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Why Stochastic ESG Modelling is the Best for Financial Forecasting

Author: Chet Velani
11 October, 2019

Why Stochastic ESG Modelling is the Best for Financial Forecasting

I’ve stopped using my in-built car navigation system. It’s GoogleMaps for me from now on. On the face of it, it seems like an unnecessary faff as it means having to plug in my phone to CarPlay and not having the directions appear on the dashboard directly in front of me. But GoogleMaps wins hands down. 

Yes, the in-built navigation shows the best route from A to B, it provides voice directions for each section on the route (in a rather hectoring tone, but that’s not why I no longer use it), it tracks how far progressed I am and the estimated arrival time for a journey of that distance. Which should be all I need, right? Wrong. What It doesn’t factor in, but which is essential to being able to predict realistic arrival times and thereby determine my mood when I reach my destination, is live traffic information. My car is about 10 years old, so to be fair the in-built navigation is ‘getting on a bit’ in terms of technology. I know most in-built satnavs now feature live update functionality. But mine doesn’t, and buying a new car to get my hands on more modern navigation technology seems like false economy. 

This is a similar problem facing advisers using cashflow tools. Don’t believe me? Ok, I’ll explain.

Many of the current crop of cashflow modelling tools are flawed because they use over simplistic deterministic projections and are unable to take into account ongoing variables that will affect the plan over time, and may also scupper the chances of reaching your desired destination on time. Deterministic tools are based on simple calculations (like my tired old sat nav). You input data, the system runs a simple algorithm, and it determines an answer based only on the information you put in at the start.  

Deterministic tools are prone to miscalculating sustainable income because they are unable to take into account those unexpected diversions, twists and turns, and bumps in the road that life throws at us. Like sequencing risk for example. Any retirement planning tool that is unable to model the effect of market down turns in the early years of income withdrawal are simply unsuitable. At risk of stating the obvious; If the technology you use cannot show you a range of possible outcomes from which you can effectively plan, then it’s not an effective planning tool. So without those ‘accident warnings’ you may never be able to get onto an appropriate accident-free route to your destination.

The use of a quality stochastic model is key to being able to show the likelihood of a customer achieving their retirement goals, which in turn also helps provide a capacity for loss check.

Factoring in the ‘live traffic updates’ of assets to models provides an essential additional dimension  for being able to act in mitigation against the often irreversible effects of a badly timed entry into drawdown and the other, often overlooked, risk of high volatility. 

The benefits of pound-cost averaging when accumulating wealth are well known. With regular fixed investments, market price volatility has the beneficial effect of buying more investments when prices are low and less when they are high. The higher the level of volatility, the more advantageous the effect. 

With drawdown, the effect is reversed. High price volatility reduces the level of sustainable income that can be afforded. The higher the volatility the worse the effect. The following example illustrates the effect in action.

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By reproducing real-life characteristics of assets, a stochastic ESG model gives sensible and realistic forecasts. ESG forecasts do not depend on historic performance. This keeps them firmly looking forward from the current economic situation and makes them particularly suited for long term projections

You can read about the many different model types and their respective merits here. But to my mind, a complete, real time picture of all the hazards that could knock a plan off course makes all the difference to realistic outcome planning. And that’s the difference between a deterministic model and a stochastic ESG model. It is not hyperbolic to say that for the customer in drawdown, it could mean the difference between attaining their retirement goals and outliving their retirement income.

Modelling future outcomes. Why stochastic is the credible choice