There’s a growing realization in the ‘Big Data’ world, of which sentiment analytics is a small part, that you can’t expect to hire any data scientist (quant analysts in finance) and get results. Data scientists not only need to be good statisticians but they need to have some practical knowledge of how their subject behaves to make good use of the data.
This is very true in forex because news or discussion about currencies is not necessarily what moves the market. News about base currencies or currency pairs is often backward looking – except in the case of technical analysis. The majority of currency news is something like ‘EURUSD went up/down because…’
There’s not much signal there. Some, but not much.
So that means our quant analyst normally has to model how a currency moves in relation to macroeconomic and geopolitical news events. He has to ask himself things like ‘How does NZDUSD react to a severe earthquake in Christchurch?’ (an easy example) or ‘how does Aussie react to higher/lower Chinese GDP?’ (more complex, obviously).
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But it needn’t be that difficult. And you’ll have to forgive me here because I want to talk about some research done using data provided by my company. An independent contractor did the research – my company just gave him the data. But the source of the data is not important; it’s the concept of how the data was used.
The researcher in question is Saeed Amen, quantitative strategist at a think tank called The Thalesians <link to http://www.thalesians.com>. Saeed set about trying to improve one of the most popular trading strategies – the carry trade.
What he found was actually relatively simple. That is, you can use news volume as a filter in your carry trading strategy and it reduces the amount of drawdowns during periods of risk aversion and hence improves risk-adjusted returns. That’s when you compare it to a filter using another risk indicator – the VIX – or no filter at all.
You’ll have to get hold of the paper from Saeed if you’d like a copy – he talks about it on his blog too.
So, you don’t necessarily need a data scientist to map out your own personal “Butterfly Effect” to make use of news data – just some imaginative use of news volume.