Will big data challenge the status quo?
27 September 2016
Laura Allen of Trading Apps explains how growing demands for big data analysis from beneficial owners is driving counterparty reporting standards
Image: Shutterstock
It’s widely reported that ‘big data’ will change the way we think about business in the years to come. It will explain our current position, plot how we got here, and offer a much-needed insight to the rewards and hurdles that lie ahead. There is a theory that algorithms will replace free thought and decision-making across many industries, and although still in its infancy there is evidence of this within securities lending.
As data processing systems grow in knowledge and power, connecting to them becomes critical for market participants. Lenders have recognised this and are no longer satisfied with the canned reports provided to them by their agents. Lenders have leveraged their position of importance to the agent by gaining direct access to the data providers. There are currently three major data providers covering the securities lending market. Each product is unique in terms of its analytics, frequency of data and number and type of data contributors. Lenders are now demanding bespoke data sets, specific reporting and comparative analysis from these data providers. Many beneficial owners still use multiple agents to lend their assets. This has resulted in a need for much more consolidated reporting whereby the beneficial owner can view performance across multiple lenders within the same portal. Trading Apps can load multiple data feeds from multiple source and aggregate that data versus configurable rule sets and filters.
But, like their agents, lenders want to go one step further. They don’t want to rely on the data providers to clean, cut and present the data, they want to absorb the raw data. Accessing the data is one thing but the value of data is what one can gain from all the possible ways it can be employed. Of course, the danger here is that endless data isn’t always a good thing. It is now becoming more important than ever for anyone analysing these different data sets to truly understand the underlying constituents and methodologies at play. Having a system like Trading Apps that can aggregate it all and put it in one place is very helpful for the end user.
Data provided to lenders on volumes, collateral, term trading, costs and improved data on income is encouraging the buy side to take control of their lending programs. It comes at little surprise that lenders are no longer satisfied with being part of a pooled program and are striving to move to intrinsic value lending. Â鶹´«Ã½ lending revenue can add additional yield to improve performance against a fund’s specific benchmark, so by using their portfolio efficiently, employing a proactive approach and concentrating resources where they can generate the most revenue, funds can outperform.
Lenders are exploring alternative methods of lending to maximise revenues. Larger funds are exploring the direct lending route initially for the portfolios they lend as ‘exclusives’ but with an eye on the possibility to transition to a full direct program. Lenders are therefore keen to understand the software solutions available to support direct lending, and over the past 18 months Trading Apps has seen a significant rise in enquiries from this client sector.
Direct lending will create new tasks for lenders that will need to either build or buy software solutions to perform portfolio valuations, manage incoming collateral and measure credit risk, tasks all previously performed by their agent lender. Even with the slow but steady emergence of central counterparties (CCPs) that reduce the barriers to market for lenders, by solving for the collateral management piece and consolidating credit risk to the one counterparty, the CCP itself, the need for system development is real.
Agent lenders recognise this shift and are reacting to their clients needs by acknowledging the requirement for a customised offering. Many are developing a set of a la carte services including operations, collateral management, valuations and indemnification. Clients can then select the services that are important to and compatible with their lending model and pay for them accordingly.
This de-bundling of agency lending services doesn’t only favour the lender. De-bundling will allow agents to set hurdle rates, accurately charge clients across different transaction types and optimise revenue for both the lender and themselves. Disparate ‘cost of trade’ calculations based upon the collateral eligibility schedules across lenders will disable fair distribution algorithms and challenge agent lenders to adjust their models to capture the profitable opportunities and avoid transactions that have capital implications and are likely to prove counterproductive.
For both agents and lenders the need for strong analytical tools is clear, neither sector can function in this changing market environment without the correct systems in place to crunch data. Market participants have expressed an interest or desire to create a universal benchmark to measure lending performance. The obvious issue with this is every lender programme is different and filled with its own intricacies, characteristics and restrictions. We recognised this need at Trading Apps and developed our ‘Benchmark App’ to solve for it. The platform loads multiple data sources, normalises it and based upon the user’s configuration will calculate an indicative fee.
Certainly the market continues to ask for data on a timelier basis. There is currently one provider that offers intraday data in the securities lending space, Trading Apps Benchmark App can load intraday feeds, and as a result those real time data points have become a fixture on our clients securities lending trading desks. We would certainly expect the market to continue to push the other providers for the same level of frequency.
Any lender active in securities financing needs the ability to accurately price, calculate risk, optimise revenue and measure agent/direct-lending performance. This requires data aggregation from multiple sources and advanced reporting and analysis, a space that Trading Apps excels in.
Likewise, agent lenders will need the ability to manage a multi faceted programme. The victors will be those that can analyse data and adapt their programmes to meet the shift in lender requirements offering them the flexibility they require and transparency on the cost of services provided. The introduction of the a la carte service menu will significantly challenge traditional providers’ technology stacks. With Glass as our core product, Trading Apps design targeted applications that are quick to build and easy to deploy, helping our clients keep pace with the rapid change we have come to expect in the global finance industry.
Of course, we need to remember that data is only as good as the person looking at it, otherwise it’s just lots of stored information. Not much use to anyone until you add two more ingredients, knowledge of what the data holds and an algorithm to find the patterns and trends within the numbers. Trading Apps recognises this need and each of our apps has an analyser attached allowing the user to manipulate the data according to their specific workflows.
In the future we’ll see much more emphasis on data analysis competencies in high-functioning organisations. Big data will become a source of competitive advantage for many firms and the structure of our industry will be reshaped. The rewards however, will accrue unequally and the winners will be found among the firms whose decisions are driven by data.
As data processing systems grow in knowledge and power, connecting to them becomes critical for market participants. Lenders have recognised this and are no longer satisfied with the canned reports provided to them by their agents. Lenders have leveraged their position of importance to the agent by gaining direct access to the data providers. There are currently three major data providers covering the securities lending market. Each product is unique in terms of its analytics, frequency of data and number and type of data contributors. Lenders are now demanding bespoke data sets, specific reporting and comparative analysis from these data providers. Many beneficial owners still use multiple agents to lend their assets. This has resulted in a need for much more consolidated reporting whereby the beneficial owner can view performance across multiple lenders within the same portal. Trading Apps can load multiple data feeds from multiple source and aggregate that data versus configurable rule sets and filters.
But, like their agents, lenders want to go one step further. They don’t want to rely on the data providers to clean, cut and present the data, they want to absorb the raw data. Accessing the data is one thing but the value of data is what one can gain from all the possible ways it can be employed. Of course, the danger here is that endless data isn’t always a good thing. It is now becoming more important than ever for anyone analysing these different data sets to truly understand the underlying constituents and methodologies at play. Having a system like Trading Apps that can aggregate it all and put it in one place is very helpful for the end user.
Data provided to lenders on volumes, collateral, term trading, costs and improved data on income is encouraging the buy side to take control of their lending programs. It comes at little surprise that lenders are no longer satisfied with being part of a pooled program and are striving to move to intrinsic value lending. Â鶹´«Ã½ lending revenue can add additional yield to improve performance against a fund’s specific benchmark, so by using their portfolio efficiently, employing a proactive approach and concentrating resources where they can generate the most revenue, funds can outperform.
Lenders are exploring alternative methods of lending to maximise revenues. Larger funds are exploring the direct lending route initially for the portfolios they lend as ‘exclusives’ but with an eye on the possibility to transition to a full direct program. Lenders are therefore keen to understand the software solutions available to support direct lending, and over the past 18 months Trading Apps has seen a significant rise in enquiries from this client sector.
Direct lending will create new tasks for lenders that will need to either build or buy software solutions to perform portfolio valuations, manage incoming collateral and measure credit risk, tasks all previously performed by their agent lender. Even with the slow but steady emergence of central counterparties (CCPs) that reduce the barriers to market for lenders, by solving for the collateral management piece and consolidating credit risk to the one counterparty, the CCP itself, the need for system development is real.
Agent lenders recognise this shift and are reacting to their clients needs by acknowledging the requirement for a customised offering. Many are developing a set of a la carte services including operations, collateral management, valuations and indemnification. Clients can then select the services that are important to and compatible with their lending model and pay for them accordingly.
This de-bundling of agency lending services doesn’t only favour the lender. De-bundling will allow agents to set hurdle rates, accurately charge clients across different transaction types and optimise revenue for both the lender and themselves. Disparate ‘cost of trade’ calculations based upon the collateral eligibility schedules across lenders will disable fair distribution algorithms and challenge agent lenders to adjust their models to capture the profitable opportunities and avoid transactions that have capital implications and are likely to prove counterproductive.
For both agents and lenders the need for strong analytical tools is clear, neither sector can function in this changing market environment without the correct systems in place to crunch data. Market participants have expressed an interest or desire to create a universal benchmark to measure lending performance. The obvious issue with this is every lender programme is different and filled with its own intricacies, characteristics and restrictions. We recognised this need at Trading Apps and developed our ‘Benchmark App’ to solve for it. The platform loads multiple data sources, normalises it and based upon the user’s configuration will calculate an indicative fee.
Certainly the market continues to ask for data on a timelier basis. There is currently one provider that offers intraday data in the securities lending space, Trading Apps Benchmark App can load intraday feeds, and as a result those real time data points have become a fixture on our clients securities lending trading desks. We would certainly expect the market to continue to push the other providers for the same level of frequency.
Any lender active in securities financing needs the ability to accurately price, calculate risk, optimise revenue and measure agent/direct-lending performance. This requires data aggregation from multiple sources and advanced reporting and analysis, a space that Trading Apps excels in.
Likewise, agent lenders will need the ability to manage a multi faceted programme. The victors will be those that can analyse data and adapt their programmes to meet the shift in lender requirements offering them the flexibility they require and transparency on the cost of services provided. The introduction of the a la carte service menu will significantly challenge traditional providers’ technology stacks. With Glass as our core product, Trading Apps design targeted applications that are quick to build and easy to deploy, helping our clients keep pace with the rapid change we have come to expect in the global finance industry.
Of course, we need to remember that data is only as good as the person looking at it, otherwise it’s just lots of stored information. Not much use to anyone until you add two more ingredients, knowledge of what the data holds and an algorithm to find the patterns and trends within the numbers. Trading Apps recognises this need and each of our apps has an analyser attached allowing the user to manipulate the data according to their specific workflows.
In the future we’ll see much more emphasis on data analysis competencies in high-functioning organisations. Big data will become a source of competitive advantage for many firms and the structure of our industry will be reshaped. The rewards however, will accrue unequally and the winners will be found among the firms whose decisions are driven by data.
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