December 13, 2015

Lessons from the last 40 years for the next 20

By Alan Brown

Einstein reputedly said: “We should always make things as simple as we can, but no simpler.” Looking back over the last 40 years, it is clear the investment community has repeatedly been guilty of oversimplification to the detriment of investors. The lessons of the past convince me that, going forward, successful asset owners and asset managers will be those that stop making things too simple. They will instead acknowledge that: tail events occur more frequently than a ‘normally’ distributed world implies; volatility serves little purpose as a risk measure; acknowledging behavioural biases is the most important step to overcoming them; and there are risks and opportunities that no backward-looking statistical measure will ever capture, but it would be extremely foolish not to account for those that should be expected.
  1. Expect the unexpected

Unforecastable events happen all the time. As investors, we need to recognise that and remain humble about our ability to predict the future.

This doesn’t just include political or geological events. Disruptive technologies, for example, come out of left-field, surprise us and have a huge impact. The exponential growth in computing power and affordability has had a profound impact on our industry. Not just in terms of the products and capabilities of the companies we invest in, but also on our own technology and understanding.

Gordon E. Moore, founder of Intel, in his 1965 paper, predicted that computing capabilities would double approximately every two years. Known as ‘Moore’s Law’, he was originally referring to the number of transistors on a chip, but today it is interpreted as speeds and memory will double every two years while prices will halve. This has stood the test of time remarkably well.

Before Moore’s Law was written, in 1952 Harry Markowitz first published his Nobel Prize- winning work in modern portfolio theory, Portfolio Selection. He introduced the concept of mean variance efficient portfolios, where risk was defined as the volatility (standard deviation) of returns. An efficient portfolio was one which gave the highest return for a given level of risk. While his theory was elegant, at the time it was impossible to implement as the necessary computing power to handle large scale matrices of returns, volatilities and correlations didn’t exist.

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