Optimisation, that’s what you need
18 April 2017
David Lewis, senior vice president at FIS Astec Analytics, explains how timely and accurate data may be the perfect balm for soothing regulatory aches and the increasing pains of rising business costs
Image: Shutterstock
As the industry continues to develop into a more comprehensive, multi-disciplined, more heavily regulated, and indeed more reported business, efficiency and optimisation will be increasingly important. While 2016 may well have been a good year for the industry income- and activity-wise, few are expecting the next couple of years to be an improvement on that, and some believe incomes may contract slightly at the same time that the costs of doing business are rising. Optimisation of collateral, counterparty, trade type and capital usage will all play their individual parts, but acting together, backed by timely and accurate data, they will help programmes to achieve their best.
In the modern securities finance world, the key to programme performance is, increasingly, optimisation. Working within ever-stricter regulatory boundaries and with increasingly scarce resources, a securities finance programme needs to be optimised to perform properly among its peers, as well as against the other resource-hungry businesses that must be battled for attention, counterparty balances and capital allocations.
Looking at optimisation from a macro perspective, our industry has already made great strides forward in combining what used to be disparate business lines, merging fixed income repo with securities lending and folding in collateral management across the enterprise to make a securities finance and collateral business. There are a number of different pressures that have brought about this change in approach, but the need for efficiency or the optimisation of industry practice has been a significant driver of change, which has been, in turn, underpinned by the increasing availability of data analytics.
Arguably the first development towards optimising a programme was about the ‘who’ more than the ‘what’. Pre-crisis, it could be said that much of the power in a trading relationship was held by the lender and beneficial owner side of the market, with a great deal depending on the willingness of an asset owner to approve the worthiness of a borrower. Post-crisis, there was a pivot towards the borrowers looking much more carefully at who they borrow from, given that they are commonly pre-delivering higher quality collateral, plus a margin. Data analytics on the stability of the lenders rapidly became a vital part of the borrower to lender relationship.
Jump forward to today, and the results of this trajectory of change can be seen very clearly.
Discussions are ongoing, largely under the auspices of the new Securities Financing Transactions Regulation reporting requirements, that will potentially change the whole dynamic of the agency lender disclosure process. Some borrowers are advocating knowing the identity of the underlying lender pre-settlement so that they can actively approve or reject them based on the relative costs of doing business with that lender, including, of course, the capital weighting certain counterparties may attract. This kind of data-driven trade optimisation may become absolutely vital for borrowers to ensure each and every trade undertaken is a profitable one or, at the very least, one that contributes positively to their book.
Optimising the use of their balance sheet will demand that borrowers have access to intra-day data on their overall book of business, including collateral, liquidity and funding ratios. Alongside this information, they will need to juggle their various objectives, which may well conflict from time to time–meeting revenue targets versus balance sheet values at reporting time, for example. Without accurate and timely data on exposures, balances and what type of collateral is held where, this kind of optimisation will be near impossible.
‘Enterprise collateral’ has been one of the many new phrases to enter the securities finance lexicon over the past few years, and it now forms a central pillar of an effective securities finance desk. In discussions with our clients, we have found that there are as many interpretations of just what collateral optimisation means as there are market participants, and those answers can change according to what time of the year, month or even day that you ask the question.
One thing all these conversations do have in common, though, is the reliance on data quality and completeness. Gone are the days of homogenous collateral schedules for clients, but we are not quite yet in the age of digital schedules, centrally stored and managed by collateral systems and triparty agents.
When looking for standardised reference data, there are even some instances when definitions of instrument types can clash. Without effectively managed and accurate data, optimisation of collateral can only achieve so much. For example, data might be incomplete, because, you have assets in Asia that cannot be counted towards your global position, the whole enterprise collateral effect and benefit can be lost.
With the combined disciplines now becoming organised under one banner of securities finance and collateral management, traders have many more tools and options available to them, allowing them to optimise the trade type to suit their needs at that moment. Subject to the capital cost, the availability of the required collateral, and their profitability threshold, traders can select a synthetic trade, repo, buy/sell-back or stock loan as required, not to mention varying types of durations and routes to market to suit the moment. Making these decisions effectively, however, requires optimised data on intra-day pricing to ensure you earn or pay the right fee, use or receive the most cost-effective or suitable piece of collateral, and understand the impact that trade will have on the overall position of the trading book compared with the prevailing liquidity, capital and profitability targets.
As the industry continues to develop into a more comprehensive, multi-disciplined, more heavily regulated, and indeed more reported business, efficiency and optimisation will be increasingly important. While 2016 may well have been a good year for the industry income- and activity-wise, few are expecting the next couple of years to be an improvement on that, and some believe incomes may contract slightly at the same time that the costs of doing business are rising. Optimisation of collateral, counterparty, trade type and capital usage will all play their individual parts, but acting together, backed by timely and accurate data, they will help programmes to achieve their best.
In the modern securities finance world, the key to programme performance is, increasingly, optimisation. Working within ever-stricter regulatory boundaries and with increasingly scarce resources, a securities finance programme needs to be optimised to perform properly among its peers, as well as against the other resource-hungry businesses that must be battled for attention, counterparty balances and capital allocations.
Looking at optimisation from a macro perspective, our industry has already made great strides forward in combining what used to be disparate business lines, merging fixed income repo with securities lending and folding in collateral management across the enterprise to make a securities finance and collateral business. There are a number of different pressures that have brought about this change in approach, but the need for efficiency or the optimisation of industry practice has been a significant driver of change, which has been, in turn, underpinned by the increasing availability of data analytics.
Arguably the first development towards optimising a programme was about the ‘who’ more than the ‘what’. Pre-crisis, it could be said that much of the power in a trading relationship was held by the lender and beneficial owner side of the market, with a great deal depending on the willingness of an asset owner to approve the worthiness of a borrower. Post-crisis, there was a pivot towards the borrowers looking much more carefully at who they borrow from, given that they are commonly pre-delivering higher quality collateral, plus a margin. Data analytics on the stability of the lenders rapidly became a vital part of the borrower to lender relationship.
Jump forward to today, and the results of this trajectory of change can be seen very clearly.
Discussions are ongoing, largely under the auspices of the new Securities Financing Transactions Regulation reporting requirements, that will potentially change the whole dynamic of the agency lender disclosure process. Some borrowers are advocating knowing the identity of the underlying lender pre-settlement so that they can actively approve or reject them based on the relative costs of doing business with that lender, including, of course, the capital weighting certain counterparties may attract. This kind of data-driven trade optimisation may become absolutely vital for borrowers to ensure each and every trade undertaken is a profitable one or, at the very least, one that contributes positively to their book.
Optimising the use of their balance sheet will demand that borrowers have access to intra-day data on their overall book of business, including collateral, liquidity and funding ratios. Alongside this information, they will need to juggle their various objectives, which may well conflict from time to time–meeting revenue targets versus balance sheet values at reporting time, for example. Without accurate and timely data on exposures, balances and what type of collateral is held where, this kind of optimisation will be near impossible.
‘Enterprise collateral’ has been one of the many new phrases to enter the securities finance lexicon over the past few years, and it now forms a central pillar of an effective securities finance desk. In discussions with our clients, we have found that there are as many interpretations of just what collateral optimisation means as there are market participants, and those answers can change according to what time of the year, month or even day that you ask the question.
One thing all these conversations do have in common, though, is the reliance on data quality and completeness. Gone are the days of homogenous collateral schedules for clients, but we are not quite yet in the age of digital schedules, centrally stored and managed by collateral systems and triparty agents.
When looking for standardised reference data, there are even some instances when definitions of instrument types can clash. Without effectively managed and accurate data, optimisation of collateral can only achieve so much. For example, data might be incomplete, because, you have assets in Asia that cannot be counted towards your global position, the whole enterprise collateral effect and benefit can be lost.
With the combined disciplines now becoming organised under one banner of securities finance and collateral management, traders have many more tools and options available to them, allowing them to optimise the trade type to suit their needs at that moment. Subject to the capital cost, the availability of the required collateral, and their profitability threshold, traders can select a synthetic trade, repo, buy/sell-back or stock loan as required, not to mention varying types of durations and routes to market to suit the moment. Making these decisions effectively, however, requires optimised data on intra-day pricing to ensure you earn or pay the right fee, use or receive the most cost-effective or suitable piece of collateral, and understand the impact that trade will have on the overall position of the trading book compared with the prevailing liquidity, capital and profitability targets.
As the industry continues to develop into a more comprehensive, multi-disciplined, more heavily regulated, and indeed more reported business, efficiency and optimisation will be increasingly important. While 2016 may well have been a good year for the industry income- and activity-wise, few are expecting the next couple of years to be an improvement on that, and some believe incomes may contract slightly at the same time that the costs of doing business are rising. Optimisation of collateral, counterparty, trade type and capital usage will all play their individual parts, but acting together, backed by timely and accurate data, they will help programmes to achieve their best.
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