How to beat the market: a hedge fund manager writes…
A book by hedge fund manager Paul Marshall takes on the sector’s critics to argue it is possible to beat the market
The active versus passive debate, the impact of new quant technology, and the trade-off concentration and diversification, are some of the issues discussed in 10 1/2 Lessons from Experience (2020), an accessible and concise tour d’horizon of the contemporary investment landscape by Paul Marshall, co-founder of one of London’s first and most successful hedge funds, Marshall Wace.
Marshall is also known for his past association with the Liberal Democrats, particularly in regard to editing The Orange Book (2004), a collection of essays that marked a turn within the party to economic liberalism and smoothed the path to the coalition government with the Conservatives.
His latest book, ten chapters rounded off with a closing reflection, offers insights accumulated from Marshall’s experience building a successful fund in a fiercely competitive industry. As might be expected there is some flag waving for Marshall Wace; indeed the essay occasionally reads as an extended chairman’s statement from an annual report. But this is an intelligent and well written reflection on the contemporary investment world that will be of interest to private investors as well as Marshall’s peers.
Though hedge funds have, in Marshall’s own words, positioned themselves as the ‘self-styled elite’ of the investment world, their reputation is compromised by the high fees many charge for mediocre performance. The book’s opening chapters offer a spirited defence of the principle of active fund management against the trend towards passive investment, vividly illustrated in recent years by the rapid growth of thematic ETFs.
Against the Chicago School
Marshall targets the conceptual underpinnings of the passive case, the theories of efficient markets developed by the Chicago School which imply that investors should simply track markets rather than try to beat them. In their purest form, he argues, the School’s theses manifest a post-Enlightenment overconfidence that real world social systems, with all their infinite complexities, can be modelled in abstract frameworks.
He takes particular issue with Eugene Fama’s efficient-market hypothesis (EMH), which maintains that in a frictionless market in which rational participants have access to relevant information, securities will always be accurately priced, making it impossible to pick stocks with any confidence that they will rise or fall. Prices follow a ‘random walk’, today’s price providing no insight into tomorrow’s.
Marshall is surely right to insist this rarefied picture bears little resemblance to real world markets, which everyday participants know to be emotional places blown by the winds of shifting sentiment, where prices rise and fall in relation to each other. Their dynamics are better captured by George Soros’s theory of reflexivity, self-referential systems in which ‘human beings are not merely scientific observers but also active participants’, changed by the act of observation. Financial markets do not only anticipate and react to economic developments, but drive them in a tight feedback loop, a process vividly illustrated by the phenomenon of ‘contagion’ often seen in emerging markets, in which speculators bet against fragile economies and weak governments. Marshall also refers in this regard to Hyman Minsky’s observations on the capacity of markets to destabilise themselves. Long periods of stability prompt risk-taking which generates a crisis, after which a chastened market observes a period of calm before temptation reasserts itself and the cycle repeats.
The Chicago School’s ideal picture of the rational investor has been further problematised by the insights afforded by behavioural economics into investors’ chronic tendency to allow emotions to drive their decision making. Market participants are subject to all manner of biases: a natural tendency to overconfidence that leads them to believe they are less prone to error than their peers; a false belief that if something happens more frequently than normal during a given period it will happen less frequently in the future; a proclivity to allow an initial piece of information to sway — or ‘anchor’ subsequent judgements; a bias towards the perception that current market movements confirm past judgements; and a tendency to sell assets that have increased in value and hold on to those that have dropped. For Marshall markets ‘are highly complex non-linear systems created by a myriad of half-informed or uninformed decisions made by fallible (human) agents with multiple cognitive biases.’
He does concede that professionalisation has made markets more efficient over time, with more decisions being made by professional managers with access to much better information than retail investors: the retail share of the US market for example has fallen from 50% to 15% over the last 50 years. But that disproves the efficient markets assumption that participants have equal access. Even passive advocates tend to concede that stock selection in specialist markets where information is less available — emerging, small cap and biotech for example — is better left to professionals with more knowledge.
Measuring active fund performance
Purists aside, most passive advocates would accept many of Marshall’s observations: the EMH is intended to model reality rather than capture it. But they would simply note by way of response that the facts don’t lie: active funds do not have a good record of beating the market, their returns tending to converge with the market portfolio over time. What metrics, then, do active proponents offer to to quantify their claims that they can outperform it?
Marshall dedicates a chapter to explaining how his fund does it. The two standard skill metrics the industry has evolved play a part: the ‘Information Ratio’, applicable to long only managers, sets outperformance of benchmark against volatility of outperformance of benchmark; the Sharpe Ratio, a measure for hedge fund managers, weighs absolute return in excess of the risk-free rate against volatility of absolute returns.
But of themselves he thinks they are of limited value for separating ‘the signal from the noise’, identifying the ‘idiosyncratic skill’ attributable to a manager distinct from external factors. They can be useful if they are measured over a three to five year period that has witnessed a variety of market regimes, challenging bear as well as favourable bull markets. Analysis must also account for factors that can charge performance, such as style biases towards momentum, value, and growth, and the fact that some markets offer more favourable opportunities than others.
Marshall pays great attention to a rather simple ‘success ratio’, the percentage of winning trades. A good ratio is surprisingly modest, an ‘alpha success ratio of 52–53% is already very good if it is consistent through time. A truly great manager will have a success ratio of 55%.’ Indeed a success rate below 50% acceptable if significant sizes are taken in winning stocks.
Identifying opportunity
So how does a well resourced fund like Marshall Wace spot opportunities in the first place? The firm’s funds are structured according to Benjamin Graham’s observation that ‘in the short run the market is a voting machine, in the long term it is a weighing machine’. Its quant funds are in the voting machine game, seeking gains by anticipating investor sentiment and emerging market trends. Its fundamental managers are in the weighing machine business, taking what seems to be a classic value investment approach, seeking to identity stocks that appear to have been misvalued, with unstable prices tending in a particular direction. For Marshall ‘the two approaches exploit market inefficiencies over different time horizons.’
Value stocks are energised through catalysts such as takeovers, strategy announcements or new product launches, likely to prompt the market to revise its assessment of their worth. Funds like Marshall Wace seek to show their worth by recognising such opportunities before others at ‘the interesting moment … when the idea is just in its dawn, half-glimpsed and half-understood.’
In addition to value stocks the fund is not averse to buying and holding quality companies in recognition that markets can be ‘intrinsically bad at discounting long-term growth and earnings streams’. But Marshall thinks over emphasis on quality is ‘intrinsically lazy.’ He acknowledges Buffet’s insight that certain companies can develop ‘moats’, defences against competitors that enable them to establish, entrench and defend market position, but argues such defences are becoming ever harder to develop and defend in a global market in which technological innovation can swiftly ‘disrupt’ and erode established business models.
Some of book’s most interesting pages indicate how rich funds like Marshall Wace take advantage of quant technology, the rapidly developing advances in AI and machine learning that allow a vast range of data to be processed that previously had to be picked over by researchers. Computers are now able to filter company accounts, fund flows, and broker, market and social media sentiment for leads, and can sort through prospects according to momentum, value and other style factors. But technology complements rather than replaces human judgement: ‘Machines typically do not fare well in a crisis. They are not good at responding to new paradigms until the rules of the new paradigm are plugged into them by a human.’ Funds that want to stay in business will have to continue to invest in technology. But discretion will still be required to sift the data it produces.
Concentration versus diversification
Marshall moves on from discussing how securities are selected to how they should be organised at the level of the portfolio. Concentrated portfolios, focused on a narrow set of carefully chosen securities, have the potential to yield higher returns than more conservative diversified portfolios. That said Marshall acknowledges diversification — ‘that rare beast — a genuinely helpful innovation to have come out of the Chicago School’ — as an essential technique for measuring the prospect of returns against risk. But he argues diversification can be used to absolve managers of responsibility for selecting a good set of assets in the first place: the principle improves the risk characteristics of bad as well as good assets. As Buffet puts it: ‘Diversification is protection against ignorance. It makes little sense if you know what you are doing.’
As private investors will know there is no perfect trade-off between concentration and diversification at the level of the individual portfolio. But managers of multiple portfolios can square the circle to a degree by combining portfolios into ‘multi-manager’ — or ‘platform’ — funds which can go some way towards tapping the inherent virtues of both concentration and diversification. At Marshall Wave managers design concentrated portfolios that seek to exploit up to ten ‘big ideas’. But when integrated into overarching funds they become elements in a diversified strategy that benefits from ‘the maximum conviction of every underlying contributor’.
Managing radical uncertainty
Marshall devotes the book’s longest chapter to another somewhat insoluble issue: risk management in the face of uncertainty. Marshall embraces the notion of ‘radical’ uncertainty — against Bayesians for whom all probabilities should in principle be measurable — defined by John Maynard Keynes and Frank Knight, who distinguished between known risks which can be probabilised and unmeasurable uncertainties, events that simply cannot be foreseen according to any metric. The concept has been discussed at length in the recent book Radical Uncertainty (2020) by Mervyn King and John Kay, who argue that in ‘a world of radical uncertainty there is no way of identifying the probabilities of future events and no set of equations that describes people’s attempt to cope with, rather than optimise against, that uncertainty.’
That being so portfolios must be designed to withstand whatever the future might throw at them. Marshall writes that the ‘art of risk management is to anticipate or identify emergent risks so you are ahead of the wave before it breaks. The science is to probability-weight those risks and stress-test portfolios in real time so you can anticipate the effect of the wave of your portfolio.’
For funds managing billions of dollars like Marshall Wace that means developing risk management strategies that anticipate not just future events but how those caught up in them are likely to react. He subscribes to the agent-based modelling techniques advocated by Richard Bookstaber in The End of Theory (2017), which aspire to take into account the ‘multiple agents, emergent phenomena, randomness, actions and reactions’ at work within the complex real world, and anticipate the most likely responses to crises:
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.
Hedge funds also face particular risk management challenges in regard to liquidity and leverage, ‘the two grim reapers of the financial markets’. Funds can retain liquidity by setting limits to the position they take in any particular company. And the maintenance of appropriate risk-return ratios through diversification allows them to continue to exercise leverage. They must also guard against becoming too big. Although the minimum scale of assets under management (AuM) required for a hedge fund to break even has risen sevenfold since Marshall Wace was founded in 1997, Marshall says that ‘the guilty secret of the fund management business is that size matters even more in the other direction. Beyond a certain level of AuM, size becomes an impediment to skill-based returns as it requires trading costs in a non-linear fashion and reduces the flexibility of trading and risk management.’ Again, position size is vital: keeping it under control automatically sets upper bounds on individual fund size.
The virtues and dangers of conviction
Marshall’s concluding remarks emphasise the virtue of conviction for successful investment, to be continually renewed ‘by re-examining every assumption, every thesis, discarding some and doubling down on others.’ Again, there are particular dangers for large funds inclined to ‘star structure’ models, where confidence can soon degenerate into hubris. Partnership structures and regular review of performance data offer safeguards: ‘as each of us is wrong on at least 45% of our trades, the data, used correctly, is a guarantor of humility’
In writing 10 1/2 Lessons from Experience Marshall was no doubt partially motivated by a desire to defend a hedge fund sector that can be hard to love. It’s a tough sell, even to open minded readers. Again, the data prompts the question: how many hedge funds secure the significant alpha returns that might justify their fees? But Marshall’s well considered essay makes clear that advocates of active management have important things to say that should be of interest to all thoughtful investors.
10 1/2 Lessons from Experience by Paul Marshall is published by Profile Editions.
