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Quantitative Methods for Finance

MET AD685 A2

Fall 2022

1. Course Overview

1.1 AD 685 Quantitative Methods for Finance

· Finance is a highly competitive and dynamic industry that demands quantitatively oriented professionals. This course will equip students with the empirical techniques used in the analysis of financial markets with strong focus on financial applications using actual data.

· In particular, we will cover classical linear regression models, time series analysis, limited dependent variable models, and a few big data models applied to topics such as: predictability of asset returns; econometric tests of the CAPM and multifactor models; volatility modelling, etc.

1.2 Introduction

A list of assigned readings and problems is given in the course schedule. Students are expected to prepare for lectures and to do research on their own. The objective of lectures is to guide and clarify student learning.

· There will be quizzes, assignments, and financial applications with actual data.

· A term paper is required. It consists of a comprehensive empirical paper (10-12 pages) to answer practical finance questions by means of the econometric methods and tests SEEN IN THIS COURSE.

· Exams will cover the readings, online material, lectures, and assigned problems. Real-world issues and applied problems will be the foundation of the course.

1.3 Prerequisites

a) Courses:  No prerequisites.

b) Labs: Self-paced Lab ADR100 (Intro to R).

c) Optional, but highly recommended: Some matrix algebra, probability theory, and statistics. (Free preparatory laboratories in Math and Statistics)

2. Basic Information

Instructor: Miguel Villanueva, Ph.D.

Classes: Wednesdays, 6:00 pm – 8:45 pm

Classroom: KCB 102

Contact: [email protected]

Office: N/A at BU. I work full time in financial consulting in Brookline 

Office Hours: TBD

3. Text & Materials

3.1 Required Text: Stock, James & Mark Watson, Introduction to Econometrics, 4th Edition, 2019. Pearson. (“SW”)

We will use this version: “MyEconLab with eText access code”. You may purchase it directly through Pearson website upon registration.

· You MUST have access to MyEconLab to do the weekly homework to be posted there.

· However, the e-textbook is optional. If you have a hard copy of the book, Pearson will give you the option to buy only the access to MyEconLab.

In Pearson’s website, please register for the course with my course ID below. You will be prompted to a student purchase options page. I have posted instructions in Blackboard to register.

Course ID: villanueva09247

3.2 Software/Lab Sessions:

The statistical package recommended for this course is R, which is open source and freely available to download (https://www.r-project.org/). However, you may use other software while you familiarize with R.

3.3 Blackboard

This course will use a Blackboard site. Students are required to have a BU ID and password to log in. If you do not have a BU ID yet, note that this takes some time so start this process well before class starts. The site is: http://learn.bu.edu

3.4 Others

· Use of cell phones in class is prohibited. Please get a business or scientific calculator for exams or to use in class, you may not use your cell phone.

· You may bring your laptop to class, assuming you will use R, E-Views or other relevant software to run regressions or the analytical material of that week.

· Laptops shall not be used for internet browsing while in class.

4. Course Overview and Details

4.1 Course Learning Goals and Objectives

The goal of this course is to provide students with a number of econometric techniques used in the analysis of financial markets based on asset pricing and corporate finance models. We will cover classical linear regression models, time series analysis, limited dependent variable models, and some Big Data models applied to topics such as: predictability of asset returns; event study analysis; econometric tests of the CAPM and multifactor models; volatility modelling, etc.

4.2 Course Expectations

a) The course will consist of weekly classroom lectures and discussions.

b) ASSIGNMENTS: Weekly homework in Pearson’s MyEconLab and random in-class quizzes. We will discuss some of these problems during lectures, focusing on those topics most challenging for the class.

c) Term Paper:

In my section, you will write a term paper on estimating a model (Y as a function of several X) or forecasting variables (one or more Y as function of several X) of any financial asset price or return of your interest (stock, bond, currency, commodity, crypto-currency).

· You must apply statistical and econometric tools and tests covered in this course (MORE DETAIL ON THIS LATER).

· A proper research paper must include a bibliography of information and works cited. The paper should be 10-12 double spaced pages, not including cover page and appendices (which will usually contain figures and tables).

· The paper should be prepared using the APA writing style and guideline for references’ format.

o The Department uses the APA style as to facilitate both, reading the paper and understanding references without being cumbersome as some of the other styles (such as Chicago or MLA).

o Students can download the student style guide from the American Psychological Association web site, http://www.apastyle.org/elecref.html.

The empirical project must be an ORIGINAL RESEARCH PAPER.

“Original” means that the estimations, calculations, tables and results, as well as the textual discussion and presentation of your findings MUST be your own. The literature review or the conceptual ideas about your topic and previous results will be an overview or summary of several sources of information (several authors who published on your topic or similar topic in the past). These conceptual ideas and previous results must be attributed to the authors using the APA format. This is your paper and not the cut and paste of someone else's work –whether text, data tables, estimation tables, figures, etc.- without proper attribution. Keep in mind that the Internet is: (1) not quality oriented as it has both good materials and not so good materials, and the Internet does not know the difference; (2) the Internet is NOT a sole source location. In particular, sources such as Wikipedia are the works of individual submitters which are not reviewed. Thus, while many entries provide excellent information, some are fundamentally flawed or just plain wrong. Keep in mind that the Boston University Library as well as your local, state and the national US Library of Congress have extensive online services. USE THEM.

d) Partial Evaluations: There will be two partial evaluations or tests and no final exam, with equal weights, and I will send information about them later in the semester.

· There will be a set of past tests, exams and solutions in respective folders in Blackboard.

· These are only indicative because topics may have changed in the first and second half of the course, AND the number of topics covered is greater than the number of questions.

4.3 Course Grading

Course grades will be a weighted average of:

A: Partial Evaluation 1: 25%

B: Partial Evaluation 2: 25%

C: Term Paper: 25%

D: Assignments: 20%

E: Successful completion of ADR100 Lab (Intro to R): Your Assignments score will receive an extra 5% (for a total of 25% weight); else you’ll receive credit only for A to D.

4.4 Schedule

Please be aware that the schedule below may be subject to minor changes along the way.

Date	Topic	Readings Due	Assignments Due: Times are ET
Session 1 Sep 7, 2022	Syllabus. Introduction, Review of Probability	SW Ch. 1, 2.	n/a
Session 2 Sep 14, 2022	Review of Statistics	SW Ch. 3	n/a
Session 3 Sep 21, 2022	Linear Regression with Multiple Regressors	SW Ch. 6	Sep 20, 11:59 pm Ch 2 – Ch 3
Session 4 Sep 28 2022	Hypothesis Test/Confid Interv in Multiple Regressn.	SW Ch. 7	Sep 27, 11:59 pm Ch 6
Session 5 Oct 5, 2022	Nonlinear Regression Functions	SW Ch. 8	Oct 4, 11:59 pm Ch 7
Session 6 Oct 12, 2022	Catchup Due: 2-page Project proposal. SEND BY EMAIL	SW Ch. 7-8	Oct 11, 11:59 pm (Ch 8) Oct 12, 11:59 pm (Project 2-pager)
Session 7 Oct 19, 2022	Partial Evaluation 1		--
Session 8 Oct 26, 2022	Introduction to Time Series Regression and Forecasting Due: Project Data series for Y, X, summary stats. Send by email	SW Ch. 15	Oct 26, 11:59 pm (Project Data)
Session 9 Nov 2, 2022	Intro to Time Series (cont)	SW Ch 15 cont	Nov 1 at 11:59 pm (Ch 15)
Session 10 Nov 9, 2022	Additional Topics in Time Series	SW Ch 17	Nov 8 at 11:59 pm (Ch 15)
Session 11 Nov 16, 2022	Additional Topics (cont)	SW Ch 17 cont	Nov 15 at 11:59 pm (Ch 17)
Nov 23, 2022	Thanksgiving Break (no class)
Session 12 Nov 30, 2022	Forecasts with many predictors and Big Data models	SW Ch 14	Nov 29 at 11:59 pm (Ch 17)
Session 13 Dec 7, 2022	Partial Evaluation 2 Last Class of the course		Dec 6 at 11:59 pm (Ch 18)
Friday Dec 9, 2022	Due: Empirical Project (completed paper)		Email by midnight.

5. Requirements, Policies and Standards

5.1. Weekly Assignments

Assignments and discussions will serve as resource for the exams preparation. There will be clearly specified deadlines for the assignments and it is extremely important to complete the assignments BEFORE THE DEADLINE, no exceptions.

5.2. Timely Presentation of Materials Due

All assignments and assessments have due dates. These are the LAST DATES that stated material is due. I maintain the right to refuse, or downgrade any materials presented after due dates. This is not a subject for discussion.

Students should organize their time and work so as to turn in the assignment before the due date. To be clear, this means that the work will be accepted anytime up to that date but not after. Students should develop a schedule so that the work is built around their personal needs and obligations. Students should allow for contingencies and plan to hand in their work well before the last minute. That way, should some unforeseen problem arise, timely presentation of work is not in jeopardy.

5.3. Discussion Expectations

Please remember that your participation in weekly lectures discussions is essential.

5.4. Student Preparation

Minimal preparation is reading the material, and being able to summarize what it is about, what the major issues are, and offer some recommendations.

Superior preparation involves being able to (i) summarize the situation or problem presented; (ii) recommend a solution to the discussed problem; (iii) support your recommendation with relevant details, and analyses; and (iv) discuss innovative solutions.

5.5. Project Requirements

You are to complete any project or assignment on your own. 

5.6. Grading Policy

Grade inflation is not in the best interest of BU students or the reputation of BU. I have a responsibility to differentiate the performance of my students, and to reward with high grades only those who do exceptionally well. A Grade of ‘A’ or ‘A minus’ will be limited only to those students truly distinguishing themselves in the course.

Based on recommendations by the Academic Policy Committee of Metropolitan College and my own variations, I have given these grade percentages:

A, A- 25%-35%

B+, B, B- 50%-65%

Other (C, D, etc.) as necessary 0%-10%

Excellent work will be rewarded with an ‘A’. An ‘A’ requires quality excellence in all aspects of the course: quizzes, discussions, project, and exam. Grades may be curved.

5.7. Requests for Extensions

The general position is that make up extensions are NOT given. Sometimes, unfortunate situations occur that make fulfilling requirements impossible and, as such, extension requests will be evaluated on a case-by-case basis. BUT any request needs to be in writing and a written verification of the incident will be expected. This is not to penalize any individual student but to ensure there is a level playing field and the whole class feels confident that no one has a unique advantage. If, for any reason, you are unable to meet any assignment deadline, please contact the instructor immediately and preferably in advance. All assignments must be completed.

5.8. Off-Syllabus Work

Students will not be allowed to submit work for consideration that is beyond that defined in the syllabus. Students will not be allowed to submit extra work to improve their grade as this will be unfair to other students.

6. Academic Conduct Policy

Cheating and plagiarism will not be tolerated in any Metropolitan College course. These instances will result in no credit for the assignment or examination and may lead to disciplinary actions.  Any instance of cheating or plagiarism will be reported to the Dean and dealt with according to the Academic Conduct Code of Metropolitan College. Please take the time to review the Student Academic Conduct Code and familiarize with it:

https://www.bu.edu/met/current-students/academic-policies-procedures/#acc

Boston University makes available to all faculty the plagiarism tools “Safe Assign” and “TurnItIn.” These softwares do a decent job, but I do my own search and follow my own algorithm.