Data Analytics for Finance

Prerequisites

Programming in Finance I,  Statistics at master level

Objectives

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 to provide the students with the tools and thinking framework to accomplish these tasks.

Description / Program

1. The nature of financial data
   Random variables, data generation, data types
2. Managing data
   ETL, encodings, data bases, quality checks, relations, data at scale, approximate data analysis, the time dimension
3. Standard data sets in finance
   Computstat, CRSP, Optionmetrics, Markit, Factset plus data access Bloomberg
4. Alternative and historic data
   Microblogging, search engines, trade, blockchain, satellite images, 19^th century stocks
5. Exploratory data analysis
   Robust and non-robust descriptive statistics, hypothesis generation, verification of assumptions
6. Advanced statistical methods
   Dealing with non-rectangular and non-numerical data
7. Data visualization
   Perception and aesthetics; exploratory, illustrational and statistical visualizations, interactive visualization
8. Additional topics

• The data economy: data as product and raw material, licensing, open data
• Data in research: sharing and publishing data sets, case studies in the value of (new) datasets
• Managing a data science project
• Storytelling with data
• Copyright, privacy

Learning Method / Style of Lessons

This course will take students from theory to practice in three steps. New topics are introduced in short lectures, which are followed by learning-by-doing in PC labs. Students finally apply their new knowledge in individual work, which is collected and presented in a student portfolio.

Exam Style

40% – Written midterm exam

60% – Portfolio

Students create portfolios of ca. 20 pages from their individual work throughout the semester, including

• Discussions of data sets and/or methods
• Documentation of a small data science project
• Data visualizations
• Discussions of a paper from the literature

Readings/Textbooks

Tukey, J.W. (1962): The future of Data Analysis, The Annals of mathematics and statistics, p. 1-67

Tukey, J.W. (1977): Exploratory data analysis