Using experience in financial research

I end up buying a lot of books. Inevitably, I end up reading far fewer books than I end up buying. The unread books peer at me from my bookcase, knowing they'll likely never be read. One book which I bought recently and immediately started reading was the Trading Game by Gary Stevenson, a former STIRT trader. All I would say is that a book about a trading desk is likely to be filled with more revelations than anything written about the life of a researcher in a bank. Perhaps a more researcher focused book would be filled with revelations about discovering INDEX and MATCH functions on Excel, and how much faster they are than using VLOOKUP (believe me, I did find this revelatory when I discovered it). Or perhaps it would devote a few pages to the inevitable discussions about how you are asked to do something which sounds trivial by the sales or trading desks, but is in fact extremely non trivial.

Ok, I'm perhaps being a bit facetious here, after all there is more to doing financial research than exploring the vagaries of Excel or responded to requests from other parts of the business. Over time, you build up a picture of how you think market works, and once you think you know it all, some event occurs which totally dispels that: markets lull you into mistakenly thinking you understand them fully. However, experience does give you a sense of where to look, when deciding what topics to research in market. The difficulty with researching financial markets is that there are countless avenues to explore. There are many datasets out there, there are many types of models you could investigate, many markets etc. Time is limited, so you need to somehow judge what projects are worth exploring. Indeed, it is something we think about a lot at Turnleaf Analytics, where we are forecasting economic data releases, in particular inflation. We continually undertake research to improve our forecasts, and a big part of that is deciding precisely what topics to research. Very broadly there are two choices you need to make when researching financial markets (I suppose you could generalise this to many areas of data science)....

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