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Active and passive investment strategies aren’t adversaries, they are both part of the same ecosystem, said a panel of experts at the Fiduciary Investors Symposium at Stanford University.

Alison Romano, senior investment officer in global equity at the $160 billion Florida State Board of Administration, told delegates the pension fund’s risk targets shape its allocation to active and passive strategies. She said active management “does and continues to work” in the global equity allocation and expects active allocations to outperform passive as markets grow more challenging.

Gene Podkaminer, senior vice-president, head of multi-asset research strategies at Franklin Templeton Multi Asset Solutions, said it was important to think about passive and active strategies not only as a trade-off between risk and return, but more as a way of shaping implementation. For example, passive strategies can help with pension funds’ liquidity calls, he said.

Farouki Majeed, chief investment officer of Ohio School Employees Retirement System, invests according to a risk and cost budget. He told delegates he chooses active and passive strategies in the various asset classes according to which ones are most likely to provide excess returns relative to the policy benchmarks. He notes less success in equity, and therefore spends less of his risk budget in equities and more in private equity and real estate, opportunistic investments and some hedge funds. However, although the fund doesn’t spend much of its risk budget on equity, most of its total risk budget is in equities. The total risk is decided by the board, Majeed explained. They set OSERS’ 60 per cent allocation to equity to meet the fund’s long-term return target of 7.5 pe cent, he said.

Majeed said it was relatively easy to acquire beta returns across the board at low cost but he noted that hedge fund returns are challenged. This led the panel to discuss the rise of factor strategies, which sit between active and passive. The panel noted that factor investment did involve a series of active decision0making. For example, around construction and acknowledging that one value factor can behave differently to another, avoiding duplication as well as decisions around rebalancing. Putting these pieces together to get the return stream you want is an active decision, Romano said.

Indeed, using smart beta or factors allowed investors to “own decisions” with significant governance implications, Podkaminer said., the panel discussed using factor benchmarks, rather than looking at active or passive solutions against a market cap-weighted benchmark, to more easily see how much beta and alpha they are receiving. For example, as volatility is likely to increase, investors could change their global equity benchmark from cap-weighted to a volatility-adjusted benchmark.

Florida State Board of Administration has worked with consultants to weigh up the value of active management in the portfolio, leading to a revamp of its active managers, Romano said. She said she wanted to spend most of her risk budget in emerging markets and micro-cap strategies, parts of the market that attract active management and lack efficiency. The panel discussed the challenge of passive strategies in fixed income, where it can lead to exposure to the most indebted companies, since they often have the highest allocations in the index. Here, active strategies allow investors to play with the duration, sector and individual security and the cost is lower than in other active strategies.

The panel mulled whether exposure to high conviction strategies was a way to complement passive. However, investors noted that they are constrained in their ability to pursue active strategies because of the risk of underperformance and an inconsistent performance. If global equities underperform, investors struggle to make up for the losses in other allocations. High allocations to equity, and the high underlying risk, cause any errors to hurt the rest of the performance.

The panel stated that passive managers take as proactive a role engaging with companies as active managers, allaying concerns that passive managers, with meaningful and large allocations, were not active ESG owners. Romano said Florida had an entire corporate governance group dedicated to engagement and the fund “didn’t treat passive holdings any differently”. Indeed, because passive investment results in bigger holdings in some corporate names, there is more potential influence to wield.

The panel noted that just because investors “happened to choose a bunch of stocks determined by an index”, there was no reason to think engagement would be slack. Moreover, it is a myth that active managers are always engaging with companies, Romano suggested.

“There is no evidence they do more than others,” she said, noting that passive managers such as BlackRock are active in corporate governance and, more broadly, ESG.

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Is relying on a hunch a good strategy for investing? It is unlikely a serious investor would answer yes. But if I replace “hunch” with “expert intuition” it becomes a more interesting question.

So what is intuition? The most widely accepted definition comes from economist Herbert Simon: “The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition.” In other words, intuition is simply pattern matching. In principle, this process is no different than how kids recognise that an animal is a dog not a cat. “Expert intuition” is acquired and applied in professional fields – playing chess or firefighting for example.

Intuition is part of a form of knowledge known as tacit knowledge. We, as human beings, know more than we can tell. Even the experts aren’t able to articulate their intuitions, let alone justify them. It happens effortlessly. It feels automatic.

If that sounds familiar, you have probably come across Daniel Kahneman, a Nobel Prize-winning psychologist, and his work on system 1 vs 2 thinking. In short, system 1 is heuristic thinking – a fast, autonomous and unconscious way of thinking. System 2 is reasoned thinking – a slower, harder and controlled way of thinking that normally kicks in when a person is faced with complex problems. In this context, intuition is a system 1 tool.

There is strong evidence to support the assertion that the intuitive judgements of some professionals are impressive. On the other hand, Kahneman tells us that system 1 thinking is prone to cognitive biases. It cannot be trusted all the time.

So can we trust expert intuition with making investment decisions?

It turns out that for intuition to be genuinely skilled, Kahneman and Gary Klein say, there are two necessary conditions.

First, there must be adequate opportunity to build up the expertise. The 10,000-hour rule might apply here. Assuming 40 hours a week and 45 weeks a year, that’s less than six years’ experience. This seems like an easy hurdle for investment professionals.

Second, and more importantly, the environment in which the expertise is acquired needs to be sufficiently regular to be predictable. The ideal situation is that the feedback is immediate and unambiguous. Think learning to apply brakes when driving around a bend. Intuition can also be acquired in the presence of uncertainty but the key condition is that the environment (underlying probability distribution) needs to be stable. Think poker: the exact chance of winning with any given hand is always the same, despite the uncertain outcome of each game.

Unfortunately, the feedback we receive from financial markets is not immediate and is far from unambiguous. There are a couple of reasons to believe intuition gained in this environment is probably spurious.

First, cause-effect linkages are weak in a high-noise environment such as investment markets. Nonetheless, Kahneman’s research suggests human minds are strongly biased toward casual explanations. We refuse to accept that we simply cannot explain why certain events happen. We come up with false explanations to satisfy our craving for coherence and reason. If sharemarkets went up by 3 per cent, fundamentals must be improving. If rising markets suggest that fundamentals are improving, we ought to buy more! Clearly, this buying-high strategy is doomed to fail.

Second, our financial education gives us a false impression that we operate in an environment where reliable intuition can be acquired – a stable underlying probability distribution. In reality, financial markets often behave for a prolonged period of time as if they resemble a stable environment, until regime shift brings out abrupt systematic changes. This leads to a false confidence that genuine expert intuition can be learnt.

System 1 is always on – it cannot be turned off. Through years of experience, it will pick up something. I would argue that, in the environment of financial markets, it is more likely to pick up unreliable hunches and cognitive biases than valid intuition.

Is it all bad news? Of course not. I can offer at least two positive observations.

Decisions facing leaders in the investment industry are much broader than how to position a portfolio for an uncertain and unpredictable future. There are numerous decisions in the area of managing the investment business and operations, along with the investment talent. Arguably, the environment for these types of decisions is more benign for developing reliable intuition.

Then there is another type of intuition in my mind. It is about the ability to assess the environment and determine when and whether to stop relying on intuition. The acquisition of this type of intuition comes through attention, effort and rigorous analysis – all signatures of system 2 thinking. But through practice, it becomes fast and almost automatic, like a system 1 intuition. Warren Buffett’s famous remark, “Be fearful when others are greedy and greedy when others are fearful” is a great example. I believe this type of intuition is worth pursuing.

Liang Yin is a senior investment consultant in the Thinking Ahead Group, an independent research team at Willis Towers Watson and executive to the Thinking Ahead Institute.

The far-reaching changes the $365.5 billion California Public Employees’ Retirement System plans for its private equity program still need final board approval. Yet one of the new model’s most important advocates, Priya Mathur, has just lost her seat on the 13-member CalPERS Board of Administration.

Speaking at the Fiduciary Investors Symposium at Stanford University, on the eve of her surprise election defeat, Mathur, a board member for 15 years who was voted CalPERS’ first female president in January, told delegates the board was still working through the governance process for the private equity program.

“I really believe this is essential for us and I hope to see the board endorse it in coming months,” she said.

Mathur’s sudden departure after losing to first-time candidate police officer Jason Perez means CalPERS needs to elect a new president, heralding a board shake-up that could delay the urgent plans.

Innovation and Horizon

CalPERS is in the final stages of approving a separate entity that would make direct private equity investments and would be governed by a separate, independent board. CalPERS Direct would consist of two separate funds. One called ‘Innovation’, focusing on late-stage investments in technology, life sciences, and healthcare; and the other called ‘Horizon’, for long-term investments in established companies that kick off cash flow. The pension fund hopes to increase investment in private equity – its best-performing asset class – to between $10 billion and $13 billion a year.

“We are a maturing system, paying a lot in benefits and don’t want to sell assets to pay benefits,” Mathur told delegates. “As truly patient capital, we are not just looking to sell after five to seven years.”

She said the pension fund needed to invest over the longer term to better match its liabilities and tap private opportunities as the number of public companies declines.

The Innovation and Horizon funds would operate alongside CalPERS’ existing private equity structure, which typically invests in co-mingled private equity funds, and a new program to partner with emerging managers, Mathur said. Many of the most established private equity general partners are entering succession periods so partnering with rising funds mitigates related risk. It also opens opportunities.

Models and structures

The structure and governance of the new model remains unclear. Mathur told delegates at the symposium that details are still being “worked out”. Discussions among investment staff and the board have referenced creating a separate corporation with its own investment staff and board. This would allow CalPERS to compensate and reward private equity professionals differently with more than what is possible in a public pension fund, to help recruit the talent required. It would also give the new entity autonomy and breathing space to innovate. Yet Mathur said the new program would still involve managers.

“We are definitely hiring a set of external managers to do this for us,” she said, again referencing the “limitations” in CalPERS’ ability to pay the large salaries needed to attract talent. She also said hiring managers would enable the fund to attract the “right talent”, particularly on the innovation side, with the ability to access the best deals.

Elaborating further on the proposal, she said the new entity would “not be CalPERS” but referred to “a firm” where an advisory board ensured assets were managed according to CalPERS’ expectations. Although it would be running the fund’s money, she said it “would not necessarily be owned by us”. She explained that CalPERS wouldn’t interfere with how companies were selected or managed but would look for alignment with its principles, and said the structure offered an opportunity for the fund to have an impact on how private companies evolved.

CalPERS still struggles to work out the fees it pays, how they are composed and whether the fund is getting value for money.

“It is still hard for [limited partners] LPs to know what fees they are paying in private equity,” Mathur said.

She acknowledged the risk of California-based investments in the private equity and wider portfolio, given that the pension fund’s liabilities are also in California. For example, Mathur said, after the GFC it was difficult for the pension fund to ask for larger contributions from struggling employers and beneficiaries in the state.

“Our investment supports jobs and this is positive, but there is risk on the liability side,” she said.

Mathur’s departure made the former president’s speech painfully ironic in retrospect. She told delegates of her pride in her long, groundbreaking tenure, which included shepherding through CalPERS’ 10 Investment Beliefs to help decision-making after the financial crisis. When she was elected in 2003, she was the only woman, she recalled, adding, “I’m still here.”

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China and America’s relationship is the biggest geopolitical risk on the global landscape, said Stephen Kotkin, speaking at the Fiduciary Investors Symposium at Stanford University. The John P Birkelund ’52 Professor in History and International Affairs at Princeton University, and senior fellow at the Hoover Institution at Stanford University, told delegates conflict could arise over Taiwan.

He discounted geopolitical risk on the Korean peninsula. Although it is dangerous and potentially catastrophic for the region, it doesn’t pose a systemic risk, he said. Nor, in Kotkin’s estimation, do Russia, instability in the Middle East, or Iran, which has a small army and uses proxies and militias to fight its wars.

He said the “Trump chaos” that has manifest in a denunciation of allies and breaking of key global agreements, looks very different from Obama’s policies. However, scratch beneath the surface and US President Donald Trump’s policies are not that dissimilar, he argued, because the administration is grappling with the same problems as Obama’s: the US is trying to reduce its foreign commitments, demand more of its allies, and find more common ground with its adversaries as America’s power in the world declines.

“Obama tried to do all these things, the style was different, but the substance is similar,” he said.

Kotkin drew on the Iranian and North Korean nuclear challenges to illustrate his point. Trump has rubbished Obama’s nuclear deal with Iran but has sought to cut his own nuclear deal with North Korea. Trump’s decision has enraged European allies but has been supported by other allies in the Middle East, such as Israel and Saudi Arabia, he said.

Kotkin said China’s growing military muscle was illustrated by its stake in many ports around the world. He said these ports had a “dual purpose” of commercial and military gain. China is laying down infrastructure to imitate US military bases around the world and drive a commercial story, he said. China’s activity in the South China Sea, where it has built naval bases on coral reefs amounts to a “huge military presence” in a region that used to be dominated by the US.

The professor said the clash between China’s growing power and the overstretched, declining power of the US would come to the fore in Taiwan. For decades, China’s claim over Taiwan, and Taiwan’s de facto independence, have been countered by preserving the status quo.

“We’ve been with the Taiwan problem for a long time, but it’s never blown up,” Kotkin said.

Taiwan now depends on China economically, yet economic integration has not bought political integration. Instead, the Taiwanese identifying as Taiwanese are in the majority, with opinion on the island now moving away from political integration with mainland china.

Beijing is applying pressure on Taiwan, Kotkin said, noting the increased military exercises around the island. That leaves the US with a problem because, in the event of a dispute, if the US fails to protect Taiwan it would threaten the US alliance in Asia.

“This is your systemic geopolitical risk,” Kotkin said. “This is what you should pay attention to.”

He said the current trade war was the opening phase of this wider potential conflict, which could eventually lead to portfolios getting “incinerated”.

He said one solution was to maintain the status quo, which could be possible if Taiwan made no move toward formal independence and the US didn’t encourage it to do so. He said diplomatic negotiation was also possible, with confident diplomats and without local politics driving the agenda. But he added that a war could unfold that would poison the water and air, destroy energy plants and create domestic chaos.

Kotkin said the trade war was part of a larger strategy by the US to destabilise the Chinese regime, which he said the Trump administration believed was unstable and vulnerable.

“Authoritarian regimes are brittle,” he said, noting that despite breath-taking economic innovation, the Chinese regime was afraid of its own people and didn’t act confidently.

Regulation of liquidity in over-the-counter markets in fixed income, commodities and currencies is having a profound impact on funding costs.

Professor Darrell Duffie explained to delegates at the Fiduciary Investors Symposium at Stanford University how OTC markets are dominated by a small number of dealers affiliated with large banks. These banks, which connect with investors via their buy-and-sell orders and connect with one another in laying off those positions, charge much more today than before the financial crisis.

The market is relatively opaque for many buy-side firms. Because the current market structure is dominated by large banks, when investors call dealers to buy or sell a position, dealers are in a “better position” and “better informed,” said Duffie the Dean Witter Distinguished Professor of Finance at Stanford Graduate School of Business. Moreover, there is little opportunity to trade directly with another buy-side investor, meaning all the market share and bid-ask spread goes to the dealers.

The structure of the current market has been shaped by regulation that has been introduced since the financial crisis. Rules around bank capital adequacy make it more expensive to trade because access to banks’ balance sheets has become more expensive.

“Dealers have to have more capital to back your positions,” he said, adding that although capital regulations are a good idea, they now dictate the cost of trading.

No longer too big to fail

Other factors have also increased the cost of accessing balance sheet space. Failure resolution rules in the US and Europe, which cover liability if a major bank fails, now force the creditors of large banks, rather than governments and taxpayers, to take those losses.

“Banks have to resolve losses with creditors, there will be no bailouts by governments,” Duffie said.

It means creditors need a higher credit spread to compensate them for potential loses. So, while the probability of a default has gone down due to capital adequacy, the potential losses for the largest banks’ unsecured creditors have gone up, and with them the net cost of debt funding. It means banks are not opening their balance sheets to arbitrage trades, Duffie said.

Before the financial crisis, the credit spreads for the largest US and European bank dealers were “razor thin”. It was indicative of the fact banks were happy to fund arbitrage trades with only a few basis points of profit, safe in the knowledge they could go to the debt markets and secure cheap funding because creditors didn’t believe banks would ever be allowed to fail.

Now, it’s more expensive for banks to use debt funding to take positions, Duffie continued.

“Investors are paying for it every time they make a trade with a large bank,” he said.

Indeed, a bank will conduct a trade only if the gross profit is larger than the funding cost, Duffie said. Even a strictly positive profit-and-loss position can be viewed by the banks’ shareholders as a negative-return trade. It has led to the introduction of funding value adjustment, costs which traders must now meet to justify making a trade.

Duffie pointed out that banks’ credit ratings have lowered in reflection of the fact they are no longer “too big to fail”. It’s the removal of so-called “sovereign uplift”.

“There is no more sovereign uplift with the largest banks,” he said. “Banks are safer, but creditors are at greater risk because they won’t get bailed out if they are holding unsecured unprotected debt.”

Solutions

Duffie suggested a couple of solutions to the higher cost of funding. If banks competed for trades, investors would get a better deal.

“It will mean the banks with the higher funding costs will be driven out of the market, and you will get lower costs,” he told delegates. Alternatively, trading platforms where dealers could respond to requests for quotes, could introduce competition. However, he noted there was fragmentation in the trading platform market, with few dealers answering requests for quotes and an absence of competition.

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AI will have a huge impact on financial services, particularly investment and risk management, in the next five years, a panel of experts said at the Fiduciary Investors Symposium at Stanford University. Machine learning systems that analyse data to mimic human cognitive behaviour around problem solving and thinking are growing as more data and cheap computer power become available.

Machine learning has the potential to generate alpha and affect business processes. Data is automatically processed, collected and presented by machines, rather than people within organisations, said Kay Giesecke, professor of management science and engineering at Stanford University, who said many more research projects in his department have been focused on AI over the last five years.

The availability of data and computer power has opened AI strategies to investors, Jens Kroeske, head of macro systematic strategies research at Aberdeen Standard Investments said. In the past, data was gathered by specialised companies that gave earnings transcripts. Now standard financial data providers present deep detail in new, easily accessible formats that professionals can plug into natural language processing algorithms.

“There has been a commoditisation of machine-learning algorithms,” Kroeske said. He added that more people can now access these programs and algorithms on specific data sets, even those without experience of programming, widening access beyond the preserve of specialised coders.

Although the growth in systematic strategies creates a natural evolution to indexing, Kroeske said the risk-management question still needs solving. In an industry built on trust and human relationships, machine learning still lacks a risk-management framework that explains it and bounds its behaviour. Once this is addressed, it will be disruptive and replace hedge funds and fundamental managers, he said.

The need to explain to stakeholders how machine learning reaches decisions is a key component. It’s something Kroeske believes is getting easier.

“Modern machine learning is designed in such a way that it should be easier to explain,” he said. Its focus on finding similarities, variables and past examples that are relevant help “explainability”. He predicts that the line between fund and quant investing will become increasingly blurred as the types of information provided to machine learning and a discretionary manager grow similar.

Data is king

As data has led to better insights, decisions and outperformance, its accumulation has become key. We will see trillion-dollar companies in the next five years that have grown not through producing widgets but through the mass collection of information, predicted Jagdeep Singh Bachher, chief investment officer University of California Regents. Taking the energy sector as an example, he said data accumulation would grow with the shift from centralised to decentralised sectors and economies.

The explosion in the flow of assets to exchange-traded funds and quant strategies has been driven by the “idea that data creates better alpha” Bachher said. Moreover, it is driving the University of California Regents’ own direct investments in companies. It now invests in Ola, India’s rival to Uber, where data patterns providing insight on behaviour and dynamic pricing are key to the company defining how it charges customers, driving the value of the business. In another example, Bachher cited the tech companies vying for access to healthcare records and data. He told delegates that investment in data and data sources was an area they should feed.

“It could lead to better insights to decision-making,” he said.

Balancing act

Balancing machine learning and fiduciary responsibility is challenging, the panel said. Investors have a fiduciary responsibility to explain where their investment performance is coming from. Machine learning involves replacing the traditional linear models used for predicting returns with a non-linear model, said Giesecke who referred to this as the “key driver” behind outperformance in machine learning. The world is not linear, and every variable depends on other non-variables, yet conventional approaches ignore this, he said. He noted the trade-off between drawing on non-linear data and “explainability”. “You can have explainabilty or give up some performance – but ideally you want both.”

Fiduciaries could introduce governance around which machine learning models they use. Conversations about this could include why certain models require so much data or ranking the importance of the data that is feeding algorithms, so fiduciaries recognise what parameters in the model made the difference.