Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Algorithmic Forecasting of Stock Price Processes

    A tantárgy neve magyarul / Name of the subject in Hungarian: Algoritmikus tőzsdei folyamat-előrejelzés

    Last updated: 2017. június 28.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Business Information Systems MSc.

     

    Specialization: Financial informatics
    Course ID Semester Assessment Credit Tantárgyfélév
    VISZM107   3/0/2/v 6  
    3. Course coordinator and department Dr. Telcs András, Számítástudományi és Információelméleti Tanszék
    4. Instructors
    Name:

     

    Position:

     

    Department.:

     

    László Györfi DSc

     

    Professor

     

    Department. of Computer. Science and Information Theory

     

    András Telcs DSc

     

    Assoc. Prof.

     

    Department. of Computer. Science and Information Theory

     

    5. Required knowledge

    Recommended: Mathematical statistics, Finance, Planning of financial investments

    7. Objectives, learning outcomes and obtained knowledge a Objectives, learning outcomes and obtained knowledge

     

    The course provide knowledge of methodology of modeling and predicting financial time series and the related portfolio strategies.

     

     

    b. Acquired skills

    By the competition of the course students are enabled to apply tools, techniques to model and forecast financial time series.  Will be able to support bank, investment founds in the planning their investment strategies.

    8. Synopsis a Lectures

     

    1.      Estimate of the density function, L1 error.

     

    2.      Estimate of the density function, histogram.

     

    3.      Estimate of the density function, kernel estimates.

     

    4.      The regression problem, the regression function, partition method.

     

    5.      The regression problem, the regression function, kernel functions.

     

    6.      The regression problem, nearest neighbor estimate.

     

    7.      The regression problem, empirical error.

     

    8.      Pattern recognition, error probability.

     

    9.      Pattern recognition, Bayes decision, partitions.

     

    10.   Pattern recognition, kernel functions, nearest neighbor estimate.

     

    11.   Pattern recognition, empirical error.

     

    12.   Optimal portfolio strategies, fixed portfolios.

     

    13.   Optimal portfolio strategies, constantly rebalanced portfolios.

     

    14.   Optimal portfolio strategies, dynamically rebalanced and empirical portfolios.

     

     

    b. Labs

     

    1.      Data acquisition, database design.

     

    2.      Data cleaning and cleansing.

     

    3.      Introduction to the software usage.

     

    4.      Exploratory data analysis, descriptive statistics.

     

    5.      Exploratory data analysis, presentation, graphics.

     

    6.      Time series analysis, overview, deterministic models.

     

    7.      Test.

     

    8.      Time series analysis, overview, application of ARIMA, ARCH, GARCH models.

     

    9.      The regression problem, the regression function, elementary, spline, NN.

     

    10.   The regression problem, the regression function, kernel functions – Gaussian.

     

    11.   The regression problem, nearest neighbor estimate implementation.

     

    12.   Test.

     

    13.   Log-optimal portfolio, algorithmic implementation static, rebalanced.

     

    14.   Log-optimal portfolio, algorithmic implementation combined experts.

     

    9. Method of instruction Lectures and laboratory

     

    10. Assessment a. Active involvement during lectures, condition of the course signature minimum, completion os lab exercises, 40% score of the two test average. Exam 70%, lab 30% in the final mark.  The second test covers the first 11 lab and lecture material.

     

    b. Oral exam

     

    c. Pre exam subject of tutor's agreement

     

    11. Recaps One re-test during the recap-weak and oral presentation 30% of missing lab exercises are possible.

     

    12. Consultations upon personal inquiry

     

    13. References, textbooks and resources 1.      Száz János: Tőzsdei opciók vételre és eladásra, Tanszék Kft, 1999.

     

    2.      R. S. Tsay: Analysis of Financial Time Series, Wiley, 2nd edition, 2005.

     

    3.      L. Györfi, M. Kohler, A. Krzyzak, H. Walk: A Distribution-Free Theory of Nonparametric Regression, Springer-Verlag, 2002.

     

    4.      L. Györfi, G. Ottucsák: Empirical log-optimal portfolio selections: a survey, http://www.szit.bme.hu/~oti/portfolio/articles/tgyorfi.pdf 2007

     

    14. Required learning hours and assignment
    classes70
    preparation for classes10
    preparation for labs14
    preparation for test36
    preparation for seminars0
    Vizsgafelkészülés50
    Összesen180
    15. Syllabus prepared by
    Name:

     

    Position:

     

    Department:

     

    László Györfi DSc

     

    Professor

     

    Department. of Computer. Science and Information Theory