The quantitative analyst or “quant” is nothing like the conventional financial analyst who, after learning the intricacies of financial metrics, capital structures, and so on, uses that skill set to analyze balance sheets. Quants often know very little of the CFA charter textbooks—and they don’t need to, because the kind of analysis they do couldn’t be any more different than the conventional ones.
To understand this, let’s talk a bit about “statarb”, or statistical arbitration, which is in many ways the origin of quantitative analysis. Statarb is the analysis of statistically significant events that are not priced into a market, even if there is no apparently logical correlation between the two events. For instance, let’s say that every time it rains on a Tuesday in May, the market goes down 1.5%. A statarb analyst identifies this pattern and then sets up an algorithm to buy every time it rains on a Tuesday in May and sell when the rain stops.
That’s a silly example, of course, and most (but not all!) statarb methods are significantly less silly. But the point is that this is simply identifying and calculating correlations from data sets. In the real world, statarb models are incomprehensibly more complex than this example, involving staggering amounts of data and complex correlations with various phenomena.
The data is so complicated that it takes a data scientist to be a top-notch quant. “Data science” is a relatively new term, and it’s an often misused one. In its purest sense, data scientists are trained in how to use big data to identify patterns and produce algorithms that identify more patterns. There are very few data scientists in the world, and they are paid staggeringly well.
On the more humble end of the spectrum, many PhDs in other heavily quantitative fields, such as mathematics, statistics, and computer science, can easily transfer those skills to a job as a quant in a hedge fund. These are very well-paid positions, with $100k starting being an absolute floor and a $250k salary being a more respectable median income. Bonuses are much higher, and can reach seven figures pretty quickly.
If you are in a quantitative field and you are considering transitioning to finance, switching majors from computer science to finance may be shooting yourself in the foot; finance grads are plentiful and often need to bolster their technical and programming skills to compete in the more complex and tech-driven finance world of today. But if your dream is one of the high paid buy-side positions, a Ph.D. may be required. The road to quantdom is a long one.