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 entrepreneurial and innovative expertise.
Honor Code and Code of Conduct:
This course will be conducted under the Code of Conduct and Code of Academic Integrity. Students are expected to know and follow them (see p. 2 for more details).
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 fifinancial 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 fifinance, and Black-Litterman model.
Pre-requisite: Fin 532–Investment Theory (Python not pre-required, but self-study needed.)
Texts (highly recommended but not required):
a). Chincarini and Kim, 2022, Quantitative Equity Portfolio Management, 2e., 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):
a). Sundnes, J., 2020, Introduction to Scientifific Programming with Python, Faller.
b). Langtangen, H., 2016, A Primer on Scientifific Programming with Python, 5e, Faller.
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), Artifificial Intelligence 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.
(a) Required: Lecture notes, slides, articles and other reading assignments.
(b) Suggested: Daily reading of Investor’s Business Daily and The Wall $treet Journal.
Tu: 10–12am; and 30 minutes right after evening classes.
Homeworks (Python computations, etc), worth 15%, will be assigned and graded by P/F. The fifinal 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 presenting them as your own; may avoid plagiarism by proper acknowledgement.
b) Cheat on Examination – receive/provide any unauthorized assistance or use unauthorized materials/too during exam.
c) Engage in other forms of deceit or dishonesty that violate the spirit of the Code.
a) Olin policies on professional behavior, etc, via the school document Integrity Matters: Olin Business School Code of Conduct.
b) University policies on COVID-19 Health and Safety Protocols and many other important matters, via web.
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