“The answer, my friend, is blowin’ in the wind…” (possibly)
I’ve just posted a report on, of all things, the UK weather.
It’s always a topic of conversation, whether to complain it’s too hot, or too cold, or too wet, or too windy. And sometimes, that can be in the space of one day!
I’ve always kept an eye on the weather forecast. Not to moan about it when it’s wrong, but to see how techniques improve over time. Whilst it’s easy to look out of the window and have a feeling that it’s going to rain, the actual science behind it involves some rather clever and complex equations.
These equations are solved via computer methods – the data is so big and the equations so difficult, it’s almost impossible to solve by regular analytical methods. In fact, a lot of the equations don’t even HAVE exact solutions. One must always chip away with the numbers, getting closer and closer, but never reaching, a solution.
The same approach is used in mathematical finance.
The equations in mathematical finance generally do not have exact solutions. Whilst they can be derived using pen and paper, they can’t be solved with pen and paper. Instead, a computer is needed to find approximate solutions, with many iterations needed before stability is reached. This then constitutes a solution.
Now, the question some will ask is – what does weather forecasting have to do with the markets?
Essentially, they are just reams and reams of numbers. Strings of data.
A meteorologist will be looking at, for example, temperature readings every hour. A trader will be looking at price action every hour. After a week of doing this, they each have a set of numbers which will need analysing.
The same methods can be used for either – after all, it’s only a list of numbers. It’s how the results are interpreted, however, which matters. The meteorologist may run his computer program and see a thunderstorm brewing in the Gobi desert, the trader will see an opportunity to sell EUR/USD.
So, armed with the idea that it’s only data, there’s no reason why a technical analyst can’t apply techniques to weather data. Hence, after a passing thought last night, I decided to look at the UK temperatures and see how they’re looking and what they’re suggesting.
I think the results are interesting – for example, how temperatures moved much more easily in the ’60s than in the ’90s. Will UK winters stay cold? There are some trends appearing in the data, but do the professionals agree?