📚What is Quantitative Finance?

TLDR: Testable and repeatable consistency.

Imagine you begin to remove the human intuition or input to an investment process. We would like to minimize discretionary judgement, reducing reliance on subjective decision making. Two main differences will arise: the rigor of mathematics, and the strategy development process.

It first becomes necessary to adhere to a data-driven framework that is conducive to strategies that are testable and repeatable. The scientific method ensures a strict standard of quality. After posing an investment question, hypotheses are designed and set up to be validated. Different datasets and assumptions justify modeling choices and constraints. Trading strategies become evidence based: algorithmic and computational.

Then, deeper levels of mathematics and programming are necessary to realize these goals. The evolution of a stock portfolio can be represented as a vector and modeled with linear algebra. The fair value of a stock option changes according to a number of factors, such as its time remaining, or the volatility and value of the underlying stock itself. The change in this price can then be represented as a partial differential equation, with an increasing complexity as certain assumptions no longer hold.

Consider how physicists first began to model how a dust particle floats and moves in a room. It'll start to fly around when you blow on it, but there's still a component of randomness in how it wanders. Quantitative finance takes inspiration from the mathematics used in modeling physical mechanics, and adapts them to be implemented into trading strategies.

This is why, often times, those who work in quantitative finance did not come from any financial background at all. Aerospace engineers learn the mathematics of force and torque before they get anywhere close to a plane. In the same way, quantitative finance often rewards mathematical reasoning over market intuition. Traditional business-school training can sometimes become a disadvantage when it encourages narrative thinking or reliance on anecdotal market wisdom rather than statistical evidence.

Those who work in this discipline are broadly called quantitative analysts ("quants"). Specific roles within this field vary wildly depending on the firm, but there are some general themes:

• Quantitative Research: Those tasked with new strategy design.

• Quantitative Developer: Those who program strategies into usable software for execution.

• Quantitative Trading: Those tasked with implementing the strategies and making decisions based on how they are performing.

You're not necessarily out to blindly maximize profits. Moreso, the importance is in consistency, uncorrelation, and confidence. Is your strategy consistent, producing generally the same results given varying market conditions? For a projected expected return, can you quantify the variance you'd expect along the way of getting there?