Type of Credit: Required
Credit(s)
Number of Students
This course is designed to help the students master the econometric tools for analysing the financial data. It covers the basic theories on estimation and inference. The application of econometric methods would focus on the time-series and cross-sectional predictability of asset returns. We will also cover the basic machine learning methods and their application in finance.
能力項目說明
After finishing this course, the students are expected to be able to apply the econometric methods and conduct an independent empirical research.
| 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 |
|---|---|---|
|
週次 Week |
課程主題 Topic |
教學活動與作業 Teaching Activities and Homework |
學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
|||
|
1 |
Introduction |
Lecture |
3 |
0 |
|
2 |
Linear regression |
Lecture |
3 |
5 |
|
3 |
Linear regression |
Lecture |
3 |
5 |
|
4 |
Forecast evaluation |
Lecture |
3 |
5 |
|
5 |
Forecast evaluation |
Lecture |
3 |
5 |
|
6 |
Panel data methods |
Lecture |
3 |
5 |
|
7 |
Panel data methods |
Lecture |
3 |
5 |
|
8 |
Bootstrap-based inference |
Lecture |
3 |
5 |
|
9 |
Bootstrap-based inference |
Lecture |
3 |
5 |
|
10 |
Maximum likelihood methods |
Lecture |
3 |
5 |
|
11 |
Maximum likelihood methods |
Lecture |
3 |
5 |
|
12 |
Regularization |
Lecture |
3 |
5 |
|
13 |
Factor model |
Lecture |
3 |
5 |
|
14 |
ML methods |
Lecture |
3 |
5 |
|
15 |
Review session |
Lecture |
3 |
5 |
|
16 |
Final Exam |
Exam |
3 |
5 |
Class participation and quizzes (30%)
Final exam (40%)
Final report (30%)
Lecture notes