Hendrik Bessembinder’s latest paper asks the question – which US stock has generated the highest long-term returns?[i] The answer is Altria Group (formerly known as Philip Morris). Over 98 years the tobacco company produced a cumulative return of 265 million percent! Based on this it would be easy to write about how lucrative it can be to sell addictive products, particularly when there are negative externalities involved, but there is something more important for investors to take away from the research – the power of compounding over the passage of time.

The cumulative total return of Altria seems astonishing, yet it equates to an annualised return of ‘only’ 16.3%.  On a standalone basis the figure does not seem remarkable, it is only when you apply time to it – long periods of time – that the dramatic impact of compounding takes hold.

Investors are inevitably drawn to high short-term returns (far higher than the 16.3% per annum produced by Altria), but these are inevitably unsustainable. That’s not an opinion, it is a mathematical reality. There is an inescapable gravitational pull as both time and size drags astronomical performance back toward normality.

Most investors neglect the power of time and the monumental advantage it bestows upon those with a sufficiently long horizon. We behave as if we are attempting to generate the highest possible return in the shortest possible time. Instead of compounding solid returns, we become destined to compound a succession of poor decisions with painful long-term consequences.  

It is not surprising that investors act in this fashion. We are wired to worry about and act on short-term risks and opportunities – it is a great strategy for evolutionary survival, just a terrible one for long-term investing. But it is more than that. The entire industry wants us to be impatient – whether it be selling the next great product or attracting our attention with the next alarming article.

Everything within us and around us seems designed to interrupt the positive force of compounding.

Investors who understand their time horizon, build a sensible portfolio and rarely make changes will be far better off than most. The trick is understanding ourselves and our environment well enough so that we avoid the temptation to veer from that course.  

Despite it being the best strategy for most investors, doing nothing (or at least very little) has a very bad reputation, so maybe it needs a rebrand. Instead, from now on, let’s call it: ‘Letting compounding do its work’.

[i] Bessembinder, H. (2024). Which US Stocks Generated the Highest Long-Term Returns?. Available at SSRN.

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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)

Investors love talking about ‘skin in the game’. It has become something of a truism that a fund manager with substantial sums of their own money invested in their strategy will make better decisions. Not only is this a dubious generalisation but it also overly simplifies what can be quite a messy incentive problem. Skin in the game is an important idea but also one we can get wrong by misjudging where it exists, what it is telling us and when it might be a problem.

Incentives drive behaviour. If we could only know one piece of information to predict how someone would behave in a given situation, it would be their incentives. The idea of having skin in the game is a form of incentive alignment, it means that people directly experience the consequence (or risks) of their actions, not simply enjoy the benefits.

The decisions that we make will be heavily influenced by the distribution of potential outcomes that we face. An individual who bears all of the upside but none of the downside in a given situation, will make different choices to someone who faces the reverse.

It is too simplistic to say that having skin in the game leads people to make better decisions, but rather that the shape of incentive structures can profoundly impact our behaviour.   

Skin in the game is a critical concept, but one which investors can easily misjudge. Here are three examples:

Our skin in their game – The most obvious and frustrating scenario is where we believe that our interests are aligned with an investor that we are allocating money to, when in fact the structure is horribly asymmetric. Here we bear the majority of the downside risk, but the fund manager captures a disproportionate amount of the upside. The classic case of this is traditional hedge fund performance fees, where client capital is put at risk and large performance fees can be generated (and crystallised) for fleeting periods of outperformance.

The worst aspect of these fee structures is that they are often framed as better aligning incentives – “we have skin in the game – when our performance is poor, our profits suffer”, but this is a sleight of hand that ignores the inherent asymmetry. If things go badly wrong who bears the majority of the painful costs? If things go right who can generate transformational wealth? The answer to these questions should be the same, and it is not.

From a utility maximising, risk / reward perspective asset managers will always want to structure fees and incentives in ways such as this (this is not just a hedge fund issue, hello private equity), but it is a terrible structure for clients and not ‘skin in the game’ in any beneficial way.

Skin in the game as a negative signal – Now for an unpopular view. What if a fund manager investing heavily in their own strategy was a sign that they were not a good investor, but rather an overconfident and imprudent risk taker? What if having skin in the game was a useful signal for which fund managers to avoid?

This is an exaggeration for effect here, but it is not clear to me that a fund manager holding a significant portion of their net worth in their own strategy is always a positive, nor likely to encourage better decisions.

I am quite keen on investors who are humble, understand probabilities and are aware of the prudence of diversification. Such investors may be less likely to invest most of their wealth into their own investment strategy – the same one on which their career is reliant upon.

Aside from this issue there are other questions, such as: How does a fund manager having a large portion of their wealth invested in a strategy impact the choices they make? Are their objectives and risk tolerance aligned with our own? Given that fund managers have been known to be infected with a slight dose of hubris – should we be emboldened by their own (over)confidence in their strategy, or worried by it?

People seem to confuse it being ‘right’ that a fund manager invests in their own strategy (which it may be), with it necessarily being a positive indicator. In some circumstances it might be, in others perhaps not. It is a complex and nuanced area, not a useful heuristic.

Skin in different games – The final area is one where we tend to ignore issues around skin in the game and incentive structures: group decision making. Somewhat bizarrely little attention is paid to how groups of people make judgements – the behavioural literature is focused on the individual – despite most of our choices being made as part of a collective.

One of the primary reasons that boards and committees are so often dysfunctional is because they suffer from profound incentive misalignment problems. There is no meaningful, unified skin in the game because everyone is playing entirely different games. Often everyone around the room will have different incentive structures, time horizons and metrics that they are measured against, which will dominate their behaviour.

The CEO worried about share price performance, CIO focused on investment returns, the CFO trained on controlling costs and the independent Non-Executive hoping nothing blows up in their tenure. This is not a recipe for aligned, high quality decision making, but individuals with their risk and rewards attached to very different things. It shouldn’t be surprising that group decisions are defined by frustration, procrastination and uneasy compromise. Internal politics are generally about trade-offs between individuals with divergent incentives.

This is not just true of boards, most group structures face this problem, but never realise or acknowledge it – they just assume that everyone has a consistent set of objectives. Our assumption should always be that a group of people put together to make a decision do not have alignment of incentives or share skin in the game, unless an express effort is made to make it so.

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Incentives are almost certainly the most important driver of human behaviour and sometimes they are incredibly easy to observe, but, as the notion of skin in the game shows, they can be a little more complex than they might first appear.  

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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).

Humans are prediction machines. We have to be. Every decision we make is based on predicting something. Whether it is the sun rising tomorrow morning, our car slowing down when we press on the brake pedal or the bank we use remaining solvent. Every choice we make is built on assumptions about the future. Despite this, I spend plenty of time annoying people by talking about how pointless (or worse) most financial market predictions are. So, are they essential or worthless? Well, it depends.

It depends because the types of prediction that investors make are wildly different – some are fiendishly complex and dynamic, whilst others are simple and stable. Before comparing some examples, let’s think about the factors that are likely to moderate the challenge posed by an investment forecast.

Our basic approach to any prediction should be Bayesian. This means that we have a set of potential outcomes that we apply probabilities to based on some starting assumptions or prior beliefs. Crucially, when we receive new and relevant information we update our priors and probabilities.

Let’s take a simplified example. I believe that there is a 70% chance that US equities will outperform European equities over the next twelve months because of stronger earnings growth (this is not true, I have no idea). Now, if US company earnings are surprisingly weak in the next quarter, I may revise down my probability.

Sounds simple, but there is a problem. While a Bayesian method is the best way to approach this scenario, it doesn’t mean it leads to good or helpful answers from an investment perspective. This is due to the forecast itself being just too difficult.

What makes a forecast hard? There are three key questions:

1) Can we define the variables that matter to our forecast? We need to be able to identify the factors that will influence the outcome of the thing we are forecasting.

The list of variables that could impact the relative performance of US equities and European equities over the next twelve months is huge and includes things that we know (such as earnings) and things that we don’t (unexpected events).

2) Is the group of variables that matter stable? The more the factors that matter change, the harder it is to make good predictions.

It is not just that the list of variables that matter is long and unknowable, but the relative importance of them is not constant. Maybe earnings will matter this year, maybe a pandemic the next.

3) How predictable are those variables? It is one thing identifying what the variables of importance are, but we still need to be able to predict them with some level of accuracy.

We don’t just need to define the factors that will influence the relative performance, we need to be able to predict them. Even if earnings growth was the most important variable in any given year, we would still need to forecast it well.

This type of forecasting is exceptionally tough, even if we take a sensible, measured approach.

All our investment decisions are a type of forecast – does this mean they are all equally problematic? No, they are not. Let’s take another example that seems similar at face value, but is an entirely different proposition.

Imagine we have thirty years to retirement and invest the majority of our long-term savings into equities. We are making a forecast here – that investing in the stock market is the best way to grow our wealth over time. What assumptions are we making in this instance?

– That economies will grow in real terms.

– That corporate earnings growth will be closely linked to economic fundamentals.

– That shareholder rights will be upheld.

Now, we might want to expand this list a little or be more nuanced, but in basic terms these are the elements that will impact our forecast. Over the long-run these are likely to be the aspects that matter, they are unlikely to change and we can predict them with reasonable confidence.

There are, of course, no guarantees. There is by no means a 100% probability that equities are the right place to be even over thirty-years – there are all sorts of remote but not impossible adverse scenarios – but the likelihood we should attach to this being accurate is far, far higher than our one-year market conjecture.

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As much as I dislike admitting in, we are constantly making forecasts about financial markets – it is inescapable. That doesn’t mean, however, that we cannot differentiate between predictions that are necessary and reasonable, and those that are impossibly difficult and almost certainly damaging.

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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).