Authored By
Neil Visnapuu
Neil describes his role as focused on product with a splash of engineering. Neil has spent more than two decades in FinTech building compliance strategies and solutions for the financial services industry.
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Neil Visnapuu, VP of product management at Arcesium, writes about the complexities of financial data management, and why evolving asset classes, securities, and regulatory requirements necessitate a flexible, dynamic approach to data mapping.
I’ve been in FinTech for 25 years, starting out in the mid 90’s when hedge funds really began to mature.
As hedge funds emerged and were marketed to a broader global audience, there was a push to think of them as an asset class. Even kind of a meta-asset class since they were blending asset classes and, in theory, offering yet another way to manage risk. But under the hood, funds were buying and selling securities, with leverage, in any market that could handle their velocity, and bringing directional stances such as long, short, or neutral.
Despite the above sounding like a springboard for things to go off the rails — it’s only happened once or twice in my career — funds still invest in a fairly objective, recognizable list of asset classes, from equities to real estate to derivatives. Each of those asset classes have growing, but objectively agreeable, sub types. Derivatives can be interest rate swaps, or currency forwards, or a lot of other things. Bonds can be sovereign or junk, for instance.
I say objective because it’s pretty finite, and enough folks in a room can probably agree on a taxonomy.
Next is the actual securities, how they fit into that taxonomy, and a whole lot of subjectivity around what a data provider ascribes to a given security via mapping to an asset class, or sub type, or a venue on which it trades.
And then, there’s what the lawyers say when firms navigate that subjective mapping. Take MiFID filings, for example. Firms trade with counterparties in various ways, and there’s a lot of creative “mapping”, when aggregating a set of positions that may or may not fall under a jurisdiction, based on that mapping. And based on how that firm sells itself to its investors and eventually to regulators.
And don’t get me started on IDs.
At Arcesium we talk a lot about getting the data house in order to make things like leveraging AI, or producing P&L, realistic, usable, and timely. Regulatory reporting is no different.
Arcesium’s operational platform, Opterra, is often described as providing a golden source of truth. I’d say a more accurate description is a golden source of truths. And it’s incredibly valuable to have a platform underpinning your downstream operations, that recognizes and actually embraces this seemingly contradictory state.
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This is a very vanilla take on asset classes, from what I remember when cramming for the Series 7: There’s a lot of terrain between stocks, bonds, cash, real estate, and commodities. For example, characterizing the underlying positions of a swap, for the purposes of MiFID reporting. Textbooks asset class taxonomy is simple, but when lawyers interpret regs, and multiple datasets, from multiple vendors, are stitched into an artifact for a regulator, there’s a need to accommodate variance.
And innovation in financial products often blurs these lines. Where products trade, such as OTC, exchange, illiquid, can also muddy the waters.
Hedge funds in particular trade exotic instruments. In that uniqueness, who’s to say, for example, if an instrument is a derivative or a fixed income proxy, given its cash flow? Exotic instruments might contain multiple assets with different triggers. I am constantly surprised at the nuance in instruments that flow through our platform. How these things are internally valued (and given risk views) is an industry in and of itself, and every attribute in a 150 array — think columns in a spreadsheet — brings information to some calculation somewhere.
A total return swap might be an equity derivative or a credit instrument. And this might change based on legal guidance, regulatory guidance, or fund mandates. When prepping quarterly systemic risk filings, how you run that group by is meaningful. For the record, it’s a bit more complicated than a group by! It’s usually a chain of them, clubbing datasets from N source partners, vendors, etc., and usually under the duress of an impending deadline.
Wrestling with country codes, which can have multiple options even amongst one data vendor and being able to make such adjustments within one framework, versus waiting for a response from a fund admin on when they might be able to handle such a change, can make a big difference at the end of a quarter and in a filing window.
Furthermore, it’s very common not to even recognize concern on such variability, until looking at a “final” calc, and walking back to how it was rendered.
Whether it’s T+1, or something annual, there’s always room to be surprised, when rendering an artifact for an external party, like a regulator. Compression of that spread, between first look and when you press submit, means technical and operational systems that bring efficiency are highly sought after.
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Line of sight is a phrase I like to use when talking regulatory reporting to our clients. Line of sight from booking a trade to summing collateral and tallying a position for a legal entity, to calculating its relationship to market cap, is critical with such common aggregations.
Knowing the composition of a calculation, a position, even a NAV, and being able to “tune” such data flows, requires both a sophisticated technology platform and domain-aware nomenclature. Arcesium is able to support a disparate investment industry because we defined the problem as a balance between generic tasks and proprietary configurations. Regulatory reporting, especially for investment funds given their trading habits, is best served via the above problem definition.
Mentioning investment funds in the context of the above problem, especially when discussing regulatory reporting, is important. Trading derivatives, a common practice, involves leverage with wild optionality. Regulators are keen to understand systemic risk in the US, Europe, and Asia. Geo-jurisdictions, along with the dynamic nature of the derivatives market, means your data flows and classifications will need to adapt.
Commodities — often thought of as real-world assets — can be traded physically. But because of that inconvenience, a large array of synthetic options exist. And with that, the same considerations.
And the latest test of conditional data flows, where the regulatory burden is still nascent — crypto!
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The point is simple — the dynamic nature of the most complex securities and instruments, forces regulators to adapt. In turn, the requirement on investment funds is to be aware of your chosen mappings, be able to change them based on guidance from consultants, lawyers, regulators, and clients, and do so in a timely manner.
The tension between innovation and regulation (we’ll avoid discussion on WHY this tension exists) lays waste to any oversimplified, static model, whether it’s technology or spreadsheet jockeys — or in the worst case, when those two things are forcibly married, because one side is stuck.
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