Modulhandbuch (Module manual)

M.184.5452 Topics in Financial and Economic Data Science
(Selected Topics in Financial and Economic Data)
Koordinator (coordinator): Prof. Dr. Yuanhua Feng
Ansprechpartner (contact): Prof. Dr. Yuanhua Feng (yuanhua.feng[at]
Dominik Schulz (dominik.schulz[at]
Credits: 5 ECTS
Workload: 150 Std (h)
Semesterturnus (semester cycle): SoSe/WS
Studiensemester (study semester): 4
Dauer in Semestern (duration in semesters): 1
Lehrveranstaltungen (courses):
Nummer / Name
(number / title)
(contact time)
Status (P/WP)
(group size)
a) K.184.54521 / Topics in Financial and Economic Data Science Seminar P 20 TN (PART)
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Empfohlene Voraussetzungen (prerequisites):

One of the following:

W5451 Statistical Learning for Data Science with R and Python

W5333 Data Science for Business

Remark: In exceptional cases, the participation in this module is possible, if a student is going to visit one of the required modules parallel to W5452.
Inhalte (short description):

This is an advanced seminar in data science which particularly covers the areas of modern statistical and econometric approaches as well as statistical and machine learning. Basic topics are on the application of suitable algorithms in those areas for modeling economic and financial data, especially for and forecasting economic and financial time series, based on known research results in the literature. For this purpose, new tools in modern areas of statistics and econometrics, such as local polynomial regression, P-Splines, quantile regression and functional data analysis, should be considered. Further new tools in recurrent neural networks, deep learning and reinforcement learning should be employed. Modelling and forecasting multivariate time series using proper adaptations of the above-mentioned approaches will also be studied. For high-level or research oriented seminar works more advanced topics, e.g. the extension of currently used methods in the literature for semiparametric modeling of long memory time series, deep learning of multivariate, functional or high-frequency financial and economic time series as well as Machine Learning algorithms for big financial and economic data can be offered.

Lernergebnisse (learning outcomes):
Fachkompetenz Wissen (professional expertise):

Fachkompetenz Fertigkeit (practical professional and academic skills):
  • ​the ability to use basic and sophisticated Statistical Learning concepts.
  • gain skills of computer intensive data analysing and for model selection.
  • gain skills to collect, manage, visualize and analyse large and complex data sets.
  • gain advanced knowledge about the programming language R.
  • gain basic knowledge about the programming language Python
  • Personale Kompetenz / Sozial (individual competences / social skills):

  • improve further skills of problem definition and problem solution
  • gain ability for managing and implementation of a small empirical study project
  • improve cooperative and team-work ability.
  • improve the ability for presenting own results
  • gain communication and conversation skills.
  • Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
  • ​gain ability of self-learning
  • gain more expertise in scientific working.
  • obtain further training in independent studying.
  • improve computing data analysis skills.
  • Improve ability for writing a detailed project report.
  • Prüfungsleistungen (examinations)
    Art der Modulprüfung (type of modul examination): Modulprüfung
    Art der Prüfung
    (type of examination)
    a) Hausarbeit 15-20 pages 50.00 %
    b) Hausarbeit mit Präsentation 10-15 pages and 15minutes, respectively 50.00 %
    Studienleistung / qualifizierte Teilnahme (module participation requirements)
    Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
    Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
    Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist
    Gewichtung für Gesamtnote (calculation of overall grade)
    Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)
    Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
    M.Sc. International Business Studies, M.Sc. Betriebswirtschaftslehre, M.Sc. International Economics and Management, M.Sc. Management Information Systems, M.Sc. Wirtschaftsinformatik, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik
    Umfang QT (participation requirements):
    Lernmaterialien, Literaturangaben (learning material, literature):

    Most important references on Time Series Analysis:

    Tsay (2010). Analysis of Financial Time Series. John Wiley.

    Hautsch (2012). Econometrics of Financial High-Frequency Data.

    Beran, J., Feng, Y., Ghosh, S., Kulik, R. (2013). Long Memory Processes. Springer.

    Selected readings for Data Science (Statistical or Machine Learning):

    James et al., (2017). An introduction to Statistical Learning with application in R. Springer, available under

    Hastie et al. (2001). The Elements of Statistical Learning (2nd Ed.). Springer
    Available under

    Baumer, B.S., Kaplan, D.T. and Horton, N.J. (2017). Modern Data Science with R. Chapman & Hall/CRC, Boca Raton.

    Haykin, S.O. (2009). Neural Networks and Learning Machines (3rd Ed.). Pearson.

    Selected readings on non- and semiparametric regression:

    Bowman, A, Azzalini, A. (2001). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford.

    Ruppert, D., Wand, M.P., Carroll, R.J. (2003). Semiparametric Regression. Cambridge.

    Ghosh, S. (2018). Kernel Smoothing: Principles, Methods and Applications. Wiley.

    Additional references:

    Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer

    Koenker, R. (2005). Quantile Regression.Cambridge

    Ramsay, J.O, Silverman, B.W. (2005). Functional Data Analysis. Springer.
    Teilnehmerbegrenzung (participant limit):
    45 Personen
    Sonstige Hinweise (additional information):

    ​Please note that besides the enrollment via PAUL an additional enquiry has to be sent via email to the lecturers.

    Semester cycle:

    This module will be taught regularly during the summer semester.

    Selection of participants under the general prerequisites:

    The number of participants is limited to 45. The students mentioned below have an absolute priority and are not affected by the limitation of the number of participants. 
    Otherwise, this seminar can be provided individually for students in the following cases:

  • IEM students with the track “Data Science in Economics” who are going to finish their   Master study
  • Students in Master of Economic Computer Science for their “Individual Study Research”  and who are going to finish their Master study   
  • IEM students, who are writing their Master theses on a topic in Statistics or Econometrics and are going to finish their Master study
  • Students in other exceptional cases

  • Teaching language is english.

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