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, FailApplies in this implementation:
-
Workload
Valid 01.08.2020-31.07.2022:
Classroom hours 24h
Class preparation 40h
Assignments/projects 60h
Preparing Exam 34h
Exam 2hTotal 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.
- Teacher: Sean Shin