Quant: path to a job in alternative data
I’m interested in both data science and finance and i was considering to specialize in financial engineering to become a quant.
The problem is that I’m not really drawn to roles like derivative pricing or high-frequency trading, but I would love to work with alternative data (e.g. CEO flight data for M&A predictions, traffic data to forecast hotel earnings…).
My question is: to pursue this path, should I focus on becoming a quant, a data scientist or stick with traditional finance?
I mean is this type of role typical of a quant or is it more something of a data scientist/a modern evolution of fundamental roles?
I work in a pod that does something similar. There is a whole world of buy-side firms that don't trade high frequency and give you time to build conviction. We regularly get vendors sending us trial datasets that my pod then experiments on. Oftentimes these are sources of great alpha.
Though this is more from a top-down POV, I would consider individuals who sift through and run models on alternative data to find alpha as investment analysts, whether quants or fundamental. Definitely not data scientists with that specific job description.
To answer your question: this can be done both via quantitative and fundamental means. IMO, the best pods at this do it with a mixture of both. Hypotheses driven by alternative data can be justified by marginal probabilities and so on, but at least for the examples that you gave such as merger arb, some understanding of finance is necessary. (What kind of acquisition is it? What's the capital structure? Is it accretive / dilutive? etc.)
there’s a name for this kind of role
at Citadel it’s sector data analyst
at point it’s market intelligence/proprietary research r
they basically do exactly what u say - look at alt data such as credit card spend airline passenger volumes etc. and give insights to fundamental pods
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