Time To Start A Relationship With Quants
Most finance students I know simply hate quants.
In Level I CFA, some of the most detested readings are .....drum rolls please.... Probability concepts, normal distributions and hypothesis testing.
It gets worse in Level II CFA. As a trainer, it’s depressing to see faces registering frustration or indifference as I try to explain the elegance and importance of multiple regression and time series analysis. I have to confess that out of all my CFA prep classes, I used to get the crappiest feedback in my Level II quants classes. And I don’t think that’s because I’m a lousy tutor.
This attitude to quants is a huge pity. Because quants is becoming even more relevant.
Quants is a prerequisite to the field of data analytics and applied machine learning. Plus, big data and AI/ML are increasingly being applied in finance (read investments) and the landscape may be dominated by a mix of AI (artificial intelligence) and HI (human intelligence).
In order to a) understand the data used to train a machine learning model and to b) interpret the results of testing different machine learning models, statistical methods are required.
For example, data raises questions, such as:
What is the most common or expected observation?
What are the limits on the observations?
What does the data look like?
Beyond raw data, you can design experiments to collect observations. From these experiments you may have bigger questions, such as:
What variables are most relevant?
What is the difference in an outcome between two experiments?
Are the differences real or the result of noise in the data?
So what do you need to know? It’s a mostly familiar list:
The Gaussian distribution and how to describe data with this distribution using statistics.
How to quantify the relationship between the samples of two variables.
How to check for the difference between two samples using hypothesis tests.
An alternative to statistical hypothesis tests called estimation statistics.
Nonparametric methods that can be used when data is not drawn from the Gaussian distribution.
Why do you need to know all this even though you’re probably not gonna be a programmer/data analyst/data scientist?
True. But you are a user. You have business and finance problems to solve. You must know the various tools and techniques (and their applications, merits and limitations) and how these problems can be solved using big data and AI/ML.
So, even if you can’t love this useful science, at least understand it.