Type of Credit: Partially Required
Credit(s)
Number of Students
This course takes a data-driven approach to making investment decisions. The first part will review financial market basics, essential mathematical tools (linear algebra, constrained optimization), statistical methods (linear regression, simulation), Excel spreadsheets, and Python programming (NumPy, SciPy, and Pandas) for investment analysis. The second part will introduce the quantitative investment framework: (i) data collection, (ii) input estimation, (iii) portfolio optimization, (iv) back-testing, (v) implementation, and (vi) performance evaluation. Students will learn how to apply financial theories in portfolio choice (MPT) and asset pricing (CAPM). For practical topics, the course will cover topics on the fund industry, passive investment, active portfolio management, factor investing, performance metrics, style analysis, and risk management.
Students should have a good background in mathematics and statistics (e.g. matrix operations, univariate calculus, normal distribution, hypothesis testing). Prior knowledge in programming (e.g. Python, R, Excel VBA, Matlab, etc.) is desirable but not required. Students without the aforementioned knowledge should expect a steep learning curve and heavy workload.
能力項目說明
1. To understand the fundamental theories of portfolio choice and asset pricing.
2. To apply portfolio management techniques in Excel and Python.
3. To acquire practical experience in working with financial data.
4. To gain awareness of the limitations of theoretical models in reality.
5. To learn about the investment management industry and performance evaluation.
6. To gain insights about trends in investment and portfolio management.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
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週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
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1 |
Overview of the course |
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Class 1 |
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2 |
Review of statistical and optimization techniques |
Lecture notes |
Class 2 |
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3 |
Financial market and security trading |
BKM Chapters 1, 2, 3 |
Class 3 |
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4 |
Market data |
BKM Chapter 5 |
Class 4 |
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5 |
National holiday |
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6 |
Capital allocation to risky assets |
BKM Chapter 6 |
Class 5 |
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7 |
Portfolio choices |
BKM Chapters 6, 7 |
Class 6 |
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8 |
Applications of modern portfolio theory |
BKM Chapters 7, 8 |
Class 7 |
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9 |
Midterm examination |
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Midterm examination |
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10 |
The capital asset pricing model |
BKM Chapter 9 |
Class 8 |
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11 |
Arbitrage pricing theory |
BKM Chapter 10 |
Class 9 |
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12 |
Applications of asset pricing models and performance evaluation |
BKM Chapters 8, 9, 10, 24 |
Class 10 |
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13 |
Investment companies |
BKM Chapters 4, 26 |
Class 11 |
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14 |
Project on investment strategies |
Group discussion and presentations |
Class 12 |
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15 |
Project on investment strategies |
Group discussion and presentations |
Class 13 |
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16 |
Optional self-learning module and revision |
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Capstone self-learning |
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17 |
Optional self-learning module and revision |
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Capstone self-learning |
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18 |
Final examination |
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Final examination |
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5 Homework Assignment - 25%
1 Group Project - 25%
1 Midterm Examination - 25%
1 Final Examination - 25%
Main textbook:
Investments, 11th Edition by Zvi Bodie, Alex Kane, and Alan Marcus, McGraw-Hill, 2019. (BKM)