You might recognise the famous quote in our headline. It’s been attributed to various people, including Mark Twain, but the truth is that no-one knows who said it first.
Like the quote itself, you can’t believe everything you see. However, you can believe us, because all our advice is evidence-based. Let’s look at some examples.
At a time when many people are panicking about the impact of the coronavirus, this chart shows how previous pandemics have correlated with market returns.
As you can see, markets do seem to react to ‘noise’ in the short-term, and rise after news hits about deadly diseases. Perhaps people suddenly start thinking about their mortality and getting their financial affairs in order?
However, correlation doesn’t necessarily imply causation. You might be aware of the hemline indicator, originally presented in 1926 by economist George Taylor. He theorised that hemlines rise in good economies as seen in the 1920s and the 1960s, and fall in poor economic times, as shown by the 1929 Wall Street Crash. Non-peer-reviewed research in 2010 suggested that the economic cycle leads the hemline by about three years.
Review of 2019
In our recent survey, we asked if you wanted to see a quarterly review of the markets. As it’s the start of a new year, we’ve gone one better. Here is the review of the whole of 2019 from our friends at Dimensional.
As you can see, all the arrows are green and pointing up, up, up. Hoorah! Look closer, and you’ll see that the US and developing markets had their best year ever! It’s quite surprising that every single sector behaved as you’d hope throughout 2019.
It’s the reason we recommend you have a diversified portfolio because you are then protected from the usual vagaries of market performance around the world.
Whether the short-term news is good or bad, we don’t think you should do anything with your investments other than wait and let them grow. Ignore the media hype about the coronavirus, and ignore the current trend in hemlines.
Finally, here’s a fun video to prove the point that correlation does not imply causation.