| Academic Year |
2025Year |
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) Fri5-8 |
| 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Different kinds of data from financial markets
第2回Cross-sectional data analysis
第3回Comparative data analysis
第4回Analysis of variance (ANOVA)
第5回Stock price adjustments and returns
第6回Stock market indexes
第7回ETFs, smart beta portforlio
第8回Construction of continuous futures price series
第9回 Indexes from derivatives
第10回Log returns and random walk
第11回Market microstructure
第12回Simple linear regression
第13回Application of linear regression: Event study
第14回Multiple linear regression II
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. |