Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

By the end of the course students, students will be familiar with basic quantitative methods used for analyzing financial data; be able to implement those skills in the context of potential applications for portfolio construction using a programming language.

Credits: 6

Schedule: 28.02.2019 - 09.04.2019

Teacher in charge (valid 01.08.2020-31.07.2022):

Teacher in charge (applies in this implementation): Sean Shin

Contact information for the course (valid 04.02.2019-21.12.2112):

Professor: Sean Seunghun Shin

E-mail: sean.shin@aalto.fi

Phone: +358-50-304-3004

Office hours: by
appointment

Times: Thursday, 13:15 -
14:45
and Friday, 10:15 - 11:45

Location: T004 Maarintie 13 (Otaniemi) with one exception (Apr 4),specified below


CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • Valid 01.08.2020-31.07.2022:

    This course covers basic quantitative skills for analyzing financial data. This course also reviews basic academic papers related to empirical topics, such as behaviors of securities prices relative to the benchmark asset pricing models.


    Details of assignments will be provided by the lecturer through MyCourses.

  • Applies in this implementation:

    This course covers
    basic quantitative skills for analyzing financial data. This course reviews
    academic papers related to empirical asset pricing topics, such as behaviors of
    securities prices relative to the benchmark asset pricing models. This course also
    introduce some applications to quantitative analysis, such as the factor-based
    investing. By the end of the course, students will be familiar with academic
    findings on the empirical asset pricing; be familiar with basic quantitative
    methods for analyzing financial data; be able to implement those skills in the
    context of developing investment strategies.


Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    The final grade (0 – 5 scale) is based on total points (max 100 points); combining assignments (50 %) and exam (50 %) points. To pass the course, you have to get at least 40% of exam points, i.e. 20 points. Conditional on that, your final grade is based on the following scale:  

    90≤x≤100: Final grade = 5
    80≤x<90: Final grade = 4
    70≤x<80: Final grade = 3
    60≤x<70: Final grade = 2
    50≤x<60: Final grade = 1
    0≤ x<50: Final grade = 0, Fail

  • Applies in this implementation:

    -

Workload
  • Valid 01.08.2020-31.07.2022:

    Classroom hours 24h
    Class preparation 40h
    Assignments/projects 60h
    Preparing Exam 34h
    Exam 2h

    Total 160h (6 op)

  • Applies in this implementation:

    Exam (50%)


    Individual
    Assignment (15%)

    There will be one
    individual assignment. The assignments will contain exercises to apply
    quantitative skills to the financial data and replicate academic studies. It
    requires programming skills and basic understanding of econometrics in addition
    to academic knowledges covered by the lecture.
    For the individual assignment,
    it is strictly forbidden to refer other’s works. Both results (or answers) and
    codes to generate the results should be submitted. Detailed instructions will
    be announced later through MyCourses.


    Group
    Works (35%)

    There will be one
    group work; developing a quantitative investing strategy such as factor
    investing. A group can have four or less members. A group of four members is
    recommended. (The maximum number of members can be adjusted according to number
    of students enrolled in the class) Each member of a group is expected to
    motivate others to participate equally.

    There will be two
    group tasks:

    1.      Short
    presentation of a key paper and project execution plan (5%)

    2.      Final
    presentation and report (30%)  

    A final report of
    maximum 15 pages and a final presentation of 30 mins will be required. The
    presentation times can vary with size of class. The report should include various
    quantitative analyses on the developed strategy, such as back-testing,
    robustness checks, and risk analyses. It does not have to be a completely new
    strategy or factor. Showing capability of quantitative skills and developing
    logics for the strategy will be main criteria for the evaluation. Finding a new
    factor will be a huge plus if it is convincingly robust. Consider yourself as a
    quant and this report as a proposal to adopt or sell new quantitative
    investment strategy. Detailed instructions will be announced later through
    MyCourses.



DETAILS

Study Material
  • Valid 01.08.2020-31.07.2022:

    Readings, slides, and other materials will be provided by the lecturer through MyCourses.

  • Applies in this implementation:

    Lecture notes will be uploaded. Also, see above reading list.


Prerequisites
  • Valid 01.08.2020-31.07.2022:

    Investment Management (28C00300) and Econometrics for Finance (28C00200), or equivalent courses.

FURTHER INFORMATION

Details on the schedule
  • Applies in this implementation:

    Schedule and Contents

    Note: Contents are tentative and are subject to
    change.

     

    #1. Course Orientation and Introduction.

    28.2, Thu, T004

     

    #2. Evaluating asset returns: Time-Series vs.
    Cross-sectional tests.


    1.3, Fri, T004

    -         
    Fama, E. F., and French,
    K. R. 1992. The cross‐section of expected stock returns. the Journal of
    Finance, 47(2), 427-465.

    -         
    Fama, E. F., and MacBeth, J. D. 1973.
    Risk, return, and equilibrium: Empirical tests. Journal of Political Economy,
    81(3), 607-636.

    -         
    Gibbons, M. R., Ross, S. A., and Shanken,
    J., 1989, A test of the efficiency of a given portfolio. Econometrica,
    1121-1152.

    -         
    Fama, E. F., and French, K. R., 1993, “Common
    Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial
    Economics, 33, 3-56.

     

    #3. Value Investing.
    7.3, Thu, T004

    -         
    Lakonishok,
    J., Shleifer, A., and Vishny, R. W., 1994, Contrarian investment,
    extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.

    -         
    Fama,
    E. F., and French, K. R. ,1996,. Multifactor explanations of asset pricing
    anomalies. Journal of Finance, 51(1), 55-84.

    -         
    Daniel,
    K., and Titman, S., 1997, Evidence on the characteristics of cross sectional
    variation in stock returns. Journal of Finance, 52(1), 1-33.

    -         
    Asness,
    C. S., Frazzini, A., Israel, R., and Moskowitz, T. J., 2015, Fact, fiction, and
    value investing.

    -         
    Individual
    Assignment 1

     

    #4. Momentum Investing.

    8.3, Fri, T004

    -         
    Jegadeesh, Narasimhan and Sheridan Titman,
    “Returns to Buying Winners and Selling Losers: Implications for Stock Market
    Efficiency,
    1993, Journal of Finance, 48,
    65-91.

    -         
    Moskowitz, Tobias and Mark Grinblatt, “Do
    Industries Explain Momentum?”, 1999, Journal of Finance, 54, 1249-1290.

    -         
    Cooper, M. J., Gutierrez, R. C., and
    Hameed, A., 2004, Market states and momentum. The Journal of Finance, 59(3),
    1345-1365.

    -         
    Hvidkjaer, Soeren, “A Trade-Based Analysis
    of Momentum,” 2006, Review of Financial Studies.

    -         
    George, T. J., and Hwang, C. Y., 2004, The
    52‐week high and momentum investing. The Journal of Finance, 59(5), 2145-2176.

    -         
    Daniel, K., and Moskowitz, T. J., 2016,
    Momentum crashes. Journal of Financial Economics, 122(2), 221-247.

    -         
    Asness, C. S., Frazzini, A., Israel, R., and
    Moskowitz, T. J., 2014, Fact, fiction and momentum investing.

    -         
    Goyal, A., & Jegadeesh, N., 2017,
    Cross-Sectional and Time-Series Tests of Return Predictability: What Is the
    Difference?, The Review of Financial Studies

     

    #5. Quality Investing, Betting against Beta, and Q-factor
    Model


    14.3, Thu, T004

    -         
    Novy-Marx, Robert, 2013, “The Other Side
    of Value: The Gross Profitability Premium,” Journal of Financial Economics.

    -         
    Asness, C. S., Frazzini, A., and Pedersen,
    L. H., 2014. Quality minus junk.

    -         
    Frazzini, A., and Pedersen, L. H., 2014,
    Betting against beta. Journal of Financial Economics, 111(1), 1-25.

    -         
    Frazzini, A., Kabiller, D., &
    Pedersen, L. H., 2013, Buffett's alpha. National Bureau of Economic Research.

    -         
    Cederburg, S., and O'Doherty, M. S., 2016,
    Does it pay to bet against beta? on the conditional performance of the beta
    anomaly. Journal of finance, 71(2), 737-774.

    -         
    Hou, K., Xue, C., & Zhang, L. (2015).
    Digesting anomalies: An investment approach. The Review of Financial Studies,
    28(3), 650-705.

     

     

    #6. Group Presentation: Key Paper and Project Execution
    Plan
    15.3, Fri, T004

    #7. Factor Investing: is it robust?
     

    21.3, Thu, T004

    -         
    Harvey, C. R., Liu, Y., and Zhu, H., 2016,
    … and the cross-section of expected returns. The Review of Financial Studies,
    29(1), 5-68.

    -         
    McLean, R. D., and Pontiff, J., 2016. Does
    academic research destroy stock return predictability?. Journal of Finance,
    71(1), 5-32.

    -         
    Jacobs, H., and Müller, S., 2017, Anomalies
    across the globe: Once public, no longer existent?.

    -         
    Israel, Ronen and Tobias Moskowitz, 2013,
    The Role of Shorting, Size, and Time on Market Anomalies, Journal of Financial
    Economics, 108, 275-301.

    -         
    Hou, K., Xue, C., and Zhang, L., 2017.
    Replicating anomalies. National Bureau of Economic Research.

    -         
    Asness, C. S., Frazzini, A., Israel, R.,
    Moskowitz, T. J., and Pedersen, L. H. (2017). Size matters, if you control your
    junk.

     

    #8. Investing Other Asset Market: Fixed Income Markets   

    22.3, Fri, T004

    -         
    Merton, R. C., 1974, On the pricing of
    corporate debt: The risk structure of interest rates. Journal of finance,
    29(2), 449-470.

    -         
    Asness, C. S., Moskowitz, T. J., and
    Pedersen, L. H., 2013, Value and momentum everywhere. Journal of Finance,
    68(3), 929-985.

    -         
    Eom, Y. H., Helwege,
    J., & Huang, J. Z. (2004).
    Structural models of corporate bond
    pricing: An empirical analysis. The Review of Financial Studies, 17(2),
    499-544.

    -         
    Gebhardt, W. R., Hvidkjaer, S., and
    Swaminathan, B., 2005, Stock and bond market interaction: Does momentum spill
    over?. Journal of Financial Economics, 75(3), 651-690.

    -         
    Jostova, G., Nikolova, S., Philipov, A.,
    and Stahel, C. W., 2013, Momentum in corporate bond returns. The Review of
    Financial Studies, 26(7), 1649-1693.

    -         
    Houweling, P., &
    Van Zundert, J., 2017.
    Factor investing in the corporate bond
    market. Financial Analysts Journal, 73(2), 100-115.

    -         
    Chordia, T., Goyal, A., Nozawa, Y.,
    Subrahmanyam, A., and Tong, Q. (2017). Are capital market anomalies common to
    equity and corporate bond markets? An empirical investigation. Journal of
    Financial and Quantitative Analysis, 52(4), 1301-1342.

     

    #9. Big Data and Machine Learning in Finance Research

    28.3, Thu, T004

    -         
    Mullainathan, S., and Spiess, J., 2017.
    Machine learning: an applied econometric approach. Journal of Economic
    Perspectives, 31(2), 87-106.

    -         
    Choi, H., and Varian, H., 2012. Predicting
    the present with Google Trends. Economic Record, 88(s1), 2-9.

    -         
    Kogan, S., Levin, D., Routledge, B. R.,
    Sagi, J. S., and Smith, N. A. ,2009,. Predicting risk from financial reports
    with regression. In Proceedings of Human Language Technologies: The 2009 Annual
    Conference of the North American Chapter of the Association for Computational
    Linguistics (pp. 272-280). Association for Computational Linguistics.

    -         
    Abe, M., and Nakayama, H., 2018. Deep
    Learning for Forecasting Stock Returns in the Cross-Section. arXiv preprint
    arXiv:1801.01777.

    -         
    McLean, R. D., and Pontiff, J., 2016. Does
    academic research destroy stock return predictability?. Journal of Finance,
    71(1), 5-32.

    -         
    Hoberg, G., and Phillips, G., 2010.
    Product market synergies and competition in mergers and acquisitions: A
    text-based analysis. The Review of Financial Studies, 23(10), 3773-3811.

    -         
    Moritz, B., and Zimmermann, T., 2016,
    Tree-based conditional portfolio sorts: The relation between past and future
    stock returns.

     

    #10. Big Data and Machine Learning in Finance Research
    (cont.)


    29.3, Fri, T004

     

    #11. Final Group Presentation 1

    4.4, Thu, Q201
    Ryhmäopetus, Väre (Note Exceptional Place)

     

    #12. Final Group Presentation 2

    4.5, Fri, T004

     

    *** The reading
    list is tentative. The exam will cover all materials and papers that are
    appeared in the lecture notes.


Description

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