Just the other day, I had a conversation with an Investment Risk Manager about one of the data problems his team was working on and he was wondering if Neo4j could help. Imagine you have about 20,000 mutual funds and ETFs, and you want to track how they measure up against a benchmark like, say, the returns of the S&P 500. I’m sorry, did I say one? I meant all of them — let’s say, 2,000 different benchmarks — and you want to track it every day for a rolling five-year period. So, that’s 20,000 securities * 2000 benchmarks * 5 years * 252 trading days a year (on average)… or... 50 billion data points. That’s a big join table if we're using a relational database. How can we efficiently model this in Neo4j?
To keep things simple, let’s say we only want to track one measure, and that measure is R-squared. When we compare the change in the value of the mutual fund vs. the benchmark, how closely did the changes track? R-squared is always between 0 and 1. A value of zero means the benchmark has nothing to do with the mutual fund, and a value of one means it has everything to do with it. So how could we model this?
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