When the Seattle Seahawks won Super Bowl LX, Aden Durde became the first British coach to achieve the feat. As Defensive Coordinator he shared responsibility for marshalling the fearsome defense that was the central pillar of the Seahawks’ accomplishment. Following the game Durde was asked to account for the team’s success and he said: “we keep each other accountable and we have a common goal.” 

While this is a very simple explanation that belies a huge amount of work, it does get to the heart of what a team needs to be successful – and is certainly a necessary but not sufficient condition for a high calibre investment team.

Held to Account

In simple terms we can think of accountability as about an individual being responsible for their actions, but from a team perspective it is more about shared trust.

Think of the NFL Defensive Coordinator, alongside the Head Coach, they define a philosophy, design a scheme and call plays. They expect and trust players to operate within that framework.

Players have to trust both the guidance of their coordinator and coaches, but also their teammates. The success of any individual is heavily reliant on other people doing their job well.

Everyone in the team is accountable for outcomes, but also their individual success is incredibly dependent on everyone else within that team. If you trust the other people in the team then you are happy being accountable; if you don’t then accountability feels like an unfair burden.

Cultures within teams are typically defined by the level of trust. If people don’t trust each other, you can forget having a productive culture.

A Trust Deficit

A recent article written by Adam Butler, which I would recommend reading, argues that trust is an incredibly valuable (and largely invisible) common good that tends to be destroyed by market forces – as it is replaced with systems, controls and metrics, which both serve to erode trust and monetise its absence.

The removal of trust and its replacement with measurement is an undoubted problem. Butler tells the story of a nurse whose care and compassion looks like inefficiency and non-compliance on her hospital’s productivity dashboard.

The inviolable pattern is that information becomes a metric which becomes a control which becomes a behaviour. This always happens. 

We see this dynamic in all domains of life – when a footballer (soccer player) is tracked on the distance they run during a game, they will run about a lot – whether or not it is the right thing to do in the context of winning the game.

Controls also always grow and never contract – the system becomes defined by an absence of trust and builds on itself – each breach needing a new control. This can be maddening but is also extremely rational – trust is ephemeral, and controls and metrics are tangible. If something goes wrong, you need to show where the control is.

Atul Gawande’s compelling ‘Checklist Manifesto’ quickly degrades into reams and reams of checks and constraints – more than defeating his noble initial purpose.

This problem matters a lot for investment teams. Both because trust is integral to a high functioning team, and because financial markets are so incredibly noisy – some metrics and measures are required and useful, but vastly more are wildly ineffective. Not only does that mean that behaviour is driven by flawed measures, but the most valuable aspects of an investment approach are lost because they are unquantifiable in any reasonable way.

If you want to understand how an investment team behaves, you really want to know what it measures, how and why.

Of course, nothing is binary. There is a balance to strike – people can be incredibly vulnerable if they rely entirely on trust without appropriate controls in place, but the extension of that is not removing trust from the equation entirely. Without trust, you don’t have much.

Shared Goals

Alongside trust and accountability, perhaps the defining feature of any effective team is that each member of it is attempting to achieve the same thing. The ideal team structure is one which combines distinctive expertise and skills with a shared goal. Too often teams achieve the first part and fail at the second.

If people in a team do not have the same objectives then it becomes virtually impossible to create an environment of trust and accountability. How can I trust the other individuals in my team, if they are attempting to achieve something different to me? This is not a malign or malevolent mistrust, simply one borne from people following different incentives in an entirely rational fashion.

If you are disagreeing with something I say, is it because of your expertise or your incentives? Without shared goals, there is no real way of telling. (Although it is almost always about incentives.)

Imagine an extreme example where an investment team has portfolio managers incentivised only by outperformance, and a risk team incentivised to limit losses. This would be a crazy arrangement, which entirely ignores the trade-offs involved in any risk/reward decision. Although this might seem extreme, it is probably not far off where many end up.

When we are assessing the strength of an investment team, it is easy to assume that the goals are the same – they all want to generate strong returns, surely? Yet it is so easy, and common, for team members to have skin in different games – someone will live and die by performance, someone who needs to sell, someone who needs to avoid anything blowing up. All rational choices for individuals, but a mess for a coherent team.

One of the key challenges for larger investment teams (and an advantage for boutique firms) is that it is far easier to have goal alignment when there are fewer people involved.

Of course, shared goals are not necessarily a good thing in themselves. They are the best way to encourage aligned and complementary behaviour, but that behaviour can be good or bad depending on what the objective is.

When assessing an investment team it is critical to ask – what are the goals of the team (including anyone who has influence on how it makes decisions) and how are team members aligned with achieving this?

Teams need to pull together and in the right direction.

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There is a lot that goes into building a defence capable of winning a Super Bowl; Aden Durde’s comment on accountability and common goals speak to the foundation that any effective team needs. We should think about these before anything else. 

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

There have been some quite significant moves in asset prices in recent times. The dramatic increase in the price of gold over the past year has probably not escaped your attention, but it is not the rise in the yellow metal that is most interesting to me, rather the behavioural implications. How does extreme positive performance from any asset class or fund make us think and act, and why?

The behavioural drivers

Availability: The availability heuristic exerts a huge influence. This is the mental shortcut where we judge something not based on the weight of evidence, but by how prominent and frequent it is. When an asset class keeps hitting new highs, we conflate the volume of ‘evidence’ with its strength. There might be lots of articles, but if they are all saying the same thing, they are far from independent reasons for us to believe the performance will continue. In these situations, availability also leads us to understate risks – because we are not seeing the downside risks, we are likely to severely neglect them.

Stories: Humans understand the world through stories, and when an asset class is on an unprecedented run of good performance, plenty of stories will be told. We will treat these stories not just as explanations as to why something has occurred, but as a prediction of its persistence. We are inherently uncomfortable with things happening without a clear explanation, so to escape this dissonance we look for convincing explanations so that the world makes sense.

Extrapolation: The combination of high-profile performance and persuasive stories inevitably leads to extrapolation – the trenchant belief that what has been happening will persist into the future. The stronger and more sustained the high returns of an asset class, the more we struggle to see anything to stop it.

Fear of Missing Out: We are relative creatures. Our feelings about most things are framed by how it compares to something else (often other people). Two things happen in this regard when an asset is producing strong, high-profile performance. Those of us that don’t have exposure to the asset class will envy those that do, and those of us that do will wish they held more.

Feedback loops

This combination of behavioural factors creates powerful self-reinforcing feedback loops that can serve to further boost an asset’s performance. It works something like this:

Stage One: High returns from an asset class receive significant attention.

Stage Two: Stories are formed to explain the performance; these stories are prominent and convincing because performance is strong.

Stage Three: The combination of high returns and compelling justifications increase the belief that the trends will persist.

Stage Four: More investors are drawn into the asset class.

Stage Five: Returns are boosted by increasing investor appetite.

And so it goes on, until it doesn’t.

These feedback loops are driven by changes in investor behaviour and sentiment, and impact all asset classes over the short-run. For assets that generate cash flows, over time the phenomenon will be outweighed by the gravitational pull of valuations and fundamentals (at some point someone might say “maybe 100x earnings is a little too rich”). This doesn’t occur for ‘belief assets‘ that are not tethered to fundamentals (such as gold or crypto), which makes the range of potential outcomes incredibly wide.

Thresholds

Another fascinating aspect of what happens when the price of an asset rises substantially is at what point do different types of investors become involved (and when might they withdraw). This is clearly a complex topic, but the best model probably comes from Sociology and the work of Mark Granovetter. He explored the subject of crowd psychology and, in particular, how people in a group will have different thresholds for engaging in collective action.

For example, in a group of protestors, there might be some individuals who have an incredibly low threshold for engaging in disorder, whereas others have an incredibly high threshold. Those in the latter group won’t start throwing bricks until almost everybody else has. Granovetter’s argument is that the composition of a group and how behaviour cascades through it will be critical to how it acts.

In a financial market context, we have a huge group of potential investors all with different thresholds for participating in the ascent of an asset class. Day traders, momentum traders, macro hedge funds, all the way to long-term valuation-driven investors. The first group have a very low threshold for engagement, whereas the latter group might only invest when the pressure not to becomes too great (perhaps their job depends on it). The longer strong performance persists, the greater the chance that those high-threshold investors get drawn in.

This threshold model also matters for the reversal of extremely strong performance trends – the key question becoming the reverse: what is the threshold for investors to exit an asset class if returns start to deteriorate? This is what is typically meant when people talk of the dangers of speculative or ‘tourist’ money. Money with a low threshold for exit can create sharp and severe downside risks.

Although the overarching behavioural patterns of investors are similar, their motivations and strategies will be different, and that matters.

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Extremely positive performance from an asset class can be caused by and encourage extreme behaviour. The more prominent the unusually high returns are, the more important it is to reflect on what might be driving our own decision making.

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

It is hard to argue against the idea that geopolitical tensions are rising, and this type of backdrop can be incredibly difficult to navigate for investors. When the news is filled with discussions of war and conflict, it is natural to lose sight of our long-term investment objectives, and instead become focused on short-term, financial market worries. Yet, contrary to how these environments are likely to make us feel, a 2024 study showed that future equity market returns tend to be higher when coverage of war is more prominent in the media.

In a paper titled: “War Discourse and Disaster Premium: 160 Years of Evidence from the Stock Market“, a group of academics analysed 7 million New York Times articles spanning nearly 160 years and identified how much coverage was dedicated to a set of specific topics each month. They then linked the strength of this coverage to subsequent equity market returns from periods of one month to 36 months.

They found that of the topics considered, ‘war’ was the strongest predictor. Between 1871 and 2019, a 1 standard deviation increase in the intensity of war coverage in the New York Times predicts a 3.8% increase in annualised monthly returns. Over three years, the same rise in war coverage predicts 2.3% higher returns over the next 36 months.

In simple terms, the more that war was a focus of the articles in the New York Times, the higher subsequent returns were all the way up to three years out.

Why might war be good for equity market returns?

Although these results run counter to our behavioural instincts – not many of us treat war or rising geopolitical risk as a buy signal – that is probably the exact reason why this relationship appears to exist. For me, there are two plausible explanations for the phenomenon described in the paper:

• Heightened coverage of war and geopolitical risk leads investors to overstate the potential impact on financial markets and unduly mark down equity prices. This leads to lower valuations and higher future returns.
  
• Increased war coverage is an indicator of rising risk of economic and market catastrophe (what we might call disaster risk) and therefore equities are prudently priced lower. The expected return is greater because risks are also now more pronounced.

We can think of these explanations as being irrational (in the first case) and rational (in the second case). The consequences of both are the same – higher expected returns because equity markets have sold off and valuations are lower. In truth, both factors are probably at play.

Does geopolitical risk create buying opportunities?

Not so fast. There are some significant limitations with the study.

The first is the spectre of survivorship bias. While the data may show that future equity market returns rise alongside war coverage because investors overstate the risk of economic disaster, this is only true because there has been no such catastrophe. The world has to survive for us to see the results – so we cannot easily tell whether a pricing anomaly actually exists.

Equity market returns are predicted to be higher following periods of increased war coverage, provided the world doesn’t end!

(It may be reasonable to argue that if the world does end, we won’t be that worried about the returns from our equity portfolio).

The paper also does not advocate investing in specific countries that are the focus of increased geopolitical risks. It deliberately looks at general coverage of war and its impact on US equity markets. If instead it observed the impact on equity market returns of a country directly involved in a conflict, the results might be somewhat different.

Don’t compound geopolitical risks

The authors refer to the results of their study as a ‘war return premium’, which suggests it is something to exploit and potentially benefit from. However, I would frame the findings somewhat differently. When risks are prominent and emotive, we are liable to allow them to overwhelm our judgement, and prone to overstate the likelihood of worst case scenarios. We should guard against this.

If a ‘war return premium’ exists, it does so because of how investors react to the increased intensity of war coverage in the media. I think we should be less concerned about collecting the premium and more focused on not being the investor who pays it by making poor decisions in stressed geopolitical environments. The evidence suggests that our instincts during such times are likely to serve us poorly.

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

In 2025, I ran a prediction game for finance professionals. This year, to keep pace with current trends, I decided instead to pose ten questions about markets in 2026 to several AI large language models (LLMs). Will they do any better than humans, and will they agree with each other?

I used three AI LLMs for this task: ChatGPT (5.2), Gemini (3), and Claude (Sonnet 4.5). Here are the questions and responses:

1) Where will the S&P 500 finish 2026?
ChatGPT: 7,700
Gemini: 7,555
Claude: 6,800

2) Where will the FTSE 100 finish 2026?
ChatGPT: 10,600
Gemini: 10,750
Claude: 9,200

3) What will the ten-year Treasury yield be at the end of 2026?
ChatGPT: 4.5%
Gemini: 3.75%
Claude: 4.50%

4) Where will the GBP/USD spot rate be at the end of 2026?
ChatGPT: 1.28
Gemini: 1.38
Claude: 1.28

5) What will the dollar price of Brent crude oil be at the end of 2026?
ChatGPT: $57
Gemini: $55
Claude: $66

6) Will there be a 20% decline in the S&P 500 in 2026?
ChatGPT: No
Gemini: No
Claude: No

7) What will the dollar price of Bitcoin be at the end of 2026?
ChatGPT: $130,000
Gemini: $135,000
Claude: $78,000

8) Will the Russell 2000 outperform the S&P 500 in 2026?
ChatGPT: No
Gemini: No
Claude: No

9) What will the Federal Funds Target Rate (upper bound) be at the end of 2026?
ChatGPT: 3.25%
Gemini: 3.25%
Claude: 3.50%

10) What will the dollar price of gold be at the end of 2026?
ChatGPT: $4,500
Gemini: $4,900
Claude: $3,100

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It was reassuring that when I initially asked the AI models to make these predictions, they were reluctant and would only offer heavily caveated ranges – suggesting they may be better calibrated than humans. I had to persuade them to produce point forecasts!

Of course, the models were right to be reticent. No LLM will be capable of making accurate and specific predictions about a system as complex as financial markets. Still, there were some other aspects I wanted to explore.

Were the predictions consistent across models?

Absolutely not. While there was some consistency between ChatGPT and Gemini, Claude made several very bold calls (gold at $3,100, for example). Interestingly, Claude’s rationale was often the most “convincing.” It behaved like a particular type of market strategist – frequently wrong, but articulate and provocative – and therefore attracting the most attention.

Were the predictions internally consistent?

This is an important and difficult question to answer. The models could certainly sound internally consistent when asked to justify their views, but some combinations looked odd. For example, Claude was the most bullish on oil while also being the most bearish on UK equities – a scenario that could happen, but feels like an unlikely pairing (given the resource heavy nature of the UK market).

Are they better than human predictions?

I was asking the models to predict outcomes that, in my view, are impossible for either humans or LLMs to forecast with any real accuracy. Can they do this as well as a human? Almost certainly. Does that make it useful? Outside of marketing copy, probably not.

There are definitely more important things to spend tokens on!

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* Thank you to my colleague Duncan for suggesting this post.

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

Having already written a piece on the similarities between how contestants in the TV game show The Traitors behave and our futile attempts to time financial markets, I thought I was finished with the subject. After indulging in the fourth UK series of the show, however, I realised there are so many excellent examples of human folly and perplexing decision-making that it felt worthy of a sequel.

For those uninitiated, there is a recap of how the game works in my previous post on this subject.

Here are some traitorous observations on investor behaviour:

We like being part of the herd. In the first banishment round of the latest UK series, 16 of the 21 contestants voted to remove the same person from the game. This broad consensus was reached despite there being no meaningful evidence to speak of. Of course, despite the agreement, they were entirely wrong.

We don’t like outsiders. After the first failed banishment, one of the 16 people who voted to remove a Faithful suggested turning their attention to a contestant who had voted differently, on the basis that this made them a potential Traitor. This was despite the majority having been incorrect in their prior accusation. It does not matter whether a group is right or wrong; it is safer to be in it.

We have a narrow circle of competence. A consistent feature of the game is that people in certain professions, such as lawyers and police detectives, believe they will be good at it and are reassuringly terrible. Part of me worries that people whose jobs involve making decisions under uncertainty do not seem to spend much time thinking about how to make good decisions. Another part simply concludes that our circle of competence is far narrower than we like to admit.

We are wildly overconfident. Along similar lines, a highlight of the most recent series is a retired police detective who plays the game with great confidence while aggressively and incorrectly accusing the wrong people of being Traitors. They also decided to keep their previous career a secret from the group, until revealing it to one other contestant who was, with wonderful inevitability, a Traitor. We are not as good as we think we are.

We are in thrall to expertise. Even though participants with so-called decision-making backgrounds are wrong at least as often as everyone else, other contestants remain in awe of their supposed expertise, no matter how often it proves unreliable. This is reminiscent of investors patiently waiting for the next insight from a market strategist who is right about as often as a coin flip.

We are attracted to new things. In the game, a banishment occurs every episode or day, and there is a strange behaviour whereby one day an individual may attract a great deal of suspicion and votes, but if they survive, they are barely mentioned the following day. We are drawn to shiny new things even when the evidence has not changed at all, much like investors latching on to the topic of the month and forgetting what they were previously obsessed with.

We are terribly calibrated. One of the most enjoyable and bewildering facets of the game is how often contestants say things like, “I am 100 percent sure X is a Faithful.” This is typically based on no evidence whatsoever and would be absurd even in situations where we have good reason to be confident. Here, participants have almost nothing to go on. We are very poor judges of how confident we should be about uncertain outcomes.

We need to survive: Although the game is focused on identifying Traitors in the group this isn’t really very important until we get near to its end because banished Traitors are replaced anyway. Participants shouldn’t care that much whether they banish a Faithful or a Traitor – they just need to make sure it is not them. Survival is the most vital and underappreciated thing in both the game and investing.

We cannot read people. Whether it is identifying a Traitor or judging a fund manager, we are awful at reading people yet remain convinced we are good at assessing both honesty and intentions. In a previous season, there was a contestant who was a professional magician whose job involved reading people. He was predictably hopeless at the game.

We ignore the most important things. Given how little useful evidence there is for identifying Traitors, particularly early in the game, the most sensible approach outside of choosing someone at random would be to ask: if I were a producer of this show, who would I select as a Traitor? Either contestants do not think this way, or, if they do, it is not shown on television because it would spoil the magic. My guess is the former.

We use salience as evidence. Judgements about who is a Traitor, especially early on, seem to be driven by what or who is salient, noticeable, or different. We mistake what captures our attention for what is meaningful. The current UK series has seen players frequently banished after making themselves the centre of attention – which is baffling behaviour.

While The Traitors is a light-hearted and convoluted game, it does a great job of revealing some of our most ingrained and peculiar behaviours. When we watch it and feel frustrated by the decisions being made, it is worth remembering that the contestants are simply being human. We do many of the same things in life and certainly when investing.

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

From a behavioural perspective, our ability to meet our long-term investment objectives is really about one thing: managing the conflict between our desire to save for the future and our craving to make ourselves feel better in the present. This has always been an incredibly exacting challenge, but it is being exacerbated by an industry that seems set on innovations aimed at exploiting our weakness for immediate satisfaction.

Back in 1997, economist David Laibson published a paper titled “Golden Eggs and Hyperbolic Discounting”. In it he contends that our long-run savings and investment goals are frequently compromised by the behaviour of our present-biased selves.

There are two key elements to this:

• We fail to save sufficiently for the future because we choose to consume more today.
  
  
• We fail to keep our savings invested for the long run because of our response to short-term volatility and temporary market losses.

Laibson argues that investors are therefore blighted by having “too much” liquidity, which gives us ample opportunity to prioritise how we feel right now over our future needs. If we know that humans have this strong tendency, then illiquidity becomes incredibly valuable – because it protects us from ourselves.

While Laibson refers to the value of illiquidity, he is not advocating a wholesale switch into private markets. What he means is the use of commitment devices: steps we can take to prevent panic selling or otherwise stop us reacting in-the-moment at the expense of our future needs.

Commitments come in many forms – the most obvious being investment vehicles holding genuinely illiquid assets with lock-up periods. Not everything has to be so punitive, however. Soft frictions, such as auto-enrolment or the Save More Tomorrow scheme in the US, can also be extremely effective. Anything that reduces impulsive behaviour can deliver value many years from now.

While tools that encourage commitment can have a dramatic impact on behaviour, there is a problem: we don’t like being asked to commit. There are several reasons for this:

• It feels like a restriction of choice and agency. Adding friction to slow our decision making or reducing our ability to act can feel like a loss of freedom. Imagine if an investment platform introduced a 24-hour “cooling-off” period for every trade. This would likely be effective at limiting hot-state behaviour, but few people would choose to use it.
  
  
• We don’t think we need them. Commitment devices are designed to counter problems caused by our present-biased selves. To value them, we must first accept that we suffer from this behavioural failing. Of course, we all do – but we are far more likely to see it in others than in ourselves.
  
  
• Commitment devices involve trade-offs. Anything that reduces liquidity or limits action for the benefit of our future self comes with a present-day cost. This may not just be frustration at our inability to act; it could also mean being unable to access funds when they are genuinely needed.

Laibson made the argument about the dangers of “too much liquidity” nearly 30 years ago, but his case feels more relevant – and more urgent – than ever. Humans remain inescapably present-biased, yet the investment landscape has evolved in ways that exacerbate (and perhaps deliberately exploit) this bias, to the detriment of our long-term savings goals. There are a host of contributing factors, but these are the most culpable:

• ‘24 hour’ trading. Financial markets are increasingly ‘always open’, providing instant liquidity at all times. There is no restriction on our ability to react in the moment. This can be framed as technology-driven liberation or, more realistically, as a behavioural disaster.
  
  
• Constant portfolio access. Not only can we trade at any time, but we can live the minute-by-minute fluctuations in our portfolio values. Nobody would argue that investors should not have access to this information, but few have seriously considered the behavioural cost.
  
  
• More news and emotional stimulus. We are bombarded by financial news and opinion, a phenomenon greatly amplified by social media. There is more news, more noise, and more negativity. Humans make poor decisions when emotional stimulus is high and friction is low – precisely the environment we now inhabit.
  
  
• The financialisation of everything. Over time, more aspects of life are becoming financialised products to buy and sell – stocks, currencies, cryptocurrencies, sports, even political events. Everything has become something to trade. The line between gambling and investing is increasingly blurred, and may soon disappear entirely.
  

All of these factors increase the likelihood that we sacrifice long-term goals for short-term fulfilment. If Laibson was worried in 1997, he should be petrified today.

We can think of the golden egg as the future consumption funded by our savings, and the goose that lays it as a sensibly diversified investment portfolio. Financial and technological innovation is making it far easier to kill the goose.

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Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2), 443-478.

(Laibson frames the ‘golden egg’ in a slightly different way in this paper, but the overarching point is the same).

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

Back in December of 2024, I ran a survey which was completed by 276 finance professionals. It asked them 10 questions on what would happen in markets in 2025.

Let’s see how they got on:

Question 1 – What will be the % total return of the S&P 500 in 2025?

Prediction: 6.5% (mean)

Outcome: 17.9%

The 21 participants that predicted a loss of 10% or greater for the US market must have been feeling pretty good in April, but by the close of the year the US market had recovered to produce a total return just short of 18%. Of the 268 responses, only 8% predicted performance of 18% or more.

The average return expectation was for a 6.5% total return from US equities in 2025. While this seems a reasonable number such ‘normal’ performance is quite rare in any given calendar year – equity returns are high because they are very lumpy!  

Question 2 – Will the Russell 2000 outperform the S&P 500 in 2025?

Prediction: Yes (60%)

Outcome: No

The respondents were moderately optimistic about a recovery in the fortunes of smaller US companies, perhaps through a combination of depressed (relative) valuations and the initial hopes for the economic impact of Trump’s second term. As it turned out, the performance of small caps in the US was decent in absolute terms, but they still trailed the mega caps by c.6%. Maybe 2026 will be the year.

Question 3 – Which equity market will produce the highest total return in 2025 (USD terms)?

Prediction: US equities (39%) 

Outcome: Chinese equities (20% of respondents)

There was a strong expectation for a continuation of US equity exceptionalism in 2025, which turned out to be entirely wrong. While near 40% of participants backed the US to be the leading market in 2025, its returns trailed all other options except for India. This was a win for the contrarians, as the much-maligned Chinese market came top of the pile – making 20% of the respondents right.  

Question 4: What is the probability of the US economy entering a recession in 2025?

Prediction: 25% chance of a recession (mean)

Outcome: No recession.

Without the ability to observe parallel universes it is quite difficult to judge the quality of responses here. All that can be said is that most participants were generally sanguine about the prospects for a US recession in 2025 and the outcomes were consistent with this. It was looking a little dicey in April and May, however, with prediction markets pricing in a 60% chance in 2025.

Oh, and the 23 forecasts of a 0 or 100% recession probability were wrong from the start.  

Question 5: Will Nvidia outperform the S&P 500 in 2025?

Prediction: No (62%)

Outcome: Yes. Nvidia returned 39%, against 18% for the S&P 500.

Much like the small-cap view, participants were expecting a broadening out of US equity markets in 2025. It seemed reasonable to believe a company of that size, trading on that valuation with that stratospheric performance history couldn’t outperform for another year, but markets have a history of being wholly unreasonable.

Question 6: What will be the GBPUSD spot exchange rate at the close of 2025?

Prediction: 1.26 (mean)

Outcome: 1.34

Although on average there was an expectation for a slightly stronger sterling / weaker dollar across 2025, the most common prediction was for sterling weakness (1.20). This probably reflects lukewarm sentiment for the UK and (thus far) unfounded optimism about Trump’s impact on the dollar. Only 16% of respondents went for a rate of 1.34 or above.

Here is a resolution for 2026 and for every year for all investors – I will not make currency forecasts.  

  
Question 7: What will the USD price of Bitcoin be at the close of 2025?

Prediction: $108,353 (mean), $95,000 (median)

Outcome: $87,647

Participants were a little too optimistic about the fortunes of this ‘belief asset’ in 2025, however, the forecasts did range from $1m to 0 so it was a little noisy. It is hard to say whether any given prediction is either serious or a joke given the nature of the asset.

Question 8: Will the S&P 500 suffer a peak to trough decline of more than 20% in 2025?

Prediction: No (60%)

Outcome: No (just)

Although US equity returns were strong across 2025, there is also a Wikipedia page for the ‘2025 Stock Market Crash’, which although seeming a little dramatic is a reminder of the negativity that surrounded ‘Liberation Day’ and the policy chaos that surrounded it. If we classify a bear market as a 20% decline, the S&P 500 just missed out in 2025 falling 18.9% between 19 February and 8 April.

Question 9: What will be the annual rate of US inflation (CPI) in 2025?

Prediction: 2.9%

Outcome: 2.7% (November print)

This is a tricky one for a couple of reasons: 1) We don’t yet have the December figure, 2) There was much scepticism from economists about the November print because of the impact of the US Government shutdown. It is probably fair to say that the average inflation expectation for 2025 was in the right ballpark.

Question 10: What will the US Ten Year Treasury yield be at the close of 2025?

Prediction: 4.20% (mean)

Outcome: 4.17%

Who said that forecasting bond yields was difficult? The crowd was wise on this topic with the average prediction being pretty much bang on the yield of ten-year Treasuries at the close of 2025. The dispersion of responses to this question was, however, wide. 20% of participants forecast the yield finishing the year above 5% and another 20% below 3.5%. Lots of wrongs can make a right.

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Making good predictions about financial market performance over short periods such as one year is incredibly difficult. In essence, we are trying to forecast how other market participants will react to events that we know will happen but don’t know the outcome and events that we don’t know will happen and (by definition) can’t know the outcome. We also need to work out how these events or developments might interact with each other. It would be hard to design a more complex prediction problem. *

(There is a reason why annual investment outlooks don’t spend much time reviewing what they said a year ago.)

Given this it is somewhat baffling that the entire asset management industry can sometimes feel like a giant forecasting game – one where people are constantly making predictions while being incurably afflicted with amnesia about the quality and content of their previous judgments. I was totally wrong last time, but you have to listen to me this time around.

The good news is that for most investors this is all irrelevant noise. Our long-term investment fortunes will not be defined by our ability to foresee what will happen in markets over the next quarter or year. They may, however, be defined by our ability to accept this and act accordingly.

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* It is not even about getting a single prediction right, you have to do it again and again.

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.

When you buy a stock, what drives your decision-making? In a new study, academics ran a series of AI-driven interviews with over 1,500 investors from across the world about their stock-picking choices, and found 13 different factors (or what the paper calls “recurrent mechanisms”) that motivate decisions to invest in a particular company. Although there were some notable distinctions across regions and cultures, Fundamental Strength and Growth / Innovation proved to be far more common reasons than Valuation / Mispricing and Momentum. [i]

The researchers employed AI to conduct field interviews with a large group of investors situated in several countries (including the US, UK, India, and South Korea). There was also a broad spread of investable assets, with 42% of the sample between $1,000 and $499,999, 29% between $500,000 and $999,999, and 29% above $1m.

Participants engaged in open-ended, AI-led conversations designed to explore their rationale for stock purchases. A GPT model was then used to assess the responses and define “recurring decision patterns” or, in simple terms, the reasons why investors were buying stocks.

The process led to the identification of 13 mechanisms that commonly described the choices made by investors around individual company investments. They were ranked as follows:

1. Fundamental Strength (40.5%)*
2. Growth Innovation (37.6%)
3. Familiarity / Brand Effect (28.4%)
4. Blue Chip Comfort (28.1%)
5. Authority / Follow (18.6%)
6. Momentum (17.9%)
7. Confluence (14.6%)
8. Dividends (14.0%)
9. Social Copy (13.6%)
10. Valuation / Mispricing (13.6%)
11. Buy the Dip (12.5%)
12. ESG / Values (10.3%)
13. Technical Analysis (6.8%)

(The numbers in parentheses show the proportion of responses where a specific mechanism or rationale was identified.)

This is a fascinating study that I would recommend taking the time to read. I have some initial observations on the results:

Investors like to own stocks that are performing well right now:

If you had asked me, prior to reading the study, to predict its findings, I probably would have said that investors like to buy stocks that have good current profitability and positive sentiment around its prospects – consistent with Fundamental Strength. This plays into so many of our behavioural biases – most notably our desire to extrapolate, our focus on recent outcomes, and susceptibility to salient stories. What is more attractive to an investor than a high-profile stock with good recent results and a captivating narrative?

In addition to this result being unsurprising, it is also worrying that so many investors implicitly believe that they have an edge in judging the prospects of these types of companies.

Investors can’t resist a good growth story:

It is also unsurprising that investors have a preference for growth and innovation. While this may be somewhat exaggerated by market conditions in recent years, the desire to invest in innovative companies with the potential for abnormally high payoffs predates the more recent success of technology and AI-related names.

Investors are comfortable with what they know:

Investors like companies that have an established history and those which they may have personal experience with. There are some powerful heuristics at play here, both in the view that a long-established company may be lower risk, and in the tendency for people to judge a company’s prospects by how positive their own experience is with the product or service it offers. I would expect this factor to be far more common among private investors, where the depth and breadth of knowledge of the universe of stocks is very likely to be limited compared to professional investors.

There is good news for value investors:

Value investors will probably have mixed emotions reading this study – both despondent that Valuation / Mispricing does not rank highly among the reasons people buy stocks, and delighted at the opportunities that may arise because valuation does not rank highly. Value investing works (over time) because it is behaviourally difficult, and because we are predisposed to care more about other things (valuation only scores as highly as copying other people).

There is probably a stigma around momentum investing:

Momentum as a standalone factor only ranks sixth, but I think this understates how important recent returns are to investor decision-making. Performance momentum is inevitably deeply intertwined with Fundamental Strength, and I have a strong suspicion that people are likely to justify performance-chasing decisions as being driven by strong fundamentals. It sounds more compelling and robust to say that you have invested because of a company’s improving operational prospects, rather than because its price has gone up a lot recently.

There are significant cultural / regional differences:

There are some fascinating regional and cultural differences in the study. For example, Fundamental Strength is stronger in Japan (60%) than in the US (30%). Also, close to half of the responses in Japan were assessed to have Familiarity / Brand Effect as a meaningful contributor to stock decisions. The extent to which these are entrenched differences or reflective of more recent, localised market performance is
unclear, but it is undoubtedly interesting.

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Why are certain factors so influential in what stocks investors buy?

If we look through the underlying mechanisms described, there is a heavy behavioural influence on the largest contributors to stock picks – Fundamental Strength (extrapolation, outcome bias), Growth Innovation (lottery-ticket pay-offs, stories), and Brand Effect (familiarity). This seems to outweigh the influence of factors that have somewhat more evidence supporting them (such as Valuation / Mispricing and Momentum).

Of course, it is impossible to know with any level of confidence what drives our behaviour and this study only goes so far as to seek to identify the stated reasons for a stock purchase. This raises two intriguing questions:

– How significant is the gap between the stated reasons and the ‘real’ drivers of our investing behaviour?

– If the stated reasons are a fair reflection of the factors influencing our stock purchasing decisions, why do investors choose those factors? (Why is Fundamental Strength more important to us than Valuation? What is the appeal of dividends?)

Questions for another post.

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* The definition of Fundamental Strength is as follows: “Investors buy stocks when audited financial information indicates robust current performance, which, to investors invoking this mechanism, also signals high expected future returns.” Definitions for each of the mechanisms are provided in the full study.

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^[i] How Investors Pick Stocks: Global Evidence from 1,540 AI-Driven Field Interviews

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My first book has been published. The Intelligent Fund Investor explores the beliefs and behaviours that lead investors astray, and shows how we can make better decisions. You can get a copy here (UK) or here (US).

All opinions are my own, not that of my employer or anybody else. I am often wrong, and my future self will disagree with my present self at some point. Not investment advice.