Washington University
Olin School of Business
Finance 532B Prof. Guofu Zhou
Data Analysis for Investments Fall, 2021
Course Syllabus
Olin’s Pillars of Excellence:
Values-based and Data driven; Global; Experiential; Entrepreneurship.
Students will: 1) embody a values-based and data-driven approach; 2) understand the
global opportunities and challenges; 3) engage business with experiential knowledge and
rigorous technical skills; 4) pursue world-changing initiatives with an entrepreneurial
and innovative mindset and skillset.
Honor Code and Code of Conduct:
This course will be conducted under the Code of Conduct and Code of Academic
Integrity. Students are expected know them (some are attached at the end).
Course Description:
The objective is to obtain an in-depth understanding of some of the major empirical
issues in investments and to gain the implementation skills. Based on recent advances,
students are required to learn the facts, theories and the associated statistical tools to
analyze financial data with Python, and with some optional tutorial and codes in R
and Matlab. The topics include portfolio optimization, factor models, factor investing,
Bayesian and shrinkage estimations, principal analysis, predictability, big data tools,
asset allocation, stock screening, performance evaluation, anomalies, limits to arbitrage,
behavioral finance, and Black-Litterman model.
Pre-requisite: Fin 532–Investment Theory (note: Python is not pre-required)
Texts (highly recommended):
a). Chincarini and Kim, 2006, Quantitative Equity Portfolio Management, MGH.
b). Grinold and Kahn, 2000 and 2019, Active Portfolio Management and Advances in
Active Portfolio Management, McGraw-Hill.
c). Litterman, et al, 2003, Modern Investment Management, Wiley.
Other Books (optional):
Python:
a). Sundnes, J., 2020, Introduction to Scientific Programming with Python, Springer.
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b). Langtangen, H., 2016, A Primer on Scientific Programming with Python, 5e, Springer.
c). Heinold, B., 2012, A Practical Introduction to Python Programming, on-line.
d). Lutz, M., 2013, Learning Python, 5e, O’Reilly Media.
Python in Finance:
a). Weiming, J., 2019, Mastering Python for Finance, 2e, Packt.
b). Yan, Y., 2017, Python for Finance, 2e, Packt.
c). Hilpisch, Y., Python for Finance: Mastering Data-Driven Finance (2019), Python
for Algorithmic Trading: From Idea to Cloud Deployment (2020), Artificial Intel-
ligence in Finance: A Python-Based Guide (2020).
d). Jansen, S., 2020, Machine Learning for Algorithmic Trading, 2e, Packt.
Concepts review: Bodie, Kane, and Marcus, 2017, Investments, 11e, McGraw-Hill.
Readings:
(a) Required: Lecture notes, slides, articles and other reading assignments.
(b) Suggested: Daily reading of Investor’s Business Daily and The Wall $treet Journal.
Office Hours:
Tu: 10–12am; and 30 minutes right after evening classes.
Grading:
Homeworks (Python computations, etc), worth 15%, will be assigned and graded by
P/F. The final is worth 70% each, and the class participation 15%.
Olin’s Code of Conduct as it relates to Academic Matters:
It is a Student Academic Violations if
a) Plagiarize - take someone else’s ideas, words or other types of product and pre-
senting them as your own; may avoid plagiarism by proper acknowledgement.
b) Cheat on Examination - receive/provide any unauthorized assistance or use unau-
thorized materials/too during exam.
c) Engage in other forms of deceit or dishonesty that violate the spirit of the Code.
Olin’s Code of Conduct as it relates to Professional Behavior:
Olin students are expected to conduct themselves at all times in a professional manner:
a) Attendance: when must miss a session, should notify the instructor prior.
b) Punctuality: expected to arrive prior to the start of the class.
See Integrity Matters: Olin Business School Code of Conduct for more details.
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Finance 532B Prof. Guofu Zhou
Data Analysis for Investments Fall, 2021
Sections 1 or 3 (in-person)
[M-W: 10:00–11:20am; 11:30–12:50pm; Simon Hall 106]
Date (Day) Topics Readings
(Lecture Notes, papers,
concepts review via BKM)
10/25 (M) Introduction and Python Ch 1–5, 13
10/27 (W) Properties of Stock Returns
11/01 (M) Mean-variance Portfolio Ch 6–8
11/03 (W) Portfolio Optimization
11/08 (M) Simulation, Bootstrap and Shrinkage
11/10 (W) Factor Models 1: Known Factors Ch 9–10
11/15 (M) Factor Models 2: Latent Factors
11/17 (W) Anomalies and Behavior Finance Ch 24–28
11/22 (M) Thanksgiving Break
11/24 (W) Thanksgiving Break
11/30 (M) Predictability
12/01 (W) Big Data and Machine Learning
12/06 (M) Bayesian Estimation
12/08 (W) Black-Litterman Allocation
12/15 (W) Final Exam
9–11am, SH 106, 110, 112 (via Canvas)
⋆ ⋆ All students enrolled in Olin school course work are subject to the ⋆ ⋆
⋆ ⋆ ⋆ student instituted and managed Honor Code regarding academic integrity ⋆ ⋆ ⋆
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Finance 532B Prof. Guofu Zhou
Data Analysis for Investments Fall, 2021
Sections 2, 4, 5 (via Zoom)
[M or W: 6:15—9:15pm; F: 1:00pm—4pm.]
Date (Day) Topics Readings
(Lecture Notes, papers,
concepts review via BKM)
10/25,27,29 (MWF) Introduction and Python Ch 1–5, 13
Properties of Stock Returns
11/01,03,05 (MWF) Mean-variance Portfolio Ch 6–8
Portfolio Optimization
11/08,10,12 (MWF) Simulation, Bootstrap and Shrinkage
Factor Models 1: Known Factors Ch 9–10
11/15,17,19 (MWF) Factor Models 2: Latent Factors
Anomalies and Behavior Finance Ch 24–28
11/22,24,26 (MWF) Thanksgiving Break
Thanksgiving Break
11/30,01,03 (MWF) Predictability
Big Data and Machine Learning
12/06,08,11 (MWF) Bayesian Estimation
Black-Litterman Allocation
12/13,15 (MW) M&W Sections’ Final Exam
(M&W) 7:00–9:00pm (via Canvas)
12/15 (F) Fri Section’s Final Exam
9–11am, SH 112 (via Canvas)
⋆ ⋆ All students enrolled in Olin school course work are subject to the ⋆ ⋆
⋆ ⋆ ⋆ student instituted and managed Honor Code regarding academic integrity ⋆ ⋆ ⋆
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