Financial Data Analysis

FIE401B Financial Data Analysis

  • Topics


    This course introduces students to the main econometric methods and techniques. The course focuses on practical applications of econometrics to financial data using R (free programming language). The mathematics of econometrics is introduced only as needed and is not a central focus.

    Topics covered:

    • Introduction to R
    • Elements of statistics
    • Simple and multiple regression models
      • Possible application: CAPM and Fama-French three factor asset pricing models
    • Regression with a binary dependent variable
      • Possible application: Determinants of the choice of the mode of payment in M&As
    • Regression with panel data
      • Possible application: Capital structure regressions
    • Instrumental variables regression.
      • Possible application: CEO succession decision in family firms
    • Time series analysis
      • Possible application: Volatility of stock returns

  • Learning outcome

    Learning outcome

    KNOWLEDGE - The candidate...

    • understands what assumptions econometric models are based on;
    • knows the econometric methods necessary for doing empirical analysis in finance;
    • is able to use R for doing econometric analysis.

    SKILLS - The candidate...

    • will be able to conduct, interpret and critically deal with empirical studies in finance and related fields;
    • will be able to identify the advantages and disadvantages of the various methods and techniques;
    • will be able to understand the relationships between the theoretical concepts taught in finance class and their application in empirical studies;

    COMPETENCE - The candidate...

    • has the tools and knowledge necessary to define, design and deliver an academically rigorous piece of research.

  • Teaching


    The course will consist of the combination of lectures and lab sessions where students will learn how to use R for financial data analysis.

  • Restricted access

    Restricted access


  • Recommended prerequisites

    Recommended prerequisites


  • Required prerequisites

    Required prerequisites


  • Credit reduction due to overlap

    Credit reduction due to overlap

    The course cannot be combined with BUS444 Econometrics for Business Research, ECN402 Econometric Techniques, FIE401A Financial Data Analysis.

    Students who have taken FIE449 Financial Econometrics (discontinued) before and wish to retake the exam are required to retake the whole course. Only grade for FIE401B Financial Data Analysis will appear in the transcript.

  • Requirements for course approval

    Requirements for course approval

    Three assignments. Students are encouraged to collaborate on the assignments, but not required to do so. Each team should have three to four members and hand in one solution per team. Assignments must be written in English and must be submitted in the same semester.

    Grading scale: Approved/Not Approved

  • Assessment


    Final exam, comprising a 4 hour written school exam. The exam and the course will be given in English, and hence, the exam questions must be answered in English.

    This exam can only be retaken under the same course code (FIE401B). This course was taught before as FIE449.

  • Grading Scale

    Grading Scale

    Grading scale A - F.

  • Computer tools

    Computer tools

    Participants should bring their laptops to the lectures. All applications covered in the course will be implemented in RStudio (an open-source software for R programming language). Download and installation instructions will be provided during the first lecture.

  • Semester



  • Literature


    Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach (fifth edition)

    Florian Heiss, Using R for Introductory Econometrics


ECTS Credits
Teaching language

Course responsible

Darya Yuferova, Department of Finance, NHH