Successful investing is always a process. Successful goal-based investing is a process that ties together the visualisation, description and quantification of the benefits sought from your money with a continuous, dynamic decision-making method that can deliver these benefits.
Our process is highly systematic, relying on computer modelling and mathematical decision rules. It draws on a set of theoretical but also intuitive beliefs about the way individuals and financial markets function. What we have originated is the combination of them in a single process as a service for individual savers and investors that can replace all their existing industry relationships. See our techniques below...
Planning to maximise wealth benefits
The benefits money can provide take both hard forms (meeting a spending or gifting target) and soft forms (clarity and peace of mind). The benefits sought are unique to each individual or household.
They are ‘discovered’ by dialogue. Its language requires only the ability to visualise purposes for your money and what outcomes for each purpose would denote satisfaction or regret, which comes from thinking about consequences. It’s about who, when, why, how much, why then. It requires no knowledge of finance or investment. Once specified,these benefits can be ‘translated’ into an economic language as descriptions of ‘utility’ or ‘welfare’.
The most efficient organisation of personal assets and liabilities, including the approach to risk taking, is that which maximises the defined welfare. It is neither fixed at every stage (benefits take different forms at different life stages) nor unchanging at any one stage (its ambitions may be modified by its own progress). The principles are widely applied in company finance and in institutional investment but are rarely used to bring logic and consistency to personal ‘balance sheets’. Our process does that. Dialogue about personal benefits is guided by our financial models to help make all planning clear, realistic and internally consistent.
Benefits come with costs – both real costs and opportunity costs. That invites trade offs. Trade offs can be better made if informed by numbers. For the numbers to be realistic they must either come from known current market prices or a model of future possible market prices.
Projections need to be as complete as possible, with a probability distribution that allows, say, an outcome with 99% confidence to be quantified (so it corresponds to a worst-case tolerance expressed by the client).
For financial goals that take the form of a stream of cash amounts, such as the pre-tax draw from capital that has to finance post-tax spending, the distribution of probable outcomes needs to be specific to every time horizon, because the uncertainty associated with investing in risky assets depends on the time horizon.
Finally, future probable outcomes need also to reflect any decision rules applied during the investment journey to keep the level of risk consistent with the agreed tolerances for outcomes. A constant attitude to risk will not translate into the same level of risk at different time horizons: as the horizons of a stream of cash flows move nearer, the level of risk will have to be reduced.
Because accuracy and internal consistency are both vital if the numbers are to be realistic as inputs to choices clients make, computer modelling is necessary as the means of generating the numbers.
Discovering 'true' risk tolerances
A key benefit of using models to inform choices is that risk preferences become specific and obvious instead of abstract and opaque.
Without specifying outcome probabilities and dates, a client’s risk preferences have to be abstract and general. That’s why you are typically asked ‘is your risk tolerance high, medium or low?’In most advice services it’s one of the first inputs to the firm’s process.
With fully-costed probable outcomes at defined dates, risk preferences emerge as an output of planning rather than as an input. For instance, if I know the money available for this particular purpose and I need to be this certain of achieving this minimum tolerable outcome, but with only enough potential for better outcomes that I really value, I can solve for the risk approach (constant attitude, changing level) that gets that job done.
Not knowing now the amount of money available (perhaps because I’m still trying to work out how much to save or how to spread my money between competing goals), I can instead solve for all the trade offs between probable outcomes and required resources that are differentiated by risk. I can then easily choose between them because I’m looking not at the risk levels themselves but at combinations of outcomes and resources (my language) that I can relate to.
Systematic portfolio management
To build a good planning model that dynamically links time, risk and resources, we had to build a portfolio model that would apply the same dynamic rules in practice.
Realising that good planning choices need information about future outcomes that reflect changing risk levels over the life of plan, rather than static assumptions about risk and returns, it was always obvious to us that the portfolio management approach needed to share the same features as the planning model. There could be some other features that were specific to the portfolio model, but the core rules had to be the same ones used for projecting probable outcomes for the plan. The model we use to plan a goal is the model used to manage the goal-based portfolio throughout its life.
Portfolio management that follows a set of rules (when I see this I must do that) is typically known as ‘quantitative investment management’. ‘Quant’ can either be judgement guided by a model or systematic following of a model. We choose the latter for two reasons.
Our model is designed to take advantage of market movements that reflect the behavioural errors to which investors are prone, so judgement will only tend to replicate those errors.
Judgement increases costs.
The original model was first built between 1998 and 2000. Though subject to refinements since, the broad principles have remained constant. It has proved robust through what have turned out to be extremely testing market conditions in the subsequent period, including the technology boom and bust, the global financial crisis and the subsequent recovery in prices of risky assets.
Leading-edge institutional techniques
There is a precedent for goal-based investing aimed at realising quantified outcomes, within agreed tolerances, at defined time horizons: Liability Driven Investing or LDI. We did not need to invent it.
Amongst financial institutions like life insurance companies and occupation pension schemes, the equivalent of outcomes is meeting their contractual liabilities, or promises to pay at set dates. Traditionally, a lot of effort and accuracy went into quantifying, categorising and date-stamping these liabilities. What then used to happen is that they were roughly matched, with none of the same precision, to some ‘balanced’ mix of asset classes and markets selected from within a small range, from low to high risk.
What LDI did is introduce to institutions the same precision in both planning and managing. The specification of the liabilities, by nature and time, and the specification of the sensitivity of the institution to each different aspect of uncertainty, now lead directly and logically to a totally-customised investment solution. No two LDI portfolios will be the same unless by chance. Standard balanced management is now the exception, not the rule.
Fowler Drew has been a leader in applying LDI techniques to individual financial goals.
The core of an LDI solution is separation of the assets held to match liabilities between ‘hedges’ and ‘bets’. Hedges are assets that match a liability with certainty - equivalent to insuring them, so they are risk free. Bets are assets chosen to try to exceed a liability but with no certainty they will, so they are risky. The combination of hedges and bets is relied on to control risk in the way the institution specifies. Dilution of risks by a risk free asset replaces diversification as the risk control.
Portfolio separation theory allows a more robust way of managing risk within agreed tolerances as it introduces known quantities (hence dilution) rather than relying on unknown, highly variable and often randomly changing quantities such as the way different risky assets move together or independently.
Because risk free or dilutive assets are also very cheap to buy and may even be free to hold, dilution lowers investors’ costs. It is only attractive to managers, like Fowler Drew, who charge flat fees - a problem institutions adopting LDI did not need to worry about as they could set their own agenda.
Fowler Drew uses cash (often retained by the client but assigned to a goal-based portfolio) to hedge short-term liabilities. Where somewhat longer-dated liabilities need to be hedged to keep their outcomes within agreed tolerances, we hold instruments with guaranteed inflation protection (index linked gilts and National Savings certificates) rather than cash.
With portfolio separation, the need for diversification to control risk is removed so diversification is only that required to produce the most efficient combination of well-evidenced and low-cost risky assets. That means geographically-spread global equities traded in public markets. Assets whose return behaviour is less well evidenced or which are expensive to buy and hold are not required and would weaken the model.
Even quantitative managers have an investment style. Ours is value seeking and contrarian. Our model exploits ‘mean reversion’ in both absolute and relative real returns.
When returns for different equity markets are calculated to combine income and price change, and are deflated by each country’s own inflation, the resulting real total returns show surprising similarities in their long-term growth trend. Returns to investors who diversify are much more narrowly distributed than would be expected given differences in national economic growth, maturity or profitability. They are more similar than might be suggested by differences in culture, market structures and taxation of investments.This compression of mean returns on investment is a logical feature of an adaptive capitalist or ‘market’ system.
Over short periods, deviations from trend are very large. But for the long-term trends to persist there is clearly some mechanism within the system, including within the structure of an index, which causes these deviations to revert to the mean. Mean reversion can be exploited by patient and contrarian investors, both within and between markets.
Ignoring this opportunity, or assuming it will not persist, implies a bet that the market system will break down and will cease to survive by adaptation. That uncertainty cannot be excluded but, were it to happen, there is no other asset or type of investment that will survive undamaged and provide a hedge. It therefore pays to bet on the system surviving, not failing. That is what we have taught our model to do.
Active asset allocation is the key to generating the required returns within a tight regime of risk control. Passive, index-tracking funds are the cheapest means of implementing exposures. Combining the two, often known as Active/Passive, increases the reliability of the overall process.
We prefer passive implementation, using index tracking collectives (both open-ended funds and exchange traded funds or ETFs) because it is the cheapest but also because the active funds industry has a truly appalling record of performance relative to its own benchmarks after all costs.
Analysis by Vanguard of performance data from Morningstar for all UK-distributed funds (including funds that were closed, usually the worst-performing) shows that across 11 different fund categories in 10 years ending 2014 the best-performing sector still had 70% of funds underperforming their benchmark. The worst fund category had 95% underperforming.
Passive is gaining market share very rapidly as a ban on sales commissions introduced by the FCA in 2013 has removed a bias to active funds. Prior to its ban, commissions were paid to agents as incentives to recommend active funds whereas low-cost passive funds did not typically pay commissions.
In our Active/Passive process:
The systematic returns of an asset allocation policy can be harvested at lowest cost
All the risk budget of the plan can be assigned to the asset allocation strategy which is better able to explain outcomes and is more reliable
Tax-saving actions being limited in scope by allowances and thresholds, the full tax-saving budget can be assigned to decisions at an asset allocation level that need to be made anyway, for reasons of risk control.