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 purpose of this course is to cover fundamental and advanced concepts in corporate finance, including investment decisions, capital structure choices, leveraged acquisitions and recapitalizations, dividend and payout policy, cash holdings, short term liquidity management and techniques for valuing investments. The course relies largely on case studies and is focussed mainly on applications rather than theory. The course is intended for students with an undegraduate background in accounting, asset pricing, and corporate finance. The course includes a series of practical sessions that will take place in the afternoon.

Course Contents

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

ECTS and Grading

On request, students may ask to be graded to earn ECTS credits. Grading will be based on the presentation of business case studies discussed in class. 

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 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. We will  also discuss how the theory extends to the pricing of exotic and American options
 
The financial crisis of 2007 has forced to add additional terms to the classical pricing due to the credit risk, which arises from the possibility that borrowers and counterparties in derivatives transactions may default. We will end the course discussing some approaches to compute default probabilities.
  
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 Contents

1. Risk-neutral valuation and arbitrage
2. Option pricing with trees
3. Itô’s Lemma
4. Pricing under Black-Scholes model
5. Greeks and Delta Gamma hedging
6. Pricing exotic and American options
7. Interest rate derivatives
8. Default probabilities

Practical Classes

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. 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

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.

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 cover fundamental and advanced concepts in portfolio theory, focusing both on theoretical and practical aspects of the models. Topics will include optimal portfolio choice with return predictability, the effects of estimation errors on portfolio performance, and various approaches that have developed 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

• 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
• Comparison of out-of-sample performance of plug-in, shrinkage, Bayesian, and machine learning mean-variance portfolios with and without portfolio constraints

ECTS and Grading

On request, students may take an exam to earn ECTS credits. The grade will be assigned based on a problem set due one week after completion of the course. The problem set includes practical implementations in Matlab of the portfolio models covered in the course.

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.

Introduction

Financial Markets are no longer as they used to be. Trading floors have been largely replaced by exchange servers, human traders by computer algorithms, and traditional designated liquidity providers by proprietary high-frequency market makers. The services sell-side intermediaries provided not so long ago are nowadays largely coded into computer algorithms operating at super-human speed. New trading venues that compete with regulated exchanges have also emerged. Some of them operate as centralized and transparent limit order markets, but others take the form of opaque crossing networks or dark pools. As a result, financial markets are nowadays more fragmented and less transparent. In this context, new types of computerized trading have raised: algorithmic trading (AT) and, more importantly, high-frequency trading (HFT). AT refers to the use of computers to automatically make trade decisions. HFT are proprietary ATs (they code their own algorithms to trade on their own account) whose trading strategies rely on speed. Their algorithms optimally design, route, monitor, execute, and cancel thousands of orders at extraordinary high speed. Nowadays, HFTs account for most of the message traffic and participate in half of the trades.

In this course, we will provide both theoretical and (primarily) empirical background on liquidity supply and price discovery in electronic markets, paying particular attention to the role HFTs play. There is a fierce debate in the industry, among regulators, in the media, and in the academic literature about whether this new type of trader/intermediary benefits or hurts market quality. We will provide an overview of the academic literature on HFT and its conclusions thus far about the pros and cons of these new high-speed players. What is HFT? What do HFTs do? What is the impact of HFTs on liquidity and price discovery? Is HFT inherently good or bad for financial markets?

Course Contents

Day I: Liquidity in electronic markets
What is liquidity and how to measure it? Who provides and who takes liquidity in electronic markets? What is the limit order book? What are the risks and costs of providing liquidity? How do liquidity providers manage that risk?

Day II: Order flow and price discovery
How prices are formed at high frequencies? Are prices mostly information-driven or noise-driven? How important noise is what drives it? Does trading contribute to price discovery? What is the link between price discovery and liquidity?

Day III: Fragmentation and dark trading
What do we mean by market transparency? What is off-exchange trading and what makes it opaque? What is a dark pool? How do these platforms operate? Are “lit” markets fully transparent? Is fragmentation of markets good or bad for market quality and price discovery? How do trading platforms compete with each other?

Day IV: HFT
What is algorithmic trading (AT)? What is high-frequency trading (HFT)? How important HFTs are nowadays? What do HFTs do (types of HFTs)? What is latency? What is colocation?

Day V: HFT and market quality
Do HFTs contribute to liquidity supply? When and how? Are they superior market makers? How do they manage market making risks? How do they profit from market making? Do the HFTs trades and orders convey information? Do they contribute to price discovery? What is the impact of HFT on price efficiency?

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 tasks involving high-frequency data (trades, orders, limit order book). Simple tasks would be solved in Excel (with a limited amount of data), and more demanding tasks would require some programming (Matlab).

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. The exam will consist of general questions covering the basic contents of the course.

About the Instructor

Roberto Pascual (Ph.D. in Economics, Universidad Carlos III de Madrid) is Associate Professor of Finance at the Business Department of the University of the Balearic Islands (UIB). His main area of expertise is Empirical Market Microstructure. His research covers topics such as high-frequency trading, circuit breakers, liquidity provision in order-driven markets, market-making costs, price formation at high frequencies, and limit order book dynamics. He has held visiting research positions at the Stern School of Business (New York University), European Center for Advanced Research in Econometrics and Statistics (Free University of Brussels,) International Center for Finance of the Yale Business School (Yale University), and Auckland Centre for Financial Research (Auckland University of Technology).

Course Overview and Objectives

The purpose of this course is to cover fundamental and advanced investing strategies in fixed income markets. Students will be given the opportunity to familiarize themselves with fixed income securities ahead of the course, and grasp key concepts such as yield to maturity, duration and the tracking error in a bond portfolio. Since bonds encapsulate three types of main risk, being liquidity, interest rate and credit risk, we spend considerable time on estimating and modelling these risks. Topics for bond investing include factor investing in corporate credits and creating dynamic portfolio strategies.

The course is designed for a mix audience of masters and graduate students, and professionals who want to expand their knowledge of investing in fixed income markets: students or professionals with a background in financial economics and moderate quantitative skills who want to pursue a career or are currently working with buy-side financial institutions, central banks or market oversight of regulators, and portfolio managers in fixed income who want to deepen their understanding in their subject field and learn about the latest academic research.

Course Contents

1. Nuts and bolts of fixed income securities
2. Liquidity in the bond markets: ETFs and liquidity measures
3. Estimating and modelling interest rate risk
4. Estimating and modelling credit risk
5. Factor investing in the corporate bond market
6. Active portfolio strategies

Requirements

The course draws from the Bond Markets, Analysis and Strategies book (8th edition) and the Advanced Bond Portfolio Management, both by Frank J. Fabozzi. Students are not required to obtain those books in order to follow the course, but can do so to deepen their understanding of the taught material.

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 investment problems in Matlab. 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.

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

Mary Pieterse-Bloem is Endowed Professor Financial Markets at the Erasmus School of Economics in Rotterdam. She obtained her Ph.D. in Finance from Tilburg University and her Masters in Economics from the London School of Economics. Her research interests span optimal asset allocation, financial market integration, factor investing, market microstructure, and the interplay between financial markets and investment decisions. Her publications have appeared in the Journal of Empirical Finance, Journal of International Money and Finance and the International Review of Economics and Finance. She teaches a course on fixed income in the Masters of Financial Economics at ESE. Mary combines her academic work with a career in the financial sector, where she is currently the Global Head of Fixed Income for ABN AMRO private bank. Prior to that she worked for APG AM, NNIP, Lehman Brothers and BNP Paribas.