The game of picking stocks is mirrored in a game of picking managers who pick stocks. The performance effects of playing the active management game are the sum of behavioural impacts at both levels: fund managers and the ‘end investor’ or his/her agent. 

Because most investors playing this game are doing so because they believe past performance is predictive of future performance, rather than random, they will naturally tend to select new holdings from the sample of managers/funds that have performed better than average over some recent period.  If, on the other hand, it really is random (or even much less predictable than they thought), there is a very high chance of disappointment – where disappointment is defined as the ‘unexpected’ slippage of the fund through the rankings table. Consistent with the beliefs they held when they bought, they will now tend to sell, because they will assume that they made a mistake or that the manager in question has lost his/her touch – in other words the new performance is predicting more of the same. Because they have not changed their beliefs, they then go through the same exercise to select the replacement fund. And so it goes on, turning random underperformance of holdings into a non-random string of portfolio underperformance – until they realise it is their beliefs that are wrong. 

This second behavioural effect is under-researched in academic studies, perhaps because the key insight itself has not attracted nearly as much attention as the effects at manager level. However, there are a number of industry firms that survey money-weighted returns in mutual funds (measuring client-experienced average returns): Dalbar, Vanguard and Morningstar. This analysis suggests playing the loser’s game costs up to 6% pa – far more than just the incremental costs of active funds (which in the UK we put at between 0.6 and 2% depending on the buyer’s agency relationships). 

The data available to researchers is fund performance (obviously) and fund flows. But when making a connection between the two there are some problems:

• Both are absolute – so if people sell after poor performance they could be making a market timing decision or a switching decision based on poor relative return but the data won’t tell them which. The two may of course be positively correlated. Given the evidence (in Dalbar’s annual surveys, for instance) that net flows are positively rather than inversely associated with market movements, it seems likely most of the effect is due to poor market timing rather than switching, so a reaction to absolute rather than relative performance.
• Flows are partly idiosyncratic decisions and partly regular contributions/withdrawals but the two are not distinguished.
• Money-weighted return calculations are affected by the sequential pattern of returns and flows – although I’m not convinced this methodology point ‘explains’ the apparent behaviour effect.

In ‘Mutual Fund Flows and Investor Returns: An Empirical Examination of Fund Investor Timing Ability’ (Friesen and Travis, 2007) the authors say this about the relative-return  impulses to trade funds:

In testimony to the United States Senate, investment guru John Bogle (2003) argued that the Dalbar (2003) study ignores an additional “selection cost” borne by investors, whereby investors place a disproportionate amount of money into actively managed funds that subsequently underperform the S&P 500 index. Bogle suggests that after accounting for this selection cost, the average mutual fund investor return over the 1984-2002 period is actually negative.

Whilst it may be difficult to measure actual effects for a universe of mutual fund investors, it is possible to model the behavioural effects to test for the likely scale of impact of the relative-return impulse, by applying some simple decision rules, based on rank orders, for both buying and selling.  This is a project for future research.

It is, however, possible to infer something today about the prevalence of this impact just from observation of a limited sample of portfolios. We have taken in new clients from a wide range of advisers/banks/wealth managers over the past six years. Because our clients have well above-average wealth, their previous agents are typically respected and popular firms, so we would expect them to be less prone than the average to systematic wealth-destroying behaviour. We also take on clients who are more experienced than average and so many have previously been wholly or partly self-directed. Our initial financial planning process for all new clients includes a critical appraisal of existing investment arrangements. We observe, in most cases, the same destructive behaviour by both agents and self-directed investors.

Even in the case of investors with agents, we suspect the investor’s own ‘predicted’ behaviour effectively encourages the agents to sell, because the agents think individuals think that poor performance tells them something about the agent’s skill.

When looking at agent behaviours, it is always sensible to consider whether incentives or game theory might explain them. Whilst I believe this is realistic in the case of fund managers, whose active-management payoffs are quite different from their investors, I do not think gaming validates the behaviour of agents selecting funds. Because both these agents and their clients are acting consistent with a set of beliefs (however false) about skill, it looks more like a case of the blind leading the blind.

‘It’s a miracle! I can see!’ cries Eddie Murphy in Trading Places when rumbled as a ‘blind’ beggar. In investing, miracles do happen and when an investor truly can see, their rumbled agents are unlikely to get away with it. They, rather than their clients, are the ones who should have known better.

Note: This article (in draft) was extensively quoted by Matthew Vincent, FT Money editor, in his Serious Money column on 30th July. It followed a piece the previous week about the first, manager, level of the active-management game titled How fund managers get paid for winning the lottery. You may need to be an FT subscriber to view these articles.

Pauline draws attention to Total Expense Ratios (TERs) as a means of estimating the ‘all in costs’  and asks for the opinion of  Stuart Fowler. “The key is whether the activity of the manager offers value for money”.

Stuart points out that portfolio turnover (a measure of how frequently assets within a fund are bought and sold by the managers)  has risen hugely over the past decade or so, but  with trading costs reducing,  the overall effect has been neutral.  He goes on to say the average active manager underperforms the benchmark by the amount of the average TER. They would underperform by more if their trading activity did not add value.

To read the article, “A lot of  indignation but no change”, please visit the FT website.

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.

The information contained in the returns themselves and in the underlying asset allocations is about our investment process itself. So an analysis of our performance is an analysis of our process in action.

The process itself addresses a much trickier question: how do you manage money when the lesson properly learnt from both theory and practice in financial markets is that a manager cannot know what is going to happen in the short term, and their opinion is of limited value to their client? How does a battle-hardened agnostic place their bets and why?

The process is an outcomes-driven approach to goal-based investment portfolios, which we term ‘Defined Outcome Portfolios’. The approach is detailed here on the website.

The key characteristic of this approach is that some bets are avoided or hedged and others tightly and dynamically managed, within a specified ‘risk budget’. The risk budget is expressed in terms of a range of tolerable outcomes, in real terms (after inflation), at the times the money is needed.

With such an approach, the outcomes at shorter horizons are the wrong ones to make bets about. To the extent they are fully matched by risk free assets, matched to the duration of that time horizon, a key insight is that clients can afford to be indifferent to the volatility of these assets. If the volatility has no impact, their short-term performance is also of little import. The volatility is zero for cash but index linked gilts, though risk free in real terms at maturity (because of the government guarantee of inflation uplift), are nonetheless quite volatile in the period prior to maturity. But the volatility has no impact on outcomes, by definition.

Only the volatility of risky assets has a bearing on the probable plan outcomes and therefore the chances of the client achieving their minimum objective. These are the bets, as opposed to the hedges.

This transforms the relevance of the volatile short term return path of the ‘current’ portfolio. It makes a nonsense of comparisons of achieved short-period returns with other managers. Our clients are out of the performance race and in a journey, of their own planning.

Private clients are largely unaware of how to use performance information where planned goal outcomes are partially protected by hedging. They are not alone, however. The advent of ‘liability driven investment’ in the institutional market, which is the closest parallel, has also forced trustees to rethink the significance of reported performance, both for their own asset manager and for comparisons between managers.

Returns
In an earlier post, part way through the market fall, we took two clients as examples. Since all clients have a different combination of explicit time horizons, outcome profiles and risk tolerance, we have to use samples, not a universe. We have updated these two examples in this article.

They both have Defined Outcome portfolios funding retirement spending. One is still ‘in accumulation’, with about 5 years before drawdown starts. The second is already in drawdown, meeting current retirement spending.

The examples were chosen to have similar risk tolerance so that the asset allocation differences, as output by their own models, are explained by the time horizons (although changing profiles for spending outcomes at different stages of retirement also make a difference).

The returns are money-weighted returns. Because the cash flows, mainly new savings or draw, typically arise at month end, there is essentially no difference in the cash-flow adjusted returns whether money-weighted or time-weighted.

The period is from December 2007 through August 2009, as this roughly corresponds to the start of the bear market and the subsequent recovery.

The accumulation portfolio achieved a return over the whole period of -11.3%. The drawdown portfolio achieved a return of -4.1%. The paths of each, indexed to 100 at 31/12/2007, are shown below in Fig 1.

Fig 1: Index returns of two Defined Outcome portfolios

Asset allocation
The building blocks of a Defined Outcome portfolio are divided into two:

• Risk free assets (matched to the nature and duration of the time horizon, such as £x real spending in year y)
• Risky assets (in the form of a mix of equity markets across the world, as represented by broad indices with all but ‘systematic’ risk diversified away).

The two are combined to ensure that the projected outcomes of a dynamically-managed ‘plan’ (not just the current portfolio), counting down to fixed dates, are always consistent with the agreed tolerable outcomes.

The return paths of the building blocks (source: Lipper Hindsight) are shown in Fig 2 below, indexed to 100 at 28/09/2007

• The cash return is a one month deposit rate (the risk free asset for durations below about three years)
• The index linked gilt return is derived from the over 5 years FTSE index (as representative of the ‘medium-dated’ maturities where we typically combine risky and risk free)
• The equity returns are for index-tracking funds provided by Legal & General (as representative of the returns that could have been earned from tracking the risky asset building blocks, after the fund expenses attributable to institutional units).

Fig 2: Indexed total returns of the portfolio building blocks

Attribution
Because the asset allocations output from each model run for each client are implemented using index-tracking funds and individual index linked gilts, the portfolio return and its variability are almost entirely explained by the allocations.

The accumulation portfolio started with an equity allocation of 100% and the drawdown portfolio 65%. The risk free return has been relatively flat due to both low interest rates on cash and moderate volatility and net change in index linked gilt yields over the period.

Considering the difference in equity exposure at the outset, the gap at its widest point between the two portfolios is surprisingly small. The largest drop for the accumulation portfolio reached 28%. This also appears relatively small compared with the returns apparently experienced by most UK-based equity investors, including those investing purely in the UK, which was down 38% at that same point.

As Fig 2 hints at, a large part of the answer is geographical diversification. We tend to hold more in European, Japanese and US markets than most investors. This follows logically from the intention of diversification and is likely to be an output of any portfolio optimisation process that seeks to maximize expected return per unit of risk. However, most investors constrain exposures to foreign markets to suboptimal levels. Diversification benefits reflect the distribution of weights to each market not just the number of markets held.

Short-term correlations between different equity markets are highly unstable (and indeed increased dramatically during the bear market). But differences in achieved returns do emerge, implying differences in future returns. With fairly similar long-term real return trends apparent in the historical data, mean reversion operates both between and within markets. Larger international exposure allows this to be exploited more efficiently.

As it turned out, much of the benefit from geographical diversification in this particular period came from currency movements. Sterling fell as asset prices were at their weakest and strengthened as equities themselves rallied.

Currency movements are not normally well correlated with market movements so most of the time these two sources of risk will not combine to increase portfolio risk significantly. What happened here, with currency dampening portfolio volatility, cannot be predicted in advance or relied on.

Asset allocation changes
Fig 3 shows the asset allocation changes for a drawdown model starting with the December 2007 quarter and ending in the two months to August 2009. The actual model runs are monthly but we have conflated the changes to quarterly, to simplify the rebalancing narrative.

The resources at the outset in this model run were £1.8m which puts the changes shown below into context. The range of monthly change in allocations at the highest level, between risk free and risky, is from 4% to 8% of the total portfolio. This is clearly a gradualist approach.

Fig 3: Changes in net equity exposure

The narrative here, even if at the time it needed some explanation and illustration,  is entirely consistent with the portfolio process and so held no surprises for the client.

• Two quarters of additions to equity exposure relative to index linked gilts as the bear market began, as expected equity returns to each planning horizon were then higher than before and higher relative to index linked gilt yields for the same horizon – hence hedges can be marginally lifted
• A small net pull-back in the next quarter as equities rallied and index linked gilts fell, lifting their real yields
• As the US housing bubble started to go wrong and the credit crisis got worse, dragging risk free yields down with it, the next three quarters saw net additions to equities
• From March 2009 the model called for net sales of equities as prices rallied and risk free rates also moved higher.

The net change over the whole period is an addition to equities of £0.25m, equivalent to 14% of the starting portfolio value. Considering the net fall in equities over the whole period, and the relatively small net fall in risk free rates, a constant risk tolerance should be expected to lead to reductions in hedging and an increase in risky exposures. Investors (or their advisers) who instead altered their risk tolerance in response to what they thought was happening in the world would have shown the opposite response, selling risky assets as they declined in price, or might have been frozen into inactivity.

The net changes filter out the rebalancing arising between different equity markets which is also shown in the table. The gross change over the whole period is 42%. In practice, some of the smaller changes indicated by monthly model runs may not be implemented, as the likely improvements in risk-adjusted return are too small relative to the costs of transactions (even when these are minimized by using exchange traded funds on an execution-only stockbroker dealing platform). Many small changes should reverse themselves from month to month. There is an element of judgement in filtering these small changes.

The main changes in country weights are as follows:

• Reductions in Japan, which had started the period at 36% of the risky allocations – high exposure in Japan is a key test of a globally-diversified approach to long-term return return opportunities
• Roughly half of the net addition to equities was in the UK, which is a natural response to increasing real return ranges in all markets, as plan outcomes can then be met with less uncertainty in the home market.

The portfolio model run for August had the following new target allocations.

Fig 4: Closing model allocations

These allocations are entirely logical given the client’s planned profile of the drawdown over the life of the plan; the client’s tolerance of uncertainty in the draw; and our projected return probabilities for the portfolio building blocks, as generated by a new model run in August 2009.

• The 16% cash allocation is matching about three years of spending targets (as an estimated pre-tax equivalent portfolio draw).
• From years 4 to about 8 the outcome targets are fully matched by a schedule of maturing index linked gilts with the same duration as the spending
• The next 6 years are partially matched by index linked gilts but equities are also held against these spending targets
• Only durations later than 15 years are fully backed by equities.

The proportion of the total plan resources that is sensitive to volatility in the portfolio value is represented by the allocations to equities from years 8 to about 15. They are sensitive because the return-generating element of the model does not assume that a fall in equity prices will be offset fully by higher real equity returns within the time frame required for that horizon. There just is not enough time to be that sure. This portfolio proportion, for which short-term performance has an impact on probable outcomes, is about 28%!

The earlier horizons (35% of current resources) are indifferent to volatility because they are fully matched. The later years, beyond 15 years, (the balance of 37%) are far enough off for mean reversion to have offset much of the fall in current value, leaving outcome probabilities little moved by the fall.

Even though there is a middle segment of the plan that is sensitive to volatility, any plan that was fully funded to meet the drawdown targets with a high level of certainty should still meet its original planning targets. This is based on the simulations of the plan, which tested randomly-generated paths for equity markets and exchange rates as well as their interaction with a pre-defined rate of draw in real sterling amounts per annum.

In practice, the progress of the portfolio is likely to feed back to the client’s comfort level in spending. Should this arise because of really bad equity markets globally, implying widespread economic stress, it would not be surprising if the client chose to trim their spending objectives anyway, even if the model stress tests suggested it was not strictly necessary.