As financial securities increasingly become more complex and with the rise of automation there has grown a huge swarm of people who like to call themselves “quants” or more formally quantitative analysts. These people are typically math and computer geniuses who are hired by top financial institutions like Investment Banks and Hedge funds.

Make no mistake here these math whizzes are not your traditional bankers; instead they are Ph.D. holders who use their skills in the world of finance to produce seemingly impossible results. They not only understand complex mathematical models that price these securities but are able to use them to generate high profits and hedge risks.

(Okay this article is going to use hedge a lot of times, just to explain hedge is basically a term in finance that means to reduce risk.)

These quants are not your typical too dressed up Wall Street “finance bros” instead they are generally much older generally because of the nature of background that they have specifically that of some doctoral degree.

However, quants are not just employed in just Investment /banks or hedge funds but also commercial banks, insurance companies and management consultancies too name a few. In an Investment Bank these math whizzes are classified into two categories:

- Front-Office: They work with traders directly providing them with pricing or trading tools
- Back-Office: They work creating new models, conducting research and creating new strategies.

One of the most popular pricing tools that quants use is called the Black Scholes Equation which is a partial differential equation which on solving gives the price of a derivative.

Phew!

A partial differential equation is typically a dynamic system in which a particular quantity depends on multiple other quantities. This requires a lot of mind-boggling calculus as one might have guessed from all the Greek alphabets presented in the equation. Only skilled mathematicians can understand the true workings of this equation and after much computer simulations actually solve it.

Further, a derivative is a financial instrument that has an underlying asset class such as a security (basically a stock). But you don’t actually have to pay for the stock; you just bet on the stock to go up (or down) to a particular point and then just profit from the difference which equals the price at which you hope that it reaches difference the price it was when you placed your bet.

This is also one of the more complex financial instruments possible just because of the fact that the methods by which it is priced also uses, no surprises here, calculus. To ensure that the bets placed on derivatives can be optimized to the level such that the profits that are ultimately reaped are maximum.

Thus, it is very evident that there has been a drastic shift in which securities were traded. Earlier when the markets were physical and floor base, traders interacted with the market makers and then would settle the price of a security. Therefore, at that time it was essential for a trader to have a good, strong and clear voice. However, as we made our way towards digitization the loud voices on the floor began to be replaced by computer-savvy techies. Further with the integration of computers into finance it has led to vast expansion of the markets leading to loads of research and more of data mining and adutomated trading systems.