Beyond Meat: Did you miss out?
15 October 2019
SLT sits down with Christopher Sappo of Tidal Markets to discuss one of the year鈥檚 hottest stocks, Beyond Meat, and review how his securities lending volatility indicator dataset revealed how some agents might have missed out on revenue
Image: Shutterstock
Introducing itself into the securities lending market at the start of the year, Tidal Markets is making waves with its new volatility indicator.
Tidal Markets was founded by Christopher Sappo, a former database engineer involved with all aspects of securities lending market data, from on-boarding client data to processing and amalgamating analytics for the securities lending industry.
After beginning his graduate studies at Boston College in 2015, Sappo continued his work by focusing on securities lending data, which led him to derive a unique proprietary mathematical formula to calculate volatility between lending and borrowing rates.
After several years of fine-tuning, the securities lending volatility indicator, known as the 鈥楽LVX鈥�, is marketed and sold under multiple datafiles by Tidal Markets.
The SLVX is a weighted average intrinsic rate standard deviation formula. In layman鈥檚 terms, the formula takes all transactional data underlying a security, assesses the volume and rates being charged, and outputs a statistical value that identifies rate disparity.
When market participants are closely aligned with the rate being charged to borrow a security, the SLVX indicator is low. But when underlying counterparties change direction and agree to lend/borrow statistically significant volumes of securities at rates deviating from the norm, the SLVX moves higher; indicating that the spread in activity is widening.
This spread in activity translates to increased revenue or a cost-saving opportunities, depending on which side of the trade you鈥檙e on, and how quickly you can react to those changes.
In simple terms, Sappo explains, if you鈥檙e on the lending side of the trade鈥搚ou鈥檙e better informed of the opportunity to increase the cost to borrow a security. If you鈥檙e on the borrowing side of a trade鈥搚ou鈥檙e looking to lock in the lowest rate possible before rates begin to rise.
To showcase how the SLVX provides clarity as a secondary metric of rate analysis, Sappo offers some simple examples (see tables, overleaf).
When counterparties are closely aligned the SLVX is low, which indicates the disparity in rates is also low (see example one).
When counterparties are closely aligned with rates being charged, even if there is an anomalous spike in rates, due to the minimal volume relative to other trades, the SLVX remains low and still indicates the disparity in rates remains small (see example two).
But, when an underlying counterparty changes direction, the SLVX spikes and indicates that the disparity in rates is asymmetric (see example three).
As exemplified in the third scenario, when the majority of the market is under the presumption that the cost they are charging to borrow AAPL is closely aligned to the weighted average rate, the reality is that the fourth lending agent is significantly profiting by lending the same amount of shares of AAPL. Therefore, unbeknownst to those who are lending AAPL, the first, second and third lending agents are missing out on significant revenue opportunity.
According to Sappo, this is the statistical clarity the SLVX provides that no other data providers exhibit.
To illustrate the potential power of the SLVX indicator in the real world, Sappo highlights how it performed against Beyond Meat (BYND), one of the hottest stocks of the year for the securities lending industry.
Beyond Meat鈥檚 initial public offer was in May 2019. The SLVX (see figure one) illustrates not only large individual spikes occurring on 11 June, 26 June, 10 July, 1 August, and more recently on 26 September, but also that the disparity in rates being charged averaged over 400 deviations during this time period. In translational terms, this means the weighted average standard deviation for all counterparties that transacted BYND was on average 400 deviations from the average rate charged.
The SLVX statistically demonstrates how there is a wide spread in rates being charged to borrow BYND.
From the lending side, this means there are some lending agents making significantly more money than their peers. And on the borrowing side, this means there are certain borrowers saving substantial money by borrowing from agents who are mispricing their loans.
Examining the most recent SLVX spike for BYND on 26 September, the SLVX computed a value of 902.30. On the same day, the weighted average rebate rate was -127.68 percent, whereas the lowest rebate rate was -327.50 percent.
While the average lending rate was -127.68 bps, other participants were profiting largely by charging -327.50 bps. This is not an anomaly, and as the SLVX statistically exemplifies, lending agents who failed to re-rate their loans closer to the maximum -327.50 percent rate were losing out, potentially to the tune of $10,000s each day.
The SLVX averaged at levels of more than 400 since Beyond Meat went public which shows the inefficiency of rates being charged by some lending agents and where such volatility can bring unusually high returns. As more beneficial owners begin watching their lending programmes with a keener eye, greater precision into the cost to borrow securities is something market participants can ill afford to miss.
The SLVX dataset analyses over 2.1 billion shares of outstanding loans daily, spanning over 40,000 global securities within 70 industries.
Historical data coverage spans from January 2010 to the present. Per security datafiles are categorically grouped on a geographic basis and disseminated via file transfer protocol daily at 7am EST.
Figure 1
Tidal Markets was founded by Christopher Sappo, a former database engineer involved with all aspects of securities lending market data, from on-boarding client data to processing and amalgamating analytics for the securities lending industry.
After beginning his graduate studies at Boston College in 2015, Sappo continued his work by focusing on securities lending data, which led him to derive a unique proprietary mathematical formula to calculate volatility between lending and borrowing rates.
After several years of fine-tuning, the securities lending volatility indicator, known as the 鈥楽LVX鈥�, is marketed and sold under multiple datafiles by Tidal Markets.
The SLVX is a weighted average intrinsic rate standard deviation formula. In layman鈥檚 terms, the formula takes all transactional data underlying a security, assesses the volume and rates being charged, and outputs a statistical value that identifies rate disparity.
When market participants are closely aligned with the rate being charged to borrow a security, the SLVX indicator is low. But when underlying counterparties change direction and agree to lend/borrow statistically significant volumes of securities at rates deviating from the norm, the SLVX moves higher; indicating that the spread in activity is widening.
This spread in activity translates to increased revenue or a cost-saving opportunities, depending on which side of the trade you鈥檙e on, and how quickly you can react to those changes.
In simple terms, Sappo explains, if you鈥檙e on the lending side of the trade鈥搚ou鈥檙e better informed of the opportunity to increase the cost to borrow a security. If you鈥檙e on the borrowing side of a trade鈥搚ou鈥檙e looking to lock in the lowest rate possible before rates begin to rise.
To showcase how the SLVX provides clarity as a secondary metric of rate analysis, Sappo offers some simple examples (see tables, overleaf).
When counterparties are closely aligned the SLVX is low, which indicates the disparity in rates is also low (see example one).
When counterparties are closely aligned with rates being charged, even if there is an anomalous spike in rates, due to the minimal volume relative to other trades, the SLVX remains low and still indicates the disparity in rates remains small (see example two).
But, when an underlying counterparty changes direction, the SLVX spikes and indicates that the disparity in rates is asymmetric (see example three).
As exemplified in the third scenario, when the majority of the market is under the presumption that the cost they are charging to borrow AAPL is closely aligned to the weighted average rate, the reality is that the fourth lending agent is significantly profiting by lending the same amount of shares of AAPL. Therefore, unbeknownst to those who are lending AAPL, the first, second and third lending agents are missing out on significant revenue opportunity.
According to Sappo, this is the statistical clarity the SLVX provides that no other data providers exhibit.
To illustrate the potential power of the SLVX indicator in the real world, Sappo highlights how it performed against Beyond Meat (BYND), one of the hottest stocks of the year for the securities lending industry.
Beyond Meat鈥檚 initial public offer was in May 2019. The SLVX (see figure one) illustrates not only large individual spikes occurring on 11 June, 26 June, 10 July, 1 August, and more recently on 26 September, but also that the disparity in rates being charged averaged over 400 deviations during this time period. In translational terms, this means the weighted average standard deviation for all counterparties that transacted BYND was on average 400 deviations from the average rate charged.
The SLVX statistically demonstrates how there is a wide spread in rates being charged to borrow BYND.
From the lending side, this means there are some lending agents making significantly more money than their peers. And on the borrowing side, this means there are certain borrowers saving substantial money by borrowing from agents who are mispricing their loans.
Examining the most recent SLVX spike for BYND on 26 September, the SLVX computed a value of 902.30. On the same day, the weighted average rebate rate was -127.68 percent, whereas the lowest rebate rate was -327.50 percent.
While the average lending rate was -127.68 bps, other participants were profiting largely by charging -327.50 bps. This is not an anomaly, and as the SLVX statistically exemplifies, lending agents who failed to re-rate their loans closer to the maximum -327.50 percent rate were losing out, potentially to the tune of $10,000s each day.
The SLVX averaged at levels of more than 400 since Beyond Meat went public which shows the inefficiency of rates being charged by some lending agents and where such volatility can bring unusually high returns. As more beneficial owners begin watching their lending programmes with a keener eye, greater precision into the cost to borrow securities is something market participants can ill afford to miss.
The SLVX dataset analyses over 2.1 billion shares of outstanding loans daily, spanning over 40,000 global securities within 70 industries.
Historical data coverage spans from January 2010 to the present. Per security datafiles are categorically grouped on a geographic basis and disseminated via file transfer protocol daily at 7am EST.
Figure 1
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