Data Analytics for Finance I & II

The course is organized weekly sessions of four hours. There are two parts:

Part 1: Financial Data – in the first half of the semester, we will study the answer to the following questions

1. What is data
   – Nature and theory of data, data generating process, measurement, variables, data types
   – Scientific papers that make innovative use of data
   – Which financial questions can be answered with data
2. How to download, store, encode and organize data
   – Data encodings
   – Introduction to relational databases
   – APIs and scraping
3. Where to obtain data
   1. Traditional data sources: Computstat, CRSP, Optionmetrics, Markit, Factset, Bloomberg
   2. Alternative data sources: Quandl, microblogging, search engines, blockchain
   3. Climate data
   4. Historic datasets
4. Fact checking and data checking in the age of AI
5. How to analyze data
   – Exploratory data analysis and an introduction to nonparametric statistics
   – Derived quantities, and data aggregation
6. How to collect your own data
   – An introduction to surveys and interviews
7. Which econometric models can overcome data shortcomings
   Robust statistics, seasonality
8. The data economy and your data strategy
   – Data business models
   – Open research data principles

Part 2: Data visualization  – the second half of the semester is entirely dedicated to data visualization, one of the most powerful tools for analyzing data and communicating your results

1. Visualization theory
   – Human perception
   – The grammar of graphics
   – Aesthetics, color,
2. Static visualizations
   Bar charts, scatter plots, pie charts, line carts, Sankey diagrams, Parallel coordinate plots
3. Statistical visualizations
   Box and violin plots, qq-plot, histograms, tree maps, forest plot, autocorellograms, Lorenz curves, Venn diagrams,
4. Data maps
   Dot distribution maps, heat maps, choropleths and alternative maps: cartograms, grid and hexagon maps, statebins
5. Interactive visualizations
   Basic web technology, user interaction, R shiny
6. Visualization workflow and refinement

Tukey (1962) defines Data Analysis to be “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

The goal of this course is 

1. to provide the students with the thinking framework for data analysis
2. equip them with tools and resources to obtain and manage financial data.
3. to foster creativity in using and exploring existing datasets as well as in creating new datasets
4. to equip students with techniques to visually communicate their results

In presence

This course will take students from theory to practice in four steps. Students prepare with reading a short text or watching a video. Then new topics are introduced in a lecture, which is followed by learning-by-doing laptop session. Students finally apply their new knowledge in individual homeworks, which are collected and presented in a student portfolio.

Programming is heavily centred around AI tools, using Github Copilot. Based on these AI tools, several programming languages  will be used throughout the course: the R and Python languages, a bit of SQL and bash (Linux) as well as Tableau (free student version). 

Students are free to use any language or tool for the assignments. Students are required to bring a laptop to every class. Students will also need a ChatGPT Plus licese for the second part of the course. Occasionally, we will work with LINUX. Student can access a server made available by the lecturer or continue to use their server from Programming in Finance and Economics II.

40% – Written midterm exam
60% – Portfolio
Students create an individual portfolio of ca. 20 pages from their work throughout the semester, including

• One discussion of data sets and/or methods
• One discussion of a paper from the literature
• Several data visualizations