Omfattning: 6

Tidtabel: 28.02.2019 - 09.04.2019

Ansvarslärare (är i kraft 01.08.2018-31.07.2020): 

Sean Shin

Kontaktuppgifter till kursens personal (gäller denna kursomgång): 

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


Undervisningsperiod (är i kraft 01.08.2018-31.07.2020): 

IV (Spring 2019) Otaniemi campus

IV (Spring 2020) Otaniemi campus

Lärandemål (är i kraft 01.08.2018-31.07.2020): 

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.

Innehåll (är i kraft 01.08.2018-31.07.2020): 

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.

Närmare beskrivning av kursens innehåll (gäller denna kursomgång): 

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.


Metoder, arbetssätt och bedömningsgrunder (är i kraft 01.08.2018-31.07.2020): 

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

Närmare information om bedömningsgrunderna och -metoderna och om hur den studerande kan ta del av bedömningen (gäller denna kursomgång): 

-

Arbetsmängd (är i kraft 01.08.2018-31.07.2020): 

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

Total 160h (6 op)

Preciserad belastningsberäkning (gäller denna kursomgång): 

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.



Studiematerial (är i kraft 01.08.2018-31.07.2020): 

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

Närmare information om kursmaterial (gäller denna kursomgång): 

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


Förkunskaper (är i kraft 01.08.2018-31.07.2020): 

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

Bedömningsskala (är i kraft 01.08.2018-31.07.2020): 

0-5

Anmälning (är i kraft 01.08.2018-31.07.2020): 

The course registration is done via WebOodi and closes always 7 days before the first lecture/ start of the teaching period. The study coordinator will check the registrations first and then discuss with the lecturer and provide the lists. We have student quotas for our M.Sc. level courses and we use prioritization list.

Tilläggsinformation (är i kraft 01.08.2018-31.07.2020): 

A maximum of 40 students can be accepted to the course.

First priority are Finance M.Sc. programme students (i.e. who have graduated as B.Sc.) and CEMS students (those courses that have been designated as CEMS courses).

Remaining seats are prioritized as follows, in the order of registration in WebOodi within one category:
1. Finance M.Sc. exchange students from other universities
2. Aalto Finance B.Sc. students with a finished B.Sc. thesis (registered in transcript of records)
3. All other M.Sc. students

Please follow carefully the registration deadlines of the courses and exams! Missing registration deadline automatically foregoes a guaranteed seat for Finance M.Sc. courses and puts prospective student at the bottom of the prioritization list.

Students may be required to confirm their registration by signing the participation list circulated during the first lecture.

Närmare information om tidtabellen (gäller denna kursomgång): 

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.


Beskrivning

Anmälning och tillläggsinformation