more borrowers at any given time of the trading day. \
Lenders can match the oversight capabilities of regulators with ticket level detail if their lending agents were to provide copies of those filings to a data trust owned by the lenders. With the benefits of ticket-level detail, lenders can manage their exposures using distributed ledger technology, especially shared ledgers and smart contracts. The benefits of the regulatory disclosures via the data trust would then be realized by securities lenders and their compliant borrowers, as well as by their supervisors and regulators.
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Data courtesy of Tidal Markets LLC.
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