金融数据分析-教学大纲

《金融数据分析》教学大纲

The Course Outline of Financial Data

Analysis

课程编号：151222B

课程类型：专业选修课

总学时：32 讲课学时：16 实验（上机）学时：16

学分：2

适用对象：金融学（金融经济实验班）

先修课程：计量经济学、微观经济学、宏观经济学、概率论与数理统计、线性代数、微积分

Course Code:

Course Type: Discipline Elective Course

Periods: 32 Lecture: 16 Experiment (Computer): 16

Credits: 2

Applicable Subjects: Finance（Finance and Economics Experiment Class）

Prerequisite Courses: Econometrics, Microeconomics, Macroeconomics, Probability and Statistics, Linear Algebra, Calculus

一、课程简介

本课程是面向金融学、经济学和管理学相关专业的高年级本科生开设的学科专业选修课程，主要介绍应用于金融数据分析中的经典计量方法，并注重培养学生的实际操作能力。

Financial data analysis is an elective course for advanced undergraduate students majored in finance, economics and management. In this course, we not only introduce basic econometric methods applied to financial data, but also train students’ practical skills of handling financial data.

二、教学目标

本课程包括以下教学目标：（1）使学生能运用所学的金融计量理论分析和解释实际金融数据；（2）着重为对金融理论进行实证分析提供所需的估计和检验方法；（3）使学生运用计算机软件（以R语言为主）进行金融数据研究。

This course has the following teaching goals: (1) equipping students with econometric tools to analyze and explain financial data, (2) focusing on the estimation and inferential methods used in empirical analysis of finance theory, and (3) enabling students to implement these techniques using computer software, primarily R programming language.

二、教学基本要求

这门课程主要讲授如何利用海量金融数据对金融理论进行实证分析，以及在处理实际金融数据时所需的计量方法和计算机技术。因此，在教学内容的讲授过程中，授课老师需要做到理论与实践并重。理论学习包括金融数据概述，平稳时间序列分析，非平稳性时间序列分析，波动率分析，股票回报率预测，等。在时间允许的情况下，我们还将介绍，多维向量自回归模型, 以及协整和误差纠正模型。对于上述所有的模型的分析，本课程提供相应的实际金融数据，以及完整的R程序与讲解说明文件。

本课程教学方法将以课堂讲授为主。由于课程内容难度相对较大，我们鼓励学生课前预习，课上积极参与讨论，并课后复习和独立完成作业。R语言的学习以课堂展示和学生上机实际操作的方式完成。

课程考核由考勤、作业、研究项目、和期末考试组成。考核成绩为百分制，各项分数分配见表（一）。

表（一）：分数分配方式

出勤10%

作业30%

期末闭卷考试30%

研究项目30%

The main content of this course is to explain how to handle a huge amount of financial data and to conduct empirical analysis based on financial theories. Thus, this course will weight equally on both learning econometric theories and real applications. Theoretical study will mainly cover such topics as introduction to financial data, stationary time series analysis, non-stationary time series analysis, volatility analysis, and stock return prediction. If time permits, we will also learn vector autoregressive models (V AR) and cointegration and error correction models. All the models are accompanied with real-data examples with complete R programs and tutorials.

The basic teaching strategy of this course mainly involves in-class lectures. Due to the difficulty of the course, we encourage students to browse the assigned reading materials before class, to proactively participate discussions in class, and to peruse lecture notes and independently complete homework after class. Students will learn R through in-class instructions and practice.

The methods of the course evaluation include attendance, homework, project work, and final exam. The grade distribution of each component within one hundred percentage points is presented in Table 1

Table 1: The grade distribution

Attendance 10%

Assignments 30%

Final Exam (closed) 30%

Project Work 30%

三、各教学环节学时分配