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Fair Enough?

When it comes to Securities Lending there is a perennial question – “Is it fair?” “Is what fair?” I hear you ask!

Let’s talk about fair algorithms and just how fair the securities lending business really is.

In the Agency Securities Lending world Agents have many fiduciary responsibilities to their underlying clients, be they *UCITS funds, family offices or retail clients. These responsibilities encompass many areas outside the scope of this paper, so for the benefit of clarity we will focus on just two of the responsibilities –

  1. Best Execution
  2. Fair Allocation

These two concepts are inextricably bound in the business of Securities Lending so we really shouldn’t talk about one without addressing the other.

Before we discuss the main topic let’s get a couple of definitions out of the way.

ESMA’s definition for “Best Execution”

"Best execution" means that, when firms execute client orders, they must take all reasonable steps to deliver the best possible result for their clients, taking into account a variety of factors, such as the price of the financial instrument, speed of execution of the order and cost. For retail clients, "best possible" means the most favorable result in terms of the price of the instrument and the costs associated with the execution.

The above is just one of many definitions of “Best Execution” from various ESMA sources, but there are also legal opinions and many different perspectives from ISLA/ICMA members.

Fair Allocation

With an Agency Lending trade there are two parties, we have the “street” party, which is the borrower (which could be a Prime Broker, Principal Borrower or other sell side party) and the “Internal” party, which is the Agent’s internal client (could be a pension fund, UCITS or any buy side entity).

For example, a generic Agency Trade –

Fair enough 1

In this example there are three funds lending an aggregate quantity of 30,000 to “STREETCPTY1”.

Borrowers don’t want to have piecemeal trades with small quantities; hence the Agent Lenders aggregate their availability so they can complete the order in “one hit”.

The Agent Lender could have thousands of possible internal clients to select from to fill the trade. The question is  - which internal clients should they select (“Allocate”) for the trade? This is the essence of “Fair Allocation.”

Fair Allocation models

There is more than one way to skin the proverbial cat, and so it is with Fair Allocation. The models that are currently used within the industry can be broken down into variations of the following models.

Equal Allocation Model

In this model every internal party gets allocated an equal portion of the trade regardless of the size of parties holding.

Although seemingly fair, the downside to this model is the sheer volume of internal allocations it can generate. It’s conceivable to have hundreds if not thousands of allocations for a single trade. This can prove expensive for the agent, as each allocation potentially has an associated settlement cost (which can be passed on to the internal client in the form of charges).

A further downside is that the more parties you have on a trade the more likely it is you will have to do a daily “reallocation” as the odds are higher that some of the parties that have lent out their shares will have sold them and will need to recall the shares to fulfil the sale. Those parties’ allocation will have to be replaced with shares from another party else the trade will have to be partially recalled. This issue is not limited to just this allocation model, it applies to any method that creates a large number of allocations.

Equal Allocation

Required quantity is equally split amongst clients with availability.

Fair Enough 2

Size Weighted model

For this model, the required quantity allocated to parties with availability is proportional to their availability. So the larger their holding (“availability”) the larger their contribution to the trade.

The downside here is that parties with small holdings will be constantly overlooked by the algorithm as it strives to meet the required quantity in the most efficient manner (i.e. smallest number of shapes).

Size Weighted

Required quantity allocated to a client with availability is proportional to their availability.

Fair Enough 3

Algorithmic Model

This model and its variants rely on a “league table” of sorts. Versions of this algorithm are regarded as the “fairest” model. To put it simply this model assigns negative points to “winners” (those parties who got to be selected for a trade) and positive points to “losers” (those parties who were not selected for a trade). Over time the opportunities even out and every party who has a holding gets to participate in a trade.

The issues...

Do we need to worry about MiFID? Is MiFID applicable to securities lending?

Yes. This topic has been debated many times. It seems that the jury is still out with many industry participants believing that it does, and many that it doesn’t. As a vendor, Broadridge has to cater for both sides of the argument which in this case means we need to worry about it! By and large the definition of best execution is mostly applicable to the cash equities market. But what about the other attributes - are they relevant for securities lending? Price? If we take ESMA’s statements literally, no it’s probably not. But if you take “price” to mean “Rebate/Fee Rate” then yes. How about “speed of execution of the order and cost”? Speed, not so much. SFTR constrains European parties to execute within an hour, but in this context does it matter if a deal is struck in 2, 20 or 60 minutes?

Surely what’s more important from a securities lending perspective are these factors? –


Are the securities available for lending and is there sufficient quantity?

Trade Type

Do I want to do a fee or rebate trade, or perhaps an evergreen?

Rate Am I offered the best fee or rebate rate? Maybe not so important for “GC (General Collateral)” business but essential for “specials.”
Term The rate is linked to the term – different rates for Open, 7 day, 30 day etc

Triparty or bilateral, and then at a deeper level, what schedule? E.g. G10 “Govies” or S&P500 Equities? Having the right collateral schedule could have a significant impact on the rate you can achieve on a trade.


Do I have an existing relationship with this party?


Do I have a sufficient credit limit? What is the RWA bucket?

Dividend %

What dividend percentage does the borrower need, and the lender allow?

If you were going to have a Best Execution Policy for Securities Lending wouldn’t the above attributes be the most relevant?

Where the algorithm is the solution and also the challenge…

Algorithms really depend on calculating the relative fairness on a fixed set of attributes, but in the “real world” these attributes may not be at all fixed. There are a lot of “What if’s” e.g.

  • What if the borrower only accepts lenders with particular Credit Rating?
    • This could rule out whole groups of clients who don’t have the requisite Credit Rating.
  • What if the borrower wants a specific term?
    • Open, 1 day, 1 week, 1 month, 1 year? That’s a lot of permutations to build into an algorithm and the associated static data needed to support it. That’s a lot of complexity!
  • What if the borrowers and lenders have set their default dividend requirements but are flexible?
    • Some lenders would be happy to give up something on the dividend if it was made up elsewhere on the trade.

The challenge with Algorithms and the importance of human intervention

The challenge with Algorithms is that the more complex the Algorithms become the more difficult it is to ascertain what would be the outcome of any allocation. It essentially becomes a “black box” to the trader.

It’s challenging to build an all-encompassing algorithm that covers every scenario and use case. It’s not really desirable or cost effective to try and do so. Where the Algorithm fails to provide the “best” solution the trader has to step in and override the algorithm.

One could say that as soon as the trader steps in and overrides the allocation the process is no longer “fair,” but one could also say that if the trader had to step in then the Algorithm failed to find the right match - and if the trader hadn’t stepped in the trade would have been lost. In this case a different lender/s were selected as opposed to “no lenders selected” and what would have been fair about that?

There’s no doubt that algorithms will evolve to support the securities lending business as it develops over time. We have to take care that those Algorithms remain as fair as is practical, but we also have to be pragmatic as to what is achievable from a technological and human point of view.

A “Fair” algorithmic allocation is the best solution when you have a simple to medium complexity business coupled with high volumes. However, if the business is concentrated on complex trades, then human beings making decisions is an effective and fair alternative.


ICMA MiFID and Best Execution - Andy Hill ICMA

ESMA Best Execution

*UCITS funds - undertakings for collective investment in transferable securities

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