| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN22801 |
Subject Classification |
Specialized Education |
| Subject Name |
Data Science of Algorithmic Finance |
Subject Name
（Katakana） |
データサイエンスオブアルゴリズミックファイナンス |
Subject Name in
English |
Data Science of Algorithmic Finance |
| Instructor |
TING HIAN ANN |
Instructor
(Katakana) |
ティン ヒェン アン |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Weds5-8 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| Homework, Data Analysis, Quiz |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
E
:
English |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
M1 & M2 |
| Keywords |
Data analysis, data processing, algorithm, quantitative finance, futures, options, indexes, ETFs, random |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
The industry landscape of investment, trading, and risk management has been revolutionized by computing technologies, data science, and quantitative finance. To progress in tandem with these changes in the industry, the topics covered in this course include alternative ETF construction, market microstructure, and algorithmic trading.
In addition to mathematical modeling, an important part of this course is the practical aspect: computational implementations with statistical tests. Given that implementation and test procedures are involved, this course is algorithmic and hands-on in nature. |
| Class Schedule |
第1回Stock Market Indexes and ETFs I
第2回Stock Market Indexes and ETFs II
第3回Prices and Returns I
第4回Prices and Returns II
第4回Prices and Returns II
第6回Financial Statistics II
第7回Random Walk and Variance Ratio Test I
第8回Random Walk and Variance Ratio Test II
第9回Asset Pricing Models I
第10回Asset Pricing Models II
第11回Options
第12回Volatility Indexes
第13回Static Replication
第14回GARCH
第15回Summary |
Text/Reference
Books,etc. |
Ting, C. H. A. Algorithmic Finance: A Companion to Data Science, World Scientific, 2022. |
PC or AV used in
Class,etc. |
|
| (More Details) |
Latex beamer and projector from notebook |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
For each lecture, the before- and after-lecture reviews are needed. |
| Requirements |
Working knowledge on differential and integral calculus, and Python programming. |
| Grading Method |
Comprehensive evaluation will be based on the reports(about 4) and active participation in lectures. |
| Practical Experience |
Experienced
|
| Summary of Practical Experience and Class Contents based on it |
Practical experiences in data sourcing, data processing, and data analysis of financial markets using Python |
| Message |
Lectures will be all in English. |
| Other |
Attendance at each lecture will be taken. No examination for this course. Grades are based on reports (timely submission of assignments). |
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |