Tue, 26/05/2015 - 11:03
Once a fund manager has got the middle office infrastructure in place – the hardware as it were – the logical next step is to work out how best to utilise it. How can the flow of data – and flow of data is not simply the software – be used in a smart, intelligent fashion to bring incremental gains to the way that the manager runs its business?
This is a question that many managers now face as the volume of data they must handle for a variety of regulatory and investor demands continues to rise exponentially. With the technology available today, managers have the capability to pinpoint specific patterns and trends in large swathes of data and derive insights that previously would have been unobtainable or even unthinkable.
“Data analysis can provide insights that you would never have imagined were there in the first place. It can help create that Eureka moment – did you know that under such and such market conditions when X happens, Y is the result? These sorts of insights can help change a manager’s trading process or enhance the trading philosophy,” says Ross Ellis (pictured), Vice President and Managing Director of the Knowledge Partnership in the Investment Manager Services division at SEI.
The best investment managers will take the outputs of their risk reporting or performance attribution and integrate them outputs back into the investment process. Beyond benefiting the front office, a solid data management strategy can also help with other activities such as monitoring counterparties, providing consistent reports to regulators around the world, and building insights into investor behaviour, not to mention delivering investors and interested parties their appropriate levels of transparency.
This ability to effectively handle data and disseminate it in a variety of forms and flavours to investors separates great hedge fund managers from good ones.
For some time now, investors have ceased to view hedge funds as a separate distinct asset class. Alternative strategies like long/short, event-driven, and systematic market neutral sit within investors’ equity bucket, along with those managed by traditional long-only asset managers, to bring diversity from a risk and/or return perspective. Together with increased regulatory and operational due diligence requirements, hedge funds have become subject to the same level of scrutiny that traditional asset managers have endured for some time.
“Investors increasingly want to know how a manager’s fund or strategy fits within their overall portfolio. Managers need much more detail and expertise from their middle office team to help explain the correlations or what the performance of the fund or strategy is relative to expectations. It’s not just a case of investors allocating to hedge funds as a separate and distinct investment but rather how they fit in to the investor’s overall portfolio,” says Holly Miller, Managing Director of Middle Office Services in SEI’s Investment Manager Services division.
“To perform this sort of performance and risk analysis, managers need position, transaction and pricing data for every portfolio managed, as well as accruals, security characteristics and possibly benchmark constituent data—and they need it to be both timely and accurate every single day. It is no longer a once-a-month snapshot.”
Increased demands from investors, prospects and regulators for customization, regulation, distribution and/or transparency have prompted fund managers to accommodate these demands with new investment vehicles, such as ’40 Act mutual funds, UCITS, hedge funds, private equity funds and/or separate accounts. As fund managers diversify their offerings by launching or taking on side-by-side funds and separate accounts that mirror the strategy of an existing fund, data management requirements skyrocket.
A single-fund manager, for example, that takes on a separate account suddenly doubles the volume of data that must be managed and analysed.
SEI and Alpha FMC, a London-based consultancy, recently surveyed a number of buy-side managers and found that whilst technology per se was not an issue or inhibitor to taking advantage of data, getting the right governance in place was.
“The problem was having an inability to explain from a business analysis viewpoint what they were looking for, what the end game was. Oftentimes the decision around the type or frequency of information that could be provided was driven by the IT department based on what technology could do rather than what the business wanted today or dreamt of in the future,” explains Ellis.
Early findings noted that, “there is no common theme or conclusion in terms of where within the organisation data management is the biggest challenge.” Asset managers refer to different pain points across the firm. At 22 per cent, the middle office was one of the biggest challenges to achieving data consistency according to the survey, with 17 per cent citing portfolio management as the biggest data consistency challenge.
Given the daily pressures of aggregating and normalising data to pass on to the front office, traders, and regulators, and growing requirements to produce customised reports for investors, this challenge with consistency is understandable. As the industry has institutionalised and evolved, the middle office has become a more integral and critical part of a hedge fund’s operations.
As such, the need for clean, consistent, accurate and timely data to disseminate, evaluate and act upon to drive change or influence decision-making is essential.
To be competitive, SEI and Alpha found that investment organisations need a holistic, strategic approach to data management — one that will equip them to run and improve all business functions while also supporting future expansion of the enterprise. Being “data-driven” is a journey, not a destination, one where insight can be gathered along the way and need not be a big-ticket, transformative leap.
An effective data management strategy is not predicated on being able to handle increasing volumes of data but rather on how to pull the right needle out of the haystack and utilise actionable data.
“About 20 years ago I worked for a global asset manager,” recalls Miller. “We would always emphasize the strength of our stock picking in RFPs. We finally ran performance attribution tests on a portfolio and lo and behold, the results couldn’t back that up. We learned we were excellent country and sector allocators but our specific stock-selection skills were not as exceptional as we thought. That was a great insight for us and helped us improve our process and fine tune our marketing approach.”
The importance of high-quality, up-to-date figures is not just about portfolio data. It applies just as importantly to investor and fund accounting data. Indeed, managers can even apply it to brokers and other counterparties to appraise them.
“Having data on broker performance would allow a manager to explain to their investors the reasons why they are paying higher commissions to one broker over another. Maybe the trade failure rate is minimal at the more expensive broker. Maybe a cheaper broker consistently issues incorrect trade confirmations and creates a mismatch ratio that is twice what it is with every other counterparty. There are myriad insightful ways we can use that data to help our clients argue their case or understand behaviour,” says Miller, who continues: “The “Greeks” and other risk metrics are generic. What you want is to spot trends in your data to better anticipate what might happen in the market. This is where it becomes more than just a middle-office function. We have clients coming to us for fund accounting data, investor-level data, which we can easily [data] mine. But we try to take it one step further.
“For example, when SEI puts reports on our online dashboard for our managers to release to their investors, we can see who is viewing those reports and sometimes even how they are using them. We can see that Investor X downloaded this report and downloaded these other six reports multiple times. This is a new evaluation of different types of data that helps the fund manager develop new insights into its investors’ needs and behaviours.”
The ability to use unstructured data has the potential, therefore, not only to encourage greater collaboration between front-, middle-, and back-office teams but also to improve how a fund manager interacts with new and existing investors. Suddenly, the rigorous ODD demands of investors become less onerous. Investor relations teams can share data not just on the fund’s performance but on counterparty performance. Indeed, the more proactive the manager is at sharing information, the more credible they are seen in the eyes of investors.
SEI’s data management survey found that over half (56 per cent) of respondents said that they viewed unstructured data as being important to their business going forward. “From an efficiency measure, big data technology can enable asset managers to structure their data more efficiently and creatively. It allows structured and unstructured data to be compared in interesting ways, for performance measures, FX, or collateral purposes,” comments Ellis.
Like SEI’s asset servicing entity, some large asset managers are beginning to change the way they view data. Historically, large institutions have tended to take a silo-based approach to using data. Finance and investor relations teams would not necessarily share data with investment and product specialist teams.
An article written by FundFire on 15 August 2014 reported that at Eaton Vance, data is a “cross-functional responsibility” between sales, marketing, analytics and product management. According to Bob Cunha, managing director of marketing and distribution strategy at Eaton Vance, having one team in charge of all the data could leave gaps, as each team looks at and understands data differently.
Without a holistic approach to analyzing data across the enterprise, managers stand no chance of building fresh insights into their trading methodologies, saving on trading costs or reaching out to new prospects.
“This is all underpinned by having the right infrastructure in place. An example of what SEI brings to bear is when a manager has a prospect asking questions that are slightly out of the norm and the manager might come to us and say, ‘I’ve got potential investors asking questions not just about the assets in the fund but the assets in the strategy they offer’. These days, managers have to talk about all the side-by-side accounts they are managing for each strategy. That’s when it’s time to start thinking about building performance composites and becoming GIPS-compliant,” says Miller.
Historically, GIPS or Global Investment Performance Standards has historically been a focus in the traditional arena rather than the alternatives market. Now, however, investors are increasingly viewing all managers they allocate to in the same way. They want evidence that they are complying with GIPS.
“For alternative managers, they’ve never been asked this GIPS question before. What we are saying to investment managers is that we understand where investors are coming from when they ask these questions. Because of our long history servicing traditional managers in Europe and the US, we can effectively coach managers to better understand and interpret what investors are asking for,” says Miller.
Managers can’t expect overnight to start building insights where once there were none. What is needed is a strategic partner that understands the manager intimately and can deliver the right needle in the haystack - not just any needle - when the manager, or indeed, anyone requires it.
“Having the right infrastructure in place ensures that you can collect all those needles. Then it’s a case of having the ability to pull out the right needle at the right time for the right purpose instead of grabbing them all and hoping for the best. It equips the manager with more intelligent data management expertise, while also improving the quality of the client service experience,” comments Ellis.
This is not just about having superior technology. Market-leading operational platforms rely on employees who have an in-depth knowledge of the systems used and how they are integrated, along with the consumers of the data and how they plan on using that data.
Explains Miller, “I was talking about our online Manager Dashboard to a hedge fund manager recently. During the discussion I said, ‘I was in such and such a city last week training people to use the dashboard when one of the founding partners stopped the conversation to ask me why. I replied that through training clients’ investment and investor relations’ teams, I learn more about how the manager thinks about investing and how they want to service their investors. If I can understand that, I can share it with our team at SEI to give our staff that intimate level of knowledge.
“That’s one way we try to better help them pull the right needle out of the haystack, not just any random needle,” continues Miller.
In many respects what is being talked about here within the asset management industry is no different to how people already mine data. On a daily basis, we use our smart phones to set birthday reminders, to view message histories, use applications to see how many people viewed our latest tweet and so on. As Miller rightly points out, smart phones have become “powerhouses of information”.
“The nub of the issue really is how does the manager turn a sea of information into useful, actionable information that can be easily articulated and readily affects decision-making,” says Miller, who concludes: “We are starting to see the best people in investment operations leaving the asset management side and join the service providers because a) we’re not just a cost centre and b) we are beginning to think about and tackle big data issues from a holistic operational, IT and governance perspective. Doing that, we can better understand how best we can help our clients transform data—faster than ever before—into the information they need to improve their risk/return profile and better service their investor base.”
Getting the right middle-office infrastructure in place is only half the battle, but an absolutely necessary one. The next challenge that managers face is making sense of the data waterfall that is fast becoming Niagara Falls in size and volume.
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