What design technology classes can teach us about investment strategy

Rob Skelton from our investment team recalls a useful school lesson he applies to every investment strategy he works on.

If you see examples of my DIY work, you’ll know straight away that design technology was not a strong subject for me at school. However, one particular DT lesson has stuck with me.

Our teacher split us into groups of two or three, gave each group a stack of thin pieces of card, some scissors and sellotape, and said:

“Your task is to make a bridge that will span the gap between two tables. The team that makes the bridge that supports the most weight before collapsing wins.”

We all went off to make our bridges and I remember – surprisingly clearly given it was 30 years ago – the design of our bridge, which had:

  • H-girders made out of folded bits of card to span the gap
  • Pillars designed to fit between the gap in the tables
  • Diagonal beams transferring the load to the pillars
  • Sellotape (surprisingly strong in tension) between the two pillars to take the load.

Our team didn’t win. The winners borrowed a wooden ruler from the stationery cupboard and hid it in card and paper! But it did teach us how to build a bridge that was as strong as possible given the materials we had.

Architects of bridges also build models. Nowadays though, they use complex mathematical models with programmed elements such as:

  • Laws of physics
  • Force of gravity on earth
  • Geological surveys of the ground on which the bridge is to be built
  • Density of the material used
  • The breaking strain of every component
  • Predictions of how these materials degrade over time
  • Mathematical models of fluid and aerodynamics to simulate the impact of wind and water on the bridge and its foundations.

Our understanding of the laws of physics and measurement of the bridge’s components and its environment combine to give us a precise measure of risk. They can accurately tell the architect how likely it is that the bridge will collapse. The architect can then design the bridge to a tolerable level of risk.

These models are both complex and expensive though. Also, the user of the model would have no idea whether the model had an underlying error in its code that skewed the predictions it made.

But what’s all that got to do with investment strategy?

It comes down to a question I always ask – What kind of risk models should we apply? Should we use the equivalent of card and sellotape (i.e. a simple model made with a spreadsheet)? Or should we invest in the equivalent of the architect’s complex modelling?

Why investment strategy is like architecture… and why it’s different

In the design of a bridge, then, the laws of physics and conditions that determine how bridges work are known to us. But this isn’t the case with investments. As we were brutally reminded in September and October last year, the rules and conditions that drive pension scheme investments can change very suddenly. Markets are not governed by intractable laws, but by behaviour, politics and other human variables.

A complex mathematical model is about as useful for investment strategy as it would be to an architect designing a bridge on an alien planet where factors like mass, geology and climate are unknown. No matter how complex, the model can’t possibly give an accurate measure of risk and the ‘probability of failure’ in such uncertain conditions.

Any model, simple or complex, is of equal use to the architect. They will give the architect a clear understanding of how to build a bridge that’s as strong as possible given the materials available.

It’s the same when modelling pension scheme risk. Both simple and complex models can help us understand how to design an investment strategy. They can give valuable pointers on how to build a strategy that’s as robust as possible given the asset classes available and the targeted returns. But neither will accurately measure the risk of it going wrong.

There are though some major advantages of a simple model over a complex one.

Firstly, it’s less likely to go wrong. A simpler model will make any inaccuracies easier to spot, lowering the risk of an unseen error that may lead to bad advice.

Secondly, it’s much more cost-effective. Consultants need to recoup the time spent programming, maintaining and checking for errors in complex models by hiking up fees.

And finally, a clear and simple model leaves the user in no doubt that the model is ‘just a model’. In contrast, a fancy ‘online stochastic modelling tool’ may make the user over-confident about the output.

For all these reasons, First Actuarial tries to make everything as simple as possible and only as complex as necessary. We won’t flog you any over-complex, over-priced modelling that will only foster poor decision-making.


Any questions or comments about this article?

Get in touch with the author, Rob Skelton.

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