OPINION | While most attention around the Federal Budget 2017-2018 was on policies, arguably the bigger driver of budget outcomes are the forecasts that are largely beyond the government’s control and out of the public eye – such as the iron ore price and the future of the Australian residential property market.
Many significant government decisions on policy, investment or finance have at their core a forecast or prediction about the future, so much so that financial forecasts shape the world we live in, whether those forecasts are made by economists, investors or corporate decision-makers.
The thing is most forecasters have no idea how good their forecasts are in the short, medium and long term, research by Philip Tetlock and Dan Gardner has found.
Forecasting mistakes are inevitable, given the complexity and inherent unpredictability of many aspects of government and investment decision-making.
To be wrong when attempting a difficult task is no crime. However, worse than merely being wrong, the empirical evidence shows that forecasts tend to be systematically biased – largely beyond the forecasters’ awareness – and that the impact of these biases is compounded by our overconfidence in the validity of these forecasts.
The good news is that forecasting has shown that it can be improved by applying targeted strategies.
In the Macquarie University Finance Professionals’ Series this month, I outline some of the warning signs and behavioural biases that can be used to help identify systematic forecasting errors. These include complexity, optimism, lack of empirical rigour, over-confidence and rational behaviour/incentives.
First, not all forecasts are prone to error. Demographic forecasts, for example, can typically be made with great accuracy into the future. For example, we have a fair idea of how many 15-year-olds will become 16, which helps determine secondary education policies. So where are errors most likely? We should go looking for forecast errors within systems with inherent complexities, non-linearity, ambiguities, sensitivities and feedback loops.
Can we forecast the outcome of the US presidential election or Brexit? Not reliably. This is at least in part because, as we saw with Trump, the outcome had some of these inherent complexities. It was sensitive to swings within small electoral colleges that could magnify forecasting errors to a presidential scale.
Second, can investors forecast company earnings, or companies forecast the outcome of projects or acquisitions? Again, depending on the context, not reliably, with systematic deviations being underpinned by a range of factors; such as being seduced by the narrative of the project or investment and under-weighting the importance of the “outside view” and mean reversion to “base rates”.
Third, we should also expect to find forecasting errors where empirical rigor is lacking. Unfortunately, the type of rigor that is ubiquitous among professional forecasters – the type evidenced by an extensive pack of supporting charts, statistics and detailed arguments – is no antidote to error. No, the type of rigor we need involves systematically collecting historical forecasts and analysing them against realised outcomes. Before accepting a forecast as having any basis more reliable than rolling a die or consulting the stars, we should assess the forecaster’s historical track record.
Fourth, and perhaps ironically, we should anticipate error where forecasts are made with the most confidence. Whether we are aware of it or not, too often we use confidence as a proxy for competence.
And finally, sometimes it can be quite rational to make systematic errors when predicting the future. A chief executive (or perhaps national treasurer) with a bias towards optimism is more likely to garner the confidence and support of their employees (or constituents). While this optimism might be unjustified by the evidence, its motivational impact might just lead the company (or economy) to better outcomes. But we need to be careful, as these incentives don’t always nicely align.
Budget forecasts will never be completely accurate, but with the application of these techniques, we can have more confidence that the newly minted policies announced in federal budgets are indeed on the right track.
Simon Russell is a behavioural finance consultant and speaker, author of Applying Behavioural Finance in Australia, and a Macquarie University alumnus with a master’s degree in applied finance.
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