Is “More Data” Hurting Asset Manager Analytics?
The push for more data is creating significantly more logistical work for investment professionals and stealing time from analytics.
Over the last decade, the investment community reached the universal agreement that more is better. Specifically, the more data you have for an underlying investment, the better. No more “following your gut.” No more selling your investment with a Hollywood-level story. Investors want data. They want it clean, they want it aggregated, and they want it yesterday. Varieties in frequencies, format, and granularities present an aggregation challenge, specifically when it comes to systemizing the process. When the time spent aggregating data surpasses the time spent analyzing it—especially for asset owners invested with multiple asset managers—analyses inevitably drop in quality. “More data” will sound great right up until teams realize the quantity of datapoints in demand is diminishing the quality of the report.
Data burdens are exceptionally good at snowballing. Data aggregation tasks that show up in routine to-do lists quickly turn into Frankenstein-esque concoctions as new data is inevitably added to the already bursting-at-the-seams machine. RIP simple spreadsheets. Each week, each month, and each quarter, teams are challenged with new extensive data projects. Speaking with investors, we’ve found the biggest storms tend to center around manager due diligence—often the most guilty culprit of data mismanagement. With each manager delivering data in their own unique way, with their own preferred frequency, and maybe forgetting a datapoint or two (or three or four), analysts find themselves sitting down to a full afternoon of data entry. If they’re lucky, the files will be complete, maybe even accurate. Third-party providers, such as multi-asset class portfolio management software systems, provide a range of benefits for investors dealing with data collection challenges. Geared specifically towards investment manager data, the examples below suggest alternatives to the traditionally internal methodologies for performing sophisticated manager due diligence.
Aggregate Data Analysis
From our vantage point, we’ve observed an increased willingness from investment managers to share data with investors—a sharp contrast from their historically tight-lipped approach. With managers now more willing to share additional transparency with their investors, the question remains: “How do we access that data?” The current trend is for investors to piece together position level information from their managers; this information is often gathered from calls, or in the body of an email chain. It is not uncommon for a junior member of the investment team to be tasked with combing through 13F filings to supplement this manager data.
The reality is that many investment managers are only providing some of their investors with access to their position level data.
In other instances, investment managers are willing to share—but require certain information be left out.
Advanced technologies such as SEI Novus℠ makes these sophisticated tasks a possibility, with features such as time-lag or slight adjustments to specific methodologies distorting the data just enough. Managers feel more comfortable sharing this data if it’s going through an established third-party software or data management company. Fortunately, modern systems can integrate directly with investment managers’ portfolio management systems, accounting software, and directly with their fund administrators to extract daily position level detail. Every institution wants more transparency, and now there are options to provide additional visibility into a portfolio.
Effective Data Normalization
In an industry that measures by uniqueness, it is no surprise that investment managers fail to deliver a simply formatted, standard monthly document to their investors. Exposure and risk reports are never the same from one asset management firm to the next. Some are formatted using a portfolio analytics system, while others are built in Excel or Tableau. They come at different frequencies. They can be accessed in various places: portals, email, links. Managers are viewing the world from completely different lenses—referencing their most flattering benchmarks, preferred periods of time, as well as characterizing sectors, market caps, and geographies to their own liking. If an allocator is relying on a small team of analysts to aggregate all this data and then build processes to normalize each data field, they will inevitably find themselves spending more time managing data than analyzing data. There is limited time before the next batch of reports are made available. These analysts become limited in their ability to deliver timely investment insights to their CIO.
Outsourced Data Management
Let’s look at a day in the life of Isabella, an investment analyst at a university endowment:
Earlier this week, Isabella’s team pulled manager data from investor portals. Today, Isabella is studying info from public filings to add details to each report. While Isabella is doing this, another analyst drops by to let her know that he has a few notes on XYZ Capital, after getting off the phone with this manager. A few hours later, he has new notes for ZYX Associates. More data!
Now it’s time for Isabella’s “favorite” task. With the enriched versions of each report, she needs to pull all of the necessary data into Excel, so that her team can appropriately analyze and manipulate the data in aggregate. Her organization has its own particular way of standardizing the data, but Isabella is used to the formatting now and is pretty quick at making the manual conversions in Excel. She’s even become familiar with each of the managers own unique way of formatting the data from doing this task so many times.
At 9pm, Isabella breathes a sigh of relief: she finally completed this month’s report. Now she can start her analysis.
If Isabella’s team decided to outsource data management, the entire data aggregation task—including public filings enrichment—becomes automated through a data management software. If managers provide position-level transparency, the need to update info based on a recent phone call is unnecessary. Now Isabella can focus all her time on the actual analysis.
Outsourcing data management provides investment analysts with more time and a more accurate, complete dataset to evaluate. Pursuing this path incorporates structure into an institution that is searching for a certain amount of consistency, routine, and confidence. Once alleviated from data entry duties, analysts can apply their talents to deeper analysis on each manager investment. By automating some of the more obvious and baseline levels of analysis on a portfolio intelligence platform, an analyst gains the freedom to focus on more research, construct more deliberate questions, and strengthen the performance of the institution.
Check out how SEI Novus approaches data management and portfolio analytics in the video below. Interested in learning more? Let's chat.
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