At this time of year our industry sees a plethora of outlooks covering the world through an investment lens, zooming in on inflation, interest rates, geopolitics and markets. Here I take the less common lens that zooms out on the big picture issues which, through the power of inter-connection, increasingly shape our investment world.

In the ever-evolving landscape of the global stage, the year 2024 holds promise, challenge, and a myriad of unforeseen events. F. Scott Fitzgerald’s notion of intelligence, the ability to hold two opposing ideas and function, resonates as we anticipate both the expected and the unexpected.

As we delve into the coming year, three key focal points emerge, each demanding our attention and thinking ahead: the ascent of systems thinking, the critical understanding of tipping points, and the growing nexus between an increasingly polarised society and new technology that is fostering a destructive post-truth zeitgeist.

Systems thinking: beyond the parts to the whole

The call for more profound systems thinking marks a paradigm shift in how we approach complex challenges. Peter Senge’s wisdom encapsulates this approach: seeing wholes instead of parts, understanding interrelationships rather than isolated entities, and working with patterns of change rather than static snapshots. In this era, success for the investment organisation hinges on comprehending and navigating the broader systems within which we operate.

As we embrace this holistic mindset, a shift in culture and learning becomes imperative. It’s about moving from zero-sum mentalities to positive-sum perspectives, going broader instead of deeper, and fostering collaboration that transcends individual success. The acronym VUCA — Volatility, Uncertainty, Complexity, and Ambiguity — becomes a guiding principle for cultivating vision, understanding, collaboration and adaptability in an ever-changing world.

Expanding our gaze to encompass earth and social systems is crucial. Anthropogenic changes have elevated the significance of earth systems, while increased interconnectedness has underscored the importance of social systems. Understanding these interconnected systems is paramount, as their intricacies hold the key to addressing the increasingly complex business and geo-political challenges.

Tipping points in climate and social dynamics

Tipping points, the moment where change becomes unstoppable, demand our attention. In climate systems, the Global Tipping Points report to COP28 warns that harmful tipping points pose grave threats to our planet’s life-support systems. And they put forward social tipping points as possible defences. We should remember that some tipping points can be positive.

The scientists’ lens is based on data and analysis and a passion to discover truths. This is one segment of our society that deserves respect for its judgements. Look out in 2024 for more influence from climate scientists. Investors need to show more imagination to gauge the power of breaches to Earth system boundaries and understand their effects on the resilience of our financial system.

At the same time, Malcolm Gladwell’s concept of social tipping points underscores the transformative power of ideas and trends once they reach a critical mass. The Global Tipping Points report advances the potential in positive tipping points like the electric vehicle transition shaped by a combination of social, financial, governance and technological forces. The pin-up here is Norway where these factors were aligned – last year 90 per cent of new cars sales there were electric. Marshalling these multiple influences is pivotal for shaping resilient societies and resilient financial markets.

Post-truth realities and navigating the misinformation era

The fusion of technology and human influence has given rise to post-truth landscapes, where appeals to emotion and personal beliefs often outweigh the trust in objective facts. We still have significant climate change scepticism despite the overwhelming weight of scientific evidence.  This erosion of trust in institutional truth-telling is fuelled by new media technologies amplified by AI and an over-promotional culture. Distinguishing truth from untruth becomes an increasingly complex task, exacerbated by the rise of deep fakes and misinformation.

Truth-seeking though remains a powerful force through the human values of humility and curiosity and the aptitude for critical thinking. This lens is what is needed to build the accurate belief about reality that is the essential foundation for any good outcome.

While much of the world still strives for truth, the fusion of different values, misaligned social media and the impact of technology threatens to pull us in the opposite direction. Vigilance is paramount, especially in the context of significant 2024 elections, where the spectre of deep fakes looms in a world where our AI guardrails seem too flimsy to protect the integrity of the system.

Navigating 2024: conclusions and actions

As we navigate the complexities of 2024, the interconnectedness of systems becomes evident. To thrive in this intricate web, embracing systems thinking, recognizing tipping points, and confronting post-truth realities are essential.

And applying systems thinking tells us that collective action across these areas is a multiplier to good outcomes. Being joined-up across people, organisations, countries, and thinking has never been more important.

By putting forward the right vision, understanding, collaboration and adaptability, we can foster a future where the test of our mettle lies not only in expecting the unexpected but also in shaping it.

 

In recent years, Iceland’s Lifeyrissjodur Verzlunarmanna, LV, has significantly boosted the passive allocation in its global equity portfolio.

The strategy, explains CIO Arne Vagn Olsen in an interview with Top1000Funds from the fund’s wintry headquarters, just south of Reykjavík, is a consequence of successful fee negotiations with the likes of index managers BlackRock, Vanguard and StateStreet and deciding to drop active managers because they struggled to outperform. Almost 80 per cent of the global equity allocation is now passive.

“Given the size of the fund and the size of our mandates, we have been able to negotiate and reduce equity management fees to a level we feel comfortable with. Our growing fund size and the increase in overseas investment has given us leverage in fee negotiations.”

LV is certainly growing. The €8 billion pension fund, still open and a hybrid of DC and DB is Iceland’s second largest; it was set up in 1956 and with 180,000 members (around half of the population) will be the largest pension fund in the country in the next decade.

Moreover, LV’s global allocation (45 per cent of AUM to global public and private markets) is significantly larger than peer funds which typically allocate around 35 per cent of their assets outside Iceland.

“The combined assets of Iceland’s pension sector is twice the size of Iceland’s GDP so it’s important for us to invest outside of Iceland to reduce systematic risk in our portfolio,” he explains.

Increasing fixed income

Olsen is also planning to increase the allocation to overseas fixed income, currently around 5 per cent of total assets. The strategy is being driven by forecast increases in volatility over the coming years because of inflation uncertainty and the impact of AI and ESG as well as higher bond yields, he says.

“Nobody really understands the impact of ESG legislation on markets,” he warns.

The boosted allocation is likely to be actively managed and focused on investment grade developed markets, but he also plans to add a high yield allocation and other instruments outside typical investment grade. “We are in the process of analysing and simulating the potential impact on the portfolio from a risk and return perspective. Nothing is decided yet.”

Although it will most likely be actively  managed, he plans to tap the same fee benefits in fixed income as he has in equity. “We don’t have the same amount of leverage to put pressure on our managers in fixed income. But part of the allocation shift will include fee negotiations and our aim is to try and get the same results as we have in equity.”

Managing inflation risk

One of Olsen’s biggest challenges is navigating Iceland’s inflation, currently running at 8 per cent. Although the fund invests in inflation-linked assets (around 35 per cent of the portfolio is inflation-linked whereby the value of the investment rises with inflation) it is unable to hedge inflation risk in its liabilities – beneficiary payments are pegged to inflation and have risen exponentially.

“Our liabilities are growing with inflation and the risk is that we may have to cut benefits if our assets can no longer cover our liabilities. Our ability to neutralise this risk is limited because we don’t have the tools.”

In contrast to the United Kingdom’s BTPS or Denmark’s ATP, LV can’t use derivatives to hedge its liabilities because its size dwarfs the market capitalization of Iceland’s banks, creating too much counterparty risk on the other side of any swap.

“We wouldn’t feel comfortable taking counterparty risk with a local bank to hedge all our liabilities,” he says, observing that ever since Iceland’s banking crisis in 2008 and the sweeping regulation in its aftermath, the banking sector has curtailed business abroad and has much stricter rules around reserves. In contrast, the country’s pension industry is growing, and increasingly international.

One solution could be investing more in inflation linked infrastructure opportunities in Iceland. However, this would require policy makers providing more support to encourage investors into the space.

In other changes, Olsen is considering using more of LV’s tactical asset allocation to ensure the investor is nimble and takes advantage of opportunities. “Tactical asset management will be more important than it used to be,” he predicts. Around 5-10 per cent of the portfolio is currently managed tactically.

LV has also rewritten its rules around rebalancing.

“If you don’t rebalance regularly, it’s not really a strategic asset allocation,” he says. “We will rebalance more regularly, but it will also come down to volatility. We need to be prepared to change our decision around our allocations if the market conditions change and we do believe we are heading into unchartered territory in the coming years.”

 

A recent investment by APG, the €517 billion Dutch asset manager, in a Woman’s Livelihood Bond that provides access to capital for women entrepreneurs in Asia and Africa provides a compelling alternative to emerging market corporate and sovereign debt or DFI issuance.

The strategy also offers a window into how APG’s responsible investment strategy has evolved to incorporate impact – in this case advancing SDG 5 and 13, a nexus of gender equity and climate action respectively. APG began integrating the SDGs in 2015 following requests from its major client pension fund ABP whose beneficiaries work in the government and education sectors. and targets 20 per cent of its AUM in the SDGs.

Singapore-based IIX, Impact Investment Exchange, manages and oversees the loan disbursements to portfolio companies – the underlying borrowers – and is a corporate issuer itself. But APG’s $30 million investment is lower risk than typical emerging market corporate debt because it has developed market government-backed guarantees.

The underlying corporates have similar default probabilities of emerging market corporates, but with the prospect of much higher debt recovery rates, due to the participation of DFIs.

“IIX secured a government-backed partial guarantee from the Swedish International Development Cooperation Agency (Sida), as well as support from the U.S. International Development Finance Corporation (DFC). That lowers the risk for investors compared to other emerging market corporates,” explains APG senior credit analyst and sustainability lead, Joshua Linder.

The investment also has relatively low volatility and little correlation with highly liquid credit investments in public markets. The bond priced with an annual coupon of 7.25 per cent.

In another facet of the strategy, it is easier to direct impact in this kind of structure compared to broad-brush emerging market investments.

“During our due diligence, we profiled each of the portfolio companies to ensure that the expected end beneficiaries align with client impact priorities. We also reviewed IIX’s historical track record for impact reporting, which includes surveys with representative samples of end beneficiaries. The detailed impact reporting – both from a quantitative and qualitative perspective – gives us further confidence,” says Linder.

Monitoring impact is one of the most complex challenges for impact investors. But APG responsible investment credit analyst, Lee Anne Hagel is impressed with IIX’s track record when it comes to reporting.

“As investors in this bond, it is crucial that we have transparency – detailed information on the activities our investment is funding and what is achieved in concrete terms. IIX uses pre-determined financial and social metrics, collects input from the end beneficiaries, and proactively verifies the impact data. In addition to annual financial reporting, we will also receive semi-annual impact reports on the individual borrowers we are lending to and on our investment as a whole,” she says.

Sustainability and digitisation are overarching themes shaping APG’s investment strategy, visible in a Sustainable Development Investment (SDI) Asset Owner Platform, driven by AI technology. The platform, launched in 2020, is designed to deliver on the SDGs and support positive outcomes. It has been created by investors for investors, and is shaped around innovation and cooperation.

Developed together with PGGM, the platform sifts through reams of structured and unstructured data to gauge the extent to which companies’ products and activities meet the SDGs.

The platform scores companies’ products and services rather than corporate conduct, the traditional ESG lens. Enthusiasts argue that SDG scores are better at integrating impact. For example, research shows that some companies with poor SDG scores can secure good ESG scores and ESG ratings can struggle to reflect positive impacts.

Meanwhile other investors like Bridgewater are increasingly incorporating risk, return and impact in a three dimensional model.

 

 

Artificial Intelligence is reaching into almost every facet of the way we live, work, and play as it reshapes society, industries and businesses. The opportunities for investors are immense, but so are the potential pitfalls. As fiduciaries for hundreds of millions of individuals whose retirement savings they invest, AI may be the greatest challenge and opportunity facing the current generation of institutional asset owners. Top1000funds.com takes a deep dive into the world of machine learning and how and why the world’s leading asset owners are embracing AI in their assessment of investments and their own internal efficiencies.

One of the greatest challenges assessing the potential impact of artificial intelligence (AI) on large institutional asset owners is not identifying the things it might be useful for, nor the investment opportunities it may reveal, but first of all defining what AI even is.

The potential for AI to help identify new investment opportunities, to revolutionise whole industries and to streamline processes and enhance productivity has been extraordinarily well hyped. But it’s better at doing some things than doing others and working out whether it’s even worth trialling it in a particular task or function relies on a clear understanding of what it is.

IBM says AI is “a field, which combines computer science and robust datasets, to enable problem-solving”. McKinsey says it is “a machine’s ability to perform the cognitive functions we usually associate with human minds”. Elsewhere, AI has been defined as “a poor choice of words from 1954”.

Jacky Chen, director of total fund completion portfolio strategies at the $C25 billion ($18.4 billion) OPTrust, says AI is “systems, tools and machines that are programmed to think and act or learn like humans”.

“Essentially, [it] is trying to replicate human abilities,” Chen says. “But it’s all mostly done by a machine.”

A central element of Chen’s definition of AI includes the technology’s ability to learn, or at least appear to learn, by refining and revising its own rules or algorithms as it goes along. He says this makes it “quite different” from other forms of technology, even very highly powered computing solutions, that run to a fixed algorithm defined and written by humans.

“That really allows the system to learn from data and improve continuously by observing patterns,” he says.

“That is quite different from some of the traditional techniques [where] you just basically have some rule-based, human-decided rules programmed into the machine and try to make the machine do some things that are human-like.”

The $C244 billion ($180 billion) PSP Investments’ managing director of digital innovation and private markets solutions Ari Shaanan says computing is moving away from deterministic outcomes to probabilistic outcomes. In a deterministic model, a given input is subject to a set of rigid and defined rules and produces a predictable outcome. In a probabilistic model, a given input can result in a range of outcomes – and those outcomes may change over time as the rules change.

“It is actually much closer to the way the human brain works, in the sense that if you give someone the same inputs, if you give them the same core set of environments and contexts, people will react differently, depending on whatever is the day, the time, the hour, the additional thousands of potential inputs in there,” Shaanan says.

“We’re somewhat non-deterministic. You can’t always – sometimes you can but not always – just predict how people work, and software it’s the same thing. AI is that shift away from deterministic code, where if you put in one, press enter, you know exactly what the output is going to be. Machine learning is much more you put in one, and you have a probability of where it’s going to go, but [it’s] not determined, [it’s] not for sure.”

Human and machine interaction

Shaanan says a critical and relatively recent development in AI is how humans interface with it, which is why language-based models, such as ChatGPT, have captured the public imagination.

“You can now have much more natural interactions with models,” Shaanan says. “You can still run deterministic things on the back end – for example, true math problems, or statistical models, regressions, whatever you’re trying to do on the back end. But instead of having to see for, example, data tables, it can be communicated back in language. It just facilitates interactions between people and computers. It’s the next evolution.”

Global head of digitisation and innovation for the EUR508 billion ($549 billion) APG Asset Management, Peter Strikwerda, says APG’s definition of AI is “The ability of computer systems to execute cognitive tasks that require human intelligence, without human intervention”.

APG’s definition of AI is: “The ability of computer systems to execute cognitive tasks that require human intelligence, without human intervention”.

At the Fiduciary Investors Symposium at Stanford University in September last year, Fei Fei Li, inaugural Sequoia Professor in the Computer Science Department at Stanford University, and co-director of Stanford’s Human-Centered AI Institute debunked the notion that AI is a fad.

“One thing I take a lot of pride as a Stanford professor is we’re not in the business of hype, we’re scholars and technologists,” she said.

“My answer to all my friends in investment is that, as far as I can see, this is a genuine inflection point of technology. I’ve been in this field long enough, I’ve seen hype cycles, I’ve seen a lot of misinformation. But I do genuinely believe that AI’s moment has arrived, in the sense that this technology is ready to really transform businesses, to deliver products and services that would really have mass value.”

AI has existed in some form or another for half a century or longer. But the implications of this wave of AI are profound for asset owners. As the ready availability of vast oceans of data combines with exponential increases in computing power, they must obviously understand and assess the impact AI will have on the companies in whose shares they invest and whose debt they buy. But they must also recognise and exploit the opportunities AI presents for themselves as fiduciaries charged with stewarding the retirement savings of hundreds of millions of individuals around the world.

Asset owners embracing AI

“The largest part is and will be investment-related,” APG’s Strikwerda says.

“So that’s our investment department, portfolio management trading. Responsible investing is a very interesting and important area, I think, for AI. But we also see activity in reporting, in risk management, and even some in operations also – for example, clearing and settlement-type of preventing breaches. But I think in the bigger picture, let’s say, the whole core investment process is the starting point for us.”

Shaanan says the biggest impact of AI for PSP will be on the assets held within its portfolios but says developing in-house AI expertise helps to support analysts and portfolio managers in their understanding of the where AI is headed, and its potential ramifications.

“That part is important to PSP and it should be important to everyone,” Shaanan says. “We spent a lot of time actually sharing the knowledge that my team has gained on these AI projects with our investor teams, to think through the impact it could have on the portfolio.

“We’ll share knowledge from our projects, but we’ll also interface a lot with our partners in what they’re doing on AI in their portfolio. And then we’re trying to bring that back again to our investors and actually more just stimulate sort of a PSP-wide level discussion around AI and upskill everybody in terms of knowledge on the topic, how to use it, where it’s valuable, where it can make a difference, where it’s going to impact society.

“We’re really trying to raise PSP’s game in this from a knowledge perspective, more than anything.”

Chen says that AI is currently being evaluated within OPTrust and used initially to manage risk. He says it is particularly well-suited to analysing vast volumes of data and very quickly identifying patterns or relationships within the data, which are then brought to the attention of the fund’s investment teams.

Chen says OPTrust has formed internal working groups to help the organisation understand better how it can harness AI to make it more productive, and to train staff on using AI-enabled tools so that when new applications become apparent it has the internal capabilities to capitalise on them.

“And that is on top of what we have been doing while we continue to use machine learning to enhance our investment process,” he says.

“What we’re essentially doing is using machine learning to understand market patterns, to see whether there’s increased uncertainty in the market that potentially informs higher risk of certain assets. And as a result of that, we can act [on] that information that was summarized by the machine.”

PSP’s Shaanan says the application of AI in any of the fund’s operations must pass a rigorous use-case test and have the clear potential to deliver enhanced returns, lower risk, or lower costs. There are no formal hurdles or thresholds for these measures, except to say the benefit must clearly exceed the cost.

The application of AI in any of the fund’s operations must pass a rigorous use-case test and have the clear potential to deliver enhanced returns, lower risk, or lower costs.

It’s difficult to prove in some cases, and these tend not to get off the ground; in others it’s relatively easy to forecast: one or two better investments made in private markets, for example, can easily compound to tens or even a hundred million dollars of additional return for a fund the size of PSP.

“Those things scale very quickly on the $500 million tickets that we write,” Shaanan says. “That’s why it’s hard to prove it with an exact science of it’s this [exact] threshold; but you do need to be able to say, yes, it’s got potential for hundreds of millions [of dollars] of impact, if you make two better investment decisions.”

Shaanan says there is no shortage of ideas put forward for AI enhancements; the trick is separating out those that will generate the greatest bang for the buck. PSP works in three phases: ideation, incubation, and then – when the use-case is proven – implementation.

Projects create their own momentum. Shaanan says that once his team has worked with one area of PSP’s business and demonstrated results, it often prompts other areas of the business to want the team to do the same for them.

“And that’s a major piece, I call it major idea stimulation,” he says.

“You’ve proven success somewhere, where else could we scale it, where else could we apply it? Yes, absolutely.”

Impact on costs and performance

But as more asset owner organisations take up AI and it becomes part of business as usual, it’s increasingly unlikely it can deliver a sustained competitive advantage. Some organisations may steal a march on competitors, even if only temporarily, as they discover new or innovative applications. But not utilising it effectively will almost certainly put an organisation at a competitive disadvantage.

And there’s another issue, too. As more and more investors use AI to analyse ever-increasing amounts of data in a bid to eke out additional investment returns, their actions will make investment markets more efficient – making it even more difficult to extract meaningful alpha.

Japan’s ¥158 trillion ($1.4 trillion) Government Pension Investment Fund (GPIF) uses AI to improve the selection of active asset managers, addressing the issue of spending tens of millions of dollars in management fees for no or even negative alpha.

The $700 million Abu Dhabi Investment Authority (ADIA) is spending heavily on in-house technology following the realisation that a reduced capacity to generate alpha was linked to a lack of investment in big data and AI.

The technology is being used by the $200 billion Teacher Retirement System of Texas (TRS), where a managing director, Mohan Balachandran describes its use as “a giant leap forward”, and is using it to identify signals found in large data sets that are then passed to a portfolio management team for further evaluation.

And it’s far from only the giant funds that are tapping into the potential of AI. The DKK217 billion ($30.6 billion) Industriens has developed algorithms to support a range of investment-related activities, including optimizing asset allocation, uncovering anomalies in data, performing automated text analysis, minimizing tracking errors and maximize Sharpe ratios. However, the fund’s investment risk and data manager Sommer Legaard cautions that human oversight is still critical, and that “we never use our models or programming without some human validation”.

“Everything we do we try to automate, but we also vet things manually to check if it looks right,” she says.

For asset owners the impact of AI is potentially at least threefold. It presents opportunities to invest in companies that themselves will benefit from the increasing adoption of AI, like semiconductor manufacturers, cloud computing providers, and companies that produce cables and colling systems. AI also has the potential to make asset owners’ internal processes more efficient, thereby lowering costs and improving net returns. And, of course, it facilitates the analysis of reams of data to uncover new investment opportunities and sources of alpha across all investment markets and asset classes.

Failing quickly

APG’s Strikwerda says that around eight out of every 10 trials of AI end up being killed off. But APG is adept at killing them off before the investment has been too significant. It starts by breaking down the potential use case into small, manageable chunks and funding those, so if it becomes apparent an idea is not going to work as it gets built out further “we manage the risk of investing in something that may not be worthwhile in the end”.

“We only scale it up, meaning we put in some serious investments, if we have proven that there is enough value, that it can be done, that it is within certain confines of risks and policies, what have you, or we kill it, if it proves not to work. We’ve done this for about seven years, and we’ve run I think close to 100 initiatives in this methodology.”

Despite the best intentions and brightest initial promise, “sometimes it just doesn’t work”, Strikwerda says.

“It also needs to be adopted in the end internally. If we have something brilliant that nobody needs or wants to buy, so to speak, it’ll sit on a shelf. It’s [about] proving that there is value, proving that it can be done at reasonable investments, costs, risks, et cetera, and having someone at enough senior level owning it.”

“An interesting example of an AI experiment that we ran a while ago…was that we would be able to predict, let’s say societal turmoil, based upon Twitter. Being pensions investors, we typically are on the radar when something happens, and then could lead to bad press.

“The hypothesis was that Twitter would be early signals, and the interesting part was that we were almost there. We used billions of tweets and quite advanced AI to process that and to predict. What we found out, as I flag it always, is that we were able to predict that there would be a fire in Amsterdam, but not exactly where. It’s quite good but it’s not actionable, so we kill it.”

But when it works, it can be spectacular. APG’s work on how individual company products contribute to the UN’s Sustainable Development Goals led to the spin-off of Entis, now a stand-alone business.

“Fast forward five years, it’s a very mature offering which we share with like-minded investors, which has been commercialised to the market – which is not our primary goal, let me be clear there,” Strikwerda says.

“It has to be a healthy financial situation, but we’re not in it for the profits.

“It’s very advanced AI that has been used, it’s been ever-evolving, it’s getting better and better [using] enormous amounts of data, structured unstructured, coming from everywhere. This is a classic and a very interesting one; they produce alpha factors for us. From the same data, we also bring in hypotheses, like if we combine this and this and this, could we find some alpha factors? That has started to pay off, too. This is quite a big one.

“This has become a whole business model, a multi-million-dollar business model.”

Productivity improvements

Sometimes an AI revolution has more humble, though no less impactful, origins. The $A75 billion ($49.3 billion) Rest superannuation fund is taking part in the Microsoft 365 Copilot Early Access Program (EAP), which embeds AI into the Microsoft 365 suite, including those stalwarts of businesses worldwide, Word and Excel.

Rest is one of the first organisations in Australia and one of only 600 in the world to be invited to take part in the program, and the fund’s chief technology and data officer Jeremy Hubbard believes AI is already delivering personal productivity improvements and serving as a good introduction of AI to the fund’s workforce.

“It sets us up for the next phase, which is our Phase Two mode: can we start using Rest data to tailor that model in a way that it’s able to help our business with the context of Rest information – information about our policies, procedures, standards, our systems, et cetera?” Hubbard says.

Hubbard says Rest has built its own version of Chat GPT, dubbed RestGPT, a “little Teams bot using [Microsoft’s] Open AI GPT 3.5 model, which enabled us to give access to all of our business via a very simple interface, being Microsoft Teams, and ability to interact with a Chat GPT-like solution, but using Microsoft’s enterprise security”.

Hubbard says Rest currently has a small dedicated internal innovation team, “but with a broader sort of virtual team”.

“We’re trying to build a community around that AI team,” he says.

“Given it’s quite a small investment at the moment, we haven’t set hard targets that we need to deliver to, but for me, what we need to be delivering to is multiple what we call proof-of-value experiments. RestGPT would be one, and I would say that adoption and usage of it is good, definitely a good example.”

There are other areas where it may be easier to put figures on the value provided to the fund and its members, Hubbard says, particularly in the software engineering space.

“There are some really prime examples we’ve found that we’re just currently experimenting with,” he says.

“If we’re upgrading a development framework, and we have to do a fairly simple rewrite of all the code to work on the new function, we will be able to automate some of those pieces.

“For me, what’s exciting there is we can estimate with our estimating methodology, this is how long it would have taken a team of developers to update, and then we can do the same thing with AI. And we’ll be able to have, I think, a really black-and-white view that this saved us x hours or x weeks, and x hundreds of thousands of dollars. That’s emerging, but that’s another space where I think we can prove very tangible value.”

At this stage, and for the foreseeable future, AI will not be autonomously making investment decisions based on what it learns. One hurdle, among a number, to using AI this way is a relative lack of data to train on. That might sound ridiculous, given how much financial data is created every second of every day, but it pales into insignificance compared to the entire contents of the internet, which is effectively ChatGPT’s training ground.

At this stage, and for the foreseeable future, AI will not be autonomously making investment decisions based on what it learns

Chen says that financial data is “not as rich compared to other areas, especially during some of the stress environments because you don’t get financial crises that often”.

“Those are the periods that really matter to us from an investment program,” he says.

“Something that I think is a great opportunity for generative AI is to help us to build synthetic data, simulated or synthetic data. So what that means is data that is not entirely the same as the one we have observed in the history, but still plausible scenarios that potentially can help us to analyse our investment strategies.

“There is some progress actually being made on this. I have actually read a paper on this recently, that you can actually use simulated data, use AI to generate something that is similar to real data that you can test on.

“That is still, not easy to be doing, because you essentially have to be able to understand the different markets and simulate all this all together. There’s still some ways to go but I think that will be really important for asset managers to have better data and better potential scenario analysis tools,” Chen says.

AI is best understood at least by the public though large language models like Chat GPT. It’s relatable, because it feels human, and it’s “a good way of expressing or showing how capable this technology is”, Chen says.

“That’s why I think a lot of people have been focusing on this. But if you’re really looking at the research that [is] ongoing, there’s many other breakthroughs in AI that is not necessarily large-language models as well.

“Just thinking about how you currently unlock your iPhone, there are a lot of deep-learning analysis involved in detecting your face, and those are things that are probably not as tangible from a user perspective.”

Regulations and ethics

Chen says these less visible developments are eventually as likely to have as great an impact on how large asset owners operate as any of the visible developments to date. But he says asset owners will be subject to the same ethical considerations and regulatory requirements as any developers as they figure out the best uses for AI.

Chen is also an adjunct professor at the University of Toronto Rotman School of Management, where he teaches an innovation course and is engaged with the school’s Financial Innovation Hub, and his research focuses on the applications of machine learning techniques for portfolio hedging, derivatives pricing, and risk management. He says AI undoubtedly is “providing a lot of benefits to us”.

“But on the other hand, it’s important to have the regulatory frameworks and all the ethical guidelines,” he says.

“If you really think about the positive of AI, it’s a lot of ground-breaking innovations that are happening, from healthcare to environmental science. And these advancements are not simply a technological evolution, it’s also actually going to enhance our capabilities and improve our life quality. It’s important to keep that positive going.

If you really think about the positive of AI, it’s a lot of ground-breaking innovations that are happening, from healthcare to environmental science.

“But at the same time, strong regulatory frameworks and ethical guidelines will be crucial. That requires us, as a society, to see collaboration between industry, governments, and also the public. We are all stakeholders in this, in these discussions, and we need to make sure that there is a collaborative effort that helps us to shape the landscape going forward, and we [that] are not just wanting to focus on the innovative side. We need to make it inclusive.”

Stanford’s Li told the Fiduciary Investors Symposium that when the university’s Human-Centered AI Institute was founded “our mission statement did not put a national boundary” around the possibilities for AI, nor around the responsibilities of those that develop it.

“I think this technology is fundamentally universal,” she said.

“I think doing good to all humanity is fundamentally important. The geopolitics today at the human level is reality but it’s also sad, but there’s many things that this technology can do, whether it’s healthcare, or climate, or scientific discovery [that] transcends or geopolitics, transcends national boundaries.

“At Stanford, we’re really privileged. We educate students from all over the world, and we build technology that I hope can be used to benefit people from all over the world.”

Industriens, the DKK 217bn ($30.6 billion) Danish pension fund, is using advanced technology and exploring  AI models to bring sweeping advantages to its risk management processes.

The hope is the benefits will do much more than speed up analysis and end having to manually hunt for errors in Excel. Instead, the technology will allow the investor to optimize its asset allocation, uncover anomalies in data, perform automated text analysis and put in place restraints, for example around ESG at a level impossible to replicate in Excel.

Elsewhere, the technology can support assimilation, minimize tracking errors, maximize sharpe ratios and feed in sentiment analysis, lists Julia Sommer Legaard, investment risk and data manager at the pension fund for the last year, brought in to help bridge the gap between IT and programming, and portfolio management.

“The idea is to develop a few generalized functions in Python, which can be used for multiple purposes,” she says. “We can find errors much faster and check for abnormalities in the market value or duration of an investment, for example.”

Data gathering is step one when it comes to developing powerful models, says Sommer Legaard. Much of her time (she works in a team of eight, including two students) is spent extracting, analysing, and validating data. It is the lifeblood of the models and solutions which provide risk management across the portfolio, spanning everything from duration risk in bonds to investment limits in sectors and countries, credit, solvency, ESG and counterparty risk, regardless of the asset class.

Diving into the detail, Sommer Legaard says successfully building a model involves optimising the code to ensure it can handle different tasks, data types and fields – warning if the data she feeds in is invalid, it scrambles the process.

Once, she recalls, the code in the model couldn’t correctly read the data because it had been set up to search for numbers rather than letters. “You can’t get a good result from the model if your input is not valid,” she explains. Real data is messy – cleaning it can be 80 per cent of the work. “There is a difference between knowing a bit of Python and applying it on real data sets. I always check if the data is valid before saving it to the database.”

Part of her job is outlier detection. Determining if an outlier is valid or not is something that requires human expertise. “We never use our models or programming without some human validation,” she says. It’s easy to think something deviates from the norm particularly during bouts of volatility, and only closer, human examination reveals that it doesn’t. Data drawn from periods of market volatility might have multiple outliers in which only one is valid. It will require looking into each one, she says. “Everything we do we try to automate, but we also vet things manually to check if it looks right.”

For investors new to technology or developing in-house models, she suggests starting slow, and phasing in support around how the code works. “A lot of it is about trust,” she says. It is often hard to trust a model because it is rooted in such complex maths, but she finds comfort by constant back testing to check how well it would have performed.  “Working with programming daily, it is important to be able to explain what your code does and what is behind the models used for AI to make it transparent.”

A key challenge is the shortage of historical data because the model’s demands often supersede the availability of data. Moreover, historical data often quickly becomes out of date because the technology is moving so fast.

Other hazards lurk too. Like the risks of using data at a time regulators, artists and media organizations (amongst others) are increasingly questioning the use and risks of data being consumed by the technology. “European General Data Protection Regulation is a case in point. You must make sure the data you are using is safely secured and that it is only used to feed the model.”

ESG FOCUS

Data gathering to support ESG integration at the pension fund brings another layer of complexity. Issues include navigating mismatches with the model’s fields and data from the pension fund’s external vendors, including ESG data providers. It often results in time consuming excel comparisons and even calculating some numbers herself.

Some of the benefits are already obvious. For example, the technology may be able to help approximate the ESG data points in the private equity allocation. Still, the need for companies to draw on ever larger lakes of data for ESG integration raises important questions around the very process of integrating ESG risk – and if AI goes against the mission of ESG.

“The amount of computer power required to store the data is unsustainable,” she says. “Investors need to minimise their computer power, but the models demand ever more data. We need to take ESG into consideration when we think about future tech advancement.”

As AI becomes more prevalent, she believes retail investors will increasingly harness the technology, potentially muting the investment edge the data is meant to give. She predicts this will lead investors to rebalance more frequently as the market will catch up with strategies faster.

She concludes that success also depends on portfolio teams grasping the technology.

Although many of her colleagues still need encouraging when it comes to programming, she questions whether pension funds really need external companies to develop AI models and applications on their behalf. “It is becoming increasingly easy to use Python so investors might not rely so much on external vendors. It is a question of pushing internally – and I am a pushy person!”

 

 

 

In a year forecast to be volatile, and with the spectre of recession still very much on the cards, Top1000funds.com finds CIOs exploring new strategies, paring back on active equity, investing in technology and wrestling with the many disparate approaches to sustainability.

“We will get through it, even if bad things happen,” said Richard Hall, president, CEO and CIO at the University of Texas Investment Management company (UTIMCO), the $69.2 billion asset manager and of one of the largest public endowments in the US, voicing the uncertainty many investors feel heading into 2024 in a recent board meeting. 

Against the backdrop of contrasting analysis from UTIMCO’s trusted advisors, amongst which JPMorgan and PIMCO predict a soft landing but BlackRock and Bridgewater Associates skew to a hard landing where high rates pitch economies into recession, Hall’s team are maintaining a neutral position – alongside modelling how much the S&P500 could potentially decline should corporate earnings take a pounding. 

UTIMCO’s correlation to equity means that in a worse-case scenario if the stock market falls 20-50 per cent it could equate to an $11-24 billion decline in the value of assets under management. 

“It’s a lot of money,” warned Hall, whose preparation for a recession includes ensuring UTIMCO has ongoing liquidity to make distributions; is not over its skis in terms of capital calls and commitments and has the firepower on hand to invest in opportunities. 

Asking Top1000funds.com interviewees focused on the long-term to share their thoughts on the year ahead is often met with reluctance, given commentary is so quickly out of date. While gathering opinions mid-December, the Fed signalled extensive rate cuts through 2024. But Hall’s advice to “hope for the best but prepare for the worst” in a likely volatile year echoes broad CIO sentiment where others counsel on the importance of diversification and warnings that AI and ESG will continue to fuel inflation. 

Exploring different strategies

With inflation still not firmly beaten, growth elusive, and allocations that worked in the past no longer as effective, asset owners will increasingly integrate different strategies to fit the new economic regime. Take Helmsley Charitable Trust, the New York-based $7 billion charitable trust, now exploring convertible bonds offering bond-like characteristics alongside an upside kicker that is less volatile than equities. 

Helmsley is also hunting buyout opportunities in Japan, opening up thanks to governance reforms that are forcing Japanese companies to embrace efficiencies and accept the tough, hands-on approach these managers deploy. 

In another reflection of uncertainty ahead, investors are boosting their ability to take advantage of opportunities as they arise by increasing and readying their tactical asset allocation. Like the $38 billion Iowa Public Employees Retirement System (IPERS) where CIO Sriram Lakshminarayanan said the ability to tactically invest requires a change in mindset that is rooted in constant communication with managers and their views on the market. 

Although there is a growing consensus that borrowing costs will fall in 2024 (“monetary tightening is over,” declared Timo Löyttyniemi, CIO of Finland’s VER in a LinkedIn post) for now higher rates continue to impact the interplay between cash and bonds. 

For the first time in years, many investors go into 2024 holding more in cash and will continue to do so as long as rates stay high. If recession comes into view and the Federal Reserve and other central banks lower rates, the environment will become better for bonds and worse for cash and asset owners will likely position to benefit from the price appreciation in bonds. 

For now, higher borrowing costs will continue to impact how pension funds approach leverage in 2024 which remains more expensive than in the past and by extension, less beneficial. It’s something front-of-mind at the $71.9 billion Pennsylvania Public School Employees’ Retirement System, PSERS and Canada’s TTC Pension Plan (TTCPP), the $7.8 billion defined benefit fund for employees of Toronto’s public transport network. 

Some investors spoke about paring back on active equity in 2024, arguing it is difficult to pick winners in a market dominated by the leading tech stocks. “We are hoping to implement decent-sized passive allocations by year-end,” said New York-based Geeta Kapadia, CIO of Fordham University’s $1 billion endowment. “I don’t think looking for long-only US equity managers that outperform is a great use of our time. I’d rather take active risk in private markets than in public equities or credit.” 

But even if investors question the value of active equity, there is much talk of enduring and compelling US equity opportunities. Technological leaps like LLM (large language models) powering the AI revolution and the new generation of life-changing drugs will continue to offer unprecedented opportunities in public markets. 

“A rising tide will lift all boats, just be invested in boats, just be invested in equity,” said Charles Van Vleet, CIO of the Textron pension fund, who will run an active stock picking strategy through 2024 but says passive allocations will also benefit from unprecedented corporate innovation. 

“Who is going to reap the benefits of this productivity? It’s not going to go to labour – it’s going to go to the capital providers; to the equity investors, and it’s already priced into the market.” 

Adjustments in private equity

Investors predict a mixed year ahead for private equity – an asset class Norges Bank Investment Management, investment manager of Norway’s $1.5 trillion wealth fund Government Pension Fund Global, hopes it will finally get a green light to invest in after years of petitioning the Ministry of Finance. 

Higher interest rates mean a higher cost of doing business that will continue to impact portfolio companies’ performance and multiples, and may not feed into valuations until 2025-2026. Meanwhile, investors are unclear if exit strategies via IPO and M&A activity will open up in 2024. 

“I have to remind our senior managers and board that returns will be more challenging going forward.  Yes, private equity is the best performing asset from an absolute return perspective, but you must also look at it from a risk adjusted basis, and private equity is the most risk-taking allocation in the fund,” said Suyi Kim, global head of private equity at Canada’s CPP Investments. 

Many asset owners go into 2024 overweight their allocations because of capital appreciation in the underlying programme and GPs sitting on companies because they don’t like current valuations, and don’t want to write them down. It means the year begins with many LPs choosing fewer managers with whom to re-up as well as selling assets in the secondary market. Still, one LP’s challenge will be another’s opportunity. For some, 2024 will open the door to invest with sought-after GPs for the first time and support fee negotiation. 

Recruitment and talent acquisition, particularly around technology, will be a key issue through 2024. At ADIA, tech prowess can be seen in its purest form in the quantitative research and development team and the asset owner says it will continue to recruit globally respected experts in diverse areas such as machine learning, strategy development and portfolio construction through 2024. 

Elsewhere Industriens, the DKK 217bn ($30.6 billion) Danish pension fund, will spend 2024 continuing to explore AI models to optimize its asset allocation, uncover anomalies in data, perform automated text analysis and put in place restraints, for example around ESG at a level impossible to replicate in Excel. Meanwhile the quest to fill CIO openings at US public pension funds including CalPERS is likely to get easier as the tide finally turns in favour of recruiters. 

Will asset owners increase their allocation to China? Investors respond that strained geopolitics, complex regulation, the increasing cost of doing business not to mention the inability to forecast exits makes direct investing in private assets in China challenging. 

Sustainability in 2024

This year may see the term ESG increasingly give way to more coherent themes around sustainability. Elsewhere, investors look forward to applying new tools to support sustainability in their large allocations to sovereign debt. 

The investor-led ASCOR project (Assessing Sovereign Climate-Related Opportunities and Risks) has just published its first independent academic assessment of 25 countries’ climate targets and policies. The analysis offers investors data and insights spanning the extent to which emissions are declining to a country’s regulatory focus. 

Adam Matthews, co-chair of ASCOR and chief responsible investment officer at Church of England Pension Board, believes it is one of the most important sustainability initiatives in years. “Investors haven’t had an academically rigorous, transparent and publicly available holistic lens through which to assess climate mitigation and adaptation in their sovereign holdings until now,” he said. 

Sustainability teams will also spend 2024 wrestling with their approach to emerging markets. For many pension funds on a net zero trajectory, emerging market holdings have a disproportionate impact on their total carbon footprint, explains Mirko Cardinale, head of investment strategy at USS. 

“The easiest way to reduce that footprint would be to sell carbon intensive companies in emerging markets, but this does little for the real world impact. We are keen to see real world change and that’s why we continue encouraging the highest emitting companies we invest in to reduce their carbon emissions.” 

The focus for investors this year will be engagement with national issuers, but also recognising the importance of emerging markets being treated fairly in current frameworks. 

“We need differentiated pathways for companies in emerging markets,” concludes Matthews.