Paul Marshall, co-founder of the $64 billion hedge fund Marshall Wace based in London, shares 25 years of investing and industry insights in his book 10½. The book offers a concise, engaging, and insightful read, distilling his most valuable lessons. Here, we share some of the key highlights that we found most compelling. Hope you enjoy them!

PREFACE

The nature of investing is such that to be successful, you need to constantly adapt.

Our business would not have succeeded without the investments we have made in optimization, trading, technology and operating systems.

INTRODUCTION: THE GREAT DISCONNECT

There can be no field of human knowledge where the disconnect between theory and practice is as pronounced as finance.

Scottish thinkers (Hutcheson, Hume, Smith) were empiricists – they chose to advance with humility, tentatively, and on the basis of what worked. Fund Managers must adopt these characteristics.

In contrast, the tradition of French philosophy was, almost from its beginnings, rational, deductive and reductive. French thinkers – like Descartes or Walras – preferred to start with axioms and then seek out the evidence to prove them. Fund Managers must avoid these ways of approaching investment management.

Science is impatient. It measures what it can with tools in its possession. Its main tool is mathematics. The kind of mathematics which was fundamental to the Industrial Revolution was basically linear in nature; predictable inputs led to proportionate outputs and axiomatic reasoning could be extended into the physical world.

Markets are an exemplar of what cannot be contained within axiomatic thought. In the end, the axioms are no better than falsifying assumptions. Understanding why they are falsifying is the beginning of financial wisdom.

Graduate recruits from academia face a sharp learning curve when they confront realities of the market. If they do not adapt quickly, the results can be catastrophic. Remember Robert Merton and Myron Scholes?

LESSON 1: MARKETS ARE INEFFICIENT

Supply and demand are virtually never in complete equilibrium. Disequilibria are resolved through price movements. The opportunities to make money in business and in markets lie precisely in the points of disequilibrium.

Efficient-market theory is so far removed from working reality that it has become the butt of many jokes. Prices can move far away from fair value because of demand and supply mismatches.

‘Scarcity Paradox’ by Adam Smith explains this. The things which have the greatest value in use have frequently little or no value in exchange; on the contrary, those which have the greatest value in exchange have frequently little or no value in use. Water is useful. A diamond has scarcely any use-value.

Water and diamonds may attract vastly different prices depending on the circumstances of the buyer/seller. A starving man in the desert can pay almost all his diamonds for a glass of water. His price elasticity is almost infinite. In normal circumstances, water is abundantly available, and one would certainly not sacrifice any diamonds to pay for it. Scarcity Paradox! Another example: during general conditions, LIBOR fund is a cheap commodity. In 2008, after the collapse of Lehman Brothers, it became scarce and precious.

Benoit Mandelbrot showed that price movements do not have the same well-behaved probability distributions that you find in physics. The ‘tails’ of the distributions are ‘fat’ because extreme price moves happen much more often than they would if the market were described by the conventional bell curve.

If normal distribution was a good way to think about market risk, the probability of the 29.2% fall in the Dow Jones Index on 19 October 1987 would have been less than 1 in 1050.

George Soros went on one step further with his theory of reflexivity. Markets not only anticipate economic developments but actually drive them and are in turn driven by them, because ‘human beings are not merely scientific observers but also active participants in the system’.

The theory of reflexivity is best illustrated by the classic emerging market phenomenon of contagion (for example, Turkey in 2018). An emerging market country with high budget deficit and high levels of foreign currency (dollar) debt experiences a speculative attack on its currency. The fall in the currency causes the dollar debt to rise further as a share of domestic GDP, aggravating the situation. The depreciation in the currency in turn leads to higher inflation, which leads to further speculation against the currency and a further rise in the share of dollar debt to GDP. The combination of a falling currency and rising inflation puts pressure on the central bank to raise interest rates and this in turn puts pressure on real assets. A vicious circle is induced.

LESSON 2: HUMANS ARE IRRATIONAL

Loyalty to tribe is much more powerful than the loyalty to truth.

Minsky moment! Hyman Minsky’s central insight was that long periods of financial stability could ultimately lead to instability because they promoted complacency and excessive leverage and risk-taking. The same principle can work the other way.

Arguably, the exceptional period of financial stability following the Great Financial Crisis was a Minsky moment in reverse.

“Stupidity does not give way to science, technology, modernity, progress: on the contrary, it progresses right along with progress.” ~Flaubert, Madame Bovary, 1857

You can’t confirm to stupidity. It does not conform to any predictable rules.

Behavioural Biases:

1. Optimism bias: a cognitive bias which causes a person to believe that they are at a lesser risk of experiencing a negative event compared to others.
2. Gambler’s fallacy a.k.a Monte Carlo fallacy: a belief that, if something happens more frequently than normal during a given period, it will happen less frequently in future (or vice versa).
3. Mean Reversion bias: it assumes that market anomalies will be quickly erased.
4. Overconfidence: the false assumption that an individual is superior to others.
5. Anchoring bias: occurs when someone allows an initial piece of information to sway subsequent judgments.
6. Confirmation bias: the tendency to interpret new evidence as confirmation of one’s existing beliefs and theories.
7. Disposition bias: the tendency of investors to sell assets which have increased in value, while keeping assets which have dropped in value.
8. Sunk Cost fallacy a.k.a Concorde fallacy: investors being influenced by the fact they already ‘sunk costs’ in an investment.

LESSON 3: INVESTMENT SKILL IS MEASURABLE AND PERSISTENT

We insist that at least 60% of the risk for any given portfolio should be attributable to stock selection rather than exposure to country and style factors and we bear in mind the split between idiosyncratic skill and style-based performance when we evaluate a manager.

Great managers can be optimists, pessimists, mean reverters, growth guys, value guys, short-term traders and long-term holders. Perhaps above all they must be resilient.

The main threats to persistent fund performance are all character related and lie outside the domain of fund management. The reddest flags for underperformance – the 3 Ds – death, divorce and disease.

LESSON 4: IN THE SHORT TERM THE MARKET IS A VOTING MACHINE, IN THE LONG TERM IT IS A WEIGHING MACHINE

Despite being a master of security analysis (as per his book by that name), Graham always left a lot of room for investor behaviour, as per his famous phrase ‘in the short run the market is a voting machine, in the long term it is a weighing machine.’

Graham was in general fairly dismissive of short-term movements.

Someone who paid a lot more respect to short-term market movements, though, was John Maynard Keynes. Keynes was an economist who actually managed money – a rare thing. And he has an audited track record. He became bursar of King’s College Cambridge in 1924 and from 1927 to 1946 was responsible for the College’s Chest Fund. Over this period, the fund compounded at an annual return of 9.1% while the UK stock market fell at an annualised rate of slightly less than 1% per cent. Between 1928 and 1932 the Chest Fund fell substantially, and underperformed the UK equity market by a wide margin. But Keynes more than made up for it in the 1930s.

The reason Keynes is interesting as an investment seer is because he understood the interplay between fundamentals and the subjective mind. All of his famous sayings about the stock market relate to the nature of subjective thinking:

‘Successful investing is anticipating the anticipations of others.’

‘If farming were to be organised like the stock market, a farmer would sell his farm in the morning when it was raining, only to buy it back in the afternoon when the sun came out.’

The task is not to identify the most attractive faces but to identify the faces which others would find the most attractive.

LESSON 5: SEEK CHANGE

Catalysts can matter simply because they create a story. Humans like stories.

Investors respond to stories. Catalysts make the stories concrete.

What everyone should know is that it is very easy to tell a story about a stock. Your ability to tell a story has almost nothing to do with your ability to pick stocks. In the case of some successful managers, it is almost inversely correlated. Yet it is the staple, still, of many due diligence processes. By all means ask questions about stocks for entertainment and to illustrate the process of the manager. But don’t give it much weight in your due diligence.
Scrutiny of industry structure and sector dynamics can generate super-normal returns from industry selection because not enough investors do it.

LESSON 6: THE BEST PORTFOLIO COMBINES CONCENTRATION WITH DIVERSIFICATION

Very few managers have a high conviction at any one time about more than about ten stocks. They simply don’t have bandwidth to analyse enough stocks to retain more than about ten high conviction names ‘on the boil’. So they should concentrate their risk where their conviction lies – in those top names. It is also why we expect our own fundamental managers to run concentrated portfolios (30-50 longs). Focusing on highest conviction ideas brings the best results.

Even with 30-50 long positions, it is not uncommon (and is fully embraced) for just ten names to make up close to 100 per cent of the idiosyncratic risk of a discretionary manager. Managers may carry more positions, but they should be dynamically sizing those positions on a continuous basis to optimise risk concentration.

To make concentration really work in a portfolio you need to be sure that as many of your positions possible are working for you. You cannot afford many ‘sleepers’. And you need a high ‘slugging ratio’ – calculated based on the realised gains on winning trades compared to realised losses on losing trades. Maximising your slugging ratio is a key skill of a successful trader. The best exponent of this is probably Stanley Druckenmiller. Druckenmiller is a believer in concentration and has strong, non-consensual, convictions. But he waits to size up his position until he gets confirmation from the price action.

Graham was a staunch advocate of diversification. Buffett dismissed it: ‘Diversification is a protection against ignorance. It makes little sense if you know what you are doing.’

How can these two perspectives be reconciled?

The answer is that you cannot fully reconcile them at the level of a single portfolio – there are trade-offs between concentration and diversification, between return and risk. But you can reconcile them at the level of the business and at the level of the client.

We combine maximum conviction with maximum diversification.

LESSON 7: SHORTS ARE DIFFERENT FROM LONGS

Shorts have ‘negative carry’. Long positions have a positive carry. The alpha from short positions has to be considered net of borrowing costs to make it comparable in financial terms to the alpha on longs.

In the past 50 years there have been six bull markets and six bear markets, if you define a bear market as a 20 per cent drawdown which is not reverse for twelve months. The average bull market has lasted 6.9 years, the average bear market 1.5 years.

When a short position goes against you it grows in size and becomes more of a problem for your portfolio. On the other hand, when long goes against you it becomes smaller and therefore less of a problem.

LESSON 8: A MACHINE BEATS A MAN, BUT A MAN PLUS A MACHINE BEATS A MCHINE

Machines typically do not fare well in a crisis. They are not good at responding to a new paradigm until the rules of the new paradigm are plugged into them by a human.

Systematic investing typically operates on the basis of diversification and multiplication (many positions, many trades) whereas fundamental investors rely on concentration and amplification (a small number of concentrated positions with lower turnover).

Maybe machines will find a way to close this gap in portfolio construction, but they will always lag in understanding new paradigms and therefore always lag behind on the really big trades.

At the time of writing, systematic hedge funds are in the midst of a protracted period of poor performance (15 months plus). The world of systematic investing is becoming more competitive and yet alpha opportunities do not yet seem to have eroded much on the fundamental side. Perhaps there is room for both.

What is also certainly true is that the two will increasingly converge.

The ability to harness and deploy technology and data will be one of the most important determinants of long-term competitiveness in the investment industry.

LESSON 9: RISK MANAGEMENT – RESPECT UNCERTAINTY

“There are old soldiers and there are bold soldiers but there are no old, bold soldiers", as the highly decorated US Army colonel David Hackworth wrote in his memoir, About Face.

There are many parallels between military strategy and investing. Both involve complex systems dependent on human agency – multiple decisions with multiple outcomes including a significant element of chance. But also reflexivity – one sent of actions by one participant can change the reality of the market/battle and therefore change the behaviour of other participants.

In battle there are usually just two protagonists (the opposing generals), each trying to impose order on complexity. If you can understand and anticipate the protagonists, you will goa long way to anticipating outcomes.

In markets there are multiple protagonists at multiple levels, all of whom can impact outcomes. Yes, there are governments and central banks who might try to impose centralised order in times of extreme crisis, but barring these interventions, you are operating within a near-perfect example of multi-agent non-linear complexity.

This poses the challenge of what can be predicted – and prepared for – and what cannot.

“Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated..... It will appear that a measurable uncertainty, or "risk" proper.... is so far different from an unmeasurable one that it is not in effect an uncertainty at all.” ~Frank Knight, Risk, Uncertainty and Profit, 1921

The art of risk management is to anticipate or identify emergent risks so you are ahead of the wave before it breaks.

Traditional parametric VaR (value at risk) stress tests assume that returns and volatility follow a normal distribution. They are useful in normalised environments. But in stressed environments? Not so much.

Each tail event is different and history never repeats itself exactly, but at least it enables a better understanding of the scale of movement and the varying nature of relationships between securities.

In a crisis, two things are going on. There are events (the drivers of the crisis) and there are the market actors’ responses, which themselves reflexively influence the unfolding of events. The actors’ responses are driven not only by fundamentals but also by competing narratives. Fundamentals can shift on a daily basis as policy makers intervene or market practitioners act (reflexively or otherwise) and so the precious skill is to anticipate the narrative which approximates what is going on and which is going to drive investor behaviour.

In a world of radical uncertainty, pragmatism and flexibility become the essential skills. You can harness the rules-based systems and Bayesian models, but the art needs ultimately to be in charge of the science.

At a 1992 address to the Air War College, Colonel John Boyd warned of the dangers of rigidity: ‘The Air Force has got a doctrine, the Army’s got a doctrine, Navy’s got a doctrine. But of his own work he said, ‘doctrine doesn’t appear in there even once. You can’t find it. You know why I don’t have it in there? Because it’s doctrine on day one, and every day after it becomes dogma.’… if you got one doctrine, you’re a dinosaur. Period.’

The way to deal with a constantly changing world, ever in flux, is through agent-based modelling. Agent-based modelling allows for the complexity of the real world – for multiple agents, emergent phenomena, randomness, actions and reactions. In its purest form this does not mean abstract modelling which reduces the agents to arbitrary categories but rather it means understanding the specifics of each major financial gent – central banks, governments, Blackrock, Goldman, Bridgewater, Citadel, AQR, AIG, etc. – and the way they may act or react in specific circumstances.

Embracing the idea of radical uncertainty has significant consequences for the way you structure investment portfolios and specifically for the way you manage liquidity and leverage.

Hedge funds have to be structured in the good times to make the maximum per unit risk but also to take account of the dangers of unknown unknowns occurring at any time.

The best hedge against unknown unknowns is structural prudence in the use of liquidity and leverage.

In relation to leverage, you always need to leave yourself enough prudential margin to survive a crisis. The definition of prudential margin is a situation where you are able to take advantage of other players’ distress rather than leaving them to take advantage of yours.

Never be in a position where a stock owns you.

LESSON 10: SIZE MATTERS

It has been estimated that the minimum scale of AuM required for a hedge fund to break even has risen sevenfold, from $50M in 1998 to $350M in 2018.

Mercury Asset Management grew to be simply too big, owning huge (10 per cent plus) holdings in many of the leading FTSE stocks. Eventually (in 1998) the firm’s UK performance blew up completely.

The pattern has repeated itself many times, most recently in the case of Woodford Investment Management in the UK. It is a pattern familiar from any number of hedge funds:

Julian Robertson was the almost legendary manager of the Tiger Fund. He annualised circa 25 per cent per annum during the 20-year life of his fund from 1980 and became a billionaire. But this would not have been your investor experience, especially if you were late to the party. The money-weighted return on the fund since inception was relatively modest. Robertson allowed the fund to grow to $13 billion but did not adapt the way he ran money, which was through concentrated high conviction portfolio. In its last three years the fund lost 4 per cent (1998), 19 per cent (1999) and 13 per cent before it was decided to close it down in 2000. Some positions were so large and unwieldy that Robertson decided to distribute them in specie to clients rather than unwind in the market, most famously his 22.4 per cent stake in US Airways.

There are natural limits to the size of any investment strategy. We measure them in two main ways:

1. Execution costs. At a certain level of AuM, trading costs begin to scale in a non-linear fashion and this represents a meaningful handicap to realised returns. The only exception to this is strategies where the turnover is sufficiently low that the hurdle is only ever modest. But low turnover (defined, say, as less than 3x per year) strategies generally do not generate that kind of supra-normal alpha that we need to deliver on our client expectations. Most good managers can deliver strong alpha on up to $1 billion of capital. Very few can deliver the same persistent above $3 billion.

2. Liquidity footprint. The size of AuM can lead a manger to own an excessive share of company’s free float. This may not matter for months or even years, but in times of crisis, the manager can find him/herself unable to trade out of the position without a significant discount. This liquidity risk can only be avoided by applying prudential approach to the amount of any company free float which the fund/firm can own. We define liquidity primarily as percentage of market cap or free float, as it is the former which matters more in most circumstances. However, we monitor and restrict both.

LESSON 10½ : MOST FUND MANAGEMENT CAREERS END IN FAILURE

“All political lives, unless they are cut off in midstream at a happy juncture, end in failure, because that is the nature of politics and of human affairs.”

What Enoch Powell said about politics is even more true of fund management.

Fund managers are not as immediately exposed to the same democratic process. They don’t stand for election/ But they are indirectly exposed to the whims of popularity in ways which magnify its effects.

When a fund manager loses his or her halo, investors can vote with their feet (unless they are gated), pushing the fund manager into forced liquidation and unleashing a negative spiral of poor performance and subsequent liquidation.

Even a really good fund manager is wrong at least 45 per cent of his or her trades and is bound to have periods – sometimes quite extended – of poor performance. It matters enormously how the fund manager reacts to these poor periods. You need deep reserves of resilience. Confidence in your convictions. A strong character will use the period of underperformance to lay the foundation for the next period of good performance, by re-examining every assumption, every thesis, discarding some and doubling down on others. A weak character will freeze, their decision making impaired. Or they might take flight, mentally at least, and avoid the difficult thinking.

Fund managers often exit the business on one of these points of failure, either because they are forced out stupidly by clients or employers (like Tony Dye in 1999 at the peak of the internet bubble) or because they lose heart or energy.

Other fund managers fail for the equal and opposite character flaw – hubris.

You need to keep your feet on the ground at all times and always remember that you are never as good or as bad as you (or others) think you are.

The Ancient Romans had a good approach to this. When a general would return home they were typically granted a ‘triumph’.

The general would be accompanied by an ‘auriga’ (a slave with gladiator status). The Auriga would continuously whisper in the general’s ear ‘momento mori’ – ‘remember you are mortal’.

Ultimately, of course, it is all about character. If you do not begin your fund management career with a sense of your fallibility, you are likely to learn it. If you do not learn it, you are likely to fail.

Thank you for reading.

Marshall's book is relatively new, but his ideas are time-tested. At DSP Pension Fund, we try to avoid most of the pitfalls discussed in 10½. Here, you can read our Framework and Process for Portfolio Management and go through our Schemes’ Factsheets. If you like what you read, consider choosing DSP as your Pension Fund Manager.

Disclaimer

Investors are advised to consult their own legal, tax and financial advisors to determine possible tax, legal and other financial implication or consequence of subscribing to the schemes of DSP Pension Fund Managers Private Limited. Tax laws are subject to change.

Past performance may or may not be sustained in future and should not be used as a basis for comparison with other investments. Returns under NPS are subject to market risk and are prone to fluctuation depending on the state of the Financial market.