Volatile markets have provided a rich hunting ground and opportunistic best ideas have come thick and fast for AP4’s new five-pronged global allocation made up of systematic equity, currency and rates, asset allocation, hedge funds/external mandates and analysis. Magdalena Högberg explains the risks and opportunities of the best ideas allocation.

Last year, AP4, the SEK548.2 billion ($56.7 billion) Swedish buffer fund, launched a new global asset allocation to harness its rich internal resources to invest more dynamically and shorter-term.

Since then, volatile markets have provided a rich hunting ground and opportunistic best ideas have come thick and fast for the five-pronged allocation made up of systematic equity (including defensive equity and environmental factor strategies) currency and rates, asset allocation, hedge funds/external mandates and analysis.

For example, higher interest rates have allowed AP4 to switch between equity and rates to various degrees. The team have profited from curve bets in fixed income, taking positions on the curve steepening and flattening at points in the year, as well as around long-end US interest rates moving up and down.

Currencies have provided a rich seam like when the usual correlation between the US dollar and Swedish Krona changed: since January this year the Swedish Krona has appreciated around 5 per cent and 12 per cent against the EUR and USD respectively.

Swedish real estate also threw open a window of opportunity in 2023 (a few months before the new asset allocation was formerly established) when it was hit by negative sentiment that caused a dislocation of the pricing in credit markets.

“We reasoned that the expected return in credit would be greater than what we could achieve in equity and sold the equity portion of the portfolio and bought credit instead,” says Magdalena Högberg, head of asset allocation, liquid markets and analysis (ALMA) at AP4 in conversation with Top1000funds.com.

Currencies and rates have provided particularly strong returns, but Högberg says AP4 hasn’t yet revealed first-year returns. Still, although the risk budget is opportunistic and will vary over time, she believes that given what is going on in financial markets, the allocation is coming into its own and she hopes her 20-person team will get more resources as the merger of the AP funds progresses.

The team has covered its open positions and is hiring a quant analyst after the summer.

The mandate captures either relative value trades within an asset class like a best ideas portfolio or takes positions across asset classes where AP4 can provide its long term, patient capital to the market at times of stress, explains Högberg who closely follows similar allocations at peer funds like AP3 and New Zealand Super where a strategic tilting allocation has become one of the investor’s top-performing programs since it was introduced in 2009 contributing approximately NZ$4.6 billion ($4.2 billion) to date.

“The relative value portfolio within the asset allocation mandate provides a way for our internal PMs to add or size up relative value trades,” she explains. “We also complement the idea generation with ideas from a dedicated team of analysts that are supported by our robust internal technical platform to capture the returns from markets behaving in interesting ways, dislocations and/or volatility.”

A typical investment will have a life of between six months to three years. Given AP4’s operational strategic asset allocation is 10 years, these positions are more short-term, but Högberg insists AP4’s edge does not lie in short-term tactical asset allocation and trading. The portfolio is merely dynamic and shaped around a medium-term outlook that takes advantage of cycles.

What are the risks?

The risk of events not playing out to plan is high. Strategies combine a quant fundamental approach with macroeconomic scenario analysis that seeks to map out ways the world could evolve. Positions depend on, say, the Fed lowering rates or inflation being high, and often require catalysts.

Moreover, successful prediction is a tricky business. Witness how although tariffs were widely predicted, the strength of Trump’s policy caught the market unaware.

“Going into April, the risks of tariffs affecting the world in some way we couldn’t foresee was high so we reduced the size of our positions as well as the overall equity risk in the portfolio. After the tariffs were announced we tried to see if the markets had gone too far, and what new positions would be most beneficial to our portfolio,” she recalls.

Another risk lies in hidden correlations. The team looks at positions from standard and statistical risk models to identify patterns to try and ensure positions don’t overly rely on, say, equity going up. Another risk comes from tying-up capital for too long in low conviction trades that utilise the risk budget and could limit dry powder on hand to take advantage of other opportunities that could be more profitable for the portfolio.

“It’s an opportunistic portfolio, so you need dry powder to step in when you see something enticing from a risk reward perceptive. We have to make sure that we have the best ideas available to us in the portfolio,” she says.

Developing an internal quantitative platform

The team has developed an internal quantitative platform that it uses for decision making based on statistics as well as time series modelling that incorporates views, and updates the models as new information arrives in the market. Specific models, or signals, also seek to incorporate things in the macro-economic environment like a spike in recession risk or signals that flag comparisons with other types of market environments.

“The quantitative platform and signals are not something we use to automatically take positions on. It’s more of an idea generation feature that identifies interesting deviations from fair value that we can analyse from a more fundamental perspective,” she says.

The model is also evolving. For example, the platform now complements time series models with one-country models, and the team is incorporating more currency models with a focus on combining long-term models like fair value using purchasing parity with short-term single country models where the team can capture other things driving returns to capture carry signals in emerging markets.

“We want to be more dynamic at identifying the regimes currencies sit within,” she says, explaining that multiple drivers influence currency markets from speculators and corporate hedging strategies to interest rate differentials or geopolitical risk. “The AP4 team has spent a lot of time on developing new, more fast-moving modules for G10 currencies and we are are now looking at ways to better trade emerging market currencies and capture signals in emerging markets.”

Antibiotics are among the most transformative innovations in medical history.

In 1928, a chance event in a London laboratory led to the discovery of penicillin and changed the course of medicine. Since then, these powerful drugs have saved countless lives by combating bacterial infections that once led to severe illness or death.

Antibiotics treat many infectious diseases as well as enable complex medical procedures, such as cancer treatments and organ transplants. They also play an essential role in safeguarding public health and supporting agricultural productivity.

However, overuse of antibiotics is driving the emergence of drug-resistant microbes, making infections increasingly difficult to treat. This leads to longer hospital stays, higher medical costs, and increased mortality.

Antimicrobial resistance (AMR) is a natural process where bacteria, as living organisms, evolve over time to withstand the drugs designed to kill them. The emergence and spread of AMR is accelerated by human activity, mainly the misuse and overuse of antimicrobials to treat, prevent or control infections in humans, animals and plants.

The World Health Organization (WHO) lists AMR as one of the top global public health and development threats. It is estimated that AMR was directly responsible for 1.2 million deaths worldwide in 2019 and that is likely to rise to 1.9 million annual deaths by 2050.

The Review on Antimicrobial Resistance, commissioned by the UK Government and Wellcome Trust and published in 2016, estimated that without intervention, the number of deaths associated with AMR globally will rise to 10 million by 2050 — surpassing cancer as a leading cause of mortality.

They also estimate that $100 trillion of economic output is at risk due to the rise of drug-resistant infections. In the UK alone, the annual cost of AMR to NHS is already estimated to be £180 million due to higher treatment costs for drug-resistant infections.

The main sectors that drive antibiotic consumption are healthcare, pharmaceutical industry, farming and food production. Antibiotics are widely used in agriculture, with much of their global use not aimed at treating sick animals but rather at preventing infections or promoting growth.

The quantity of antibiotics used in livestock is substantial. In the United States, for instance, over 70 per cent (by weight) of antibiotics classified as medically important for humans by the U.S. Food and Drug Administration (FDA) is used in animals. This significantly contributes to the development of resistant bacteria that can spread to humans through food, water, and the environment.

Lack of innovation in antibiotic development is also a contributing factor, as pharmaceutical companies face economic and regulatory challenges. The commercial returns on investment for antibiotics are lower compared to drugs for chronic conditions, which has led to a decline in research and development.

In addition, there are inadequate regulations and enforcement in many countries allowing over-the-counter sales of antibiotics, further promoting misuse.

There is a strong link between AMR and climate change, where both exacerbate each other. Intensive farming practices often lead to soil and water contamination with antibiotics, fostering the spread of resistance genes and disrupting local ecosystems. These contaminated environments become breeding grounds for resistant bacteria.

Climate change also alters ecosystems, creating conditions that favour the survival and spread of resistant bacteria. In addition, rising temperatures and changing weather patterns increase the prevalence of infectious diseases, necessitating more frequent use of antibiotics, which further accelerates the development of AMR.

Addressing AMR aligns with the United Nations Sustainable Development Goals (SDGs), particularly those related to health (SDG 3), clean water (SDG 6), responsible consumption (SDG 12), and climate action (SDG 13).

Sustainable practices in healthcare, agriculture, and environmental management are essential to mitigate the AMR crisis.

Investors can play a role in combating antimicrobial resistance by:

  • Supporting sustainable and responsible practices in industries that contribute to the problem.
  • Investing in companies that prioritise reducing antibiotic use in agriculture and developing alternative methods for disease prevention.
  • Investing in companies focused on developing new antibiotics, rapid diagnostic tools, and alternative therapies.
  • Funding initiatives that prioritise sustainable healthcare and environmental practices. This includes supporting technologies for wastewater treatment and antibiotic residue management.

Investors can also influence corporate behaviour by engaging with companies to adopt better practices. Supporting initiatives that promote antibiotic stewardship and investing in public health infrastructure can also contribute to mitigating AMR.

Antimicrobial resistance is a pressing global challenge that threatens human health, economic stability, and the environment. While antibiotics are a foundational pillar of medical progress, their overuse and misuse have led to the growing threat of AMR.

This challenge, driven by practices in healthcare, agriculture, and environmental management, poses severe risks to public health and ecosystems with potentially significant economic costs.

Addressing AMR requires a comprehensive approach that includes stronger regulations, increased innovation, and global collaboration.

Governments and industries must support initiatives aimed at reducing antibiotic use, developing new treatments, and improving public health infrastructure.

Investors can play a pivotal role by driving sustainable practices, engaging with companies to improve stewardship, and directing capital toward innovation in antibiotics, diagnostics, and alternative therapies. Through collective efforts, we can mitigate the impact of AMR and safeguard the effectiveness of antibiotics ensuring a sustainable and resilient future.

Anastassia Johnson is a researcher at the Thinking Ahead Institute at WTW, an innovation network of asset owners and asset managers committed to mobilising capital for a sustainable future.

 

Jagdeep Singh Bachher, chief investment officer of UC Investments which oversees the University of California’s $198 billion pension and endowment assets, argued the case for investing more in equity and keeping strategy simple at the latest board meeting.

Building on a narrative from previous meetings, Bachher said that his initial concerns that tariffs would have a negative impact on stocks have been offset by the impact of AI on the economic landscape, as well as the US administration’s stimulative polices around tax and deregulation in industries like banking and energy.

Pressure on the Fed to commit to cuts will also stimulate stocks and real estate, he predicted. “I do believe interest rate cuts will be part of the story going forward. Cuts are coming,” he said.

Defence spending, particularly in Europe, will also fan economic growth. “When you add all these things together [it is a reason] to stay calm and ride the equity market,” he said.

Trustees heard how the pace of annual revenue growth for companies at the forefront of AI has never been seen in history. Moreover, the AI revolution is being driven by large companies, which means equity investors stand on firmer terrain than during the dot-com bubble which was led by tech companies that didn’t have robust earnings.

Bachher also stressed the importance of investors “making the most” of opportunities to buy equity when they appear.

“We’ve learnt that a miss is more expensive than anything else. The dips are the opportunities to actually buy. We will have a decline at some point, when that happens, let’s take advantage of it.”

Time to dial down private assets – starting with hedge funds

The strong winds fanning equity markets combined with higher interest rates mean allocations to stocks and bonds are attractive once again. Investors can dial down their allocation to private assets and lower fees and reduce complexity, which is why the university has decided to remove hedge funds from its portfolio altogether. Hedge funds used to account for 10 per cent but have been whittled down over the years.

“We can’t be good at everything, and hedge funds is not one of the things we are good at doing,” he said.

In a swipe at managers, he said that rather than supporting the investor through market dislocations like the global financial crisis and the COVID pandemic, hedge funds actually exposed the fund to drama, illiquidity and high fees.

“I’m all about less is more; lower fees and less complexity and being able to sleep well at night,” said Bachher. “We saw a 17 per cent rise in the equity market this year; 20 per cent last year and 17 per cent the year prior. If you compound at these rates in the equity market, it’s not a bad place to be.”

He explained that when interest rates were zero, institutions invested in private assets to earn a return above bonds but now interest rates have moved higher, the picture has changed. Investors now have a new tool in the box and can afford to take less risk in private assets.

Amidst the opportunity, the investment team also pointed to risks.

High on that list is the US government’s ability to service interest on the ballooning deficit.  As long as the US economy continues to grow, the government will be able to pay the interest on its borrowing, but the challenge will come if the economy slows and goes into a recession.

Although China is also set to grow in AI in parallel with the US, many opportunities remain out of reach for US investors. Bachher said he has “changed his view on China” and will pay more attention to simple ways to execute and invest in China.

That is in contrast to his more negative outlook on China earlier in the year. The university reduced its allocation to China three years ago, and he said he had no plans to increase the allocation outside MSCI ACWI exposure because of uncertainties around tripping executive orders.

AUM about to hit $200 billion

The university’s assets under management, now $198 billion, will have grown to $200 billion by September 2025. This year alone, the portfolio increased by $18 billion – it has added $68 billion in the last five years. Assets are divided between the Retirement Fund ($154.4 billion), the Endowment (31.1 billion) and the Working Capital Fund (12.5 billion).

In another nod to his belief that simplicity pays, he attributed the rapid growth of AUM to the board’s decision to add more equity in 2018. Another crucial decision that has swelled assets under management was going short-duration on bonds.

“We would have lost $5 billion had we not make the decision to move from long to short duration,” he said.

However, the investment team also came under pressure to explain the poor funding ratio despite the growth in assets. In 2008, the fund was 103 per cent funded but is only 83 per cent funded today.

Bachher responded that the funding ratio will only be fixed by increased contributions, which are also imperative because of changing demographics.

“When things go well, the Faculty and the Senate and Staff say let’s cut contributions, but you can’t make the maths work. We are not a group of magicians, and employees, employers and the State have obligations. It’s not a rosy picture.”

Under strict sustainability guidelines and pressure from stakeholders to “know what they own” when investing, Dutch pension funds have developed an affinity for concentrated equity allocations in recent years. 

Some chose to have a concentrated carve-out that is actively managed, often with impact investing themes. But one prominent proponent, Huisarts & Pensioen, overhauled its entire equity allocation in 2024, which represents about a quarter of its €10 billion assets. It reduced its holdings from several thousand companies to 65 names in a radical pursuit of accountability and transparency to its members who are general practitioners in the Netherlands.  

But while a concentrated equity strategy makes ESG and reputation risks more manageable, a new study from the Rotterdam School of Management at Erasmus University flagged the diversification risk and higher volatility it introduces into the portfolio.  

Professor of Finance at the Rotterdam School of Management and co-author of the new study, Mathijs van Dijk, says the number of stocks needed to fully diversify a global portfolio today is around 750 – if the asset owner can tolerate a few extra basis points of volatility, then 100-250 stocks would do the trick. 

“If you have less than a few hundred stocks… then you’re running financial risks that at least you need to consider and be able to explain,” van Dijk tells Top1000funds.com. 

“Maybe they [the pension funds] want to accept the trade-off – it’s up to them, but I think it’s important to recognise that there might be a trade-off.” 

The research acknowledged that classical finance theory suggests owning 30-40 stocks is enough to fully diversify the idiosyncratic risks, per Meir Statman’s widely referenced 1987 paper, How Many Stocks Make a Diversified Portfolio?. But the paper itself is out of date and its subsequent derivative studies tend to have a US-focused stock sample. 

Ongoing debate 

The push towards a smaller equity portfolio has been an ongoing conversation in the Dutch pension industry in conjunction with political pressure on asset owners to invest more domestically.  

In a position paper published this month, Dutch investor group Eumedion – whose board members count Gerard Fehrenbach (senior advisor of responsible investment at PGGM) and Alfred Slager (investment committee chair at ABP) – said passive investing has watered down asset owners’ commitment and stewardship at their investee companies.  

It recommends that pension funds apply “more focus and concentration in the equity portfolio”, which could also benefit Dutch listed companies as asset owners would have larger stakes in them. This stability in the shareholder base would encourage companies to pursue longer-term goals.  

“Asset owners are also expected to act as stewards of their investee companies and to truly understand these companies. An overdiversified equity portfolio does not fit in with this,” the position paper states.  

Van Dijk says he also sees the case where a fund may want to adopt a concentrated portfolio for risk management purposes.  

“It’s a little bit harder to assess… [but] if you truly want to understand the risk of the company that you invested in – particularly the forward-looking climate related risk – like might there be stranded assets 10 years down the road – that’s probably going to be hard to do for 3000 stocks,” he says.  

“But there might be a trade-off also for having better risk assessment.” 

Huisarts & Pensioen declined to comment on the research findings when approached by Top1000funds.com but later issued a statement on its website. Despite worries that smaller portfolios have higher volatility, it said the difference is very small and within an acceptable range.  

The fund also brushed off suggestions that it is building too much concentration by only holding 65 stocks, saying that it limits its risk through sound stock-picking and not choosing companies randomly or based on size.  

“We do not want companies that focus on the riskier innovations, but rather companies that supply the crucial components for their realisation,” read the statement published in Dutch.  

“By compiling our own portfolio and not investing in all companies, the return in the short term may deviate from the broad stock market, both positively and negatively. Over the long term, this will balance out and we expect to achieve a comparable return.” 

FOMO risk 

While that might be the case, van Dijk says it’s still very hard to predict and invest in winning stocks as only 2 per cent of companies accounted for all stock market wealth creation or equity premium over his research’s 40-year sample period (1985-2023).  

Concentrated portfolios also run a higher risk of missing out on returns if they fail to select the very few winning companies dominating the markets – right now, those are the Magnificent 7. The research termed it the FOMO (fear of missing out) risk.  

“If you have fewer stocks in your portfolio, you increase the probability that you miss out on the top performance,” van Dijk says. 

“On average, it’s not the case that you’re going to do worse [than the benchmark], because, of course, there’s also a possibility that you do pick the Magnificent 7 within your 100 stocks, and then you might actually outperform. But it’s the uncertainty that’s the risk. 

“Maybe you’re very lucky, but you could also be very unlucky.” 

FOMO risk is a consideration not only during the stock-picking process, but also in deciding each stock’s portfolio weight, the rebalancing frequency and the trading strategy, the research said. The only way to reduce the FOMO risk would be to follow a portfolio strategy that minimises the risk of missing out on the next performing stock – effectively predicting the next Magnificent 7 – which is an extremely difficult objective as evidenced by active management’s struggles in recent years.  

But to optimise diversification within a concentrated portfolio, the research suggested that asset owners can implement maximum weights for individual stocks or maintaining industry composition and select stocks with low correlations.  

“In our simulation of 100 stocks, so a very concentrated portfolio, if you have very lucky picks, you will have an annual return on the portfolio – at least based on our historical sample – that is 3.5 percentage points per year higher than if you have a very unlucky pick of those 100 stocks,” van Dijk says. 

“As a pension fund, you have a 30-year horizon, if you have that year-on-year difference, it’s quite striking how important that [FOMO risk management] is.” 

In its bid to be an early adopter of AI, CalSTRS, the $358.4 billion pension fund for California’s teachers, has written a set of generative AI (GenAI) policies and guidelines, conducted an AI cost-benefit analysis and identified a pipeline of scalable use cases for the technology that all demonstrate business value.

CalSTRS is also well down the road on board and staff training efforts (staff will attend an AI bootcamp this September) to upskill the team in a sweeping change management effort.

Documents from a recent board meeting presented by April Wilcox, senior investment director, David Liao, director of enterprise IT solutions and innovation and Vaishali Dwarka, director of enterprise strategy management, laid out the next stage in a strategy where CalSTRS has committed to becoming a leader in the field.

As of August 2024, a total of 25 candidate AI use cases have been documented and analysed across more than ten business programs, six of which sit in investments.

Here, the technology will support predictive dashboards, cash flow forecasting and data augmentation and will help staff draft memos and knowledge share. It will be applied to portfolio and research intelligence, manager due diligence and facilitate the legal documentation behind the investment process, including reconciliation in private assets.

GenAI will be used to query data, generate summaries, gain insights into markets, ask questions and provide correlation analysis, they detailed. It will enhance risk management, optimise portfolio management and create operational efficiency where CalSTRS estimates the technology could increase efficiency by 85 per cent whereby processes that used to take 2.5 hours, take just 15 minutes.

In a joint presentation,  Accenture’s Ted Kwartler and Eyal Darmon detailed how integrating AI comes with important questions for institutional investors. CalSTRS must ascertain if its people, data and technology are ready for AI and investors also have to know where they will apply AI, and how to balance the value and risk of the technology.

Risks include the potential for unreliable outputs or the unauthorised disclosure of confidential information. Alongside security vulnerabilities like fraudulent attacks and disinformation, the technology also carries the risk of bias and will usher in changes in the workforce, which can exacerbate user mistrust of AI.

Integrating AI impacts investors’ risk exposure and shifts cost models. Decision-making models will be data-driven and CalSTRS will be dependent on ecosystem partnerships, they said. AI implementation also comes with multiple costs like tech infrastructure, data management and governance as well as the cost of running pilot projects, innovation labs and talent development.

The role of the board

In their presentation, Kwartler and Darmon also detailed the important role of the CalSTRS board in monitoring the rollout of AI.

For example, the board needs to stay informed about market trends which means monitoring the evolution of AI and how peer organisations are leveraging the tech. The board needs to encourage agility in AI decision-making to respond to industry shifts and have a firm grasp of the strategy. They also need to maintain an understanding of the AI program’s alignment with CalSTRS’ strategic goals and stay abreast of how AI investment is budgeted and its performance measured.

The consultants counselled on the importance of tying costs to mission-driven outcomes – suggesting starting small and scaling strategically. Quick wins are a good way to showcase upfront value which in turn builds momentum for long-term investments and will drive the transformation.

Using board notes that charted the evolution of AI, Kwartler and Darmon illustrated that Agentic AI, which acts on our behalf, planning and executing tasks end to end, is now capable of placing orders, scheduling repairs, or optimising workflows without needing a human to step in.

Alongside text (the most advanced domain, which has already passed medical, law, and business exams), AI is now able to write code for programmers and translate foreign languages.

AI also has multiple use cases outside the investment process including supporting payments to reduce repetitive interactions and freeing up staff time for other tasks.

Andrew Lo, finance professor at the MIT Sloan School of Management and quant investing pioneer, believes large language models (LLMs) can bridge the gap between fundamental and quantitative investing in a way that was unfathomable five or 10 years ago.   

While there are still a lot of “hallucinations” or misleading results in AI outputs, Lo said the finance industry is now at an “inflection point” in creating more effective ‘quantamental’ investment strategies which would bring together the best of both worlds.  

Lo, who founded quant alternatives asset manager AlphaSimplex in 1999, said the biggest difference between quant and fundamental strategies, or the obstacle standing between their integration, is fundamental strategy’s lack of scalability.  

“You don’t need a lot of quants to be able to manage billions, even hundreds of billions of dollars. It’s incredibly scalable,” Lo said during a presentation at the Machine Learning in Quantitative Finance Conference at Oxford University. 

“Fundamental analysis has its limits, because it just takes time and effort to bring all of these disparate pieces of information, much of which is not numerical,” he said. Taking his own healthcare investment venture as an example, Lo said the average fundamental analyst can only manage to follow 20 companies and come up with around five trades a year, and the only way to scale up is by hiring more fundamental analysts – until now.  

“It turns out that generative AI and specifically large language models, can now process both text and numerical information together,” Lo said. 

“It’s not just a bag of words that machine learning algorithms, when first applied to do natural language processing, tried to accomplish. It is much, much more powerful, and that is really a sea change which will allow fundamental analysis to be scaled.” 

While Lo acknowledged that no one has discovered how to specifically implement this scale-up yet, he is optimistic that a solution will come out in the near future.  

“Can quant and fundamental analysts live happily side by side and be able to be more productive than either by themselves? That’s what I mean by ‘quantamental’ analysis,” Lo said. 

But to make further progress on this integration, it’s essential that investors figure out how to conduct fair backtests of strategies involving LLMs. 

“I can run a backtest now of ChatGPT using data from 10 years ago. But the problem is ChatGPT has all the information over the last 10 years,” he explained. “I need to make ChatGPT dumber by somehow excising the last 10 years of knowledge, because otherwise it can just use the data for prices over the last 10 years, and yes, ChatGPT can perfectly predict future prices, given future prices.” 

“So here’s an idea [for the big AI firms]… create a financial product that allows you to license large language models by vintage – monthly large language models that are trained on data only as of that month and prior.  

“You can’t necessarily replay fundamental analysis over time, but you can certainly now run the quantitative part applied to fundamental data in a perfectly legitimate fashion.” 

Pattern spotter 

LLMs also have the potential to upend one of the most debated corners of investing, technical analysis, and lead to huge implications in fields like currency trading where pattern spotting is still prevalent, Lo said.  

Technical analysis refers to the method of examining past trading patterns of a security, commodity, currency or index to predict future price movements. Analysts decode the geometric shapes in graphs and charts that they surf through every day – ‘head and shoulders’, ‘double top’ and ‘double bottom’ and so forth – to identify signals and trading opportunities.   

Technical analysis’s reliability is often questioned and according to Lo, the very practice of it is looked down upon elsewhere in the industry with some referring to it as “voodoo finance”. It stands in contrast to fundamental analysts’ close examination of company balance sheets and macroeconomic factors and hinges its forecast on hints from historical market data, which are also highly open to interpretation.   

 “Someone once said… fundamental analysis is to technical analysis, like astronomy is to astrology,” Lo said. 

But at the heart of technical analysis is visual cues and pattern recognition, and Lo said AI has proven to be so much better at identifying them than humans. 

“They can actually identify patterns that humans cannot even begin to comprehend. We use double bottoms, head and shoulders, triangles, because we’re used to those shapes. How many shapes can large language models identify?” he said. 

“Large language models will become extremely good at trading foreign currencies among other asset classes. 

“Because in foreign currencies, if you’re trading, there’s really nothing else. Not a whole lot of changes between 10:03 and 10:04pm or am in terms of the trade deficit. So what are you going to do? You need to trade based upon something. This is an area where large language models can completely revolutionise the field.” 

The ultimate task 

In three years, Lo is optimistic that AI will be able to perform the most important mission in fund management: satisfying the fiduciary duty as defined by the law. 

Lo is currently conducting research around LLMs’ role as financial advisers and said it can already satisfy two out of three crucial requirements to provide sound financial advice: having expertise in the domain, and being customisable. The final criterion is developing trust and ethics, which Lo believes will be achievable in the foreseeable future.  

The boundary between human and AI is not as defined as we think, Lo said, and he pointed to prior academic efforts that aims to understand intelligence as a whole regardless of their forms, including Norbert Wiener’s exploration of cybernetics and John von Neumann’s posthumously published study, The Computer and the Brain, that discusses how the brain can be viewed as a computing machine. 

“My personal hero, Marvin Minsky… said ‘I don’t want to build a computer I can be proud of, I want to build a computer that can be proud of me’. 

“I believe that today we are on the verge of building computers that can be proud of us. That is both exhilarating and absolutely terrifying.”