Finance and Markets for Engineers and Scientists#

Description#

In today’s society, finance holds a vital position in both the progress of technology and the lives of individuals. Despite this, traditional engineering or physical science curriculums seldom teach financial forecasting, modeling, and decision-making. Many engineers and scientists pursue careers in finance and consulting, adding to the need for financial knowledge. This course aims to bridge these gaps by introducing engineers and scientists to financial systems, markets, and quantitative finance tools and approaches. We will model, analyze, and optimize financial systems and decision-making processes using engineering, statistics, data science, and machine learning tools.

Times, places and websites#

  • Credits: 3-credits

  • Lectures and discussions: Lecture at TuTh 1:25 PM - 2:15 PM and Discussion at W 1:25 PM - 2:15 PM

  • Website: Canvas and GitHub.

  • Schedule: The lecture, lab and problem set schedule can be viewed (requires Cornell login): here.

Outcomes#

Upon completion of this course, students will be able to:

  • Outcome 1: Analyze financial data sets using tools from statistics, data science (DS) and machine learning (ML)

  • Outcome 2: Identify quantitative models of asset pricing and process performance using historical and real-time financial data sets

  • Outcome 3: Demonstrate mastery of quantitative decision-making and risk management approaches in the context of corporate finance and personal wealth management.

Topics#

Lectures are organized into units, each unit having roughly four weeks of class time:

  • Unit 1. Financial Basics will introduce the basics of financial systems, markets, and instruments. We’ll also introduce the basics of financial accounting and financial statements.

  • Unit 2: Tools for Wealth Creation will introduce the basics of wealth creation, including analyzing fixed-income, equity, and derivative assets and computing the risk and return of various assets.

  • Unit 3: Modern Portfolio Theory will introduce the basics of portfolio management, including analyzing portfolio risk and return and constructing optimal portfolios.

  • Unit 4: Financial Decision Theory will introduce the basics of financial decision theory, including analyzing rational decision-making under uncertainty and using Markov processes and reinforcement learning to model sequential decision-making.

Prerequisites#

There are no formal prerequisites for this course. However, general familiarity with common programming languages such as Python/Matlab/Julia and mathematical and computing topics such as probability, statistics, optimization, and data science tools such as Jupyter notebooks, DataFrames, etc., will be helpful.

Grading#

Grades will be computed from 3 \(\times\) take-home prelims (dates/times indicated on the course calendar), approximately 6 - 8 \(\times\) problem sets (that can be done in teams), a final group project (scheduled by the University), and a participation score. The final grade will be computed as follows (subject to change):

Percentage

Component

10%

Participation. Course participation (code reviews, questions and TA/course evaluations) will be an important component of the course. Participation opportunities will be identified throughout the semester.

40%

Problem sets. Approximately 5 - 6 problem sets will be administered over the semester. Problem sets will be distributed via Canvas or GitHub classroom (typically released on Wednesday afternoons and due one-week later). Problem sets can be completed in teams.

30%

Take-home exams. Three take-home examinations will be distributed. The distribution dates are listed on the course calendar, and once distributed, students will have a specified number of hours (at least 36 hours) to complete the exams.

20%

Final project. Development of a research project by each student (semester long with additional emphasis during the final 5 weeks of the course). The projects will conclude with a written report and perhaps an oral presentation to the class near the end of the semester. The final written reports will be due on the University assigned date for final projects.

This course will have weekly hands-on case studies completed as teams in the discussion section(s). The problem sets will be similar to the hands-on case studies. We expect everyone to attempt every case study and to participate in the discussion section:

  • The teaching team reserves the right to collect and grade entire case studies or specific questions of case studies throughout the course.

  • Teamwork is welcome (and encouraged) on case studies, problem sets and course projects, but the take-home exams must be completed individually.

Course policies and procedures#

  • Students must know and abide by Cornell’s Code of Academic Integrity. You may discuss problem sets with classmates, but all submitted work should be yours. Take-home exams must be completed individually without the help of students, teaching assistants, faculty, and other sources. Please review Cornell’s academic integrity policy

  • Late problem sets: You are granted 5 \(\times\) free late days to use throughout the semester. You may use these late days however you wish, but once they are used up, late homework will be penalized. After you consume your late-day budget, 25% will be deducted from your grade each day the homework assignment is late.

  • Exam re-grade requests must be submitted in writing within one week of the return of the exam document. The entire exam document will be re-graded in addition to addressing the re-grade request.

  • If you need to request an extension on a problem set because of an extenuating circumstance, please get in touch with Professor Varner before the problem set is due to discuss an extension.

  • This course will officially support numerical computing in the Julia programming language. We’ll use Visual Studio Code (VSCode) as our Integrated Development Environment (IDE). However, students are free to use whatever free open source language and tools they wish, e.g., Python, the C-programming language or R for course assignments (but no help unless you use Julia).

Do over policy#

In addition to the free late days, you will be granted 3 \(\times\) do-overs for problem sets and prelim questions. You may use these do-overs in any way you wish, but re-submissions will not be accepted once they are used up.

Disabilities statement#

If you have a disability-related need for reasonable academic adjustments in this course, please provide Professor Varner with an accommodation letter from Student Disability Services. Students are expected to give two weeks’ notice of the need for accommodations.

Diversity statement#

We aspire to create an environment for learning, intellectual discourse, and professional and social interactions where everyone feels welcome and valued. We recognize all course participants as responsible adults with differing perspectives based partly on their unique experiences. We espouse the broader mission and values of the Smith School.

Disclaimer and Risk Statement#

This content is offered solely for training and informational purposes. No offer or solicitation to buy or sell securities or derivative products, or any investment or trading advice or strategy, is made, given, or endorsed by any member of the teaching team.

Trading involves risk. Carefully review your financial situation before investing in securities, futures contracts, options, or commodity interests. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. Trading is generally inappropriate for someone with limited resources, investment or trading experience, or a low-risk tolerance. Only risk capital that is not required for living expenses.

You are fully responsible for any investment or trading decisions you make. Such decisions should be based solely on your evaluation of your financial circumstances, investment or trading objectives, risk tolerance, and liquidity needs.