When it comes to determining the growth of savings/investment/pension accounts, within the software, one has 2 choices – to use either:
- 'Fixed Growth' rates, or
- 'Market Assumptions' (i.e. an asset-allocated portfolio)
Note that these options are not mutually exclusive, and it's perfectly possible to utilize some combination of the 2 methods within the same client plan.
Fixed Growth Rates
Within the software, 'fixed growth' rates can be applied to any savings, investment, or pension account, but will – at the very least – be appropriate for accounts that are assumed not to be subject to short-term market fluctuations: e.g. deposit-based, structured, with-profit, or other investment types that offer protection against short-term fluctuations.
NB: By default, the software exempts all accounts entered via the Savings screen from any of the market-based functionality, meaning that returns to these accounts are assumed to be given, in the first instance. This default setting can be turned on, and off, as appropriate.
In the context of 'fixed growth' rates, the user makes a single assumption about the rate of return that a particular account will achieve. Consequently, the assumed rate of return is entirely deterministic, in the sense that one's assumption will be the sole determinant of growth (excluding any fees, of course) and, by the same token, no variation in the rate of return will occur, without simply changing one's assumption – this stands in contrast to the probabilistic approach, outlined below:
The major benefit of utilizing market assumptions, within the software, is that one will be able to make full use of the software's market-based functionality, and will get a more nuanced response when doing so, in contrast to the use of 'fixed growth' rates – obviously, different asset classes will behave differently, in the face of market fluctuations, and the software will be able to reflect this, enabling one to carry-out a risk-based analysis of a client's situation.
When utilizing market assumptions, therefore, the rate of return achieved by any given account will be a function of (1) the specified asset allocation of the account, and (2) the underlying assumptions about the (long-term) returns of the various asset classes themselves. What follows from these assumptions is (1) that there is a range of possible outcomes (for any given asset allocation), from an assumed minimum investment return, to an assumed maximum, with the actual return based on a probability distribution and (2) that all possible outcomes are always relative to the mean – see below for more details:
More about market assumptions and the probability distribution
Regardless of the specific asset classes, and asset class assumptions being employed, Voyant software assumes that investment returns are normally distributed, i.e. that returns over a suitably long time period will conform to a bell curve distribution. The shape of the bell curve distribution, illustrated below, reflects the fact that some investment outcomes (i.e. those closer to the mean) are relatively more probable, than others.
As illustrated, above, the bell curve distribution assumes that (approximately) 68% of all outcomes (investment returns) will fall within 1 Standard Deviation (SD) either side of the 'mean', and that (approximately) 95% of all investment returns will fall within +/- 2 SDs of the mean. The probability distribution defines, or constrains the range of (assumed) possible returns, from the worst possible return, approximately 3 SDs below the mean, to the best possible return, approximately 3 SDs above the mean.
Given the above, it follows that every point under this curve falls somewhere along a continuum, between 0% (far left-hand side, approximately 3 SDs below the mean) and 100% (far right-hand side, approximately 3 SDs above the mean value). Every data point on this continuum (between 0 and 100) is known as a percentile value. By definition – as illustrated in the chart shown below – the mean return falls at the 50th percentile. As this chart also shows, a return of -1SD (i.e. 1 SD below the mean) will fall on the 16th percentile, and a return of +1SD (i.e. a return 1 SD above the mean) will fall on the 84th percentile:
A familiarity with the bell curve distribution and the associated percentile values will be extremely useful when using any of the market-based functionality within the software (e.g. if choosing to model a 'major loss') because the language of "percentiles" gives us a precise way to express whether investment performance has, in fact, been good, bad, or merely average, relative to the asset allocation(s) being utilised.
Market-based functionality within AdviserGo
AdviserGo incorporates several pieces of market-based functionality – each item in the list is summarized briefly, and a link provided to a more detailed explanation, as follows:
- The 'Loss Capacity' insight, 'Market Crash' insight and 'Major Loss' special events
- The 'Monte Carlo' insight
- The 'Performance' insight
- The 'Historic' insight
The 'Loss Capacity' and 'Market Crash' insights and related 'Major Loss' functionality
The 'loss capacity' insight determines how much a plan can lose from investments and pensions at a specific event while still achieving plan objectives. Both the 'market crash' insight and 'major loss' special events are intended for modelling a market crash/market correction, of whatever magnitude. Given the magnitude of the correction (i.e. the inputs), the insight will provide an output, in terms of a baseline investment return that will suffice, to avoid any shortfalls occurring. The 'Major Loss' event functionality simply enables one to build-in a market correction, as an effectively permanent feature of a client's financial plan.
You can read more about the Loss Capacity insight here. To learn more about the Market Crash insight click here. We also have short videos covering each of these:
The 'Monte Carlo' insight
The 'Monte Carlo' insight is a tool for stress testing a financial plan, to gauge the tolerance of a plan to market forces. The tool will run a series of iterations, each of which entails randomizing investment returns over the life of an individual's financial plan, so as to ascertain its likely success. You can read more about this here.
The 'Performance' insight
The 'Performance' insight provides a quick tool for dialing down (or up) one's assumed long-term investment return. Each 'notch' on the slider represents either a 1% up/down shift, or a move along (up/down) the probability distribution, of 6 percentile points. Click here to read more about this insight.
The 'Historic' insight
Finally, the 'Historic' insight is designed for mapping volatility onto one’s financial plan. The chart displays the performance of the FTSE All Share index between 1900 and 2019, and allows one to select a time period between these years. Every year selected represents a level of return (for the FTSE All Share) relative to the long-term mean, and it is this relative measure – specifically, the percentile value (for each of the selected data points) – that is then mapped onto one’s own client plan. Read more about this here.