Page 13 – Top1000funds.com

For asset owners to stay the course of a long-term investing view, the trick is not only just getting their own investment teams behind the objective, but also making sure their board and external asset managers are aligned.

Otherwise, the Fiduciary Investors Symposium heard, pension investors might find themselves fighting an uphill battle in a market where short-termism is increasingly prevalent.

Mario Therrien, head of investment funds and external management at Caisse de dépôt et placement du Québec (CDPQ), said while it’s easy to outline long-term investment goals in a mandate, the challenging part is making sure that managers stay on track over time.

CDPQ is one of Canada’s Maple 8 pension funds and has C$434 billion ($315 billion) assets under management.

“We try to outline the investment policies, risk appetite, benchmark, post investments and everything [in our mandate], but how do we execute on it? How do we make it live?” Therrien said.

“And also as asset allocators, [we need to decide] what is our tolerance for pain. Because especially in the last 15 years, we’ve seen… really smart teams underperforming markets.

“And we’re kind of forgetting the thesis of first of all, why did we invest [with these managers]? In which environment were they supposed to add value or detract value? Our role, when we go in front of investment committees, is making sure that everybody around the table understands what this is all about.”

CFA Institute chief executive Margaret Franklin said the total portfolio approach, “in its broadest, most philosophical sense” is also an important driver of long-term visions.

“What I call ‘systems thinking’ really manifests itself in a total portfolio approach, putting all the pieces together rather than heuristics or embedded systems that we have – that were developed 30 years ago, partly because between technology, modern portfolio theory, and CAPM [capital asset pricing model], we could put those into place efficiently and cost effectively,” she said.

“Those systems were designed for the previous 30 years’ problems, so 60 years later, we need a new way of thinking about these things in a much more complex world where we don’t have the playbooks.

“I think what it [TPA] does allows for innovation, allows for purpose, and has to necessarily have a long-term view, but it also recognises the importance of the short-term.”

FCLTGlobal chief executive Sarah Williamson said the difference between long-term and short-term investors is that the former thinks about the disruptive forces in the future, and does not make the poor assumption that “the future will be like the past”. FCLTGlobal describes itself as a not-for-profit organisation whose mission is to focus capital on the long term to support a sustainable economy.

“Our shorthand for thinking about this [long-term investing view] is the five Ds of disruption,” she said, these being de-leveraging, demographics, decarbonisation, de-globalisation and digitisation.

There are questions worthy of asking if asset owners wish to evaluate whether they are a long-term focused organisation, she said, such as whether they are formally separated from political cycles, whether senior staff are accountable for the total fund’s multiyear performance, whether they engage with portfolio companies on long-term issues, and whether they use internal charges for key unpriced externalities like carbon.

Keith Ambachtsheer, a pioneer of the Canadian pension model and University of Toronto Rotman School of Management executive in residence, said asset owners also need to generally articulate their investment methods in a more understandable way, which could encourage more long-term practices.

He said organisations should use toolkits such as the Integrated Reporting model, which can help articulate key aspects including purpose, governance, business model, results and strategy of the organisation in a concise way (in that order, notably).

“We have a lot of half sentences about this thing and that thing… it goes on and on,” he said.

“I think what we need to do and practice is an understandable way of describing how you actually invest.”

The rise of artificial intelligence as an actually useful business tool presents multiple issues for asset owners. They must take stock of the impact of AI on the businesses they invest in on the one hand, while at the same time assessing the implications of AI for their own businesses, including making investment decisions.

The Fiduciary Investors Symposium in Toronto earlier heard from Geoffrey Taber Chair in Entrepreneurship and Innovation and Professor of Strategic Management, University of Toronto, Rotman School of Management Ajay Agrawal that at its current stage of development, AI is typically applied in one of two ways.

The first is a short-term, specific use-case approach that enhances productivity by improving an existing process, but otherwise leaves the process largely unchanged; and the second is more systems focused, where entire workflows are reimagined and re-engineered with AI at their core.

APG Asset Management global head of digitalisation and innovation, Peter Strikwerda, said that “the true answer is it’s a bit of a mix”.

“In practice, what you see is sometimes just very small problems in a process on automation, on specific information gathering or analysis or whatever we’re trying to fix, that typically fits the use-case driven approach,” he said.

“We take small areas, but I think increasingly you see that bigger areas, and maybe that you could call that a system-type of approach, are being addressed.

“One example…is the whole process of information gathering, organising, standardising analysing, predicting [and] decision making in private markets, because it’s very different from public markets in terms of data availability, standardisation, quality, et cetera. I’m not really sure if you could call that ‘systematic’, but what I see there is that the width of the usages is broader.”

OPTrust director of total fund completion portfolio strategies Jacky Chen said there are “a few things that I would recommend people to think about” about applying AI to systems and processes in the short term.

“One is how to get started,” Chen said.

“If you don’t get started, you’re never going to be able to accumulate the knowledge to discover what are some of the key workflows. Inaction at this point is not an action because you really have to think about what are some of the early wins. You have to get started in order to accumulate the knowledge, get some skin in the game, in the short term.

“There are already some low hanging fruits that you can really do for you to improve the operational efficiency standpoint.

“You need to get your hands dirty in order to start doing that.”

Chen said that when considering the long-term applications of AI, it is important for asset owners to consider carefully who they’re working with. He said that it is unlikely asset owners will have “a whole division that just building this type of technology”.

“A lot of time you’re going to be buying, and who are the partner[s] that you’re going to work with?” Chen said.

“There’s a bit of competition going on, and once there’s established a first mover advantage, we need to think about who’s going to be the second and the third mover. A lot of time, you have to find a proven winner who has the ability to continue to pivot.

“Internally, you have to remain very nimble and agile in your approach, and externally, if you’re working with a partner on this, you have to remain very cautious about who you’re working with, and continue to pick the right the people that you believe that as it’s continued to evolve…they will be the provider that can help you to reach there.”

PSP Investments managing director digital of innovation and private market solutions, Ari Shaanan, said that PSP, like other asset owners, is currently focused on short-term applications of AI but, echoing what APG’s Strikwerda suggested, is finding the application of AI becoming broader.

“The applications are growing both in breadth and in what you’re able to do”.

“And also in size and scope, it just feels like it’s more and more accessible now,” Shaanan said, which is in part a function of more readily available data.

“Clearly there’s just more data available just being, practically speaking, sold by third parties, vendors that we could all now leverage,” he said. “[It’s] much more practical, easier to get in the door these days.”

Shaanan said there’s a second aspect of AI applications relevant to asset owners focused on generative AI and both large and small language models.

Small language models manifest as agents that can carry our specific tasks, while large language models can be developed to undertake tasks such as research on specific industries, sectors or geographies.

“You can build in an LLM internally to do something like that, and…then run an analysis on fundamentals. And you could run an analysis on how that fits in the portfolio. And you could actually stitch together now four or five or six different agents, and have those working together.

“And I think that’s more and more the world we’re going to head in where it’s not just one answer for everything in one model running, call it portfolios, but it’s many agents that can be stitched together that can be leveraged by analysts and our PMs.”

APG’s Strikwerda said the starting point for the organisation’s adoption of AI is its broader business strategy, and while it’s willing to test AI applications internally it’s also fully prepared to kill off a test if it does not achieve the expected result.

“We look at the application of AI as a means, we judge it, as a means to these ends,” Strikwerda said.

“If you’re an alpha strategy, we look at AI as an opportunity to generate alpha, always combined with data.

“When you look at running index products, it’s maybe not about alpha, it’s about having a more efficient operation to support that.”

“We never approach it from the AI, we approach it from what we are for, our purposes company, and then see how we can apply it…and then try to be able to gather proof points, support that and expand from that,” Strikwerda said.

“Or kill it, if it goes south. That’s also what’s happened.”

Strikwerda said APG’s strategy also includes being leaders in responsible investing, and there are obvious opportunities there for the application of AI because of the state of available data.

“That’s where I see a lot of growth potential, and not yet a level playing field,” Strikwerda said.

“And so the commoditisation…in capital markets, you see that data is very much commoditised to a large extent, [but] in responsible investing that’s still growing.”

The hype and near-hysteria around AI and its potential to revolutionise businesses and industries can be difficult to see past and presents asset owners with a difficult decision: to invest in new, rapidly evolving technology and run the risk of backing something that doesn’t work out in the long term; or wait and see, and run the risk of missing some stellar investment returns.

Identifying the specific impact of AI on businesses is vexing at a time when US companies are already “as profitable as they’ve ever been”, Employees Retirement System of Texas chief investment officer David Veal told the Fiduciary Investors Symposium in Toronto.

“That makes sense at some level,” Veal said.

“The question is, okay, does that mean revert, or is there another leg to this? That’s where this really starts to factor in that some of that thinking is, look, you can do new things or enhance margins even further, potentially. That’s great news for corporate America, which is generally good for the stock market.”

But there will also be industries and businesses that are negatively affected – the benefits of the technology will not be evenly distributed economy-wide, “and it’s worth thinking through what that looks like, as well”, Veal said.

Veal said ERS has an internal public equities team that has been considering these questions in depth for some time, and so far so good.

“We’ve owned Nvidia at size, we’ve gotten this trend right, which is one of the reasons our performance has been as good as it has been,” he said.

“But the question is, how do you sustain that? How do you stay on top of these trends?”

Impact on internal practices

Veal said he also worries about the impact on ERS’s internal business practices.

“A paper out of the University of Chicago last week talked about the fact that AI, Chat GPT, is actually better at predicting earnings than human analysts,” he said.

“Okay, how do you think about that? One of the other conclusions from that paper was the fact that Chat GPT plus a human analyst is actually better than either one individually. That’s something we can work with.”

From an investment perspective, seeking to capitalise on the potential of AI presents a series of risks, not least of which is concentration risk, because recent AI development, at least, is being driven by a very small number of very large organisations, such as Google, Microsoft and, of course Nvidia.

It also presents the downside risk of backing the wrong horses in the AI race. And it presents very clear and present career risk for investment professionals who consciously avoid AI opportunities, take a wait-and-see approach and miss the potentially enormous upside.

“The hardest part is, is where do you make your commitments?” Veal said.

“Do you change the way you commit capital? We don’t have a lot of exposure to venture capital, for example, [but] that’s been by design – we didn’t feel like we have the scale. But does that need to change? Is it too late to change? Something that we are really wrestling with is: have we missed the boat in some way?”

Jennison Associates managing director Nick Rubinstein told the symposium that AI is “at a seminal moment” from an investment perspective.

“AI essentially takes all of the [enabling technology] pieces that we’ve put into place, along with the predictive learning element, and enables us to essentially make predictions, streamline businesses, and potentially augment both top-line growth and cost efficiencies within organisations, as it democratises access to all of this data that we’ve created for decades so far,” Rubinstein said.

“And also it will add incredible amounts of efficiency to processes that previously were incredibly inefficient.”

Take-up will accelerate

The symposium heard earlier that initially the take-up of AI across businesses and industries has been limited, but it will accelerate exponentially as a wide range of use-cases are validated and results become tangible.

“So far, we’ve basically put the building blocks in place, and you’ve seen the growth, especially in companies like Nvidia and what we like to call the picks and shovels providers,” Rubinstein said.

“The cloud companies build the infrastructure, but then you need the applications to run on them. So the way we think about it is looking across industries. Where can these efficiencies be distributed?”

Rubinstein said there are practical applications of AI emerging across economies and often in some unexpected areas, such as agriculture where it builds on already existing automated practices.

“But now the next wave will be in the farming industry,” he said.

“How do you do predictive farming? How do you take inputs of weather patterns of past years? Which areas of your farm crops did better? How do you see those crops and embed all of that intelligence into an industry that used to be an incredibly manual process?”

Other industries such as healthcare, travel and customer service were candidates for AI-driven enhancements, Rubinstein said.

“There’s going to be a lot of diagnosis that goes on. We’re in the early days of measuring returns. And I think [Ajay Agrawal’s] example is very good, which is, can you get a 20 per cent productivity advancement in two years? If you can, you’ll probably make that investment regardless,” he said.

“But if you look over a longer-term framework, and suddenly the impact of that return multiplies and essentially goes geometric, then I think that will knock down the walls of mass adoption across industries.”

AI is even being applied to AI itself, Rubinstein said. Nvidia, has used the technology to cut its own product cycles.

“Product cycles that for Nvidia used to take two and a half years suddenly became two-year cycles,” he said.

“Within the past few years, that’s gone down to a year and that’s because they take the building blocks of what they had done for prior product cycles, applied them to go forward, and suddenly, their time to market was essentially cut by more than half.”

The most underrated area of innovation in artificial intelligence is not in computing, nor is it in the development of algorithms or techniques for data collection. It is in the human ability to recast problems in terms of predictions.

Leading economist and academic Ajay Agrawal told the Fiduciary Investors Symposium in Toronto that it helps to think of AI and machine learning as “simply a drop in the cost of prediction”.

Agrawal serves as the Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management, as well as being a Professor of Strategic Management.

“AI is computational statistics that does prediction,” Agrawal said.

“That’s all it is. And so, on the one hand, that seems very limiting. On the other hand, the thing that’s so remarkable about it is all the things we’ve discovered that we can do with high fidelity prediction.”

Agrawal said prediction is, in simple terms, “taking information you have to generate information you don’t have”. And it’s “the creativity of people to recast problems, that none of us in this room characterised as prediction problems, into prediction” that underpins developments in and the potential of AI, he said.

“Five years ago, probably nobody in this room would have said driving is a prediction problem”.

“Very few people in the room would have said translation is a prediction problem. Very few of you would have said replying to email is a prediction problem. But that’s precisely how we’re solving all those things today.”

Whether it’s predictive text when replying to an email or enhancing investment performance, the supporting AI systems are “all implementations of statistics and prediction”, Agrawal said.

These prediction models reached a zenith in large language models (LLMs), where machines were trained on how to predict the next most likely word in a sequence of words that made up sentences, paragraphs and whole responses.

“If you think about language, let’s say English, every book, every poem, every scripture that you’ve ever read, is a resequencing of the same…characters: 26 letters, a few punctuation marks just re-sequenced over and over again makes all the books. What if we could do that with actions?” Agrawal said.

From LLMs to LBMs

The principles of LLMs (next most likely word) are now being applied to large behavioural models – robots – by training them to predict the next most likely verb or action.

“In that case, we could take all the tasks – think about everyone that you know, every job they do, and every job probably has 30 or 40 different tasks, so there’s hundreds of thousands of tasks. But what if all those tasks are just really sequences of a small number of verbs?

“So what they’re doing is they’re training that robots to do a handful verbs – 50, 80, 120 verbs. Then you give the robot a prompt, just like chat GPT. You say to the robot, ‘can you please unpack those boxes and put the tools on the shelf?’ The robot hears the prompt, and then predicts what is the optimal sequence of verbs in order to complete the task.”

It is, Agrawal said, “another application of prediction”.

Agrawal said that businesses and industries are now facing a “tidal wave of problems that have been recast as prediction problems”.

“So we now are pointing machine intelligence at many of these.

“The problem is, it has come so hard and so fast, that people seem to be struggling with where do we start? And how do we actually point this towards something useful?”

Agrawal said it pays to be very specific about the metric or the performance measure that needs to be improved, and then “[point] the AI at that”.

“AIs are mathematical optimisers, they have to know what they’re optimising towards,” he said.

“If the problem is a tidal wave of new solutions available, and the problem is we don’t know how to harness it, here is a way to think about the solution – a short-term and a long-term strategy.”

Agrawal said short-term strategies are basically productivity enhancements. They’re deployable within a year, aim for 20 per cent productivity gains, and have a payback period of no more than two years.

“And here’s the key point, no change in the workflow,” he said.

“In other words, it’s truly a technology project where you just drop it in, but the rest of the system stays the same.”

Genuine game-changers

Long-term strategies take longer to deploy but they’re genuine game-changers, offering gains 10 times or more greater than short-term deployments. But critically, they require a redesign of workflows. Agrawal said AI, like electricity, is a general-purpose technology.

A useful analogy is when factories were first electrified and started to move away from stream-powered engines.

In the first 20 years after electricity was invented, there was very low take-up – less than 3 per cent of factories used electricity, and when they did, “the main value proposition…was it will reduce your input costs” by doing things like replacing gas lamps.

“Nobody wanted to tear apart their existing infrastructure in order to have that marginal benefit,” Agrawal said.

“The only ones that were experimenting with electricity were entrepreneurs building new factories, and even then, most of them said, ‘No, I want to stick with what I know’” in terms of factory design.

But a few entrepreneurs realised there was a chance to completely reimagine and redesign a factory that was powered by electricity, because no longer was it dependent on transmitting power from engines outside the factory via long steel shafts to drive the factory machinery.

When the shafts became obsolete, so did the large columns inside the factories to support them. And that opened the door to lightweight, lower-cost construction, and factory design and layout changed to having everything on one level.

“They redesigned the entire workflow, Agrawal said.

“The machines, the materials, the material handling, the people flow, everything [was] redesigned. Some of the factories got up to 600 per cent productivity lift.”

Agrawal said initially, the productivity differences between electrified and non-electrified factories were very small.

“You could be operating a non-electrified factory and think those guys who want the newfangled electricity, it’s more trouble than it’s worth,” he said.

“But the but the productivity benefits just started taking off from electricity.

“Now we’re seeing the same thing with machine intelligence [and] the adoption rate of AI.”

This one learns from us

However, Agrawal said the characteristic that “makes AI different from every other tool we’ve ever had in human history, is this the only one that learns from us”.

He said this explains the headlong development rush and the commitment of so much capital to the technology.

“The way AI works is that whoever gets an early lead, their AI gets better; when their AI gets better, they get more users; when they get more users, they get more data; when they get more data, then the AI the prediction improves,” he said.

“And so, once they get that flywheel turning, it gets very hard to catch up to them.”

Agrawal said AI and machine learning is developing so quickly it’s virtually impossible for companies and businesses to keep up, let alone implement and adapt.

“The thing I would pay attention to is not so much the technology capability, because obviously that’s important and it’s moving quickly,” he said.

“But what I’m watching are the unit economics of the companies who are first experimenting with it, and then putting it into production,” he said.

“Cost just keeps going down because the AI is learning and getting better. And so that, like my sense there is, just pay very laser-close attention to the unit economics of what it costs to do a thing.

“And you can go right down the stack of every good and service watching how, when you start applying these machine intelligence solutions to that thing, do the unit economics change?”

The Canadian pension fund market has grown to around $4 trillion in value and is dominated by eight major players – the so-called Maple 8. But that hasn’t stopped a high degree of innovative and flexible business models, all built on the considerable groundwork undertaken by those who went before them.

The Top1000funds.com Fiduciary Investors Symposium in Toronto heard that the endgame for any fund in terms of its structure and processes and its path to get there depends on the starting point.

Barbara Zvan joined the University Pension Plan (UPP) as chief executive in 2020 when “literally, at this point, they had a plan text, and they didn’t really have regulatory approval yet, and there was, I kid you not, not a stapler to be found”.

“There was nothing,” she said.

“I had 24 great years at Ontario Teachers, apparently working with a bunch of legends and lots of great colleagues. I was very comfortable with what $100 billion or $200 billion looks like. UPP is $12 billion – I’m missing a zero. It’s a lot harder to manage $12 billion than $120 billion because everything costs 10 times more when you look at it as an MER. So I called 18 friends.

“I started saying, so what do you do? And what do you have? What have you been able to do? I also called CEM [Benchmarking]… and used some of the product data. And you saw a real differentiation: after $10 billion they started doing some privates and things.”

Zvan said early decisions included what aspects of investment management should be internalised, what should be external, and how should external managers be assessed.

“At the top of the house in terms of research, knowing that I will have a complex portfolio of liabilities and also from the study of talking to 18 peers, not a lot of that group did asset liability work or risk analysis,” she said.

“We made an explicit investment to do that internally, ourselves.”

Zvan said that while she was at Ontario Teachers, she oversaw the establishment of three different risk systems, and given the volume of data the fund held, the last one took six years.

At UPP she is “putting in Aladdin in a year”, she said.

“There are so many more tools that you can leverage today.”

Logical and progressive

Zvan said it was critical to build systems and processes in a logical and progressive way and not try to do everything at once.

“Have you ever watched a crab walk across a beach? Look at the legs, they don’t all move at the same pace, but it gets to a direction,” she said.

“So really when building UPP, that is the balance between what do I need to build now, which controls first, what depends on what we’re doing, and we’re taking that approach.”

Zvan said there were five principal enablers in building UPP into an effective pension organisation from a standing start. She said the first was staffing, and “making sure that talent is incentivised, it’s innovative and it can come with ingenious solutions, and there’s a good culture”.

The second was appropriate systems and controls, so that if a university wanted to conduct due diligence on UPP before committing staff pensions to it, “we wanted excellent marks”.

“They’re giving us their money, the members are giving us the responsibility for their pensions, we want to make sure we have the right controls,” Zvan said.

Zvan said UPP spent considerable time on the third enabler, namely developing the right structure, including governance. At the outset “not only there was no organisation, I can tell you there was not even a delegation to management, there was not one committee mandate, there was no board operating guidelines”.

“We had to build all that framework of how this board would work,” she said.

Compensation was also critical as the fourth enabler, and Zvan said UPP wanted to ensure “we had the right incentives in place”. She said the organisation hired around 200 people and “for the vast majority of them, we had no incentive program yet”.

“I gave [them] a letter, some people can attest, [saying] there is a salary, this is your first bonus, trust me,” she said.

“That was that was the extent of it.”

And finally, UPP was able to leverage technology as an enabler. Zvan said technology had advanced in leaps and bounds since the early 90s.

“One reason why we could get up so much quicker is, one, we had the playbook; but two, we leveraged technology completely,” she said.

“We were in the middle of COVID, we did cloud software as a serious service. We don’t code one line of code today, we just move data around and we leverage and try to be good buyers in that area.”

The $113 billion Investment Management Corporation of Ontario was formed in 2016 and chief executive Bert Clark told the symposium that “I don’t think anyone can understate the work that that Claude [Lamoureux] and the early legends, pioneers, of the Canadian model did to establish the importance of good governance”, the benefits of which funds today continue to reap.

“I still think that’s the bedrock of what we do,” Clark said.

“It gives us operational freedom; ideally, lack of political interference – well, there’s always that perpetual risk [and] we’ve seen it recently.”

New avenues open

Clark said developments in the industry mean the newer funds have some new avenues open to them that weren’t as readily available to older funds established many decades ago.

One such theme is cost efficiency, Clark said.

For some older funds, cost efficiency was captured by internalising investment management and building out large teams.

“But when [they] were internalising, Brookfield was $20 billion; today, Brookfield is a $1 trillion,” Clark says.

“There’s nothing alternative today about private assets, they’re half our portfolio and probably half of others’.

“For us, there’s just a very different calculus today about how we get at cost efficiency. We actually made a very deliberate decision not to build big internal teams, but to still target cost efficiency through partnerships.”

Clark noted that “it makes no sense for us to try and compete with [asset managers like] Apollo or Antares or Aries”.

“What we need to be doing is figuring out how to partner with them on the most efficient terms because they have origination and value add capabilities that we just cannot replicate,” he said.

“That’s awesome. We have we have a different size, frankly, than a CPP, or a [Ontario] Teachers, even today. I think the spirit of cost efficiency is something that that is still very much alive and well.”

Clark echoed Zvan’s comments about advances in technology and the options and flexibility it presents to funds today.

“When I arrived at IMCO, I had no email system,” Clark said.

“I used to get [told] ‘we promise we’ll pay you’ and they paid me in checks. Literally, I remember I got a check and had to go to the [bank]. I hadn’t done that in a long time.

“And in five years we put in an email system, in less than five years actually, an HRIS system a risk system, a total portfolio system, a custodian. In fact, we’re on our second risk system, and so to me there are there are massive advantages we have in starting today and massive cost advantages that we can access through actually outsourcing, as opposed to internalisation.”

Clark says cost efficiency arises “by having operational latitude to make the right decisions between internal, external, paying people the right level or paying external managers”.

Investment and operational innovation

Clark says that from the outset, Canadian funds have demonstrated high levels of investment and operational innovation

“They were…doing swaps, they were investing in private assets. They were some of the early bidders on infrastructure when only a couple of bidders showed up to buy things like [Highway] 407, where you’d now have a lineup to buy it, or AltaLink.

“There’s probably nothing that’s ever totally new. But I think that spirit of innovation is still something you find in all the Canadian funds.”

Clark says any organisation also needs to understand and operate within its own limitations.

“[Thinking] you’re going to outsmart everyone, as you know, is not a good strategy,” he said.

“We’re very disciplined about saying we don’t have to do everything, and let’s make sure that if we’re trying to do something, if we’re trying to generate that very elusive net value add we have some credible basis for doing that. So [the big things are] the right asset mix, adequate liquidity, avoiding the big bet and only compete where you have an advantage.”

Even though the Healthcare of Ontario Pension Plan (HOOPP) was established in 1960, chief investment officer Michael Wissell said things are being looked at today “perhaps a little bit differently than [they] might have been looked at…in the early 2000s”, including, for example, managing liquidity.

“The entire industry has been tenaciously focused on liquidity all the way along, as an industry we’re realising [that] there was a mentality years ago that ‘cash is trash’, it was out of the endowment model which was [to] be fully invested [at] long-term maximum risk all the time, and over time that would harvest the greatest returns,” Wissell said.

Wissell says that in recent years “you’ve seen a little bit more of an adaptive world, where maintaining some firepower, so to speak, or cash or leverage availability or what have you [and] basically being liquid and being able to take up tomorrow’s opportunity set, actually sowing those seeds in those environments are where you really can meet your returns.”

‘Fewer people than years ago’

A continued focus on operational efficiency and containing costs and enabled by relatively recent advances in technology and processes, and evolving relationships with external asset managers, means that today “you probably can manifest a team that runs with fewer people than maybe we thought years ago”.

“And actually, that has more than just the benefits of keeping the economics controllable,” Wissell said.

“You have to remember every profligate dollar you spend doesn’t compound for the next 50 or 75 years. A profligate dollar spent today doesn’t compound and, as Einstein said, the most powerful force in the universe is compounding.

“But in addition to that, you can create other exogenous problems…if you let your teams grow beyond the ability for them to engage in meaningful work.

“This industry is in evolution. We all benefit by the learnings of the of the funds that came before us [and by thinking about] liquidity and cost containment and how big do you want these teams to be. Are you trying to compete with GPs, or are you trying to leverage GPs? These are elements of the industry that are truly being rethought and reimagined as we go forward.”

Alberta Investment Management Corporation (AIMCo), established in 2008, manages pension funds on behalf of a range of individual clients “and that makes us a little more complicated in certain ways”, its chief investment officer Marlene Puffer, who took up her current role only last year, told the Symposium.

“We don’t have one pool of capital that we can easily manipulate,” Puffer said.

“We can’t easily layer on derivatives and allocate back out to the clients. We can, but it’s not easy.

“As a result, we have pools and we have to deal with some pools we are able to have as open pools for our clients; some have to be closed pools and reissued in different vintages. It’s a pretty complex platform.”

Puffer said she personally had not worked at Ontario Teachers before joining AIMCo, but many individuals there had, including her immediate predecessors, and they “set up AIMCo’s systems and approaches in the image of Teachers, and there’s lots of great things about that”.

“But some things that are make things challenging, because there’s it started out as a focus on total fund,” she said.

“We actually have 17 clients and 32 pools of capital and we actually need to pay close attention to each one of those and make sure we’re delivering what each client actually needs.

“We’re really rebuilding, in some ways, some of that approach and how we tackle our asset management and the systems related to it.”

That means the operational demands of the organisation are quite distinctive, Puffer said.

“We need a team on client relationship management, we need a team that is paying attention, not just at the total portfolio or total fund level, but we need to actually pay attention to each of these clients individually.”

Strategy is everything

Puffer said that “strategy has to drive everything” as the organisation grows and develops.

“The strategy and how we’re internally versus externally managed, all of those things that [other panellists] have articulated so well, are so key,” she said.

“And yet, the starting point matters.”

Puffer said AIMCo started with a significant internally managed direct investment portfolio, which was global and diversified.

“It already is there; I can’t shut that down [and] I don’t want to shut that down,” she said.

But at the same time, the organisation is rethinking and becoming more strategic about the relationships it has with external managers to harness growth and scale, and “getting smarter” about creating “a real strategic partnership model, not just for infrastructure, but for every one of our asset classes, and importantly, across our asset classes.

Puffer said large asset managers “have an incredible global platform, and we want to be a part of that.”

“But we also don’t want to be beholden to them entirely,” she said.

“A model of co-investment alongside direct investing is at the heart of it. And then you back out of that, what does the staffing have to look like?”

Puffer said AIMCo is establishing global offices and expanding from its home base of Edmonton into Calgary, Toronto, New York, London and Singapore, and “part of the reason for that is to be able to hire people with the right expertise and the right level of experience”.

“It’s all driven by strategy,” Puffer said.

“And a big pillar of our strategy today is expanding our diversification globally.

“So it all interconnects. You can’t talk staffing without talking about your business model and strategy.”

Much can be said about Canada’s so-called Maple 8 funds and their pension models which have provided stellar learning materials, with pupils like California Public Employees’ Retirement System (CalPERS) and others around the world.

The collective consists of eight public pension funds with a number of shared characteristics: extensive in-house investment capabilities; freedom from political interference; significant ownership in illiquid and alternative assets; and the ability to offer competitive market compensation for talents, just to name a few.

However, at the Top1000funds.com Fiduciary Investors Symposium, president and chief executive of the C$128 billion ($93.5 billion) Ontario Municipal Employees Retirement System (OMERS), Blake Hutcheson, said the Maple 8 are actually all “dramatically different”.

“Newspapers write about us as though we’re one – we couldn’t be more different, couldn’t come from more different places, and couldn’t have more different structures,” he told the symposium in Toronto.

“So [for] CPP… the state looks after the liabilities, they are an investment arm governed by the Feds with a really long view. PSP is also governed by the Feds, no stakeholders or members per se. They can take a very different view of their balance sheet than we do.

“OMERS, for example, we have 620,000 members, every decision we take is in respect to our own liability, and our liability stream.

“All our asset allocations are to dovetail with how we get or keep 100 per cent funded, and how we make sure that our cash flow matches because today, our outflows are bigger than inflows.”

OMERS currently has fewer than two active members for every retiree and according to the fund’s own estimate, it will have fewer than one active member for every retiree by late 2030s. If member life expectancy increases faster than the fund has assumed in its valuations, its pension liabilities will also increase.

Liquidity matters

“Every place we start is what’s our liability? What do we need to do to pay pensions? How do we stay 100 per cent funded? And how do we construct a portfolio based on our known needs – independent of any other philosophical way to invest?” Hutcheson said.

OMERS’ allocation today consists of 20 per cent equities, 30 per cent fixed instruments (bonds, private and public credit) and, with some leverage, it holds 50 to 60 per cent in private assets (real estate, infrastructure, and private equity).

Speaking of the shift into a higher interest rate environment and what the future holds for investors, he said one “can’t be really wrong and still be right for long”.

“A lot of people were really wrong and still right for a while, and there was a massive transfer of wealth from those who were typically savers to those who were borrowers. Anybody [who] was borrowing was gaining rank against the rest of us, because money was free.

“Now, the day of reckoning is coming for those who are over levered – that didn’t work so well. And for those who have capital, there’s a massive opportunity.”

When the cost of money increases, Hutcheson said it’s more important than ever for investors to “move quickly”. OMERS’ pivot to credit in the shifting economic environment is one such example, he said, and its fixed instrument portfolio is now getting a consistent 10 per cent return.

“We can lend money into assets we know and we understand and get 500 to 700 basis points more than we would have a couple of years ago,” he said.

“Rather than taking the cost of money and making it a negative, you can transform your portfolio to take advantage and make it a positive.”