Data Analytics for Finance I & II
Persone
Docente titolare del corso
Descrizione
Prerequisites
Programming in Finance and Economics I, Statistics at master level.
The R programming language (together with a bit of SQL and Linux) will be used for most part of this course. Students are free to use other languages for the assignments.
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 with the help of the computer along the entire tool chain of financial data science: from obtaining data to organizing and merging it to analyzing and visualizing it.
Description / Program
The course is organized weekly sessions of four hours. There are two parts:
Part 1: Financial Data – first half of the semester
- Introduction to Financial Data Science
Nature and theory of data, data generating process, measurement, data types - Obtaining Financial Data
Standard datasets: Computstat, CRSP, Optionmetrics, Markit, Factset, Bloomberg
Alternative datasets: Quandl, microblogging, search engines, blockchain
Historic datasets
How to find or create missing data
A recap of data APIs - The problems with data
Data encodings and documentation
Quality and consistency checks - The economics of data
Flow vs. stock, P vs. Q data
The data business - Database design
Design principles for databases
Dimensioning a database server - Data merging and relational databases
- Exploratory data analysis
Robust and non-robust statistics
Aggregation, subsampling
Introduction to Nonparametric statistics - Advanced datacentric econometrics
Assumptions about data and their verification (seasonality, ADF, out-of sample R^2, …)
Advanced methods for dealing with problems/limitations in the data (bootstrap, MIDAS,...)
Part 2: Data visualization (not only) for financial Data – second half of the semester
- Visualization theory
Perception and aesthetics, color, the grammar of graphics - Static visualizations
Bar charts, scatter plots, pie charts, line carts, Sankey diagrams, Parallel coordinate plots, - Statistical visualizations
Box and violin plots, qq-plot, histograms, tree maps, forest plot, autocorellograms, Lorenz curves, Venn diagrams, - Data maps
Dot distribution maps, heat maps, choropleths and alternative maps: cartograms, grid and hexagon maps, statebins - Interactive visualizations
Basic web technology, user interaction, R shiny - Visualizations in R
ggPlot and shiny
Additional topics (time permitting)
- The data economy: data as raw material and product, business models, 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
- Alternative plots: Cernov faces, trees and dendograms,
- Copyright, GDPR (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
- Discussions of a paper from the literature
- Data visualizations
Requested Material
Students are required to bring a laptop with R and R Studio installed to every class. Students will additionally profit from continuing to use the Linux data server, which they have set up in Programming in Finance and Economics II.
Readings/Textbooks
Tukey, J.W. (1962): The future of Data Analysis, The Annals of mathematics and statistics, p. 1-67
Offerta formativa
- Master of Science in Economics in Finance, Corso di base, Digital Finance, 2° anno
- Master of Science in Financial Technology and Computing, Corso di base, 1° anno