I’ve been looking at markets for over a decade. Throughout my career, I’ve always been looking to find unusual and exciting datasets to shed new light on markets and find alpha. Back in 2008, I published an article for Lehman Brothers FX Research examining the use of Google Trends to trade currencies, which was a pretty exotic dataset at the time. These days the number of unusual datasets available have mushroomed. The term alternative data has evolved as an umbrella term for such datasets.
Whilst quants have been obsessing over the value of data to trade markets, they have accelerated their search for alternative data in recent years, to gain better insights into markets and create new trading strategies. Large quant funds now often have dedicated data strategy teams scouring the world for exciting new datasets for their traders to use. There are also services striving to find datasets for hedge funds such as Neudata. In recent months, it’s become more apparent that macro discretionary traders are also keen to explore quant techniques and alternative data, to see how they can them an edge in markets. Indeed, I spoke about this subject on Bloomberg TV recently with Joe Weisenthal and Scarlett Fu (see video clip here).
So what is the source of these alternative datasets? Often it is “exhaust data”. Many companies generate data as part of their daily business. Take for example newswires such as Bloomberg News. On a daily basis, thousands of articles are written by their journalists. The main consumers are traders reading these articles on Bloomberg Terminals. However, a new outlet for this “news data” are computers reading these articles in a machine readable format. Indeed, machine readable news is perhaps one of the earlier forms of alternative data which has come to the market. Millions of tweets are published daily on Twitter for the consumption of other users. Just as with machine readable news, it is possible to access these tweets via an API for computers to process. By distributing this data in a new and novel way from what it was originally intended, companies such as Bloomberg and Twitter are creating new products from existing data. Just as Gaudi used “exhaust ceramics” to create beautiful artwork as above, “exhaust data” can be used to create new and exciting observations about markets!
However, the source of alternative datasets can often be companies which are very far removed from the tech economy and financial markets! Is your company sitting on an alternative dataset which could be monetised by selling to traders in a machine readable way? In order to answer this question, it is important to see how your dataset could be used by traders. Often, this needs specific understanding about markets to come up with hypotheses about how it can be used and furthermore research to validate these hypotheses. If you can give use cases and research on how your data could be used by traders, it can help cut down the time needed for traders to incorporate the data into their trading. How do you get this expertise to monetise your dataset?
That’s where Cuemacro can come in! We can examine your company’s dataset and conduct quant research to see how it can be used by traders. We can also aggregate your datasets into indices which are more easily digestible by traders and also used to market your company. We have already worked with many clients such as Investopedia to do this (see my Bloomberg TV clip, where I discussed our project for Investopedia). If your company is sitting on a large dataset and want to see whether it can be monetised, providing a new revenue stream for your company or used to help market your company, contact me! Who wouldn’t want a new revenue stream??!