## 基本素養 Basic Literacy

Culture transition

Historical consciousness

Artistic literacy

Civil literacy

Social concern

Benefit people and the society

International Perspective

## 核心能力 Competence

Consolidate fundamental knowledge in economics

Acquire diversified knowledge from interdisciplinary fields

Be familiar with tools for economic analysis

Expand economic applications

Language proficiency and communication ability

## 課程概述 Course Description

This course is an introduction to computational finance and data science applied to finance. The course covers computer programming and data analysis in R, econometrics (statistical analysis), financial economics, microeconomics, mathematical optimization, and probability models. The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data. This involves: (1) exploratory data analysis; (2) specification of models to explain the data; (3) estimation and evaluation of models; (4) testing the economic implications of the model; (5) forecasting from the model. The modeling process requires the use of economic theory, matrix algebra, optimization techniques, probability models, statistical analysis, and statistical software.

## 課程學習目標 Course Objectives

• Understand basic financial theories of asset risk-return trade-off.
• Apply econometric concepts of distributions, standard errors.
• Learn how to obtain, import, and manipulate financial data.
• Learn how to perform statistical analysis using R and Excel.
• ## 課程進度 Progress Description

進度說明 Progress Description
1Course Introduction, Time-Value of Money and Financial Returns (1)
2Time-Value of Money and Financial Returns (1); Univariate Random Variables (2)
3Univariate Random Variables (2); Bivariate Random Variables (3)
4Introduction to Time-Series (4); Matrix Algebra Review (5)
5Descriptive Statistics (6); The Constant Expected Return Model (7)
6The Constant Expected Return Model (7)
7The Constant Expected Return Model (7); Bootstrapping Methods (8).
8The Constant Expected Return Model (7); Bootstrapping Methods (8).
9Midterm
10Introduction to Modern Portfolio Theory (9)
11Introduction to Modern Portfolio Theory (9)
12Modern Portfolio Theory and the Markowitz Algorithm (10)
13Modern Portfolio Theory and the Markowitz Algorithm (10)
14MPT with No Short Sale Constraints (11); Statistical Properties of Efficient Portfolios (12); Portfolio Risk Budgeting (13);
15Portfolio Risk Budgeting (13);The Single Index Model (14);Paper Draft Due
16The Single Index Model (14); The Capital Asset Pricing Model (15)
17The Capital Asset Pricing Model (15)
18Final Research Paper Due
以上每週進度教師可依上課情況做適度調整。The schedule may be subject to change.

## 課程是否與永續發展目標相關調查 Survey of the conntent relevant to SDGs

本課程與SDGs相關項目如下：
This course is relevant to these items of SDGs as following:
• 就業與經濟成長 (Decent work and Economic growth)
• 責任消費與生產 (Responsible consumption and production)

## 有關課程其他調查 Other Surveys of Courses

1.本課程是否規劃業界教師參與教學或演講? 否
Is there any industry specialist invited in this course? How many times? No
2.本課程是否規劃含校外實習(並非參訪)? 否
Are there any internships involved in the course? How many hours? No
3.本課程是否可歸認為學術倫理課程? 否
Is this course recognized as an academic ethics course? In the course how many hours are regarding academic ethics topics? No
4.本課程是否屬進入社區實踐課程? 否
Is this course recognized as a Community engagement and Service learning course? Which community will be engaged? No