Successful pooled schemes such as Border to Coast should be open to other clients because they are good at what they do
Asset pooling by Local Government Pension Scheme (LGPS) funds, seen as a means of reducing the costs of asset management, was first formally suggested in 2015 by the then UK chancellor of the exchequer, George Osborne. Plans for each pool were finalised by early 2018 and eight pools were created. In the subsequent five years, each pool has absorbed at least some of the assets of most of their LGPS fund clients. Despite this, UK ministers have recently criticised pooling for not moving fast enough. While pools each year publish data to prove claims of great success through huge fee reductions, these are met with scepticism.
This is a shame, because my experience of the pools has been extremely positive. So I want to set the record straight in this article, which relates to what I think is the first independent and quantitative study of the asset management costs of pooling and, specifically, the Borders to Coast Pension Partnership (BTC).
Asset management costs
In 2022, BTC invited ClearGlass to benchmark the asset management costs of the products it produces for its LGPS fund clients. This process involved BTC giving Cost Transparency Initiative (CTI) and performance templates to ClearGlass for each of its products. This is generally a simple process, but there were complexities involving the attribution of central BTC costs to the underlying costs of external managers, which also had to submit CTI templates, to build aggregate templates. We got past this and built a bank of BTC data with which we could make like-for-like comparisons with the large database ClearGlass holds on institutional fund cost and performance data.
Since ClearGlass launched in 2019 it has collected data on almost 1,000 UK defined benefit pension schemes ranging in size from less than £10m (€11m) to over £50bn, with a combined value of more than £1trn. Data came from over 530 different asset managers, in portfolios of all sizes, and data has been segmented into 44 pooled portfolio strategies and 12 segregated portfolio strategies.
Each strategy has sufficient data to generate accurate scale curves across the full range of portfolio sizes, a total of over 30,000 portfolios. Data is granular and each data point is always deal-specific. This is an important point as the CTI templates require client-specific deal data and are rejected if managers insert only share-class level data. In other words, rebates, indirect fees, implicit costs, and fees paid are captured and checked. The database is curated to ensure like-for-like strategy matching.
With this data we can do interesting things. The first is to compare the data we received from BTC, strategy by strategy, to our database. And the results were interesting. Every strategy run by BTC had costs that were better than the market median and therefore could be classified as good value for money, with most also being better than the best-quartile and therefore classified as excellent value for money. We have worked with over 1,000 schemes, including some LGPS funds, and have not seen similar success elsewhere.
But is this not to be expected? Scale counts for a lot in asset management and BTC is huge, with more than £40bn in assets under management, and the basic use-case of pooling is to derive benefits from scale. However, our benchmarks are scale adjusted, so by every measure BTC is doing well.
Unique fee benchmarks
We can create a unique fee benchmark for each asset owner. We do not look at the total size of a scheme, because two funds of the same size are rarely comparable as they have different asset allocations and use different strategies. Because fees are dependent on the strategies used, the size of the portfolios, and whether the portfolio comes from a pooled fund or is a unique segregated structure, total costs will vary. To give a simple example, a £1bn scheme with a 30% allocation to private markets will have much higher costs than a £1bn scheme with a lower allocation to private markets. Instead, we apply our benchmark fees to each portfolio and weight each fee according to the asset allocation.
For BTC the results were, frankly, incredible. Given its asset allocation and portfolio sizes, it should carry a total ongoing charge of 42bps. We calculated instead that BTC was only paying 16bps, a ‘saving’ of at least 26bps, or £121m per annum. That is £121m that BTC is saving its client LGPS funds each year and in perpetuity. Compound that value up over 50 years or so and imagine what it does to the asset values of underlying clients. Deficit wiped out, to put it bluntly.
What do I mean by ‘at least’? The 42bps ‘unique fee benchmark’ we calculated was based on portfolios of the size that BTC can build, which are huge. How much would be saved if we created a benchmark fee level, with the same asset allocation, but based on the portfolio sizes that a typical LGPS fund would deploy? For this we scaled back portfolio sizes to roughly one twentieth, which is where a £2bn LGPS fund would operate. Under these circumstances, the benchmark fee level would have been 47bps. In this version of events, BTC has reduced fees by an incredible 31bps.
Using actual data from a BTC client, we calculated that BTC had reduced fees on the migrated assets by 25bps.
ClearGlass also built a Fee Savings Index with BTC ranked at number 1, indicating that it has achieved the largest reduction in fees, in relative terms, in the UK. The index also includes larger schemes than BTC, so its ranking is truly remarkable.
How did BTC achieve these supra-normal levels of fee reduction? Basically, it is operating well beyond the limits of its scale and has not only forced external managers to deliver low fee levels: it has also built an internal management model that adds little to the costs of its fund-of-fund or direct fund management model. One external fund manager recently told me: “They really got a fantastic deal, almost unbelievable.”
The explanation for BTC’s negotiating power is manifold.
Firstly, the pool started with a clean slate. It did not onboard managers and spend time rationalising lots of small portfolios. It went to market with notional AUM totals and, having won the deals with its external managers, onboarded the assets from the LGPS funds rather than onboarding the managers that were already being used.
Secondly, BTC built FOMO (fear of missing out) among managers, whom I recall being captivated by the potential of winning multi-billion-pound portfolios to manage, and a negotiating war ensued that was related not just to the scale of the portfolios, but the idea that, in winning, they would exclude competitors.
Thirdly, and probably most importantly, governance and independence played a huge role. My experience of most pools is one of consummate professionalism. Decisions are made based on rational analysis and independent decision-making, which means that managers are picked because they are good.
Finally, and this applies to all institutional asset owners, you can always negotiate on liquid asset classes if you have good benchmarking data. But you can only negotiate with private markets managers up-front. The threat of losing a mandate as a manager if you misbehave or are too expensive is not real in the world of illiquids.
I therefore believe that asset pooling works best if you have a clean slate, excellent governance and are independent and rigorous. This is great news for LGPS pooling, even if there are questions around how to pool illiquid assets. It should also be great news for wider pooling initiatives in the private sector, such as the Pension Superfund and other aggregator mechanisms. It might also mean that large schemes that are good at asset pooling, fee negotiating, and independent manager selection, have a role to play in the wider market. In other words, when will BTC be open to clients other than LGPS because, based upon our analysis, they would do an excellent job?
Chris Sier is CEO of ClearGlass Analytics, an independent cost transparency data provider
Country Report – Pensions in UK (May 2023)
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