Page 64 – Top1000funds.com

Innovation in asset allocation and portfolio construction will come from creating the right structures to allow investment teams to flourish, the Top1000funds.com Fiduciary Investors Symposium heard.

University of Toronto Professor of Finance Redouane Elkamhi told the symposium that he is “a big believer that structure drives everything”.

“People realise that asset allocation matters way more than security selection. You [could] be like a genius at choosing stocks or bonds, but then … if you lose 40 per cent of the portfolio [that] is dramatic,” he said.

“What have we noticed after that? We have noticed a rush toward doing some new structure thinking,” he said.

“You have to give credit to thoughtful people trying to basically think outside the box and say we need to give possibility to management tilting portfolios and to make asset allocation value-add.”

But he noted that “what you want people to do and what you design them to do are two different things”.

“You want people to be able to do asset allocation tilting, and you also want them to do some security selection.

“That’s really what you want them to do. You have to ask, do you [really] want them to do that? There’s nothing wrong, you may actually decide no, I don’t want them to do that.

“It’s a choice. You agree it’s a choice? It doesn’t mean it’s wrong.”

But Elkamhi said asking investment professionals to do one thing but putting them in a structure that doesn’t allow them to do that – or which encourages them to do something else – is counterproductive. He said it’s like taking a kid to a playground and then telling them they can’t play on the slide.

“That’s a bad design,” he said.

“You design the sandbox for which they can play, but then you let them play. Play means invest. Don’t design the sandbox, tell them you can play but you don’t really let them play. And I noticed this almost in every fund.

“If you believe in asset allocation, hire the CIO that can do it. And if you hire a CIO that can do it, give them a structure that allows them to do it without screwing everybody else if they get it wrong. And that’s an important design.

“There are some approaches people are thinking about it. It’s going quite well, hopefully things are moving. But that’s what I think [of] as new thinking in the pension industry in terms of structure.”

Elkamhi said the key to innovation was bringing together two different types of thinking – one scientific and often largely theoretical, and one more like engineering, rooted in real-world solutions.

“An engineer is making something work, but doesn’t have to find the law of physics,” he said.

It’s the scientist’s job to define the laws of physics, “but you need an engineer to make something out of it”.

“This is something I think missing a lot in the asset allocation – it is not missing in risk management, by the way, or pricing models – but in the asset management industry, you almost don’t see this bridging the gap easily between what I call an engineering-type of structure…and build-it-on-paper or the academic trying to find the best optimization, the best way to do things on the portfolio construction,” he said.

Elkamhi said you “need a brain” to bring the two sides together, because an approach that is little more than crystal-ball gazing, or “you have somebody who just says ‘no, I have a feeling’, then the discussion, the communication can never happen”.

Elkamhi said it’s important for pension fund boards to “create an environment that allows scientists to be very open minded about what they do. But [to solve] the problem you need to have an engineer that understands what they do”.

“In my opinion, to flourish [you need] collaboration between the way of thinking about things as a scientist, and the way of thinking about this as an engineer.

“It means don’t create silos, create collaboration.

“You have to think of structure way more deeply. And I don’t think anybody yet solved that problem, because they just switch from one to one. And remember, there is no perfect solution for everyone. It’s just the consistency there is a problem between the incentive what the Board want [and] the structure you put in place.”

Elkamhi that innovations in asset allocation also depend on being able to translate discoveries in academic research into practical real-world solutions.

“One of the things I noticed personally through the years is that few research papers found adoptions in the asset allocation space.

“You have almost like a tale of two stories,” he said. On the one hand, you have academics undertaking research largely for the sake of research, and on the other, you have professional investors trying to create portfolios that are both resilient and which meet the return targets needed to support pensions.

Elkamhi said the role of a researcher in a university is to “get tenure and push the boundaries of research”. They do not need to concern themselves with the practical applications of what they discover.

“That’s not part of the objective function of research,” he said. “But that doesn’t mean it’s not useful.

“We need to spend some time to understand how to make them useful. And this is really what we’ve been thinking about.”

Elkamhi said there is “very good innovation in the academic literature that actually can find very useful application in the industry”, it’s just a question of identifying it and then adopting it effectively.

“You just need a way to bridge the gap between what have been developed in all the academic research.”

Computing power has advanced to the point that the once-impractical process of reinforcement learning is now a viable tool for asset owners, the Top1000funds.com Fiduciary Investors Symposium has heard.

Reinforcement learning trains software to make decisions by mimicking trial and error and is used in investment decision making to generate the best potential result.

John Hull, Maple Financial chair in derivatives and risk management at the Joseph L. Rotman School of Management, told the symposium that reinforcement learning has several advantages and outperforms simpler modelling approaches.

“It gives you the freedom to choose your objective function – it’s a danger with some of the simpler hedging strategies and so on that you’re just assuming good outcomes are as bad as bad outcomes,” he said.

“You can choose your time horizon, tests indicate that it’s robust… and gives good results during stress periods and there’s a big saving in transaction costs. Why are we talking about it now? Well, because computers are now fast enough to make it a viable tool.”

Hull said reinforcement learning techniques can reduce transaction costs by as much as 25 per cent compared with traditional hedging approaches.

“It’s a way of generating a strategy for taking decisions in a changing environment – you’re not just taking one decision, but a sequence of decisions,” he said.

“Perhaps you’re taking a decision today and then you take another decision tomorrow, and so on. Let’s suppose you’re interested in a strategy for investing in a certain stock and say what’s a good strategy for this stock – I think it’s going to work out okay, but it may not. What strategy should I use over the next three months. What do you do?”

Hull said normally a stochastic process – which assesses different outcomes based on changing variables – would be used to assess a stock.

“It’s uncertain how the stock price is going to evolve and you might use a mathematical stochastic process, you might use a historical data on the stock price behaviour, something like that. You have some model for how the stock price behaves,” Hull said.

“Then your problem is defined by what we call states/actions/rewards.”

Hull said the aim is quite simply to decide what action should be taken in each possible state to maximise the expected reward.

“You’d say okay, we don’t know how this stock price is going to evolve but it will evolve in some way, and so there will be certain states we find ourselves in. We should take a certain action, and that’s what we’re trying determine, and there will be a certain reward,” Hull said.

“In other words, you’ll make a profit or a loss. The way I think about it, it’s just sophisticated trial and error.”

This means by starting off with having “no idea at all” about what a good action to take is and to then try different hypothetical outcomes.

“It works well or it doesn’t work well, then you try a different action and so on and then eventually you come up with what seems to be the best action to take when a particular state is encountered,” Hull said.

Hull said reinforcement learning traditionally is computationally expensive, takes a lot of computation time and is “data hungry”, but that’s not the case these days.

“But fortunately, the other thing that’s happened that makes this a viable tool… is that we can now generate unlimited amounts of synthetic data that’s indistinguishable from historical data,” he said.

“You collect some historical data… maybe a couple of thousand items of historical data [and] you can generate as much synthetic data as you want to that is indistinguishable from that historical data.”

Hull said that while his experience has mostly been in applying reinforcement learning to the hedging of derivatives, he noted there’s many other areas where it can also be applied.

“Because really it can be applied in any situation where the goal is to develop a strategy for achieving a particular objective in changing market,” he said.

“There’s something out there that’s going to change in a way you don’t know, and you have to model that.”

Financial Innovation Hub, or FinHub for short, carried out the research that Hull prestned to the symposium.

Hull said one of the distinctive features of FinHub is that it’s not just academics within the Rotman School of Management that work on its projects, but also practitioners and the university’s engineering faculty.

Reinforcement learning is just one of the projects FinHub has been working on, with Hull explaining the centre has also been doing work on natural language processing, amongst other initiatives.

“We’ve worked with the Bank of Canada on monetary policy uncertainty,” Hull said.

“We’ve done work on modelling volatility services and using natural language processing to forecast different market variables.”

A rigorous governance process needs to be at the top of investors’ minds if they wish to have a portfolio management approach that is versatile enough to adapt to changing investment environments and still provides sufficient accountability, according to three prominent pension investors from Canada, the US and the UK.

Despite pension markets’ varying levels of maturity, the Top1000funds.com Fiduciary Investors Symposium in Toronto has heard the goal of combining portfolio resilience with meeting fund objectives is the same, and it can be achieved through different manifestations of governance structures.

The State of Wisconsin Investment Board (SWIB) head economist and asset and risk allocation chief investment officer Todd Mattina said governance plays an important role in setting the overall fund objective.

SWIB has close to $150 billion in assets under management, of which the majority is the Wisconsin Retirement System, which funds approximately 667,000 participants – or one in 10 Wisconsinites, to put the number into perspective.

The fund’s liability is dividend-based – a pensioner receives a benefit on retirement, then accrued dividends over time – and the dividends are a function of SWIB’s average rate of return, Mattina said.

“There’s a risk-sharing that involves the pensioners and the system. This has allowed us to keep the system fully funded over time, which is quite unique Stateside,” he said.

“To the extent that we make average returns over a discount rate that’s set in the law, our pensioners receive dividends. To the extent that we have average returns below that key threshold, we actually claw back benefits.”

To that end, SWIB has the objective of achieving a long-run rate of return of 6.8 per cent a year to keep the system fully funded and provide a stable dividend.

“Our asset allocation has an explicit allocation to policy leverage, which is currently 12 per cent of the fund. That’s approved by our board just like the allocation to public equities and private markets.

“What that allows us to do, essentially, is achieve our 6.8 per cent target rate of return while [being] able to leverage up a more efficient portfolio, which includes a significant amount of fixed income – we have 19 per cent allocation to TIPS [Treasury Inflation-Protected Securities] – and gives us some of the resilience factors.”

Accountability first

Meanwhile, the UK’s Local Pensions Partnership Investments (LPPI) chief investment officer Richard Tomlinson said for him, one of governance’s crucial roles is providing accountability in investment teams.

LPPI is a part of the bigger Local Government Pension Scheme (LGPS). In 2015, the UK government began a process that saw individual LGPS funds (state and local authority pension funds) gathered into larger pools for purposes including cost reduction, and created seven consolidation vehicles, of which LPPI is one.

LGPS collectively has £360 billion ($400 billion) in assets and 91 underlying funds, and LPPI manages approximately £25 billion ($32 billion) and has three clients.

“We have a formal way [of governance] in the UK… which is the three lines of defence model so that the risk function does not report to me as CIO, it’s segregated through a CRO [chief risk officer] and we try and work in partnership,” he said.

“The governance very much runs through how we operate from the way we’re structured.

“For our clients, they know who’s accountable for their portfolio performance, which is basically myself and the investment team, as opposed to some other pension structures where you have this fragmented governance structure where it’s not always clear where accountability sits.”

Tomlinson said he has been enjoying the different lenses this governance structure brings to the portfolio.

“I really like having a segregated risk function, who think completely differently to me, because I’ve worked in places where the biggest risk factor has been the grouping of the lead PM or the CIO,” he said.

“You find they’ve built all the models, they’ve built the risk architecture, and there’s a glaring commonality of the way they’ve built it.

“It leaves you open to a certain assumption, so having that diversity of thought and model to me is really, really important.”

Tomlinson said of the seven consolidated vehicles, LPPI is the one closest to the Canadian model – this is in relation to internalisation, access to private markets and the fiduciary model.

Forms of independence

However, even within the Canadian model there are differences in governance models. British Columbia Investment Management Corporation (BCI) vice president and head of investment risk Samir Ben Tekaya said the fund, unlike its Maple 8 peers, doesn’t have a standalone CIO.

BCI has C$233 billion in assets under management and 80 per cent of that is from pension clients; the rest consists of insurance company money. Over the past eight years, Ben Tekaya said BCI has been internalising more assets and moving into more complex strategies.

“One of the bases [of the strategy] is investment process governance, but also risk management,” he said.

“We don’t have a CRO per se. We have me, I’m head of the investment risk, which I report to the head of strategy and risk, which is portfolio construction. Our CEO is the CIO.”

BCI operates with a dual-accountability model which makes it accountable to both clients and the BCI board. The seven-member BCI board consists of four directors appointed by four largest pension plan clients and three are appointed by the Minister of Finance (two of which need to be client representatives).

“So some people ask governance-wise how independent we are, Ben Takaya said.

“But I think at the same time, if you have the right governance, the independence is there – we don’t need to be independent in terms of reporting, et cetera.

“You have the governance, you have the exposure to the board of the client, and the BCI board. But what helps is we are the same group as the portfolio construction and asset allocation, and it really helps to have the culture of risk there.”

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.”