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When cashflow plans can be ‘simplistic and misleading’

Financial planners need to tread carefully with cashflow tools where they ignore the fundamental principles of how real-life portfolios work, i.e. the randomness of returns, says FinalytiQ MD Abraham Okusanya

I saw this interesting image on Twitter (via @farnamstreet) the other day that I think financial planners should share with their clients. It’s a graphic image of how clients think their investments grow over time versus how their investments actual grow or, if you like, Client’s Plans Vs The Market’s Plans for their money!

abraham graphicWhat worries me is that financial planners (and I mean that in the true sense of the term) may actually be contributing to this inaccurate perception of how investing works.

Case in point is the use of cashflow models
Many financial advisers have adopted cashflow modelling to guide clients in making decisions on a ‘safe’ withdrawal from their portfolio. I hate to be the one who breaks it to all the cashflow modelling evangelists out there, the way these tools are used by the vast majority of UK planners means the outcomes are far too rudimentary and risk misleading clients. Many planners rely on deterministic models, which treat investment returns as linear (i.e. average annualised returns over time) and ignore randomness of returns. This underplays the dangers of negative sequence-of-return, and there is a grave danger of misleading clients.

To get a better understanding of sequencing risk, let’s take an example of two portfolios with the following yearly return in the table below. First, Portfolio A, has random returns every year, while Portfolio B, has a yearly return of 5.73%. Portfolio A is closer to how a portfolio might perform in real life, while Portfolio B represents the kind of model produced by assuming an average annualised return. If the client invests £100,000 and withdraws £5,000 a year from these portfolios starting from their 60th birthday, he/she will run out of money with Portfolio A on their 83th birthday, although Portfolio B continues to support the level of income withdrawal indefinitely.

Abraham tableThe point here is that while average annualised return on both portfolios is exactly the same i.e. 5.73%, in reality the outcomes for the client couldn’t be more different under these scenarios. This drives home the point that, when in a drawdown the order in which returns occur is perhaps more important that the average return over a period of time.

Pound cost averaging in reverse gear
A negative sequence of market returns early in retirement can cause funds to erode to the point where what seemed like a reasonable income level quickly becomes unsustainable, even if portfolio performance recovers in later years. This is because taking income from a portfolio in a falling market leads to ‘reverse pound cost averaging’, where a client is essentially forced to sell units in their portfolio when prices are falling, in order to pay the required income.

Deterministic modelling tools hide the danger of negative sequence-of-return, especially in the early years of retirement.

Financial planners need to tread carefully as these tools ignore the fundamental principles of how real-life portfolios work – i.e. the randomness of returns. This is especially so where with the new pension freedom proposed in the 2014 Budget, we are in uncharted territories, as people who would have been advised to buy annuities in the past may now end up in drawdown.

abrahams chart 2For starters, we need better tools to model potential outcomes in retirement. The problem is that deterministic cashflow models treat expected outcomes as linear and do not consider the range of possible outcomes that clients may experience. On average, deterministic models will have approxiately a 50% success rate; meaning that there is 50% likelihood that client could run out of money. This is not good enough!

Stochastic models (such as Monte Carlo simulations) are a major improvement on the deterministic models, which most planners currently use. Monte Carlo models account for randomness, not just of investment returns but other factors such as life expectancy, inflation, etc, and express potential outcomes in terms of probability of clients meeting their objectives. This is valuable information for planners to consider, and communicate with their clients. This goes right to the heart of communicating and demonstrating clients’ capacity for loss.

 

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