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:

### Market Assumptions

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 50^{th} *percentile*. As this chart also shows, a return of -1SD (i.e. 1 SD below the mean) will fall on the 16^{th} percentile, and a return of +1SD (i.e. a return 1 SD above the mean) will fall on the 84^{th} 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.