In the course “Advanced Empirical Finance” we repeatedly ask: (How) can state-of-the art methods improve financial decision-making?

While the lecture covers all relevant theoretical aspects and is based on very recent academic papers, you should spend most of your effort on this course on actually doing empirical work! Get your computer ready to work on real problems for financial applications and discuss your code with your peers to acquire the necessary skills to make a difference either in the Finance industry or academia. ## Things to get done before the first lecture {.unnumbered}

To dive right into it, there are a couple of prerequisites you should get done before the first lecture:

  • Install R and RStudio. To get a walk-through of the installation for every major operating system, follow the steps outlined in this summary. The whole process should be done in few clicks. If you wonder about the difference: R is an open-source language and environment for statistical computing and graphics, free to download and use. While R runs the computations, RStudio is an integrated development environment that provides an interface by adding many convenient features and tools. We suggest to do all the coding in RStudio.
  • Open RStudio and install the tidyverse. Not sure how it works? You find helpful information on how to install packages in this brief summary. The tidyverse is “a framework for managing data that aims at making the cleaning and preparing steps much easier”. We are going to work almost exclusively with tidyverse packages.
  • Check if everything works: Open Rstudio and type library(tidyverse). You should then get a message like in the picture below. Works? Done with the software setup!

If you are new to R, we recommend to start with the following sources:

  • A very gentle and good introduction into the workings of R can be found here. Once you are done with setting up R on your machine, try to follow the “weighted dice project”.
  • The main book on the tidyverse is available online and for free: R for Data Science by Hadley Wickham and Garrett Grolemund explains the majority of the tools we use in this book.

What is R?

In this course we work with R. Together with Python, R is nowadays the de facto standard tool in finance and is unparalleled when it comes to handle data science applications.

But so far I learned to code with Python, Ox, Matlab, …

Excellent if you have prior knowledge in another coding language. Your start with R will be way easier. Also, as a Python user you can actually run your Python code in R (and vice versa) - so if you have already written some analysis for a prior project, no need to translate everything! If you are interested to do this at some point in time, check out this resource (we will provide a brief TA session on cross-platform operability later in the course).

I have no experience whatsoever with programming

Great, you nevertheless make the bar for the course requirements. We will start entirely from scratch (but with a really steep learning curve). Besides the applications in Finance, we will focus on state-of-the-art data science concepts that cover the entire life cycle of a successful empirical analysis. No matter if we consider a research project, a thesis, seminar paper or a thorough portfolio back testing procedure at an investment bank: the challenges are always similar. The figure below illustrates the components we will talk about: How do we get R connected to our datasets? How can we clean the data such that our analysis makes sense? How to visualize or change variables in order to estimate parameters or make financial decisions? And, finally, how can we communicate our results such that we are sure that our analysis is reliable, correct and flexible enough to quickly find out what happens if we have to change some input parameters.

This sounds like a lot of work on the data science front. How do I get started?

There are a couple of ways for you to get started, below a list with relevant material and suggestions.

  1. As a member of this course you have free access to all online tutorials of DataCamp which provides excellent browser-based tutorials on the basics of R, the tidyverse and a lot of applications in Finance. In order to enjoy the benefits of completely free access, sign up to our own course site using this link (careful: registration is only possible with an econ.ku.dk mail domain)
  2. Only way to learn how to code is by getting your hands dirty. Make sure your system is ready to go by completing all technical requirements.
  3. A very gentle and good introduction into the workings of R can be found here. Once you are done with setting up R on your machine, try to follow the “weighted dice project”.
  4. The main book of this course is available online and for free: R for Data Science by Hadley Wickham and Garrett Grolemund explains everything we need. Throughout the course we will cover almost the entire content of the book. I advise you to read the first 3 Chapters for a gentle dive into the material before the lectures starts.

Help, I am stuck! Where do I get help?

To struggle in the beginning is absolutely fine, and you are not alone. Indeed, there are around 40 other students in the course that have similar questions. Make use of this community!

  1. You can always post questions in the Discussion forum. The TA’s, your colleagues and I will try to help you out.
  2. You do not have to do all the work on your own. Search for a coding buddy to discuss your problems in teams (for the assignments you can hand-in in groups of up to 3 students!).
  3. If you still feel like you are stuck (which is fine and can happen) reach out directly in class or during the office hour!