Michael Roberts

Undergraduate

Data Science for Finance

This elective course was taught at Wharton from 2020 to 2022.
Data Science for Finance introduces students to data science for financial applications using the Python programming language and its ecosystem of packages. Students investigate a variety of empirical questions from different areas within finance including: FinTech, asset management, corporate finance, corporate governance, personal finance, venture capital, and private equity. The course highlights how big data and data analytics shape the way finance is practiced by focusing on problems currently confronting finance professionals.
The course objectives are threefold:

1. Improve financial decision-making with data analytics and the scientific method.
2. Provide a foundation for performing data analytics in finance-related roles inside (e.g., commercial and investment banking, venture capital, private equity, asset management) and outside the financial sector (e.g., consulting, general management, corporate development, treasury).
3. Train “data translators” who can bridge the gap between technical professionals (e.g., computer scientists, statisticians) and strategic/ operational professionals (finance, sales, marketing, operations, human resources, etc.)

Corporate Finance

This core course was taught at Wharton from 2004 to 2008.
Corporate finance provide a rigorous introduction to the fundamental principles of financial economics and their application. The organization of the courseis based on three main principles:

1. The time value of money. Here you will learn to value financial assets such asbonds, stocks, futures, and options, and to appraise investment projects andcorporate strategies using a rigorous framework.
2. Diversification and risk. These classes provide a thorough grounding in thetrade-off between risk and return given by modern portfolio theory. The tools enable you to assess financial risks as well as business risks.
3. Arbitrage and hedging. I demonstrate how to hedge stock and commodity market risk, interest rate risk, and foreign exchange risk using futures and options.

MBA

Doctoral

Empirical Methods in Corporate Finance

This is a Ph.D. course taught at different times from 2009 to 2023.
Provide students with a toolbox and working knowledge of microeoconometric empirical methods for use in corporate finance research. What does this mean? The ‘toolbox’ refers to a variety of methods commonly employed in empirical research – not all but a representative sample of older and more recent econometric techniques. The ‘working knowledge’ means that you are going to learn these methods via a three-pronged approach.

1. You will learn the econometric intuition behind each method. This will be accomplished by lectures and light econometrics readings. This is not a theory course so well will not be spending time deriving asymptotic properties of estimators or searching for UMP hypothesis tests. This is a course for end-users of econometric tools and you don’t need to know how to build the tool in order to use it. However, you will learn how to use each tool properly.
2. You will see these methods implemented by other researchers in published and working papers. I will rely on examples from corporate finance when possible, though I will also reference examples from other fields in economics including: labor, industrial organization, development, and public finance. Students will be required to read and discuss a paper that illustrates the empirical method in a (short) seminar-like setting. This exercise will not only reinforce your understanding of the material but it will also give you practice in presenting to an audience.
3. You will implement each method on live data – learning by doing. There will be a number of empirical exercises that will require you to manipulate and analyze data using the various econometric techniques. Since this is technically a finance course, the data and applications will typically be corporate finance related.

Executives

Since 2008, I have had over 140 engagements with companies and executives spanning most industries and functional areas. Topics covered include:

• Financial accounting and financial statement analysis
• Financial modeling
• Discounted cash flow analysis
• Sensitivity/Robustness analysis
• Optimal financial and payout policies
• Cost of capital estimation and management
• Risk management
• Personal finance
• Investment management
• Corporate valuation
• SaaS investing
• Mergers and acquisitions
• Leveraged buyouts
• Growth equity
• Capital raising (IPOs, SEOs, Bank capital)
• Leveraged lending
• Collateralized loan obligations and securitization
• Data science for finance
• Machine learning

Current Courses (Fall 2026)

• FNCE1008 - Corporate Finance (honors)

  This course provides an introduction to the theory, the methods, and the concerns of corporate finance. The concepts developed in FNCE 1000 form the foundation for all elective finance courses. The main topics include: 1) the time value of money and capital budgeting techniques; 2) uncertainty and the trade-off between risk and return; 3) security market efficiency; 4) optimal capital structure, and 5) dividend policy decisions. ACCT 1010 + STAT 1010 may be taken concurrently. Honors sections require MATH 1400 or MATH 1070 as a prerequisite. Application process.

  FNCE1008301 ( Syllabus )

• FNCE6110 - Corporate Finance

  This course serves as an introduction to business finance (corporate financial management and investments) for both non-majors and majors preparing for upper-level course work. The primary objective is to provide the framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. The approach is rigorous and analytical. Topics covered include discounted cash flow techniques; corporate capital budgeting and valuation; investment decisions under uncertainty; capital asset pricing; options; and market efficiency. The course will also analyze corporate financial policy, including capital structure, cost of capital, dividend policy, and related issues. Additional topics will differ according to individual instructors.

  FNCE6110005 ( Syllabus )

All Courses

• FNCE1000 - Corporate Finance

  This course provides an introduction to the theory, the methods, and the concerns of corporate finance. The concepts developed in FNCE 1000 form the foundation for all elective finance courses. The main topics include: 1) the time value of money and capital budgeting techniques; 2) uncertainty and the trade-off between risk and return; 3) security market efficiency; 4) optimal capital structure, and 5) dividend policy decisions. ACCT 1010 + STAT 1010 may be taken concurrently.

• FNCE1008 - Corporate Finance (Honors)

  This course provides an introduction to the theory, the methods, and the concerns of corporate finance. The concepts developed in FNCE 1000 form the foundation for all elective finance courses. The main topics include: 1) the time value of money and capital budgeting techniques; 2) uncertainty and the trade-off between risk and return; 3) security market efficiency; 4) optimal capital structure, and 5) dividend policy decisions. ACCT 1010 + STAT 1010 may be taken concurrently. Honors sections require MATH 1400 or MATH 1070 as a prerequisite. Application process.

• FNCE2370 - AI and Data Science in Finance

  This course will introduce students to data science for financial applications using the Python programming language and its ecosystem of packages (e.g., Dask, Matplotlib, Numpy, Numba, Pandas, SciPy, Scikit-Learn, StatsModels). To do so, students will investigate a variety of empirical questions from different areas within finance including: FinTech, investment management, corporate finance, corporate governance, venture capital, private equity, and entrepreneurial finance. The course will highlight how big data and data analytics shape the way finance is practiced. Some programming experience is helpful though knowledge of Python is not assumed.

• FNCE3990 - Independent Study

  Integrates the work of the various courses and familiarizes the student with the tools and techniques of research.

• FNCE6110 - Corporate Finance

  This course serves as an introduction to business finance (corporate financial management and investments) for both non-majors and majors preparing for upper-level course work. The primary objective is to provide the framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. The approach is rigorous and analytical. Topics covered include discounted cash flow techniques; corporate capital budgeting and valuation; investment decisions under uncertainty; capital asset pricing; options; and market efficiency. The course will also analyze corporate financial policy, including capital structure, cost of capital, dividend policy, and related issues. Additional topics will differ according to individual instructors.

• FNCE6210 - Corporate Finance (Half CU)

  This half-semester course serves as an introduction to corporate investments for non-majors. The primary objective is to provide a framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. Topics covered include discounted cash flow techniques, corporate capital budgeting and valuation, investment decisions under uncertainty, and capital asset pricing. The approach is rigorous and analytical but the course will not cover several topics included in the full semester Corporate Finance course, including: market efficiency, corporate financial policy (including capital structure, cost of capital, dividend policy, and related issues), and options.

• FNCE7070 - Valuation

  The focus of this course is on the valuation of companies. The course covers current conceptual and theoretical valuation frameworks and translates those frameworks into practical approaches for valuing companies. The relevant accounting topics and the appropriate finance theory are integrated to show how to implement the valuation frameworks discussed on a step-by-step basis. The course teaches how to develop the required information for valuing companies from financial statements and other information sources in a real-world setting. Topics covered in depth include discounted cash flow techniques and price multiples. In addition, the course covers other valuation techniques such as leveraged buyout analysis.

• FNCE8990 - Independent Study

  Independent Study Projects require extensive independent work and a considerable amount of writing. ISP in Finance are intended to give students the opportunity to study a particular topic in Finance in greater depth than is covered in the curriculum. The application for ISP's should outline a plan of study that requires at least as much work as a typical course in the Finance Department that meets twice a week. Applications for FNCE 8990 ISP's will not be accepted after the THIRD WEEK OF THE SEMESTER. ISP's must be supervised by a Standing Faculty member of the Finance Department.

• FNCE9260 - Empirical Meth Corp Fn

  The course will cover a variety of micro-econometric models and methods including panel data models, program evaluation methods e.g. difference in differences, matching techniques, regression discontinuity design, instrumental variables, duration models, structural estimation, simulated methods of moments. The structure of the course consists of lectures, student presentations, and empirical exercises. Published studies will be utilized in a variety of fields such as corporate finance, labor economics, and industrial organization to illustrate the various techniques. The goal of the course is to provide students with a working knowledge of various econometric techniques that they can apply in their own research. As such, the emphasis of the course is on applications, not theory. Students are required to have taken a graduate sequence in Econometrics, you should be comfortable with econometrics at the level of William Green's "Econometric Analysis of Cross-Section and Panel Data".

• Jensen Prize – First place paper on corporate finance and organizations published in the Journal of Financial Economics, 2016 Description

  Best paper on corporate finance and organizations published in the Journal of Financial Economics

• Helen Kardon Moss Anvil Teaching Award, Nominee, The Wharton School, 2015
• Core Curriculum Teaching Award, The Wharton School, 2015
• Marshall E. Blume Prize – First Place, 2014
• Greener Family Fellowship, The Wharton School, 2013
• Jacobs Levy Equity Management Center for Quantitative Financial Research Fellowship, The Wharton School, 2013
• Rising Stars of Finance, 2012
• Financial Research Associate, Best Discussant, 2012
• Helen Kardon Moss Anvil Teaching Award, Nominee, The Wharton School, 2012
• Geewax, Terker, and Co. Prize, Distinguished paper published by Rodney L. White Center, 2010
• Rodney L. White Center Research Grant, The Wharton School, 2009
• Brattle Prize, Nominee for Best Paper Published in the Journal of Finance, 2008
• Brattle Prize, Distinguished Award for Best Paper published in the Journal of Finance, 2008
• Excellence in Teaching Award, The Wharton School, 2008
• Greek Executive Council Honorable Recognition, University of Pennsylvania, 2007
• Aronson, Johnson, and Ortiz Research Fellowship, The Wharton School, 2007
• Rodney L. White Center Research Grant, The Wharton School, 2007
• Excellence in Teaching Award, The Wharton School, 2006
• David W. Hauk Award for Outstanding Teaching, The Wharton School, 2006
• Greek Executive Council Honorable Recognition, University of Pennsylvania, 2006
• Geewax, Terker, and Co. Prize, Distinguished paper published by Rodney L. White Center, 2006
• Brattle Prize, Distinguished Award for Best Paper published in the Journal of Finance, 2006
• Review of Financial Studies, Outstanding Referee Award, 2005
• NYSE Fellowship, The Wharton School, 2005
• Rodney L. White Center Research Grant, The Wharton School, 2005
• Rodney L. White Center Research Grant, The Wharton School, 2004
• Daimler-Chrysler Core Teaching Award, Duke University, 2004
• Excellence in Teaching Award, The Wharton School, 2004
• Southwestern Finance Association, Best Paper in Corporate Finance, 2004
• Financial Management Association Award, Best Paper in Corporate Finance, 2004
• CIBER International Research Grant, Duke University, 2003
• Hartman Center Grant, Duke University, 2003
• CIBER International Research Grant, Duke University, 2002
• Elizabeth Schott Award for Excellence in Statistical Research, University of California Berkeley, 2000
• UC Regents Fellowship, University of California Berkeley, 2000

Financial Proficiency

Empowering people to make better financial decisions

The Financial Proficiency Initiative (FPI) was started in 2021. It’s objective is to level the financial playing field by providing the best financial education at the lowest cost possible. Practically, this means high quality educational material and training resources for high schools and their communities.

Why an initiative?

Many societal challenges are related to financial proficiency.

• 2008 housing crisis
• Ongoing student debt crisis
• Retirement savings crisis
• Social mobility and income inequality

Further, a lack of financial proficiency disproportionately affects the most vulnerable who have neither the means nor recourse to overcome financial mistakes.

How is financial proficiency different from financial literacy?

The difference is perhaps best illustrated with an analogy. Swimming literate means you can identify a pool, a diving board, etc. But, throw someone who is swimming literate into a pool and they’ll drown. Swimming proficient means knowing how to swim.

Financial literacy is largely descriptive, telling people what financial concepts are and in some cases how they work. It is focused exclusively on personal finance as if finance used in business is somehow different. And, it teaches finance as if comprised of many disparate topics with little connection to one another.

Financial proficiency is decision-centric, focused on how people should make financial decisions in their personal and professional lives. It emphasizes a few, intuitive principles that can be applied to any financial challenge thereby easing the cognitive load and ensuring people are able to adapt to an ever changing financial environment.

How can I engage?

We work with companies, educational institutions, educators, and individuals. For more information please contact me via email (mrrobert@wharton.upenn.edu) to discuss engagement options.

Wharton Financial Analytics

The Wharton Financial Analytics initiative (WFA) initiative rests on three pillars: education, research, and practice.

It is difficult, if not impossible, to imagine a financial application in which data does not play a central role. Financial education must recognize and embrace this reality. WFA is re-imagining and modernizing financial education by placing data analytics at the center of all financial teaching. Students will develop fluency in working with and interpreting financial data for the purpose of making financial decisions in personal and profession settings. By integrating data and analytics with financial theory, students will develop a deeper understanding of financial principles and be better prepared to engage financial challenges.

Research is the engine providing answers to todays and tomorrow’s questions. WFA supports empirical research that expands the frontier of financial knowledge by answering those questions. Specifically, research support will be both financial and nonfinancial, the latter of which includes aiding scholars with data acquisition, the development of sharable data warehouses and codebases, the integration of research into the classroom, and the promotion of Wharton research.

Industry engagement, or “practice,” ensures the relevance of the educational and research initiatives, as well as providing a conduit for the transfer of knowledge between academia and industry. Engagement can take many forms including co-teaching, guest lectures, data sharing, collaboration on educational materials, event sponsorships and participation, student internships, and pro bono consulting.

WFA views the pillars not as independent functions, but as integrated solutions aimed at empowering people to make better financial decisions. It is the intersection of education, research, and industry engagement that will enable WFA to achieve its vision.

If you or your company are interested in WFA, please contact me via email (mrrobert@wharton.upenn.edu) to discuss engagement options.

Data Labs

Data labs are case-like exercises in which students are asked to solve busines problems using the scientific method, and data and analytics. In each lab, students execute a complete data science worklow including: data acquisition, ingestion and verification, data cleaning and manipulation, exploratory data analysis, statistical modeling/machine learning, and inference. Their deliverables consist of annotated Jupyter Notebooks clearly articulating the problem, hypotheses, empirical evidence, and plan of action supported by their analysis. Below are examples of data labs used in the Data Science for Finance course.

• Retirement Savings
• Stocks for the Long Run
• Riffled AI
• Crypto Investing
• Indigo Acquisition
• Diamonds
• Raterx
• Socially Irresponsible Investing

Student Projects

Part of the Data Science for Finance program is a captstone project in which students undertake an empirical investigation on a topic of their choosing. The choice of topics is unrestricted, determined entirely by students’ interests. At the end of the semester students are required to deliver all code, a 35-page writeup, and a slide deck that they present in class. Below are examples of slide decks presented in past classes.

• Airbnb Value Drivers
• Retail Investor Motivation
• EV Charging Networks
• Mobile Payments in Latin America
• Financial Viability of Super League Football
• Space Utilization at Wharton
• Airplane Ticket Prices
• Startup Success
• Mass Transit and Economic Performance
• Global Warning and the Wine Industry
• Cannabis and the Economy
• Success in F1
• Success in the Music Industry
• Predicting Flight Delays
• Mimicing Hedge Funds
• Global Happiness Determinants
• Who Will Win the 2022 World Cup?

• Wharton launches executive program to educate on fixed income and credit market investing, Daily Pennsylvanian - 02/21/2024
• The Share of Americans Who Are Mortgage-Free Is at an All-Time High, Bloomberg - 11/17/2023
• Apple stock buybacks are still in full swing, and that’s good news for Warren Buffett, Yahoo! Finance - 09/07/2023
• What if you don’t pay back student loans? TikTokers are wrong about the ‘ostrich’ method, USA Today - 09/24/2022
• How Wharton’s sold-out ‘data science for finance’ class is giving future bankers and hedge funders the tools they need to win on Wall Street, Business Insider - 03/21/2022

Knowledge at Wharton

• The Impact of AI on Financial Literacy | Michael Roberts, Knowledge at Wharton - 4/16/2024
• Is Making Extra Mortgage Payments Worthwhile? | Michael Roberts, Knowledge at Wharton - 1/9/2024
• Does Generative AI Solve the Financial Literacy Problem?, Knowledge at Wharton - 6/27/2023
• Should I Pay Off My Mortgage Early in This Economy?, Knowledge at Wharton - 5/22/2023
• Making Sense of Stock Buybacks, Knowledge at Wharton - 3/7/2023
• How Analytics Is Changing Finance, Knowledge at Wharton - 11/29/2022
• Why Erasing Student Debt Won’t Fix the Problem, Knowledge at Wharton - 6/21/2022
• Why Finance Should Be Mandatory for High School Students (and Their Parents), Knowledge at Wharton - 10/5/2021
• Shopping for a Mortgage? Consider the Trade-offs, Knowledge at Wharton - 4/5/2021
• Think Twice Before Paying Off Your Mortgage Early, Knowledge at Wharton - 3/15/2021

Wharton Stories

• New Wharton Academy Welcomes Undergrads from Around the World, Wharton Stories - 09/10/2024
• Empowering High School Students with the Essentials of Finance, Wharton Stories - 08/19/2022
• How Mentorship Enables the Transition from PhD Student to Research Colleague, Wharton Stories - 11/13/2014

Book

Financial Decision-Making

This book on financial decision making is a work in progress and distills the last 20+ years of teaching undergraduates, MBAs, executives, and, most recently, high school students. Most of the book is aimed at people who make financial decisions in their personal and professional lives, i.e., everyone. The last several chapters are more narrowly aimed at current and aspiring finance professionals. I’m posting the manuscript and some of the accompanying resources as I write/edit for anyone who is interested. Feedback via email is greatly appreciated (e.g., typos, comments, lack of clarity, repetition, errors).

Disclaimer. All materials contained on this website are provided for general information purposes only and do not constitute professional advice on any subject matter.

Manuscript

Financial Decision-Making

Chapter Spreadsheets

2. Retirement Savings and the Value of College

3. Financing Purchases

5. Project Viability

6. Project Selection

7. Bonds

8. Stocks

9. Portfolios

10. Cost of Capital

11. Financial Policy (Coming)

12. Corporate Valuation (Coming)

Problem Solutions (Instructors)

2. Retirement Savings and the Value of College

3. Financing Purchases

5. Project Viability

6. Project Selection

7. Bonds

8. Stocks

9. Portfolios

10. Cost of Capital

11. Financial Policy

12. Corporate Valuation

Teaching Notes (Instructors)

1. Finance framework

2.2. Cost of college

2.3a. Personal budgeting

2.3b. Retirement savings

2.4. Going to college

3.1. Interest rates

3.2. College payment plan

3.3a. Auto lease (McLaren)

3.3b. Auto lease (Porsche)

3.4a. Mortgage basics

3.4b. Mortgage refinancing

3.4c. Mortgage pay early

3.5. Paying cash for a car

3.7. Term structure

4.    What is a firm

5.1. Decision criteria

5.2. Free cash flow

5.3. Dell tablet debrief

6.1 Mutually exclusive projects

6.2. Project selection with constraints

6.3. Projects with different lives

11.1 Financing a home – returns