No Monkey Business is scathing about the ‘let’s go shopping’ culture that is characteristic of the annual ISA season, and probably all too common amongst self-directed individual investors who want just this sort of guidance from the Investors Chronicle. To counter the tendency to pick the current ‘hot’ investment areas, Stuart picked two regions he believes are poorly represented in most private client portfolios: Japan and Europe (ex UK).

In the risky asset component of our own portfolios (so excluding risk free or hedging assets), which is always well-diversified by geographical spread and does not normally bias to the UK as ‘home’ market, Japan and Europe ex UK together currently account for about 38%. Contrary to perceptions that both have been well worth avoiding, they were key to our producing better performance in the bear market for the risky component of our portfolios than conventional diversifed benchmarks like APCIMS Growth.

As the Investors Chronicle article comes across, Stuart might seem ambivalent about using active or passive funds for either region. In fact his advice was explicit. Active managers of UK-based OEICs and unit trusts, including offshore funds in the analysis, have an appalling record of delivering consistent past (and therefore plausible future) ‘alpha’ or risk-adjusted outperformance in both Japan and Europe. Tracking has to make more sense.  

Leonora did pick up on Stuart’s comment that achieved investment returns are not well-explained by expected or even actual growth differences – also the theme of his recent post on China and emerging markets. Reading the recommendations of the other managers quoted in her article, there is a clear bias to growth stories. The No Monkey Business take is beware of the story: even if it is true, by the time you are hearing it it is probably already in the price.

The challenge for private clients our title alludes to has two dimensions: giving them the right job and ensuring they do the right job well. The typical portfolio approaches for private clients focus on short-term constraints that are not consistent with maximum satisfaction with horizon-specific outcomes.

Using as an example No Monkey Business portfolio returns over the last two years of large falls and rises in markets, we illustrate how to set objectives and assess performance better. Because all our portfolios are different, for reasons that will be obvious from reading this Insight, we reference the same two portfolios as we did in an earlier Insight, Anatomy of a bear, one in ‘accumulation’ and one in ‘drawdown’.

Performance and confidence

Performance appears to be an important reason, perhaps the most important, for individual investors deciding to change their investment arrangements. This is not because they have a lot of information about their managers’ performance and are able to assess it rationally. In fact, they probably only get to see their own returns, compared with benchmarks, every three or possibly six months. Even then, without a lot more data history than their patience may tolerate, and without attribution (was it policy choices, market timing or selection choices?), there is limited information to be extracted from the reports.

If it is not continuous well-informed appraisal that leads clients to change manager, what aspects of performance do? It is usually because they are surprised by what has happened and in the absence of proper explanation they lose confidence that their adviser has understood the constraints or the objective or both. The surprise is all about expectations but the interpretation is all about confidence.

In other cases, however, investors will realise that the adviser or manager they appointed did a good job given the mandate but the mandate was wrong. It can be hard to leave a manager who has done a good job against the wrong mandate. In selecting us many clients have moved their money from managers with very good performance as well as from those with disappointing performance.

Wrong job, right job

We identify five common causes of disappointed expectations and mismatched mandates that are prevalent today:

1. The spread of a ‘factory’ approach to portfolio organisation that lacks close links to relevant personal objectives
2. The fact that equity returns over the past decade have not typically delivered the ‘risk premium’ professionals led clients to expect
3. The failure of diversification across the traditional asset classes to smooth portfolio volatility as portfolio theory apparently posited
4. The failure of ‘alternative’ asset classes to perform as expected, in terms of absolute returns, correlations and premiums for illiquidity
5. The ‘false prospectus’ that the fad of ‘absolute return investing’ turned out to be for many of its followers.

What all five have in common is that they are policy choices: high-level decisions about the general approach to how your money is to be managed. They belong to the client, who makes that policy choice. This is not to say the industry is blameless in adopting the easy sale in preference to a more exacting route to developing optimal investment solutions. The easy sale not so long ago was, after, all unitised with-profits.

We suggest that ‘outcomes driven’ investing, which Chris Drew and I developed ten years ago in parallel with the emergence of Liability Driven Investment in the institutional market, has avoided these problems and allowed for much clearer expectations and therefore more stability and consistency in following a planned and appropriate strategy.

The expectations effect works by being more explicit about what can happen, with more quantification of risks. But is also works via a feedback from greater confidence about outcomes to greater tolerance of short-term volatility. Consistency on the part of our clients means we are free to follow a model-driven discipline which is likely to increase wealth outcomes. Inconsistency means investors tend to raise their risk tolerance when times are good and lower it when markets and the economy are doing badly. This destroys wealth.

To illustrate how our clients can assess the job we are doing we reference our two actual portfolio examples. We do this in terms of three key questions we think all clients should want answers to:

• How is my portfolio structured to deliver what I want?
• How is risk being controlled?
• Are both evidenced by the activity in the period?

How is my portfolio structured to deliver what I want?

‘Delivering what I want’ translates, in investment theory, to ‘maximising the benefits I want to get from my money in the form I specify’. In this context, ‘benefits’ translate into ‘utility’ or (as sometimes expressed in the theory) as ‘welfare’. Utility is specific to a goal not general to an ‘investment personality’, assuming such a thing even exists.

In practice, utility is mainly about the consequences of uncertainty and how individuals express preferences in response to those consequences. In the factory model, the presumption has to be that these are common to everyone, not idiosyncratic. In reality, we make value judgements that are highly specific, such as:

• preferring to push possible bad outcomes further into the future, where we think we can better bear the consequences, or nearer where we can deal with them through adjustments to behaviour (such as a household budget change)
• setting explicit constraints on the consequences, such as a minimum spending level in retirement which no incremental potential spending chance should put at risk
• preferences that are dependent on progress in funding an objective that calls for a particular pot of money, which (whether the progress is good or bad) could invite taking either more risk or less risk.

You will know personal utility was never addressed if you did not start with planning conversations that identified these preferences and described a portfolio solution that matched them.

You will also know it if the conversations about ‘risk tolerance’ did not address whether your utility was best expressed in terms of path risk, or the short-term volatility in the path of the portfolio in money terms (as measured by those performance reports) or outcome risk, as in the form in which you ultimately derive the benefits from your wealth, such a future level of real spending (after inflation) say 20 years out. The first is not a substitute for the second.

We find that in practice most investors, when encouraged to plan in terms of specific goals for their money, can see clearly when (and why) path risk or real outcome risk dominates. The investment solution will be radically different depending on which dominates.

For the client who owns our drawdown example, the goal is meeting a schedule of annual draw in real terms from capital (part in pension, part not) subject to the constraints of i) not running out of capital before age 95 and ii) sustaining the spending plan without being forced to cut it, except to the extent of a planned schedule of tapering minimum spending at different stages of retirement. In this plan, risk taking is constrained by the agreed range of tolerable outcomes, as ‘real’ money available in a cash account to meet the next three years spending, in a schedule of three-year time slices from 50 to 95. With known resources, risk is solved for by reference to the range of probable outcomes (which come from our model) and the consequences (which must come from the client).

The key element of our quarterly performance reporting is therefore where the client now stands in relation to the goal outcomes: is the ‘new’ portfolio value sufficient, with ‘new’ expected returns, to meet the agreed drawdown targets with the same confidence?

Because there is a degree of volatility in the ‘funding status’ (though as the return-generating element of the model is designed, this is much less than the volatility of ‘the market’), we report (as a monetary amount and as a percentage of the ‘fully-funded’ position) the ‘interim projected shortfall or surplus’. What the client wants to know is:

• Has the portfolio done so badly that I may now breach the plan constraints and therefore need (in this example) to cut spending, contrary to the objective of sustaining it a planned real rate?
• Have I done so well that I can change the targets or risk level or assign surplus assets to a different goal?
• Is the change just ‘noise’ I should not attach any significance to?

We provide this guidance in every report, every quarter, drawing on stochastic simulations of a long-term plan and its changing interim funding status. Usually we will be suggesting that no action is called for by the client, particularly in the first 6-10 years of a plan.

A secondary aspect of the portfolio progress report is that, because the target outcomes were planned in real terms, we need to adjust them each quarter by the actual inflation in the quarter before calculating the new funding position. We report that change too.

How is risk being controlled?

In the typical investment solutions that dominate the IFA, banking and wealth management business models, risk is managed by diversification. This is not a control. It was never put forward by theorists as a risk control. Its origin was in the separation of:

• diversifiable risks that provide no reward and therefore should be eliminated from a portfolio and
• systematic risks, common to any exposure to particular asset classes or markets however you select within them.

Relying on diversification between asset classes and markets reduces risk to the extent the returns from each are less than perfectly correlated and so for a given level of resulting risk there is a set of possible portfolios that will yield the highest expected return. These portfolios are ‘more efficient’ in using risk than portfolios that have lower returns per unit of risk. That is all it means.

To serve as a risk control, there would need to be sufficient of these asset classes and markets with both i) low or negative correlations (or co-movement) and ii) stable and predictable correlations to reduce portfolio risk reliably to acceptable proportions. As investors worshiping at the new altars of ‘absolute returns’ and ‘multi-asset classes’ discovered, correlations do not have these highly desired characteristics. And they converge when you most depend on them, such as when liquidity tightens up and asset prices are falling. Hence the disappointment.

In a liability-driven approach, risk is controlled by combining risky exposures and hedges. Hedges are assets that perfectly match a liability, or goal outcome. If the outcome is a target level of money in real terms 10 years out, for instance, that exact amount can be produced, on time, with certainty, by buying an index linked gilt with the same duration.

When investors have preferences for outcomes that involve trading off possible higher wealth against some minimum wealth, there has to be a range of probable outcomes, with risk taking, that is acceptable and efficient. The size of that range is controlled not by diversification, although that is part of an efficient solution, but by the mix of risky assets and hedges, or risk free assets.

What is the evidence?

So when we report our transaction activity for a client in the quarter, it should be seen to be consistent, at a high level, with the process of risk control as market values alter. It becomes the visible proof of a risk management discipline. It will be particularly seen as a discipline if the actual activity appears (at the time) counter-intuitive.

In Fig 1 we show the market returns from the start of 2008, just after the bear market began, up to the end of 2009. This describes the environment for each of cash returns, index linked gilts (the FTSE over 5-year index whose duration most closely corresponds to the time slice outcomes we hedge) and the four equity markets and regions (in sterling terms) we use as building blocks for the risky asset portfolio.

Fig 1 Index total returns in £ for portfolio building blocks

Two aspects are important to our model:

• The expected return on equities rises as markets fall in price, because we accept the evidence that equities generate a trend of positive real return over long horizons (capitalism requires it) and ’revert to the mean’
• The attraction of any expected equity return depends on the competition it faces from hedging assets, at their own ‘certain’ real return.

In Fig 2. we show how we moved money between the hedge portfolio and the risky portfolio over the course of the market cycle, responding to both effects. The bars show the net addition to (positive) or sale (negative) of equities in each quarter of the two-year period.

Fig 2 Net flow from risk free to risky assets as % risky

At the start of the bear market, equities fell but so did the risk free rate, so we added to the equity position. As this was followed by a reversal of the fall in risk free rates and slightly higher equity prices, we took some bets off the table and added to index linked gilts. As the bear market turned nasty, and the crisis in the banking sector spilled over into expectations of a global recession or new depression, additions to equities were mainly driven by falling price, rising expected returns, rather than by changes in risk free rates. More recently we have started taking bets off the table as both equities and index linked gilt prices have been very strong. Though mean expected equity real returns are still above average, the incentive to take risk is greater because index linked gilt yields are at record lows. However, the incentive has been lessening simply because of the scale of the recovery.

We suggest that few managers have shown such consistency in their risk taking approach during this market cycle. Looking at their activity will probably tell a clearer story than the performance they reported or the comparisons they showed. Over the long term, there is clear evidence that adopting a consistent attitude to risk, which is not at all the same as the same level of risk, produces higher returns because it avoids selling low and buying high.

Backward looking performance

Even on a pure accounting basis, we need to report what actually happened to our clients’ portfolios in each quarter and we do that too, in a fairly conventional way.

• We calculate the money weighted returns in each month (so adjusting for cash flows into or out of the portfolio during the month according to roughly when they arose) and multiplying them through to produce what is then very close to a time-weighted or internal rate of return for that quarter
• We show the returns for the assets we use a building blocks in the same period, so that the actual return can be viewed broadly in the context of the environment in which we were operating
• And from now on we plan to show something we have resisted hitherto: a measure of what clients might have earned with a more conventional approach, for which purpose there is nothing ‘better’ than the benchmarks developed to match the different version of the factory model by the Association of Private Client Investment Managers and Stockbrokers.

In a liability driven approach, the changing market values of the hedging assets are meaningless in terms of outcomes, as the two are perfectly matched: a rise in price will reduce the expected real return symmetrically leaving outcomes unchanged. So realistically it is only the risky portfolio whose volatility is meaningful, but in our view its meaning is in the impact on funding adequacy, and so requires a forward-looking measure. As a backward-facing measure of industry returns, the closest APCIMs benchmark for the risky portfolio is the Growth index although it is not still not representative of equity returns alone, as it includes cash, bonds, property and hedge funds (to the extent in full of 22.5%).

In Fig 3 we show the returns of two portfolios, one in drawdown (and so combining hedges and risky assets, the latter averaging about 55% over the period) and one in accumulation (with long enough horizons to have been fully invested in risky assets throughout the two-year period).

Fig 3 Quarterly portfolio and benchmark returns (indexed)

We can note, in terms of attribution, that we were helped by the extent of our diversification geograhically, as we tend to more equal weightings than conventional managers and have less of a bias to the UK as home market. This helped particularly in the bear market because of currency gains, although this was partially offset in the following year.  The returns to these structural characteristics, or policy features, in any particular period contain relatively little information except that diversification does not necessarily require lots of different assets. As the APCIMs benchmark shows, conventional diversification, even with the addition of 7.5% in hedge funds, did not produce either better performance or less volatility than our risky portfolio.

Though you can make comparisons with your own performance, we suggest the guidance in this Insight as to how to interpret what your manager is doing is probably much more important than a crude comparison of the numbers in a short period. As my book suggested, investment is best viewed as a journey not a race. You are not picking the winner so much as the best planner, navigator and driver, all rolled into one.

Life lines

A show of hands revealed that virtually all the IFAs attending each conference used ‘cash flow modelling’. Many probably use the same as the speaker who asked the question: a spreadsheet-like application called Truth, from Prestwood Software. These ‘deterministic’ tools, including own-design spreadsheets, exist to compound assumptions out into the future for income and expenditure and relate these flow outcomes to stocks of financial resources (existing or incremental) which are also compounding at linear growth rate assumptions. The resulting graphical displays show the changing income statement and balance sheet for the household for the assumed length of the planning period. What results is changing balances. Advisers will use these as measures of sufficiency or ‘financial independence’ that will be relied on to provide the clarity and confidence that (we know) individuals value as the immediate payoff from planning.

If the linear projection rates for each item are ‘reasonable’ as a reflection of what might be thought of as ‘normal’ (which means they have to be normal for the starting point as well as the end point), I suppose the programme could be termed Half Truth. As a measure of actual predictive accuracy, as the chance of being right rather than just better or worse, it should (being generous with rounding) be called 1% Truth.

I can well understand why advisers’ clients would see these graphical representations of their changing household economy as both relevant and useful and why it would be tempting to rely on them for confidence. It is impossible, even, to imagine any planning for the household (or a business) without forward projections. Unfortunately, these simplistic projection tools do not contain the information clients really need because they are deterministic, not probabilistic. They conceal the uncertainty in both sets of inputs: their household cash flows and the payoffs from participating in capital markets by borrowing and investing.

It might even be argued that, by encouraging false confidence, they do more harm than good. Our own model for projecting outcomes in real terms suggests that, even with quite low risk tolerance, worst-case outcomes at different time horizons might be between 25% and 50% below the mean – and that assumes a model-driven approach of tightening up risk as horizons shorten, rather than ‘buy-and-hold’. That is a margin that, left unaddressed, could lead to errors in spending and saving that can never be put right without hardship. The same silence or ambiguity about the possible payoffs from with-profits endowments recently led to similar drastic consequences, so the industry has no excuse for not learning to deal with its own shortcomings.

The information clients need is about what can go wrong but they cannot respond to that information without also knowing how much better it could be with different decisions they have the power to make.

Some of the biggest sources of disaster are insurable and the advisory industry is generally good at selling catastrophe insurance, such as the impact of early death or disability. But as soon as people participate in financial markets to achieve objectives like replacing employment earnings in retirement, they take on inflation, investment and (possibly) longevity risks that together make their financial outcomes highly uncertain. The range of uncertainty can be narrowed, but at a cost. So the information people need is

• How bad can it be – as in the worst-case or ‘floor’ outcome
• How much does it cost to ‘raise the floor’
• What am I giving up in terms of better outcomes.

Only probabilistic models can answer these questions with hard numbers.

Quoting the odds

My talk to the advisers was on the differences made when using probabilistic approaches to modelling outcomes. This is the Defined Outcome goal-based portfolio approach described on this website under What we do. What it is about this approach that changes the client’s experience of investment is our ability to quantify ranges of relevant outcomes so they can relate to the process even if they are not interested in investment. With choices differentiated by hard numbers, they can make more confident decisions about the things that really do matter to them: earning, spending, saving and risk taking.

What controls the effect of risk taking on the probable outcomes is also something people can readily relate to: insuring risk versus bearing risk – bets off the table or bets on the table. Insuring risks in an investment sense means avoiding or immunising them, by holding something that perfectly matches (or hedges) the required outcome. But (as they also know) insurance costs money, so raising the floor (or worst-case outcome) by hedging will also lower the entire range of probable outcomes. This is a better measure of Truth than a single linear projection that treats investment risk and cash flows as if they were independent.

Diversification is not enough

Taking bets off the table was (I argued) a much better way to manage outcome risks than investment diversification, and not just because diversification takes them into the world of investment maths they are not interested in.

Most of my fellow speakers were proposing solutions that depend on diversification between types of asset to control risk. But they were in defensive mood. This is because diversification conspicuously failed to deliver the expected benefits in this bear market, as all forms of risk premium collapsed together, under the weight of liquidity-induced risk aversion. As illiquid assets could not be sold, high quality assets had to be sold instead. Nothing escaped.

Knowing what was coming up, I was able to slip in a reminder that diversification was never supposed to be a form of risk control, but rather a process for getting rid of unrewarded risk exposures and maximising the combined risk-adjusted returns (or utility benefits) of the undiversifiable or ‘systematic’ asset exposures that were left – these being the ones that are rewarded, with ‘risk premiums’. Risk control is always likely to require limiting those exposures: bets off the table.

Why is No Monkey Business on such a different page from our industry peers? I think the best explanation is that, for all that they read and all the conferences they attend, they really don’t understand just how uncertain the world is. Or, if they are worldly enough to understand that, they are perhaps not able to translate it into realistic numbers. You can sense this from what pops out of their mouths: the 1% or 50% truths presented as ‘The Truth’.

The lessons of 2008 are consistent with the No Monkey Business principle that the only reliable form of risk control is to dilute exposures to risky assets by holding risk free assets. With risk controlled at this high level, you are safe to use a narrow range of core, low-cost assets: equity market trackers, index linked gilts and cash. Understanding the reasons will give you confidence to ignore, and keep ignoring, the industry’s widely hyped alternatives. Trackers should be diversified across the world’s major markets and by definition are fully diversified within each market. This is the diversification that can be relied on.

As in other aspects of finance, the main focus of all this creative activity was risk management, because it offered payoffs in the two valuable forms: the avoidance of ruin and the maximisation of profit. In the motor industry, better brakes, better road holding and the wearing of seat belts have raised average speeds. In finance, the perceived improvements in risk management have led to increased risk taking.

We can now look back with the benefit of hindsight and see that most of the Darwinian evolution of techniques, structures and strategies for transferring, transforming and managing financial risks is doomed not to survive.

The evolutionary dead ends mostly involve debt and this is rightly what the media spotlight is now falling on. Examples are:

• shifts in finance theory that mis-specified the effects of replacing equity by debt
• securitisation structures that sought to transform the underlying credit risk of a mortgage portfolio
• derivatives that transferred credit risk to third parties
• ‘value at risk’ models used by banks to drive asset volumes and capital adequacy

Whilst debt dominates the dead ends, equity investing has not been immune to failed experimentation in the area of risk management. We characterise these common failures as having to do with that irreproachable principle: diversification.

The failures take two linked forms:

1. Unrealistic reliance on diversification effects as a form of risk control
2. Unrealistic estimates of the effects themselves

Portfolio theory focuses on risk-adjusted returns and so the required inputs that determine the diversification effects are expected returns, the standard deviation of uncertain returns and (because we are addressing portfolios not individual investments) the correlations (or co-movement) between the different building blocks in the portfolio.

There will always be mistakes made about the expected returns of individual investments and even historical evidence can be misleading about standard deviations. But the more telling mistakes involve correlations. When the mistakes are in the direction of underestimating the extent to which asset prices move together, there is a natural bias to relying more than is justifiable on diversification as the main means of controlling risk, as in confining uncertain future portfolio values within some defined range.

Reliance on diversification for this specific form of risk control has increased at the same time that investors have become more aware of and also more sensitive to the consequences of volatility. This looks completely inconsistent but it is not the first time an increased need for a solution to a problem has encouraged false confidence in the solutions coming forward.

In institutional investment, the impact of volatility has increased because of regulated accounting changes that trigger wealth-reducing changes in exposures, such as selling low instead of buying low. Specific instances where regulations have radically redefined investor ‘utility’ include occupational pension schemes, life insurance companies and principal trading desks in a bank. In retail investing, we cannot blame regulatory influences. But the general decline in opaque long-term savings contracts in favour of more transparent and tradable investments has made it more likely investors will see, and then respond to, volatility in portfolio values in ways that are also wealth-reducing. The driver here is not regulatory but behavioural. The effect is the same: they buy high and sell low. They also trade too frequently, incur cancellation penalties and pay new setting-up charges for substitute products. Whilst investing too much in particular assets, they may also invest too little in aggregate to achieve the outcomes they desire or need.

Dilution
The 2000-2003 bear market, marked by the bursting of a popular investment bubble, crystallised many of these risks in institutional and retail decision making. The obvious conclusion to draw was that the problem lay in excessive exposure to volatile assets that did not provide hedges for their financial needs, and hence a simple solution would be to manage their total exposure to risky assets better, by diluting them with risk free assets.

Dilution is different from diversification because it does not rely excessively on uncertain correlations. Instead of focusing on how the bets are managed, it forces investors to take some or all bets off the table if the consequences of bad outcomes do not appear to warrant the risk.

The effect of taking bets off the table is to narrow the range of future possible returns but because the risk free hedges have lower returns than the likely mean return of an asset with a risk premium attached, such as equities, it will also lower the entire range. There is no free lunch.

This principle is illustrated below.

Considering that the most likely risk free hedge for most private client needs is index linked gilts, it is particularly ironic that they had performed almost as well as equities for most of the bull run since 2003, let alone outperforming dramatically in the subsequent bear market. After the event, this looks like a free lunch but it was not something that was predictable before the event.

New solutions for risk control
Dilution was the solution the institutional marketplace came up with, in the form of liability driven investment (LDI). This focused client by client on the tolerable outcomes. It controlled the levels of exposure to bets, as opposed to hedges, that left risk on the table but only to an extent consistent with the tolerable outcomes. But with the exception (as far as we can tell) of No Monkey Business, this was not the solution the retail investment industry came up with.

Bruised as they were by the false promise of technology investing, private clients were still eager for new product paradigms, particularly if they looked anything like a free lunch. Into this receptive audience the investment industry fed a steady stream of solutions that promised unrealistic risk control: so-called absolute-return funds, hedge funds, funds of hedge funds and multi-asset class investing based on the spectacularly successful endowment funds of Yale and Harvard. They also fed investors’ appetite for structured products with downside guarantees. Though not new, these provided full insurance of downside risk (unlike the disastrous precipice bonds that cut the cost of the upside option, and so increased upside participation, by leaving very high downside risk not just uninsured but actually geared). As a substitute for cash, there may be investors who would deliberately stake pre-tax interest and buy options on risky assets. But as a substitute for risky assets, or for taking risk off the table, the payoffs of a strategy of rolling over a series of call options in practice highly complex and, like all forms of permanent portfolio insurance, self-defeating in theory.

Perverse business incentives
Compared with taking bets off the table, these product or portfolio constructs were much more attractive options for the industry, both because they like selling solutions that look like a free lunch and because they hate to lose assets under management.

The key insight here is that the industry’s portfolio-based fees model, which No Monkey Business rejects, pushes the single most important set of decisions, total risk exposures, into a parallel universe marked by gaming activity: clients hold back assets they do not want a fee attached to and managers try to maximise the base of assets to which they attach their fee, even if it means having to play down the resulting level of portfolio risk. Indeed, to the extent the solutions encourage higher levels of risk taking in aggregate, they may increase assets under management. Moreover, if the products are more profitable than those they replace, they can increase profits that way.

Hedge funds
Diversification effects, via correlations, have proved critical for two of these popular solutions for volatility: hedge funds and multi-asset class investing. But the errors in correlation estimates in each case have been subtly different.

The hedge fund argument relied on the assumption that most of the managers’ returns were from alpha, which can be roughly translated as risk-adjusted return that is not explained by exposure to systematic (and non-diversifiable) risk factors known as betas. Beta is the principle source of returns earned by exposure to equity portfolios when the portfolio’s stock-specific risks have been well diversified. The separation of beta and alpha uses the mathematical technique of regression analysis and so is only as good as the specification of the systematic risk sources and the proxies used for the returns to those risk sources, such as an equity index. By definition, the maths make alpha uncorrelated with beta and so it is highly prized by investors who already have lots of beta exposures or who want to get rid of the volatility of market betas and settle for the less volatile but lower returns of pure alpha generation.

When No Monkey Business: what Investors need to know and why was published in 2002, hedge funds were lumped with ‘entrepreneurial’ investments on the basis that these alpha payoffs were completely unpredictable. Since then, the direction taken by academic research is that the returns are in fact more predictable but only because the betas were mis-specified: there are actually a whole set of systematic risk exposures but they are different from those commonly observed in traditional portfolios. Examples are credit risk, illiquidity and volatility but there may be more that may only be identified in the future. There is also now much more conventional beta in the hedge fund universe as a whole, particularly the US equity market.

Multi-asset class investing
Yale and Harvard set a lead amongst US endowment funds by embracing ‘alternative’ investments. There are several strands to their thinking unified by a search for lower correlations. Backing hedge funds, their thinking followed the same course described above: finding alpha. Backing private and illiquid investments held as limited partners, they were making investments that stood up on their own merit, relying on business management skill rather than investment management skill, but whose return paths were also bound to be uncorrelated with public markets just because the valuation frequency and recognition of accounting gains and losses are different. This valuation effect on correlations is shared by investors in direct property, incidentally. But these early adopters of multi-asset class investing also turned to new asset classes to increase the diversification effects via genuinely lower correlations, notably forestry and commodities.

When clients asked us if we could provide richer diversification than the core investments used in our equivalent to LDI, ‘Defined Outcome’ goal-based portfolios, we were careful to explain that the whole strategy was not just a bet on manager skill but also a bet on correlations. We explained that these are both highly unstable and poorly predictable. We also warned that logically the benefits of diversification would be smallest precisely when they were most needed: to cushion large losses shared by capital markets in general. We chose to relate this inconvenient truth to the economic cycle and so this became the basis of our multi-asset class portfolio structure, with the proviso that it still needed decisions about exposure levels, or dilution, to control the risk.

In this extract from a 2005 position paper we suggested the following categorisation of entrepreneurial alternatives to well-evidenced assets.

“The dependencies that lead to the clustering to the left are economic cycle risks, as these explain the convergence of correlations in times of distress. The relevant client exposures are usually equities and property but could also be the business risks a client is personally exposed to.

Economic risks associated with distress link property prices and the credit risk premium on bonds. Property strategies for individuals include buy to let residential investments as well as commercial property via life company investment bonds. The housing and equity cycles, though showing some unpredictable leads and lags, have historically been quite highly synchronised in bear markets.

Illiquidity is also likely to affect many of the assets grouped to the left in the same way, so to the extent there are market prices to establish portfolio values they will be set low enough to reflect buyer” higher required liquidity premium. This can also serve to reinforce correlations between assets or vehicles that are normally not highly correlated. Individuals will have a wider choice of vehicle for commercial property and more diversified residential property when real estate investment funds are launched. Experience in the USA suggests these will be much more highly correlated with equity markets than are other property vehicles (except shares of property companies themselves).

Private equity and VCT returns can appear to be largely dependent on idiosyncratic risks rather than the level of public markets but it is the latter that provides the main exit route for funds and so may be critical to the timing and level of cash flows back to the investor. In the case of private equity, when the vehicle is an investment trust higher correlation with public securities is inevitable.

More intuitive dependencies are emerging markets with developed markets and unlisted equity with public markets.

We position high grade government bonds of wealthy creditor nations away from clients’ other assets because of their almost unique function as a deflation bet.

Though commodities are likely to be linked to the economic cycle they are a cost to industry and consumers and so not well correlated with equity returns.

The cluster of assets least correlated with clients’ core assets is dominated by hedge fund strategies. Within our three high level hedge fund categories there will inevitably be some lower level strategies pursued by individual hedge fund managers that are systematic in nature. However, since they mostly can bet in either direction, the assumption of low correlation with systematic returns is generally sound. The least dependent are relative value bets, market neutral and arbitrage strategies – all of which we loosely group under arbitrage as being characterised by betting on differential price movements rather than absolute price movements of single assets (which could be systematic).

Pure currency bets are likely to be made within global macro funds but can also be made via currency funds.”

In 2008 the collapse of the debt-fuelled global liquidity boom in financial and property assets has proved our point precisely. Diversification has not helped, as correlations have converged dramatically. That is what lies behind the 22% fall in Harvard’s endowment fund in just one quarter last year (we do not know Yale’s yet), the 20% typical falls in the hedge fund universe (also in dollars), the unsparing drop in all developed and merging equity markets and falls of about 40% in commodity index strategies.

It is not what happened that means we were right. It was always right because it could happen. But we clearly had a better understanding, however imperfect, of the correlation problem than was evident in the industry at the time.

Conclusion
Diversification, as portfolio theory claims, is an integral part of risk management as it leads to a more efficient set of bets, in terms of risk-adjusted expected returns. It is not a means of controlling the range of uncertain outcomes from a portfolio of bets. This can only be assured by controlling the level of bets relative to hedge assets that match liabilities with certainty and are therefore impervious to volatility.

The bets our Defined Outcome clients leave on the table are in what we call ‘evidenced’ equity markets, which means the expected real returns and standard deviations can be quantified using past history, but the predicted range of some mix of hedges and bets still relies on correlation assumptions for the different equity markets. Here too we have been realistic about the diversification effects. Portfolio return projections assume that these can converge in either very good or very bad times, dramatically widening the range of possible returns compared with more ‘normal’ returns. But the solution to the problem of constructing optimal combinations of equity markets places a high value on diversification of risky exposures, just like apple pie and motherhood, so we use a low correlation assumption when optimising the equity portfolio. How this translated into returns in 2008 is the subject of a separate article.