PFA Pension Case Study

Firm Profile: Corporate Pension Fund

Products: Analytics, Holding Analysis

PFA Pension is Denmark’s largest commercial pension fund, managing money for multiple company pension plans, and serving around 1.2 million individuals. It had a balance of 607 billion kroner of assets (€81 billion) at the end of 2016, making it one of Europe’s largest pension funds. Senior portfolio manager, Rasmus Bartholdy, has been with PFA since 2003, and has seen the pension fund thrive through challenging market conditions.

The Need to Access Data Quickly

When PFA conducted searches in the past, raw data would be collected from a legacy system and imported into a factor model developed by Bartholdy to assess skill. “The returns are the basic input of the model, but we also extracted some soft data about the investment process, the team and AUM,” he explained.

PFA started talking to eVestment in 2014, and after running the legacy system side-by-side with eVestment, decided to opt for eVestment as its strategic data partner. “Accessing information quickly was a challenge historically. In terms of what we are searching there isn’t a big difference, but now it’s easier to find information, and the update frequency is faster with eVestment. It is about twice as fast to complete similar tasks compared to our previous system,” Bartholdy said.

Manager Selection Process – Reducing 400 Down to 10

PFA needs fast, comprehensive access to data to manage both large and small external mandates, in corporate investment grade, high yield, emerging market debt, global equities, U.S. equities, Asian equities and global emerging market equities. Bartholdy said selecting managers typically involves an initial meeting with management to agree to limits and constraints of the mandate, the process, what should be excluded and how active the mandate should be.

Bartholdy will then go into eVestment and look at a universe, pulling out data, and excluding some outliers and names if they don’t fit. He next will run returns data in his model to separate alpha and beta, and to check consistency. “After this, I have a pretty clear idea which managers may have skill and those that don’t,” Bartholdy said.

Qualitative data about the investment process, and the team are considered next. “If I see inconsistencies – say a fund is labelled ‘quality’ by the manager, but I don’t see any quality focus, that will raise a flag. I like to see a correspondence between the qualitative and the quantitative data,” he said. “Then I ask someone else to have an independent look, and next we meet to agree which strategies are more interesting than others. Then we form a shortlist, and ultimately hold RFPs with due diligence and on-site visits, before making allocations.”

PFA has recently implemented a $1 billion global emerging market equity mandate with two managers. Bartholdy said his team used eVestment to screen an initial universe of 400 strategies down to the final eight to 10 that were invited for the RFP.

Monitoring Existing Managers

After engaging a fund manager, ongoing monitoring of managers and reporting are crucial. “It’s important to understand why returns look the way they do. What is luck versus skill? Where is the risk? Under which circumstances would the portfolio struggle?” he said.

“I look at exposures to risk factors and the extent to which they explain performance. Sometimes you see a manager underperforming because of headwinds from risk factors despite alpha being positive. That’s a skilled manager who is unlucky. It would be wrong to terminate a skilled mandate because of headwinds from risk factors if you think the headwinds are only temporary. You also experience skilled managers that produce abnormal excess returns because of a tailwind from risk factors. That’s a case for trimming a mandate if you think the tailwind will revert to the mean.”

eVestment is used to compare managers against peer groups. “In some cases, the benchmark is not a good yardstick to evaluate a manager, so you must look at a peer group. A relatively illiquid asset class like high yield is a good example of this.”

Advice for Asset Managers

Bartholdy encourages asset managers to have a high level of data discipline. “When I prepare for a meeting with a potential new manager, my data source is eVestment. Without updated data in eVestment my preparation for the meeting is insufficient and the quality of the meeting declines. It’s a lose-lose situation for me and the manager.”

He says that there are dozens of data points he looks at regularly. Up-to-date investment returns are crucial, as are AUM numbers and holdings data is important too. And then there is the “soft data” as he describes it. “How is the portfolio constructed – what is the investment process? Is it a quant portfolio? Is it bottom-up process? Have there been changes in the investment team? All this static data is very important also,” Bartholdy said.

‘All the Information I Need’

Bartholdy describes eVestment as “intuitive and easy-to use” and compliments the platform on its user interface. “You can tell the people who built it have done manager selection themselves. It’s very comfortable to use. Every time eVestment develops a new version, I normally adopt it straight away. It has all the information I need.”

“Accessing information quickly was a challenge historically…but now it’s easier to find information, and the update frequency is faster with eVestment. It is about twice as fast to complete similar tasks compared to our previous system.”

Rasmus Bartholdy, Senior Portfolio Manager

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