Research Methods & Quantitative Analysis (RMB302)
Vietnamese title: Phương pháp nghiên cứu và phân tích định lượng
Credits: 3
Prerequisites: MAS202

Description:
This module provides students with an understanding of the quantitative methods for finance and investment and assists their thesis writing. This includes the ability to formulate problems into quantitative models, to aid the successful resolution of the problem. Students will learn how to apply statistical methods to analyze past data and make forecast on future trends. Using output from mathematical and statistical models, students will learn to analyze, interpret and derive potential outcomes from financial information. Topics covered include financial data collection, probability, sampling, panel regression, time series analyses, and decision tree. Teaching method is flexible. Lecturers and tutors can mix between traditional teaching method with project-based learning, problem-based learning and flipped classroom.

Learning outcomes:
Understand the econometrics methods necessary for doing empirical analysis in business, particularly in finance
Understand underlying assumptions of different econometrics models
Be able to identify the advantages and disadvantages of various methods and techniques and choose appropriate methods for specific datasets
Ulitize common statistical packages to conduct empirical research
Be able to conduct, interpret and critically deal with empirical studies in finance and related fields
Be able to work in a team or individually in making business decisions based on quantitative analysis; present one's self decisions.
Apply tools and knowledge to define, design and deliver an academically rigorous piece of research
Show awareness of statistical errors and understand of being ethical when conducting quantitative research

Student tasks:
- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments given by instructors in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU CMS at http://flm.fpt.edu.vn for up-to-date course information

Tools:
- Textbook and all learning materials provided on CMS
- Internet and a statistical computer package

Materials:
description: Introductory econometrics for finance
author: Chris Brooks
publisher: Pearson
published_date: 2019
edition: 4th
description: Introductory econometrics: A modern approach
author: Jeffrey M. Wooldridge
publisher: South-Western Cengage learning
published_date: 2012
edition: 5th or newer

Assessments:
category: Essay test
type: on-going
weight: 20.0
duration: 60'
question_type: LO1, LO2, LO3, LO4, LO6, LO7
no_question: Comprehensive exercises
knowledge_and_skill: Problem solving of 2-3 questions
grading_guide: Integration of studied knowledge and skills of all chapters
note: In class, by instructor
category: Group assignment
type: on-going
weight: 15.0
duration: During the semester
question_type: All LO
no_question: Analysis, reports and presentation
knowledge_and_skill: Case study
grading_guide: Comprehensive reading, understanding issues, presenting problems,
Personal qualities: Individual responsibility, self-esteem, sociability, self-management, and integrity.
Cover all studied chapters
note: - Guided by instructor
- Groups of students are required to choose a topic in finance, collect and analyse their data. Students are expected to write up a professional report that describes their methodology with clear adjustment of their choice of models, findings and discussion and implications. Finally, students need to present their work in class to instructors and other groups. Report are graded to groups and presentation is graded on individual.
category: Individual assignment
type: on-going
weight: 10.0
duration: During the semester
question_type: All LO
no_question: Analysis, reports
knowledge_and_skill: Case study
grading_guide: Comprehensive reading, understanding issues, presenting problems,
Personal qualities: Individual responsibility, self-esteem, sociability, self-management, and integrity.
Cover all studied chapters
note: - Guided by instructor
- Examples of assignment design: 1) Students are required to perform an analysis of a given set of data (include discription and regression). Students are expected to write up a report that describes their analysis, findings and discussion and implications.
Or 2) Students are expected to perform a simple (systematic) review of literature on finance research to find out the "State of art" topics and methodology
category: Mid-term exam
type: on-going
weight: 15.0
question_type: LO1, LO2, LO3, LO4, LO6, LO7
no_question: Comprehensive exercises
knowledge_and_skill: Problem solving of 2-3 questions
grading_guide: - Integration of studied knowledge and skills of all chapters from chapter 0 to chapter 9
note: In class, by instructor
category: Quiz
type: on-going
weight: 10.0
duration: 30'
question_type: LO1, LO2, LO3, LO4, LO7
no_question: Multiple choices or written questions.Marked by Computer or a suitable format
knowledge_and_skill: 10 to 30
grading_guide: - Integration of studied knowledge and skills of chapters assigned- Quiz 1: cover chapter 1-5- Quiz 2: cover chapters 11, 12 and 14
note: In class, by instructor
category: Final exam
type: Final exam
weight: 30.0
duration: 80'
question_type: All LO
no_question: - Multiple choice. Marked by Computer
knowledge_and_skill: 50 MCQ
grading_guide: All topics, knowledge, skills covered in the classes
note: By exam board

Schedule:
session: Course Introduction
Review of statistic basis
Chapter 1 Introduction and Mathematical Foundation
materials: RMB302
session: Chapter 2 Defining and collecting data
Probability and probability distributions
materials: RMB302
session: Review of statistic basis
Chapter 2 Defining and collecting data (cont)
Descriptive Statistics
session: Chapter 2 Defining and collecting data (cont)
Types of Data and Data Aggregation
Solve questions and problems for chapter 1 and 2
session: Chapter 3 Brief Overview of the classical linear regression model
What is a regression model
Regression versus correlation
Simple regression
The assumptions underlying the model
session: Chapter 3 Brief Overview of the classical linear regression model (cont)
Properties of the OLS estimator
Precision and Standard Errors
An Introduction to Statistical inference
A special type of hypothesis test
session: Tutorial
Solve questions and problems for chapter 3
session: Individual assignment release
Group assignment release
session: Chapter 4 Further development and analysis of the classical linear regression model
Generalising the simple model
The constant term
How are the parameters calculated?
Testing multiple hypotheses: the F-test
session: Chapter 4 Further development and analysis of the classical linear regression model (cont)
Data mining and the true size of the test
Qualitative variables
Goodness of fit statistics
Quantile regression
session: Tutorial:
Solve questions and problems for chapter 4
session: Chapter 5 Classical linear regression model assumptions and diagnostic tests
Statistical distribution for diagnostic tests
Assumptions
Multicollinearity
Adopting the wrong function form
session: Chapter 5 Classical linear regression model assumptions and diagnostic tests (cont)
Omission of an important variable
Inclusion of an irrelevant variable
Parameter stability tests
session: Chapter 5 Classical linear regression model assumptions and diagnostic tests (cont)
Measurement errors
A strategy for constructing econometrics models
session: Tutorial: Solve chapter 5 questions and problems
session: Quiz 1: Chapter 1, 2, 3, 4, 5
Tutorial
Introduce to statistical software packages
Guide to use a statistical package
session: Chapter 6 Univariate time-series modeling and forecasting
Some notation and concepts
Moving average processes
session: Chapter 6 Univariate time-series modeling and forecasting (cont)
Autoregressive processes
The Partial autocorrelation function
session: Chapter 6 Univariate time-series modeling and forecasting (cont)
ARMA processes
Exponential smoothing
Forecasting
session: Tutorial
Solve questions and problems for chapter 6
Guide to use a statistical package
session: Chapter 7 Multivariate Models
Simultaneous equations bias
How can simultaneous equations models be validly estimated?
Can the original coefficients be retrieved from the π ѕ?
session: Chapter 7 Multivariate Models (cont)
A definition of exogeneity
Estimation procedures for Simultaneous equations systems
Vector autoregressive models
Does the VAR include contemporaneous Terms
session: Chapter 7 Multivariate Models (cont)
Blocks significance and causality tests
VAR with exogenous variables
Impulse responses and Variance decompositions
session: Tutorial
Solve questions and problems for chapter 7
Guide to use a statistical package
session: Chapter 8 Modelling long-run relationships in finance
Stationary and Unit root testing
Cointegration
session: Chapter 8 Modelling long-run relationships in finance (cont)
Equilibrium correction or error correction models
Testing for cointegration in regression: a residuals-based approach
session: Chapter 8 Modelling long-run relationships in finance (cont)
Equilibrium correction or error correction models
Testing for cointegration in regression: a residuals-based approach
session: Tutorial Solve question and problems of chapter 8
Guide to use a statistical package
session: Chapter 9 Modelling volatility and correlation
Models of volatility
Exponentially weighted moving average models
session: Chapter 9 Modelling volatility and correlation (cont)
Autoregressive volatility models
Autoregressive conditionally heteroscedastics(ARCH) models
session: Chapter 9 Modelling volatility and correlation (cont)
GARCH models (EGARCH, basic GARCH)
Testing non-linear regression
session: Chapter 9 Modelling volatility and correlation (cont)
Forecasting covariances and correlations
Simple covariance models
Dirrect correlation models
session: Chapter 9 Modelling volatility and correlation (cont)
Testing non-linear regression
Forecasting covariances and correlations
Simple covariance models
session: Chapter 9 Modelling volatility and correlation (cont)
Dirrect correlation models
Tutorial
Solve questions and problems for chapter 9
session: Guide to use a statistical package
session: Mid-term exam
session: Chapter 11 Panel data
Introduction of panel techniques
What panel techniques are available
session: Chapter 11 Panel data (cont)
The fixed effects model
Time-fixed effects models
session: Chapter 11 Panel data (cont)
Investigating Banking Competition
The random effects model
session: Chapter 11 Panel data (cont)
Panel Data Application to Credit Stability of Banks
Panel unit root and cointegration tests
session: Tutorial
Solve questions and problems for chapter 11
Guide to use a statistical package
session: Chapter 12 Limited Dependent Variable Models
The linear probability model
The logit model
session: Chapter 12 Limited Dependent Variable Models (cont)
The linear probability model
The logit model
The probit model
session: Chapter 12 Limited Dependent Variable Models (cont)
Choosing between the logit and probit models
Goodness of fit measures for linear dependent variable models
session: Tutorial
Solve questions and problems for chapter 12
Guide to use a statistical package
session: Chapter 14 Additional econometrics techniques for financial research
Even study
session: Chapter 14 Additional econometrics techniques for financial research (cont)
Tests of the CAPM and the Fama-French methodology
session: Chapter 14 Additional econometrics techniques for financial research (cont)
Extreme Value Theory
The Generalised Method of Moments
session: Tutorial
Solve questions and problems for chapter 14
Guide to use a statistical package
session: Quiz 2: Chapter 11, Chapter 12, and Chapter 14
session: Chapter 15 Conducting Empirical research or doing a project or dissertation in finance
What is an empirical research project
Selecting the topic
The research proposal
session: Chapter 15 Conducting Empirical research or doing a project or dissertation in finance (cont)
Working papers and literature on the Internet
Getting the data
session: Chapter 15 Conducting Empirical research or doing a project or dissertation in finance (cont)
Choice of computer software
Methodology
session: Tutorial
Consulting to choose research topics
Guide to use a statistical package
session: Group assignment presentations
session: Group assignment presentations
session: Group assignment presentations
session: Group assignment presentations
session: Group assignment presentations
session: Essay tests Revision and Wrap-up