Finance Summer School

Course Overview and Objectives

This course aims to cover fundamental and advanced concepts in portfolio theory, focusing both on theoretical and practical aspects of the models. Topics will include return predictability, the effect of estimation error on portfolio performance, and various approaches to cope with estimation error and parameter uncertainty in portfolio construction, including recent applications of financial machine learning.

Course Contents

1. Modern Portfolio Theory. Mean-Variance optimization. Portfolio constraints
2. Time variation in returns. Present value formulas and return predictability
3. Machine learning algorithms for return predictability and portfolio selection
4. Factor structures and portfolio risk decomposition
5. Bayesian methods and Black-Litterman model for portfolio choice
6. Robust optimization, shrinkage portfolios and out-of-sample performance

Practical Classes

The course includes a series of practical sessions that will take place in the afternoon. Students will implement in Matlab and Excel the portfolio optimization techniques covered during the morning lectures. Practical exercises will include:

• Estimation and implementation of multi-factor models; portfolio optimization with constraints on factor exposures.
• Practical implementation of the Black-Littman model to international asset allocation.
• Out-of-sample performance: Comparison of plug-in, shrinkage, Bayesian, and machine learning mean-variance portfolios with and without portfolio constraints

About the Instructor

Francesco Sangiorgi is Associate Professor of Finance at the Frankfurt School of Finance & Management. He received his Ph.D. in Economics from Universitat Pompeu Fabra. His research interests span asset pricing, market microstructure, credit ratings, and the interplay between financial markets and corporate decision making. His publications have appeared in the Review of Economic Studies, the Review of Financial Studies, Management Science, and the Journal of Economic Theory. He has taught courses in financial economics, asset pricing and investment management in Master of Science, PhD and executive programs at the Stockholm School of Economics and the London School of Economics.

Course Structure

This is an introductory course in corporate finance with a focus on capital structure, liquidity, payout policy, and capital budgeting. It comprises a mix of lectures and case studies.

We first analyze the capital structure decision, accounting for the impact of taxes and other frictions to explain the observed differences in capital structure across firms and industries.

We then shift the attention to liquidity and payout policy and to how these policies are shaped by market imperfections, such as taxes, agency costs, transaction costs, and asymmetric information. We discuss why some firms pay dividends and some do not, and why some firms prefer share repurchases.

Finally, we discuss the various methods for computing the value of equity in a firm.

The course is intended for students with an undergraduate background in business or economics. The course includes a series of practical sessions that will take place in the afternoon. The practical sessions are dedicated to the solution of real business cases, which provide a direct application of the theory discussed in the morning. The business cases require calculations and modelling in Excel.

Course Contents

• Investments decisions
• Capital structure choices
• Leveraged acquisitions
• Leveraged recapitalizations
• Dividend and payout policy
• Liquidity management
• Techniques for valuing investments and firms

About the Instructor

Filippo Ippolito is Associate Professor of Financial Management at Universitat Pompeu Fabra and research affiliate at the Centre for Economic Policy Research (CEPR), London, and Director of the Master in Finance at the Barcelona Graduate School of Economics. Professor Ippolito holds a PhD in finance from Said Business School, Oxford, and an MPhil in Russian and Eastern European Studies from the University of Oxford. He has work experience in the financial and consulting sectors. His research focuses on corporate debt, capital structure, corporate liquidity management and private equity. Professor Ippolito has published in the Journal of Finance, Journal of Financial Economics, Journal of Financial Intermediation, Journal of Monetary Economics, Journal of Money, Credit and Banking, and Journal of Corporate Finance. 

Course Overview and Objectives

The course provides an introduction to the most common empirical methods used to address questions in Corporate Finance. This outline describes the econometric topics covered in the course. Each topic will then be illustrated with applications drawn from the broad corporate finance literature, which includes capital structure, corporate governance, banking, and law and finance. When necessary the course will draw from simulated data or from other subjects, such as for example labor economics, for illustration purposes.

The course is ideally suited for students who aim to pursue a career in central banking and in financial and regulatory institutions, and more generally for students who aim to pursue a career in financial consulting, investment banking, and corporate finance.

Requirements

Student need to have a good knowledge of econometrics. Jeffrey Wooldridge's Econometric Analysis of Cross Section and Panel Data is a good reference.

Course Outline

1. Introduction
   • The fundamental problem of causal inference in corporate finance
   • Potential outcome framework, randomized evaluations
   • Applications: Capital structure, corporate governance
2. Causality in a regression framework 1
   • Conditional Independence Assumption (CIA), selection into treatment
   • Heckman selection model
   • Application: Corporate bankruptcy
3. Causality in a regression framework 2: Differences-In-Differences (DID)
   • Before and after estimator v DID estimator
   • Parallel trend assumption
   • Application: Law and finance
4. Instrumental Variables (IV) as “Quasi-Experiments”
   • Angrist-Imbens-Rubin causal model and IV mechanics (constant effects case)
   • Local Average Treatment Effect (LATE) and its IV interpretation
   • Application: Family firms
5. Regression Discontinuity Designs (RDD)
   • Sharp RDD, Fuzzy RDD, Multidimensional RDD
   • Applications: Financial contracting, mergers and acquisitions
6. Selection on Observables and Matching
   • Selection on observables
   • Estimation of ATT with matching based on propensity score; sensitivity to CIA
   • Application: Diversification discount

Practical Classes

The course includes a series of practical sessions that will take place in the afternoon. These sessions will develop STATA codes to replicate some of the results from the articles discussed in the morning sessions. While doing so, the students will be exposed to practical issues that researchers encounter commonly in empirical corporate finance research.

The practical sessions are optional and are included in the price of the course. 

Every participant taking this course will receive a time-limited personal free license of STATA several days before the start of the Summer School.

ECTS and Grading

On request, students may take an exam to earn ECTS credits. The exam will take place during the afternoon session of the last day.

About the Instructor

Stefano Rossi is a Professor of Finance at Bocconi University where he holds the Generali Chair in Insurance and Risk Management. He is an IGIER Fellow, a Research Affiliate of the Centre for Economic Policy Research (CEPR), a Research Associate of the European Corporate Governance Institute (ECGI), an Executive Editor of the Journal of Law, Finance, and Accounting, and an Associate Editor of European Financial Management. Stefano holds a PhD from London Business School and an MSc and BA from Bocconi University. Stefano’s research interests include corporate governance, bankruptcy and financial distress, debt financing, international finance, sovereign borrowing, financing of innovations, and quantitative trading. His research has been published or is to appear in top peer-reviewed journals such as The Journal of Finance, The Journal of Financial Economics, The Review of Financial Studies, The Journal of Monetary Economics and The Review of Finance and has been featured in publications such as The Financial Times and The Economist. Stefano's research on the evolution of ownership (with J. Franks and C. Mayer) was awarded two international prizes for Best Paper by the European Corporate Governance Institute.

Course Objectives

The course provides a comprehensive introduction to asset pricing models, with a special focus on their implementation and empirical testing.

Course Outline

• Introduction
• Preferences and portfolio choice
• Mean-Variance Analysis
• Efficient Portfolios
• Asset pricing in equilibrium
• The CAPM
• Applications
• The CAPM in practice
• Empirical evidence
• Testing the CAPM
• Anomalies
• The Arbitrage Pricing Theory
• Arbitrage
• The Law of One Price
• The APT
• Multifactor models in practice
• Factor Analysis
• Macroeconomic variables as factors
• Fundamentals

Practical Classes

The course includes a series of practical sessions that will take place in the afternoon. In these sessions students will be exposed to practical implementation of asset pricing problems in Matlab and Stata. Every participant taking this course will receive a life-time personal free license of MATLAB several days before the start of the Summer School.

ECTS and Grading

On request, students may take an exam to earn ECTS credits. The exam will take place during the afternoon session of the last day.

About the Instructor

Javier Gil-Bazo is Associate Professor of Finance at Universitat Pompeu Fabra and Affiliated Professor of the Barcelona GSE. His research deals with the study of institutional investors and asset pricing modelling. His work has been published in academic journals such as Journal of Finance, Journal of Banking and Finance, Quantitative Finance,  Journal of Financial Econometrics, Journal of Business Finance and Accounting, Journal of Economic Behavior and Organization, and Economics Letters. His work has been awarded with the Best Paper Award at the European Conference of the Financial Management Association, the Honorable Mention of the Moskowitz Prize for outstanding research in socially responsible investing and the BME Best Derivatives Paper Award at the Annual Meeting of the Spanish Finance Association.

Course Objectives

The purpose of this course is to introduce the machinery of Itô calculus and show how it can be applied to solve the problem of pricing and hedging financial derivatives such as options and interest rate derivatives under the Black-Scholes model.

The course is designed for a mix audience of masters and graduate students, and professionals who want to learn the theory and practice of pricing and hedging financial derivatives. A basic understanding of calculus and probability theory is necessary.

Course Outline

1. Risk-neutral valuation and arbitrage
2. Option pricing with trees, binominal model
3. Itô’s stochastic calculus
4. Pricing under Black-Scholes model
5. Implied volatility and Greeks
6. Interest rate derivatives

Required Activities

*In order to participate in practical sessions, you must bring your own portable computer.

The course includes a series of practical sessions that will take place in the afternoon. During these sessions, students will implement in Matlab and Excel the pricing techniques covered during the morning lectures.

The practical sessions are optional and are included in the price of the course.

Every participant taking this course will receive a life-time personal free license of MATLAB several days before the start of the Summer School.

List of References

• Joshi, M.J. The Concepts and Practice of Mathematical Finance Second Edition Cambridge University Press, 2008.
• Paul Wilmott introduces quantitative finance, Wiley, second edition, 2007

About the Instructor

Eulàlia Nualart is Associate Professor at UPF and a Barcelona GSE Affiliated Professor. Before this she had a permanent research and teaching position at the Department of Mathematics of the University of Paris 13, after doing a PostDoc at the University of Paris 6, with a research fellowship from the National Swiss Foundation. She earned her PhD in Probability from the École Polytechnique Fédérale de Lausanne in 2002. She broadly works in the field of stochastic analysis and its applications to stochastic differential equations, stochastic partial differential equations, and mathematical finance.