Within a recent piece prepared for Citywire Stuart sets out our findings from analysis of the different Chinese investment vehicles available. As often happens, the headline doesn’t quite match the article since clearly an ETF can not change the market risk, but the article is still well worth reading.

And then in response to a piece on the FT’s Money Matters blog, Stuart sets out exactly why Mr Bolton’s fund is not for us.

Click here for the Citywire article and here for the Money Matters blog.

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.

I single it out because outsmarting the smarts with simple thinking is very No Monkey Business. In fact, the same data has regularly featured in No Monkey Business posts since 2006 (about a year behind Paulson) as an obvious parallel with the Nationwide deflated index of UK average house prices which I regularly monitored on the No Monkey Business blog from about 2002. Just don’t ask me where my $4b is.

What drew me to the study of the trend and deviations from trend in real house prices was that it was itself a parallel application of an important insight about equity returns: historical time series for ‘real total returns’ (ie cumulative indexed performance with income reinvested, deflated by a consumer price index) contain valuable predictive information for investors, and more so than conventional valuation measures like price/earnings multiples and dividend yields. This was one of the big but simple ideas set out in my book in 2002. Since 1999 it has been the basis of horizon-specific probabilistic return projections in a model we now use to manage Defined Outcome portfolios.  

Such an apparently naive solution to a complex problem is deeply offensive to most investment professionals as it implies redundancy for much of their industry. I naturally therefore warmed to the naivete of the Sunday Times account, as typified by this extract.

'Everybody said home prices never had declined on a nationwide basis except during the Great Depression,” Paulson later recalled. He sent Pellegrini scurrying back to his cubicle to determine how overheated the property market was.Tracking interest rates over the decades, Pellegrini concluded that they had little impact on house prices. But as he reviewed academic and government literature and figures, Pellegrini grew frustrated. He couldn’t quantify how excessive housing prices were or show when a bubble might have started. He couldn’t even prove the price surge was distinct from historic moves.Grasping for new ideas, Pellegrini added a “trend line” to the housing data; this illustrated how much prices had surged lately. That’s when Pellegrini took a step back to view things over a longer period, ordering up data on home prices all the way back to 1975. Suddenly, the answer was as plain as the paper in front of him: housing prices had climbed a puny 1.4% annually between 1975 and 2000 after inflation was taken into consideration. But they had soared by more than 7% a year in the following five years, until 2005. The upshot: US home prices would have to drop by almost 40% to return to their historic trend line. Not only had prices climbed like never before, but Pellegrini’s figures showed that each time housing had dropped in the past, it fell through the trend line, suggesting that an eventual drop was likely to be brutal. Pellegrini sat upright, staring at his trend line, amazed at how simple and clear it was.The next morning, he raced in to show Paulson. “This is unbelievable,” said Paulson, unable to take his eyes off the chart. “This is our bubble. Now we can prove it.” Pellegrini just grinned, unable to mask his pride.'

The standard source material for real house prices, deflated by general inflation as measured by the CPI, was the S&P Case-Shiller Index. Pellegrini refers to data going back to 1975 but there is in fact earlier data which I came across a little later in the form of index creator Professor Robert Shiller’s submission to a Congressional commitee. In September 2006 I posted an item on the No Monkey Business blog called US house prices: you thought we had a problem which included a chart of over one century of real prices for single-family homes.

It was important that the data be real. Real prices are perhaps better decribed as relative house prices, because they measure prices relative to general price inflation. But even real price series were rarely publicised because of people’s obsession with changes in the price level, treating house price inflation as the key information rather than the level of relative prices itself. This was a widespread problem in the US just as it was in the UK. I still berate newspaper editors for repeating this error.

As I pointed out in my article, fitting a regression trend to the long time series data was not helpful because there were several distinct phases of price behaviour, including just two since the end of WWII, up to and after about 2000: no real growth followed by a growth explosion. In my article, I contrasted the absence of any overall trend in US real house prices prior to 2000 with the trend in post-war real prices in the UK, which has been fairly persistent at about 2% per annum, in line with growth in personal incomes.  The 0% trend in the US for some 50 years means that relative to personal incomes US housing had become progressively more affordable. I agreed with Prof Shiller’s interpretation that this was due to easy access to new development land, an endowment we clearly do not enjoy in our planning-constrained small island.

More important, the only plausible explanation for the change in price dynamics after 2000 was the change in credit availability. The fact that people wanted to believe in endless rapid growth, a new twist to the American dream, was a necesssary condition but it also required bankers and mortgage investors to throw caution to the wind or the boom would have simply run out of fuel. And it was the massive stock of derivatives created on the back of securitised mortgages, two symptoms of the credit-induced madness that overtook the housing market, that gave Paulson & Co the instruments, in the form of credit default swaps (insurance contracts that would pay out if the underlying securities fell in price), which had the inherent leverage to turn £147m into $20b.  That was convenient but it was also smart and gutsy.

Pauline Skypala, formerly the scourge of the retail investment industry when on the FT Money team, and now FTfm editor, usually focuses on institutional investment but in her Opinion piece last week she referred to a recent interview with Zvi Bodie in a US financial planning journal in which he argues that individuals would choose to hold inflation-indexed bonds in preference to equity-based investments if they were only told the ‘true’ risks of equities and were offered investment services that implemented a ’safety first’ approach.

As this post deals with a fundamental principle of modern planning and portfolio management, it forms a new part of our Research Resource. It accompanies my Feedback response published in FTfm today.

 In this post we

• explain the approach advocated by Prof Bodie and implemented in our Outcomes Driven portfolios
• provide a technical explanation without assuming technical knowledge
• observe what clients actually do when given complete information in an unbiased way. 

It is written for the three categories of people we know read our posts:

• professionals, both pure financial planners and wealth managers, who should feel challenged by this simple and robust alternative to conventional asset allocation techniques
• individual investors, who (with the exception of No Monkey Business clients) are not being offered this powerful alternative
• financial writers.

So No Monkey Business

Because we

• adopt the risk management technique for portfolio construction that Bodie Kane and Marcus (amongst others) set out
• base portfolios on personal outcome targets defined and set by clients with our help but not our direction
• charge flat fees loosely related to wealth levels but not to particular asset values or asset types

therefore we can

• tell clients the truth about risk and what it costs (in resources, outcomes or certainty) to avoid it 
• be economically indifferent to what risk they then choose to embrace or avoid

the consequences of which are

• the process leads where it leads
• if 36% of total holdings are now in inflation-proofed government securities that largely reflects the average age and risk preferences of our clients.

The FTfm article

We thought it would be interesting to share with FTfm readers where it actually leads. The answer appears to be that clients given complete and unbiased information take more risk, not less risk. This is not at all what Zvi Bodie expects or does himself (which he has made common knowledge).

This is not the article I most wanted to offer the FT. When Professor Blake of Cass Business School wrote a piece in FTfm recommending designing pensions from real spending outcomes back to resources and risk taking, FTfm published our response saying ‘clever us, that’s just what we do’. When Edhec Business School wrote a piece for FTfm saying it was time the private wealth management industry adopted the institutional techniques of asset liability modeling and liability driven investing, Pauline published a piece from us saying ‘clever us, we’ve been doing exactly that for the last five years’. So the article I would have like to write was ‘clever us, we did what Prof Bodie suggested and it works – for us and for our clients’. But we have limited bragging rights with the media so sharing practical insights about where it leads was a smaller ask.

Explaining the method

Like so much of what we do, the core of our portfolio construction approach is no longer innovative, if viewed in the wider context of institutional practice rather than retail investment services.  Its text-book definition is the Two Asset Portfolio, because it divides a portfolio into two independent components: risky and risk free.

Technical background:

The Two Asset Portfolio sits within the general theoretical framework of Modern Portfolio Theory (MPT) – the link opens Wikipedia in a new window – and the concepts captured by the Capital Asset Pricing Model (CAPM), the distinctive feature of which is the Capital Allocation Line. In imaginary space (usually depicted in a graph) defined by return and risk, it is the line which connects a risk free rate with the opportunity set of risky alternatives, such as different equity markets.

It does not, however, require you to assume that all the risky assets lie on the line. They could be distributed quite randomly in risk/return space in which case there are lots of ’optimal’ diversified combinations that share the characteristic of having the highest return per unit of risk, all of which maximum-effiency portfolios then mark out an Efficient Frontier. 

In a Two Portfolio approach, the optimal location on the Efficient Frontier is defined by the highest possible excess risk-adjusted return, which (graphically) is where the Capital Allocation Line anchored on the risk free rate is tangental to the frontier.

This general principle is adaptable to specific definitions of risk and return, which will in turn suggest the appropriate risk free asset. In most applications of MPT today, including the ‘factory model’ of standardised asset allocation, risk is defined as short-period variance in returns in nominal terms (usually monthly or quarterly standard deviation, annualised) and, consistent with that, the risk free asset is usually cash or Treasury bills.  However, if the portfolio utility is better defined by long-period purchasing-power outcomes, because of the nature of the client’s goal and how the client will measure satisfaction, the returns, risk and risk free asset should all be redefined to ensure they fit that definition of the utility.  

The Two Asset approach is often described by the term used in corporate finance: the Separation Theorem, because it treats the risky asset (or venture) as capable of being optimised separately from the identity and risk preferences of its owner. In the context of the ‘factory model’, which for convenience treats all portfolio utilities as identical and uses the same nominal short-period risk and returns, the idea that all clients could hold the same risky component of their overall portfolio is logical. In fact, however, the ’factory model’ treats the risky portfolio as the entire portfolio. This is what firms would be tempted to do if they could only raise fees for managing the risky portfolio and not for cash, hence the conspiracy theory.

We have established that the risk free asset and the optimal risky portfolio are the two separate building blocks. Therefore:

• how they are combined determines the overall risk of the portfolio 
• and becomes the way of personalising the risk exposures and managing them.

The theoretical solution for working out the right combination is indifference. It refers to being ‘equally satisfied’ by the prospect of earning exactly the known risk free return or the range of probable returns provided by the risky portfolio. You would be indifferent between the certain return and the uncertain returns. 

But we like the implication too that it is a choice that the professional advising you is indifferent to:

• we have no interest in what you choose
• and only you can choose.

Planning in plain English

The trick when translating a choice described by algebra into one clients understand is this:

• keep with numbers
• but apply them to outcomes, not returns
• and make them outcomes that they can relate to and are relevant (or maybe even vital) to them. 

With retirement planning this is usually dead easy (thanks, Prof Blake): it is sustainable real rates of annual gross ‘income’ or drawdown, where the probabilistic range of outcomes reflects both inflation and capital market uncertainty and how they each develop with longer time horizons. At some blended tax rate (depending on how the asset are held and taxed), gross draw translates into sustainable spending. ‘Sustainable’ means all of three things: it does not need to alter with changes in stock market levels but can be increased with inflation and the capital will not run our before the job is done.

Not only do clients understand this language but they can also control the conversation and take it on. They can elaborate their target outcomes, such as wanting more early in retirement, then less but allowing for more again at the end. They can talk about conflicts, such as between their own spending and benefiting children or charities. They can resolve them in terms they understand, such as gifting (or reserving) only as much capital as would leave sufficient to meet tolerable worst-case spending outcomes at a chosen level of risk taking.

The meaning of ‘risk free’

This solves the problem of a shared language but it requires one other thing, a unique risk free asset (thanks, Prof Bodie): inflation-indexed bonds. This is because most goals that individuals plan for have ‘natural’ outcomes, as the clients think about them (not as advisers do, note), that are defined by purchasing power at some future date.

Dealing with real outcomes at different horizons immediately tells us advisers that we have to deal with two risks, arguably equally momentous: capital markets and inflation. As noted in our technical explanation, most applications of portfolio theory, including the ‘factory model’, ignore the different effects of inflation at non-standardised horizons.

In the special case of drawing down capital for lifetime spending, the uncertain duration of the plan is a third source of risk, because we do not  know how long we will live. 

Why ILGs, not bonds

Because index linked gilts have a fixed duration and are inflation-proofed, and because they bear only the principal risk of the British government, they can be described as ‘risk free’ in terms of both inflation risk and equity risks (in a previous Insight we explained how most risky assets share the same sources of risk as equities and are derivatives of equity, as loosely implied by ’business risk’).

Non-indexed bonds, whether corporate or government issue, cannot perform this risk free role. Though bonds are the conventional ‘balancing’ asset in what we call the standard ‘factory model’ of diversified, multi-purpose portfolios, they only serve to transfer the risk exposure from business risks (which are actually positively linked to general inflation) to naked inflation bets. In the same way that a ‘level’ annuity without inflation protection is a gamble on the predictability of inflation, fixed income is not only not risk free, it is a big bet. It is also a bad bet.

In our opinion, conventional bonds do not even belong in the risky portfolio, once risk is being managed by dilution or substitution, because the weak diversification value of bonds is then redundant. The reduction in real outcome uncertainty relative to equities is small (unless you want to assume the historical instability and unpredictability of the inflation process is not relevant) and the reduction in expected return is very large. A bizarre product of traditional planning is that individual investors for most of their life hold conventional bonds and at the same time mortgage debt but without ever making any connection between the two. 

The power of the method

Our application of the Two Asset Portfolio follows  Zvi Bodie’s idea of a ‘ladder’ of stepped, horizon-matched ILGs combined with an optimal combination of global equity markets (using trackers - why blow all the good work on risk and cost control done so far?) where risk tolerance or indifference is directly exhibited by the way clients choose between different ‘model runs’ illustrating different combinations of 

1. resources assigned to the goal 
2. ranges of real outcomes at different dates 
3. the thing that makes them balance at any required level of confidence: a level of risk tolerance.  

The power of the method lies in the fact that clients do not need to know what #3 means, or what it looks like when dropped into risk/return space so it cuts the Capital Allocation Line (pretty though that is). They only need to know what changing #3 does to #1 and #2, as in its effects on the certainty of achieving the target outcomes for a given level of resources (present capital plus future contributions). These effects they see. They can see as many runs as they want till they get to a solution for each goal, and between competing goals, that gets the job done in the way most satisfactory for them.

The evidence about equity risk

Using a financial model, clients will only be confident about the numbers you show them if they can see where they come from. Here, Pauline Skypala and Zvi Bodie are both right that the industry does not seem to want you to know how bad real outcomes can be, even with long expected holding periods. They have used every trick in the book to disguise risk. Although this partly carried over from an early tradition of paternalism, I do not believe this excuse holds any more and therefore lean towards conspiracy (otherwise No Monkey Business would have been a dishonest title for my book).

The Feedback article describes a process we use of walking new clients through a set of charts that show the cumulative real returns of the major equity indices for up to a century of data. We point out the long periods of flat or even negative returns, which are the real product of equities dealing badly with the inflation or business environment and investors making bad decisions about discounting the continuation of bad times. This is the flip side of the long bull market phases that selective memory (and advertising) might otherwise bias towards.

Here is one example, for the US market, used in a recent post discussing the valuation of the S&P.

Fig 1 Real total returns and fitted trend: S&P 500 (semilog)

The trend for the entire data history from 1925 (the unbroken orange line) is 7% pa. This is the ‘engine’ driving the funding of future financial goals using equity investments. But this underlying driver is literally overwhelmed over short periods by the scale of the deviations from it. Note how frequently real returns are flat for decades at a time. If you bought at the height of the ‘nifty fifty’ growth-stock boom in the 1960s you had to wait over 20 years to get you money back, when inflation is taken into account. Talk of a double dip if market falls again now is not quite accurate. If we look at the period since the dot com bubble burst in 1999, another dip will be the third in a long ‘correction’. We are already over 10 years in.

You hardly need our gruesome charts for Japan to see that equity returns can be negative or flat in real terms for very long periods and that you will almost certainly experience extreme disappointment in a lifetime of investing. Welcome to the world of true risk. 

Feedback: what clients actually choose

Zvi Bodie’s point is appealing: why would anyone run these kind of risks? The answer is simple: if they cannot afford, or do not like, the alternative.

Emphasising the risk is itself a bias unless we are also careful to explain and illustrate the cost of avoiding risk. It is the high cost of avoiding risk, in possible outcomes permanently given up or inefficient use of capital, that leads our clients to opt for a higher level of risk taking than is assumed by academics.

I did not have room in the FT article to illustrate this but I show below an example we have used in retirement seminars. It uses numbers (real or purchasing-power pounds) to show the cost in resources applied or in consumption enjoyed of replacing all equity bets with index linked gilts (’taking bets off the table’) and the further cost of removing the longevity risk by buying an index linked annuity (’leaving the casino’). The resources assigned are £2m; the resources have to last for 35 years at least and the client values higher spending early in retirement. The three alternatives are:

1. Sustainable draw with optimised risk taking: you could start at £125,000 pa with a 50% chance it will need to taper down to £105,000, and a 99% chance of not breaching a worst-case floor draw tapering from £125,000 to £57,000 in the client’s late 80s if markets are consistently bad.
2. Allocating 100% to ILGs throughout the plan: to match the worst-case outcome above takes capital of £2.2m and to match the better possible outcomes at 50% confidence (the median with optimised risk taking) takes £3.2m – a ‘resource cost’ of £1m.
3. Avoiding all risks including longevity: £2m buys a real income of £64,000 pa – an ‘outcome cost’ of £61,000 pa in the early years and £41,000 pa at the end.

The point is, the numbers speak for themselves. Given complete information about both the scale of the risk and the cost of insuring or avoiding them, as factors in this realistic practical example of half or twice, you are likely to stay at the table.

Incidentally, the multiple of two that describes the value of an inflation-proofed annuity applies also to the value of final salary scheme pensions. This is what it would cost you to replace what is effectively a large ILG ladder on your personal balance sheet. It means that one of the hardest decisions high-earners often face is whether to bear the risk that their pension scheme and its sponsor will both collapse under the intolerable burden of meeting its over-generous promises, with the consequence that a pension of £150,000 a year drops to the ‘insured’ level of £30,000. Up to the insured level, it is a risk free asset but above it there is a very small chance of catastrophe. Some advisers believe that even the insured level, being underwritten only by scheme levies to build up a protection fund, could be reneged on if the fund is overwhelmed by system-wide failures.

Are our clients representative?

This is not a controlled experiment: most of our clients are in the top 1% by wealth and income. Other investors and other advisers might assume they can afford to embrace more risk. This misses an important point. People take bets instead of laying them off because they value the payoffs. They think about the possible payoffs in terms of consequences. For goals such as retirement spending, the resources may be higher but so are the levels of spending at which the consequences look unpleasant or even intolerable. It is all relative. Rich people as well as poor people make rational gambles. It is mainly when you drop below the typical IFA target customer that the gambles are not constrained by own resources and are framed largely in terms of externalities, such as social security. 

In my Feedback piece I also suggested that the wealthiest, who could actually afford to live off the cash streams, capital or interest, from their ILGs (like their Victorian forebears who ‘clipped coupons’ on their Consols and never thought to work), choose to to take risk instead. A common motivation we see is that ‘capital should work hard’, because they worked hard to get it. Less common but just as important is that playing the game is its own reward, so satisfaction or success is measured as replacing (through return on investments) the money consumed. 

Is it more expensive?

In another important respect our clients may not look representative because they can afford the economic cost associated with planning goal-based portfolios where resources, risks and outcomes are the product of interaction with sophisticated technology (which is expensive to develop or license) and where confidence in the process calls for education and explanation as well as illustration (which is time consuming).

Our answer to this has always been simple: pay for it with the savings you make by opting out of the industry’s deeply conflicted factory model and its dependence on active management. These pile up the costs but have no economic worth.

The ILGs cost between £9.50 and £12 to buy and nothing to hold; the equity ETFs or tracker funds cost between 0.2% and 0.5% pa to hold.  At our average ILG holding of 36% (reflecting the sum of all customised combinations) for Defined Outcome portfolios, we cut typical implementation costs to 0.24%. This compares with published charges from industry giants like Towry Law of 1.6% (on which may be loaded asset-based fees for new contributions and tax wrappers). Many smaller IFAs will apply total costs of 2% or more, by loading up portfolios with expensive active products they cannot discount, and private banks load them up with their own products where they have no incentive to discount. Savings of 1.36% to 1.76% pa pay for an awful lot of unbiased, high-quality advice and education.

This example demonstrates that there is very high umbrella of inefficient charges resulting from traditional industry structures and investment processes. There is no reason why modern, technically-proficient firms should not compete successfully under this umbrella, offering better solutions at a lower price at a decent profit for themselves.

Is he right?

In this increasingly popular view, the actions by governments to pump money into the stalling financial system are seen as giving stocks a reprieve before they reached the sort of valuation levels that properly discounted the post-credit crunch realities facing economies and businesses. Hence the anxiety now as the patient looks to be coming off life support. 

John Authers’ preferred measures of value, using replacement value of assets (Tobins q) and a trailing 10-year average of earnings, supports the story that US equity investors, like the banks, got lucky and were bailed out before reality had sunk in to share prices.

I don’t think we can agree with that. Our own measure of value suggests that the last move down in the S&P at the start of 2009 took the market to an extreme level with only a few historical precedents in any major equity markets, all equally important as long-term buying opportunities.

Whilst we attach a great deal of importance to our measure of value as a basis for forecasting long holding-period return probabilities that can be used in asset allocation, it still does not mean there is a negligeable chance that the S&P will fall through the earlier low. Though it would be unprecedented for the US market, it happened in the UK in 1974 and in Japan in the 90s and again in 2003. What can happen in one place can happen in another.

There are also several historical examples where extremely high predicted value has persisted for a decade or more, Japan being one but Continental Europe in the late 70s and early 80s is a less familiar one, being disguised at the time by high inflation.

Though the FT piece was about the market most likely to influence the general direction of global equities, it is worth noting more parochially that on our measure the FTSE All Share Index is the least undervalued of all the major markets and did indeed recover well before reaching a historically exceptional valuation.

Lateral thinking about equity valuation

We agree with John Authers that Tobin’s q and trailing 10-year earnings will dampen the effects of extreme cyclicality in current fundamental inputs to valuation, particularly reported earnings. As an accompanying chart in his column showed, reported earnings in real terms reached a low point last seen in the 1930s although clearly this is not what has happened to ‘earnings power’ for American business.  The q ratio dampens variance simply because replacement value of assets is very stable, so the significant information comes from price change. Averaging earnings over several years will also dampen the valuation variance but is transparently arbitrary and may also leave price variance as the important information. These may therefore be better than conventional price/earnings ratios when earnings are disturbed but they are still not rigorous indicators of extreme valuation, high or low.

As market historians, Chris Drew and I have since the late 1990s adopted a lateral approach to equity market valuation, formalised in a return-generating model called Lambda. We ignore fundamental inputs, because of the measurement problems, in favour of tracking the path of achieved real total returns from each market and comparing it with its own long-term trend, using ’best fit’ regressions. The model has not been invalidated by subsequent return behaviour and has indeed handled several episodes of extreme valuation very well.

On this measure, the recent low for the S&P was as low as the previous extremes of the early 30s and 40s as well as 1974 which was revisited (in real but not money terms) in 1982.

Fig 1 shows the S&P continuously-compounded logarithmic returns with gross dividends reinvested and deflated by the CPI from 1925, as an index. The whole-history regression trend (colour) is 6.8% pa but the hindsight-free trend using data up to each point in the series (colour) was above or below this as a function of the particular period. In my book I showed a Wilshire series for US equities for nearly two centuries that revealed a similar trend in both centuries, in spite of massive contextual differences. Data for other major equity markets also suggests about 5-7% pa as a trend, with much narrower dispersion of the differences than economic context and culture might suggest. Global evidence suggests regression trends are not just a statistical accident but an economically sustainable quantity that is a core element of the equity return process. We believe there are strong theoretical underpinnings for this, based on both economics and risk aversion.

Fig 1 Trend and deviations from trend in real equity returns: S&P 500

This idea of sustainable real equity returns over long holding periods is really important. It conflates into a single measure two crucial influences on returns: i) fundamentals (like earnings and dividends) and ii) investor behaviour changes (like risk aversion, sentiment and momentum following). Historically, even over long holding periods, the second set of uncertainty sources has usually dominated the first, which makes the traditional focus on fundamental inputs look odd. If jointly the two sources of uncertainty lead to bounded deviations from trend, as a further core element of the return process, we do not need to disaggregate them to model either returns or risk.

Fig 2 expresses the level of the real total returns index as a ratio of its own trend, or detrended, which we call the Market Value Ratio. We use this as a basis for adjusting upwards or downwards the mean expected returns extrapolated naively from the trend alone. Low price denotes high value and high future returns and vice versa. By making some assumption (itself based on historical analysis) of the time-dependent reversion to trend, we can estimate real return probabilities for every holding period as often as we gather new historical return data. The actual frequency is monthly.

Fig 2 Market Value Ratio: S&P 500

What can we do with value?

Fig 2 makes the point of this post that the S&P low in March last year was extreme. This observation was sufficient to justify exceptionally high expected future returns, provided long holding periods could be assumed. For investors with naturally long horizons, this amounts to a lot. It is why we added with confidence to clients’ total equity exposures up to the low point, while others were selling, and reduced them either side of this recent year end. See Is your manager doing a good job? (22nd January). 

Observing extremely low value by March 2009 may even have been enough to warrant assigning a very low probability to further downside risk over short horizons, which would also amount to a lot if, it could be relied on.  We do not believe it can. We prefer to treat short holding-period returns as a ‘random walk’. That was not the basis of our decision to buy.

An aside for more numerate readers: short-run returns, whether deflated or not, also have the highly inconvenient statistical characteristic of fat tails, or extreme outliers that would not be predicted if assuming a normal distribution. Long holding-period real returns, on the other hand, appear to be normally distributed. 

We sympathise with John Authers who is obliged (as a good journalist rather than a good story teller) to remind us that even a ’true’ valuation of the market tells us very little about the chance of the market falling or rising or staying the same over the next few years. This might read as ’sitting on the fence’ but it is more insightful to see that good valuation measures simply have restricted uses.

For us, as financial planners and even as portfolio managers, using them to project long holding-period real outcomes is actually more important than using them to try to time markets. Imagine yourself in 20 or 30 years time looking back and deciding you got the outcomes you wanted and planned for. Is it most likely you will credit the many market timing decisions that made up the path your money took or rather making the right long term decisions? We have no doubt and we don’t think our clients do either.