Research in Finance - 104 - 2nd year of master's degree

LEVELLING COURSES - Select 2 courses

• Python for finance (Bloc 1/3 of the Certificate "Fundamentals of Data Science")

  Python for finance (Bloc 1/3 of the Certificate "Fundamentals of Data Science")

  Lecturer :
  HOUCINE SENOUSSI
  

  Total hours : 18

  Overview :

  Our aim in this course is to implement some key concepts in quantitative finance using popular Python packages such as :

  • NumPy : the fundamental package for scientific computing.
  • Matplotlib : the main 2D plotting library.
  • SciPy : another scientific computing library containing toolboxes dedicated to optimization, statistics and many other fields.
  • Pandas : A library offering powerful data structures and tools for data analysis.

  The data we will use is extracted from free online sources (Google, Yahoo, ...). The main parts of the course are the following :

  1. Python basics : Data types, data structures, programs structure and packages.

  2. Numpy, Matplotlib : discovering these packages with application to Monte Carlo simulation (look at the potential evolution of asset prices over time/Random walk).

  3. Scipy : Introduction and application to a regression analysis of stock prices.

  4. Pandas and Matplotlib. Introduction and Application (I) : importing, visualizing and analysing Time series financial data.

  5. Pandas and Matplotlib. Advanced aspects and application (II) : Volatility calculation, Algorithmic trading, Creating, testing and improving a trading strategy.

  Coefficient : 1

  Require prerequisites :

  Basics of algorithmics.

  Learning outcomes :

  Mastering the structure of the Python language, a good knowledge of the most important libraries for financial applications (Numpy, Matlplotlib, Scipy, Pandas).

  Assessment :

  Several programming assignments (one for each class).

• Monte Carlo Simulations in finance - MathLab

  Monte Carlo Simulations in finance - MathLab

  Lecturer :
  IRINA KORTCHEMSKI
  

  Total hours : 21

  Overview :

  Lecture 1 and 2: Introduction to MATLAB. Tutorial with numerical optimization of Rosenbrock’s function and simulation of the Brownien Motion. Markowitz portfolio optimization.

   

  Lecture 3: Binomial options pricing model. European, American, Butterfly and Barrier Knock - Out options. Simulation of a Binomial tree and assets trajectories.

   

  Lecture 4: Black and Scholes Model. Monte-Carlo method for option valuation. European option. Correlated Brownian motions. Basket et Exchange options.

   

  Lecture 5: Black and Scholes Model. Strongly Path-dependent options. Asian option. Lookback and Choosers. Stochastic volatility models. Euler-Maruyama approximation of Stochastic Differential Equations. Option and asset pricing in the Heston model.

   

  Lecture 6 and 7: Merton Model. Poisson distribution. Simulation of assets trajectories with jumps. Option pricing in the Merton model.

  Coefficient : 1

  Recommended prerequisites :

  The notions of stochastic calculus, Black and Scholes models, Ito's formula.

  Learning outcomes :

  The students will learn important principles of implementation of financial models and master algorithms of evaluation of different types of derivative securities: European, American, standard, barrier and path dependent options on stocks.This course gives a comprehensive introduction to Monte Carlo and finite difference methods for pricing financial derivatives. At the end of the course, the student should have a thorough understanding of the theory behind Monte Carlo methods, be able to implement them for a range of applications, and have an appreciation of some of the current research areas.

  Assessment :

  Control of Knowledge: Defense of a Project.

  Bibliography-recommended reading

  Reading List: 1) S E Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, Springer 2004. 2) P Glasserman, Monte Carlo Methods in Financial Engineering, Springer-Verlag, 2004. 3) P Wilmott, S D Howison and J Dewynne, Mathematics of Financial Derivatives, CUP, 1995.

• Financial Econometrics I

  Financial Econometrics I

  Ects : 3

  Lecturer :
  GAELLE LE FOL
  

  Total hours : 24

  Overview :

  This course is an introduction and/or refresher course in Econometrics that focuses on techniques for estimating regression models, on problems commonly encountered in estimating such models, and on interpreting the estimates. The goal is to provide participants with the basic skills and knowledge necessary to undertake empirical research and to prepare them to the advanced course in Econometrics of Financial Markets. If Gretl will be the econometric software used in the course, it is possible to use R. Course outline

  • How to build an econometric model and how to use it?
  • The (simple and multiple) linear regression model
  • Inference, hypothesis testing and prediction
  • Specification and diagnostic testing (heteroskedasticity, autocorrelation, model specification)
  • Selection criteria
  • Alternative to OLS (2SLS, ML, GLS, Quantile regression)

  Coefficient : Coefficient 1.5 : M1 Financial Markets

  Recommended prerequisites :

  First course in programming

  Require prerequisites :

  Mathematics and Statistics (bachelor level)

  Learning outcomes :

  Theoretical and practical knowledge of linear regression models estimation technics. Being able to set up an econometric analysis.

  Bibliography-recommended reading

  • Adkins L. C., Using gretl for Principles of Econometrics, Version 1.041, August 2018, Free copy;
  • Brooks C., Introductory Econometrics for Finance, Second Edition, Cambridge University Press, 2014 ;
  • Gelman A., J. Hill and A. Vehtari, 2021, Regression and Other Stories, 1st Edition, Cambridge University Press, 2021;
  • Gujarati D., Basic Econometrics, McGraw Hill Higher Education; 5th Revised edition edition, 2009 ;
  • Hill C., W. Griffiths and G. Lim, Principles of Econometrics, Wiley, 5th Edition, 2018 ;
• Introduction to corporate finance

  Introduction to corporate finance

  Lecturer :
  OLIVIER LEVYNE
  

  Total hours : 21

  Overview :

  Context

  This course is dedicated to students who have not studied the financial structure of the firm and practiced corporate finance. In that context, it presents the central place of valuation in finance and the usefulness of financial theory to deal with it properly. It also proposes an introduction to the Black-Scholes-Merton pricing model and to LBOs Corresponding corporate finance principle are evidenced and illustrated by real examples.

   

  Table of contents

  1. The place of valuation in finance
  2. Peers approaches
     1. Market cap and enterprise value
     2. Listed peers approach
     3. M&A peers approach
  3. DCF
     1. Principle
     2. Terminal value
     3. Discount rate
  4. Focus on financial structure
     1. Traditional approach
     2. Modigliani & Miller approach without tax
     3. Modigliani & Miller approach with corporate tax
     4. Miller approach with personal tax
  5. Usefulness of the Modigliani and Miller approach for valuation
     1. Adjusted cost of capital
     2. Unlevered/re-levered beta: the Hamada formula
     3. Introduction to the trade-off theory
  6. Option pricing models and corporate finance
     1. Black & Scholes formula
     2. Usefulness to value equity and debt
     3. Probability of bankruptcy
  7. Conclusion
     1. Holdings and conglomerates: risk, return and valuation
     2. Introduction to LBOs

  Coefficient : 1

  Assessment :

  Test after the last course (2 hours)

• DIGITAL Finance

  DIGITAL Finance

  Lecturer :
  RAGHAVENDRA RAU
  

  Total hours : 24

  Overview :

  Over the last decade, the speed of technological change has accelerated. New technologies appear and disappear every few months. Exotic new technologies – decentralized finance, cryptocurrencies, blockchains, smart contracts, AI - and intermediaries – P2P platforms, retail platforms - are being introduced (and in some cases, disappearing) rapidly. In the last year, workforces all over the world have had to deal rapidly with working remotely vs. working in an office. How should we make sense of all these changes? How can we adapt to a rapidly changing world? How should we make sense of all these changes? How can we adapt to a rapidly changing world? The biggest change underlying all these technologies is how it affects our ability to coordinate. This course demonstrates how technology is changing the way we coordinate. It documents how boundaries between firms and markets are shifting, disrupting entire industries in the process. We will discuss how firms and employees need to adapt, how they can avoid being disrupted, and what new technologies and innovations are on the horizon

  Session 1. Analyzing information

   

  Topic:

  Understanding information flows in firms and markets

  ? Imperfect information, asymmetric information, and behavioral biases ? Digitalization and information flows within firms and markets ? Capturing multi-dimensional preferences and data from stakeholders ? Deciding between firms and markets

  Session 2- Distributed Ledgers, Blockchain, and P2P

  ? What is bitcoin? ? What is a distributed ledger? When is it useful? ? When are blockchains useful? ? Mining blocks in class ? Understanding smart contracts ? The problems with poor smart contracting

  Session 3- Robo-advising, AI, and Machine Learning

  Session: AI and Machine Learning ? How are AI systems created? ? Do AI systems help understanding employees a nd customers better? ? Can deep learning help detect fraud? ? The problems of AI systems ? The dark side of technology

  Coefficient : 1

  Learning outcomes :

  Topics covered

   

  · The coordination problem: firms or markets?

  · The types of information problems: Imperfect information, asymmetric information, and behavioral biases

  · How does digitalization solve the imperfect information problem: Examining information flows in markets and firms

  · How does digitalization solve the asymmetric information problem?

  · Mechanisms to induce trust through technology

  · Mechanisms to bypass the need for trust: Distributed ledgers: Bitcoin, Blockchain and beyond

  · How does digitalization help in dealing with behavioral biases?

  · AI and machine learning

  · Taking advantage of behavioral biases for manipulation

  · Data privacy

FUNDAMENTAL COURSES - Select 4 courses for 24 ECTS (Option 1) or 5 courses for 30 ECTS (Option 2)

• Game theory

  Game theory

  Ects : 6

  Lecturer :
  DAVID ETTINGER
  

  Total hours : 36

  Overview :

  Chapter 1: Normal form games: pure and mixed strategy Nash equilibrium; weakly/strictly dominated strategies , iterated elimination of dominated strategies.

   

  Chapter 2: Dynamic games: Backward induction, subgame perfect Nash equilibrium, repeated games.

   

  Chapter 3: Incomplete information (in static games): Bayesian Nash equilibrium; introduction to some applications (auctions, finance...)

  Coefficient : 1

  Require prerequisites :

  The student must be at ease with some basic mathematical notions such as: derivations, first-order conditions...

  Learning outcomes :

  The objective of the course is to give some fundamental background in interactive decision making and its applications. After having attended the classes, the students will be able to understand the basic tools of game theory and the importance of this field in economics and finance.

  Assessment :

  A mid-term exam and a final exam

• Term structures : theory, models and empirical tests

  Term structures : theory, models and empirical tests

  Ects : 6

  Lecturer :
  DELPHINE LAUTIER
  

  Total hours : 30

  Overview :

  The term structure is defined as the relationship between the spot price and the futures prices of a derivative instrument, for any delivery date. It provides useful information for hedging, arbitrage, investment and evaluation: it indeed synthesizes the information available in the market and the operators’ expectations concerning the future price of the underlying asset.

  In many derivative markets, especially in interest rates and in commodity markets, the concept of term structure is very important, because the contract’s maturity increases as the markets come to fruition. In the Eurodollar market, for example the maturities reach 10 years.

  Chapter 1 presents a general introduction to derivatives today.

  Chapter 2 examines the traditional theories of commodity prices and the explanation of the relationships between spot and futures prices. It proposes an empirical review of the results obtained through these frameworks and explains why these theories are still investigated today. It finally shows how to apply these theories to other assets: exchange rates and interest rates.

  The traditional theories are however a bit limited when the whole term structure is considered. As a result, there is a need for a long-term extension of the analysis, which is the very subject of the Chapter 3. We first present a dynamic analysis of the term structure. Then the focus turns towards term structure models. The examples rely on the case commodity prices but can be extended to interest rates. Simulations highlight the influence of the assumptions concerning the stochastic process retained for the state variables and the number of state variables. We then explain the econometric method usually employed for the estimation of the parameters. In the presence of non-observable variables, there is a need for filtering techniques. We present the method of the Kalman filters. Finally, we study two main applications, i.e. dynamic hedging and investment valuation.

  Chapter 4 is devoted to the study of structural models, ie micro-founded equilibrium models that also examine the interactions between the physical and the derivative markets. In this situation the spot price becomes endogenous. The interactions between prices are studied thanks to rational expectations equilibriums.

  Coefficient : 1

  Recommended prerequisites :

  Students who choose this course must also attend the course “Finance in continuous time”

  Learning outcomes :

  At the end of this course, the students must have a broad knowledge about the term structures of derivative prices: the theories, the valuation methods, the econometric techniques, the empirical tests as well as the applications.

  They will also be trained to use their knowledge on this topic in order to develop a critical view on recent research articles.

  This course is mandatory for all students enrolled in the cursus PhD Qualifying Year. It is optional for all other students of the M2 104.

  Assessment :

  Ongoing assessment, 20% One final exam, 80%.

  Bibliography-recommended reading

  - Danthine J.P., Donaldson J.B., Intermediate Financial Theory, 2d Ed., Elsevier, 2005. - Hull J., Options, futures and other derivatives, 9th Ed. - Kolb R.W. , Overdahl J.A. , Futures, options, and swaps, 5th Ed., Blackwell, 2007. - Williams J., The economic function of futures markets, Cambridge University Press, 1986 - Wilmott P., Paul Wilmott on Quantitative Finance, 3-volume set, 2nd Ed., Wiley, 2006.

  Adresse du site de l'enseignant : https://sites.google.com/site/delphinelautierpageweb/

• Finance in continuous time (mandatory course, unless validated previously)

  Finance in continuous time (mandatory course, unless validated previously)

  Ects : 6

  Lecturer :
  RENE AID
  

  Total hours : 30

  Overview :

  Asset pricing, contingent claim, stochastic process, brownian motion, Itô's formula, optimal stopping time. This course is an introduction to "Derivative pricing and stochastic calculus II". It introduces the standard concepts and tools allowing to understand arbitrage theory in continuous-time. The requirements from probability theory are made as basic as possible to make the lectures accessible to studends without a strong background in applied mathematics.

  Coefficient : 1 (Master Finance) 3ECTS - Coefficient 1 (M2 Quantitative Economics)

  Learning outcomes :

  In the end of this course, the students must be comfortable with:

  i) Basic concepts of contingent claims,

  ii) the binomial model;

  iii) stochastic integrals and Itôs calculus;

  iv) the Black and Scholes model,

  v) Merton's optimal porfolio problem.

  Bibliography-recommended reading

  Steven Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, 2005.

  Steven Shreve, Stochastic Calculus for Finance II: Continuous-Time Models , 2005.

• Derivative Pricing and Stochastic calculus II (prerequisite: finance in continuous time)

  Derivative Pricing and Stochastic calculus II (prerequisite: finance in continuous time)

  Ects : 6

  Lecturer :
  JULIEN CLAISSE
  

  Total hours : 24

  Overview :

  The aim of this lecture is to present the theory of derivative asset pricing as well as the main models and techniques used in practice. The lecture starts with discrete time models which can be viewed as a proxy for continuous settings. We then develop on the theory of continuous time models. We start with a general Itô-type framework and then specialize to different situations: Markovian models, constant volatility models, local and stochastic volatility models. For each of them, we discuss their calibration, and the valuation and the hedging of different types of options (plain Vanilla and barrier options, American options, options on realized variance,...).

  Course outline:

  I. Discrete time modelling I.1. Financial assets I.2. The absence of arbitrage I.3. Pricing and hedging of European options I.4. Pricing and hedging of American options

  II. Continuous time modelling II.1. Financial assets as Itô processes II.2. The Black-Scholes model II.3. Markovian models in complete markets II.4. Local volatility models II.5. Stochastic volatility models

  • General setting
  • Tree markets
  • Risk-neutral measures
  • Fundamental theorem of asset pricing
  • The super-hedging problem
  • The complete market case : example of the CRR model
  • Approximate hedging in incomplete markets
  • Examples: binomial and trinomial tree markets
  • The Itô process framework
  • Discussion of the Absence of arbitrage opportunity
  • Complete and incomplete markets
  • The general pricing and hedging principle for European and American claims
  • Characterization of complete Black Scholes markets
  • Explicit formulas : European call option (Black-Scholes formula), barrier option (reflection principle)
  • PDE valuation (plain vanilla, barrier, Asian, American options
  • Greeks and hedging
  • Tracking error and convexity
  • Dupire’s formula and calibration to the volatility surface
  • Su per hedging prices
  • Completion of the market with options : general principle, Approximate static hedging: example of the variance swap hedging problem
  • Specific models : CEV, Heston, SABR,...

  Coefficient : Coefficient 1 (M2 Research in Finance) Coefficient 3 (M2 Financials Markets)

  Require prerequisites :

  Students must have past Financial Derivatives and Derivative Pricing& Stochastic Calculus 1.

  Learning outcomes :

  The lecture starts with discrete time models which can be viewed as a proxy for continuous settings, and for which we present in detail the theory of arbitrage pricing. We then develop on the theory of continuous time models. We start with a general Itô-type framework and then specialize to different situations: Markovian models, local and stochastic volatility models. For each of them, we discuss the valuation and the hedging of different types of options : plain Vanilla and barrier options, American options, options on realized variance, etc. Finally, we present several specific volatility models (Heston, CEV, SABR,...) and discuss their specificities.

  Assessment :

  Final exam

  Bibliography-recommended reading

  Bouchard B. et Chassagneux J.F., Fundamentals and advanced Techniques in derivatives hedging, Springer, 2016. Lamberton D. et B. Lapeyre, Introduction au calcul stochastique appliqué à la finance, Ellipses, Paris, 1999.

• Corporate finance (prerequisite: introduction to corporate finance)

  Corporate finance (prerequisite: introduction to corporate finance)

  Ects : 6

  Lecturer :
  EDITH GINGLINGER
  LUC RENNEBOOG
  

  Total hours : 30

  Overview :

  Part 1. Prof. Laurent Frésard (mailto:Laurent.fresard@usi.chLaurent.fresard@usi.ch)

   

  Course Objectives

  The objective of this part of the “ Corporate Finance ” course is to introduce you to key topics in corporate finance through the lens of empirical research. Corporate finance is largely a non-experimental field with lots of data. The nature, scope, and detail of available data continue to expand rapidly. These data are used to generate empirical insights to validate or invalidate existing theories and constitute a basis for further theories. In this class, we will discover central topics and mechanisms in corporate finance by focusing on how researchers have used data and empirical methods to develop novel knowledge that is relevant for the practice of finance. The overall approach in this class is to read and understand (selected) prior empirical work and replicate or extend some of these studies. The topics have been selected to make you work with specific datasets and methods. The primary expertise necessary is the understanding of how to use or manipulate datasets. You will need to appreciate the methods, approaches, and intuition of econometrics including and beyond a first graduate level of econometrics. I will cover some of the underlying approaches in class but our objectives will be different from those of an econometric course. Rather than a formal derivation of the underlying assumptions and tests, we will assess why something works the way it does.

   

  Deliverables - Empirical exercises

  You will have three exercise sets and a mini project to hand in. They are designed to get you up and running with financial datasets and empirical methods. There is a lot of work going into extracting databases and matching datasets. You should treat this as a permanent lifelong investment and the costs will seem more bearable. You will have to extract data from the relevant source, run the assigned tests, and answer to question I will specify. You will write a short report for each assignment, explaining all your steps and interpreting your results.

   

  Course outline and Readings

  All chapters and articles marked with an * should be carefully read in advance. As we will discuss these papers in class, not reading makes your attendance almost useless. I will ask questions related to these articles in class.

   

  Reading list

  for part 1.

  • Selected chapters from the Handbook of Corporate Finance: Empirical Corporate Finance. Edited by B. Espen Eckbo: North Holland, 2007. (HCF)
  • Cameron, A. Colin, and Pravin Trivedi, 2009, Microeconometrics: Methods and Applications, ISBN-13 #: 978-0-521-84805-3. Published by Cambridge University Press. (CT#1)
  • Cameron, A. Colin, and Pravin Trivedi, 2009, Microeconometrics Using STATA, ISBN-13 #: 978-1-59718-048-1. Published by STATA Press. (CT#2)
  • Angrist, D. Joshua, and Jorn-Steffen Pischke, 2009, Mostly Harmless Econometrics: An Empiricist ’ s companion. ISBN-978-0-691*12035-5. Princeton University Press. (AP)
  • Scott Cunningham, 2021, Causal Inference: The Mixed Tape, ISBN-978-0300251685. Yale University Press. Free online version at: https://mixtape.scunning.com/. (CI)

  COURSE

  Identification and Causality

  • AP, chapter 2
  • CI, chapter 4
  • Roberts and Whited (2012), section 2
  • Bowen, Frésard, and Taillard (2017)*
  • Morck and Yeung (2011)
  • Leamer (2010)
  • Ruhm (2018)
  • Ravallion (2020)
  • Ackerlof (2020)*

  Event studies

  • HCF, chapter 1
  • Fama, Fisher, Jensen, and Roll (1969)
  • Kolari and Pynnonen (2010)
  • Khotari and Warner (1997)
  • Thomson (1995)
  • Ahern and Dittmar (2012)*
  • Kogan, Papanikolaou, Seru, and Stoffman (2017)

  Instrumental Variables

  • CT#1, chapter 4
  • CT#2, chapters 6 and 9.2
  • AP, chapter 4
  • CI, chaper 7
  • Roberts and Whited (2012), section 3
  • Angrist and Krueger (2001)
  • Bennedsen, Nielsen, Perez-Gonzalez, and Wolfenzon (2007)**
  • Chaney, Sraer, and Thesmar (2013)**

  Difference-in-Differences

  • AP, chapter 5, Section 2
  • CI, chapter 9
  • Bertrand, Duflo, and Mulainathan (2004)
  • Giroud (2013)**
  • Roberts and Whited (2012), section 4
  • Leary (2009)

  Regression Discontinuity Design

  • *Roberts and Whited (2012), section 5
  • *Malenko and Shen (2016)

  Textual Analysis

  • Gentzkow, Kelly, and Taddy (2019)*
  • Frésard, Hoberg, and Phillips (2020)*
  • Bowen, Frésard, and Hoberg (2021)*
  • Hoberg and Phillips (2010)*
  • Hoberg and Maksimovic (2014)

  Part 2. Luc Renneboog

   

  Part 2, Topic 1. Corporate Social Responsibility and ESG

  We will deal with the following questions:

  • Why do we see such diversity in CSR levels within and across countries?
  • What are the foundations of CSR: legal systems, social preferences, … .
  • Are firms that adopt a CSR policy well governed firms or firms that are prone to agency problems? Does CSR create value?
  • What is the relation between culture and CSR adoption?
  • Does CSR activism generate higher returns?
  • Do state-owned corporations impose higher CSR standards or not? And what is the implication for firm value?

  Part 2, Topic 2. Dividend Policy / Bond Markets

  We will deal wit h the following issues:

  • What is a payout policy? Dividends vs share repurchase: main theories.
  • How does top management set the dividend policy?
  • Do dividend clienteles drive the dividend policy?
  • What is a stock dividend? What is an optional stock dividend/ scrip / drip ? Why do an optional stock dividend?

  Part 2, Topic 3 Mergers and Acquisitions

   

  Part 2, Topic 4. Executive Remuneration Contracting / CEO Characteristics and Corporate Policy

  We will deal with the following topics:

  • What are the elements of a managerial remuneration contract?
  • Are female top managers discriminated?
  • Do superstar CEOs generate higher returns?
  • CEO narcissicm and corporate decision making

  Coefficient : 1

  Require prerequisites :

  Introduction to corporate finance

  Learning outcomes :

  The objective of this course is twofold: a. to introduce the student to state of the art econometrics applied in empirical corporate finance (e.g. to address endogeneity issues, to determine an identification strategy), b. to introduce the student to some important topics in the scientific literature on empirical corporate finance. Each class will focus on a single topic and discuss different research designs and econometric approaches.

  Assessment :

  Part 1. The evaluation for the class consists of the exercise sets (45%) and a written final exam (55%). Part 2. Project

  Bibliography-recommended reading

  Some Background resources

  Michael Roberts and Toni Whited (2013) “ Endogeneity in Empirical Corporate Finance ” , in George Constantinides, Milton Harris, René Stulz (eds) Handbook of the Economics of Finance, vol 2, Amsterdam, North Holland. Joshua Angrist and Steffen Pischke (2008) Mostly Harmless Econometrics, MIT Press.

   

  Mandatory readings associated with part 2.

   

  1. Corporate social responsibility

  - Ferrell, A., Liang. H. and L. Renneboog, 2016, Socially Responsible Firms, Journal of Financial Economics, 122(3), 585-606.

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2464561

  - Liang, H. and L. Renneboog, 2017, On the Foundations of Corporate Social Responsibility, Journal of Finance 72 (2), 853-910. Victor and Jaouad

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2360633 : Jaouad + Victor - Flammer, C., 2015, Does Corporate Social Responsibility Lead to Superior Financial Performance? A Regression Discontinuity Approach, Management Science 61, 2549 – 568

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2146282

  - (Just Skim in order to familiarize yourself with the law and finance literature; some other papers are below) Djankov, S., La Porta, R., Lopez-de-Silanes, F., Shleifer, A. 2008. The law and economics of self-dealing. Journal of Financial Economics 88, 430-465. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=864645

   

  2. Social Responsible Investing

  - Barko, T., M. Cremers, and L. Renneboog, 2022, Shareholder Engagement on Environmental, Social, and Governance Performance, Journal of Business Ethics, forthcoming.

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2977219

   

  3. Dividend policy / Bond markets

  - (Not for class discussion but skim to familiarize yourself with the literature) Survey paper: Farre-Mensa, J., R. Michaely, and M. Schmalz, 2014, Dividend Policy, In Annual Review of Financial Economics, Volume 6, edited by Andrew W. Lo and Robert C. Merton. Palo Alto, CA: Annual Reviews.

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2400618

  - Feito-Ruiz, I., L. Renneboog, and C. Vansteenkiste, 2020, Elective Stock and Scrip Dividends, Journal of Corporate Finance 64, 101660.

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3245060

  - Crane, A. D., Michenaud, S., & Weston, J., 2016. The effect of institutional ownership on payout policy: Evidence from index thresholds. Review of Financial Studies, 29(6), 1377-1408. Francesco and Wilson

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2102822

  - Manconi, A., Neretina, E., & Renneboog, L., 2021. Underwriter Competition and Bargaining Power in the Corporate Bond Market, Working Paper. Ghalia and Dhia

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3098005

   

  4. Mergers and acquisitions

  - (Not for class discussion but do skim this p aper to get a quick overview of the main topics in the M&A literature related to long-term performance) Renneboog, L. and C. Vansteenkiste, 2019, Failure and Success in Mergers and Acquisitions, Journal of Corporate Finance 58, 650-699. (on ssrn.com, the paper has this title: What goes Wrong in M&As? On the long-run success factors in M&As)

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304601

  (for an overview paper on the past M&A literature but then classified by means of the main takeover waves: see Martynova and Renneboog, JBF, 2008: see recommended readings below) - Martynova, M. and L. Renneboog, 2009, What Determines the Financing Decision in Corporate Takeovers: Cost of Capital, Agency Problems, or the Means of Payment?, Journal of Corporate Finance 15 (3), 290-315.

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=941731

  - Servaes, H. and A. Tamayo, 2014, How do industry peers respond to control threats?, Management Science 60, 380 – 399 Armine and Walid

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2283625

  - Vansteenkiste C., 2018, Try Before You Buy: How Do Two-Stage Acquisitions Affect M&A Outcomes? Working Paper. Sarah and Jinteng

  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3255983

   

  5. Executive Remuneration Contracting

  /CEO turnover

  - Jenter, D., and Kanaan, F. 2015. CEO turnover and relative performance evaluation, Journal of Finance 70.http://papers.ssrn.com/sol3/papers.cfm?abstract_id=885531 : Shu Kai - Geiler, Ph. and L. Renneboog, 2015, Are Female Top Managers Really Paid Less?, Journal of Corporate Finance 35, 345-369. Fatima and Yitu - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2378762

   

  6. CEO Characteristics and Corporate Policy

  - Nihat A., E. de Bodt, H. Bollaert, and R. Roll, 2015, CEO Narcissism and the Takeover Process: From Private Initiation to Deal Completion, Journal of Financial and Quantitative Analysis.

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1784322

  - Malmendier, U., and G. Tate, 2009, Superstar CEOs, The Quarterly Journal of Economics 124 (4), 1593-1638.

  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=972725

   

  Adresse du site de l'enseignant : https://www.tilburguniversity.edu/staff/luc-renneboog ; https://people.lu.usi.ch/fresal/

• Management of Credit Risk : Theory and applications

  Management of Credit Risk : Theory and applications

  Ects : 6

  Lecturer :
  OLIVIER TOUTAIN
  

  Total hours : 30

  Overview :

  This course is an introduction to Credit Risk in its different dimensions (Default/Recovery/Transition), starting from a description of the phenomenology of Credit Risk, the different intruments subject to credit risk to the different modelling approach both for single name or portfolio exposure. Numerous concrete examples illustrate the concepts introduced and the mathematical model are studied through exercises. The aim is to cover the broad domain of credit risk from retail products (credit card, mortgages) to sovereign credit risk, looking at the existing practicla issues that students would have to solve in their future employment either as Risk Managers, Traders, Asset Managers, Credit Risk Officer, Analysts, ...

  A book covering the different concepts presented in class is made available and corrected exercise are also available to the students.

  Coefficient : 1

  Recommended prerequisites :

  Basic knowledge of fixed income products and interest rate notions.

  Basic knowledge of probability / statistics is a plus (Theorem of Total Probability, Law of Large Number, Markov Chain, Univariate distributions)

  Learning outcomes :

  The key concepts pertaining to credit risk should be understood by students and a solid framework would allow an easier analysis of credit risk and its management in their future job.

  Assessment :

  A final exam mixing (i) questions on topic seen during the class and (ii) quantitative exercises to measure credit risk.

  Bibliography-recommended reading

  Credit Risk - Pricing, Measurement, and Management - Darrelle Duffie - Princeton Universirty Press Credit Risk Modeling - David Lando Credit Risk - Tomasz Bielecki, Marek Rutkowski

• Asset pricing theory

  Asset pricing theory

  Ects : 3

  Lecturer :
  JEROME DUGAST
  

  Total hours : 27

  Overview :

  In this course, we will discuss a wide range of topics ranging from optimal portfolio, the CAPM, factor models, consumption-based asset pricing, and arbitrage pricing, to more special ones including asymmetric information, and limits to arbitrage.

  1. Optimal Portfolio Theory and the CAPM
  2. Factor Models
  3. Decision Making under Uncertainty
  4. Consumption-based Asset Pricing
  5. Arbitrage Pricing
  6. Dynamic Asset Pricing
  7. Asymmetric Information and Asset Prices
  8. Limits to Arbitrage

  Coefficient : 2

  Learning outcomes :

  Master the theoretical concepts of asset pricing

  Assessment :

  Evaluation: assignment 20%, final exam 80%

• Fixed income derivatives

  Fixed income derivatives

  Ects : 6

  Lecturer :
  AYMERIC KALIFE
  

  Total hours : 30

  Overview :

  Interest rate derivatives, investment and hedging The objective of the course is to give an all round comprehensive knowledge and understanding of the theory and the day-to-day use of interest rates derivatives, for both investment and hedging purposes. Various views about the level and shape of the yield curve are implemented with selected absolute and relative value trades. across “Directional” and “Volatility” strategies. Finally, this course introduces to the the sustainable investing landscape (“ESG”) which has met some growing and significant appetite over the past decade, while providing insights and methodology for managing fixed income ESG investment strategies.

  Coefficient : 1

  Learning outcomes :

  Participants will learn how banks, portfolio managers and corporate treasuries use rates derivatives alike in the management of risks, for trading, hedging and arbitrage and their role in the day-to-day running of the finances of businesses.

  Assessment :

  Take home exam: trade idea Table exam

  Bibliography-recommended reading

  Fixed-Income Securities: Valuation, Risk Management, and Portfolio Strategies, Lionel Martellini, Philippe Priaulet

  Fixed Income Analysis, CFA institute,Barbara S. Petitt (Author), Jerald E. Pinto, Wendy L. Pirie, Bob

  Interest Rate Risk Modeling, Wiley, Sanjay K. Nawalkha, Gloria M. Soto, Natalia A. Beliaeva

  Fixed Income Mathematics, Analytical & Statistical Techniques, Frank J. Fabozzi

   

SEMINARS - Select 2 courses for 6 ECTS (Option 1) or 0 (Option 2)

• Machine Learning in Finance

  Machine Learning in Finance

  Ects : 3

  Lecturer :
  PIERRE BRUGIERE
  

  Total hours : 21

  Overview :

  Methods of Statistical Learning, applied to some financial problems of credit rating, anomaly detection and yield curve approximations

  Coefficient : 1

  Recommended prerequisites :

  Basic linear algebra and differential calculus.

  Require prerequisites :

  Basic linear algebra and differential calculus.

  Learning outcomes :

  Vapnik Chervonenkis dimension, PAC learning, calibration versus prediction, SVM (Support Vector Machines) classifiers, Mercer's theorem, C-SVMs, mu-SVMs and single class SVMs. Basics of decision trees, random forests and penalized regressions.

  Assessment :

  Exam

  Bibliography-recommended reading

  Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning, Springer Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani : An Introduction to Statistical Learning, Springer Christopher Bishop: Pattern Recognition and Machine Learning, Springer

• Regulation and Financial Markets

  Regulation and Financial Markets

  Ects : 3

  Lecturer :
  THIBAULT GODBILLON
  

  Total hours : 21

  Overview :

  Banks, and the financial sector more broadly, operate in a highly regulated environment. Financial regulations have evolved over time, in response to key events, such as the 2008 Global Financial Crisis, emerging risks (e.g., data, cyber security, FinTech, etc) and more recently the COVID19 pandemic, or the failure of SVB and Credit Suisse. Regulations have broadened to encompass all parts of the financial system: banks and non-banks — insurers, market infrastructures, credit rating agencies, hedge funds, etc. Global policymakers (including BCBS, FSB, IOSCO) have developed international standards to support the G20 mandate — ensuring the stability and resiliency of the global financial system. At the local and regional level (in the EU for instance), prudential and market regulators are tasked with transposing these global standards in their own framework, which may cause some variations in the way regulations are implemented across jurisdictions. This lecture aims to provide students with an understanding of the global regulatory architecture, ensure they understand where regulations come from, and how to stay up-to-date with a complex and constantly evolving topic. The course will also provide students with an overview of the current rules and regulations applying to banks and financial market operators in general. Via the drafting of a two-page note on a specific topic from the course, students will practice their written English communication and capacity to summarise complex matters. Finally, via the participation of experts from various background, the course will provide students with an insight into working for global organisations.

   

  Course outline:

  1) An introduction to financial regulations 2) Prudential regulations (Basel standards, CRD/CRR, DFA) 3) Crisis management (FSB standards, BRRD/CMDI, DFA) 4) Overview of Market regulations (International standards, MIFID/EMIR) 5) Sustainable Finance (Key risks, FSB/BCBS stan dards, EU taxonomy/GBS/ SFDR) 6) Digital Finance (Key risks, FSB/BCBS standards, DORA/MiCAR) 7) Outro (Wrap-up, critical considerations on financial regulations)

  Coefficient : Coefficient 1 (M2 Research in Finance) and Coefficient 1.5 (M2 Financial Markets)

  Learning outcomes :

  Master the regulatory prudential and market reforms, at the global level and across regions

  Assessment :

  Each students will be asked to prepare a two page note aimed at summarising a key issue of the programme.

• Structured products in practice

  Structured products in practice

  Ects : 3

  Lecturer :
  AYMERIC KALIFE
  

  Total hours : 21

  Overview :

  Structured products, evaluation and control. This course is an initiation to new structured products. It shows how to value such products, and how to control the associated risks

  Coefficient : 1

  Learning outcomes :

  Participants will lear how financial institutions can build and structured products, how they value them, and what they are done for.

• Microeconomics for finance

  Microeconomics for finance

  Ects : 3

  Lecturer :
  JEROME DUGAST
  

  Total hours : 24

  Overview :

  Syllabus:

  1. Equilibrium in an Exchange Economy 2. Decision Making under Uncertainty 3. Equilibrium in Markets for Securities 4. Investment Decision under Market Imperfections: the Principal-Agent Problem

  Coefficient : 0.5 (M1 finance) 1 (M2 Finance)

  Recommended prerequisites :

  Basic notions of mathematical analysis and algebra are required.

  Learning outcomes :

  This 24 hours course aims at acquainting students with relevant microeconomics methods to tackle finance issues.

  Assessment :

  Final exam and assignment

METHODOLOGY OF RESEARCH

• Frontiers in Finance

  Frontiers in Finance

  Lecturer :
  DELPHINE LAUTIER
  

  Total hours : 15

  Overview :

  Content:

  • Presentation of the research team and academic careers (Carole Gresse)
  • Presentation of the Dauphine PhD program in finance (Jérôme Dugast)
  • Series of seminars on topics situated at the frontiers of finance.

  This course is mandatory for the students enrolled in the cursus Phd Qualifying Year. It is optional for all others.  

  Learning outcomes :

  The course Frontiers in Finance is a serie of seminars, proposed by academics of the university PSL Paris Dauphine. Most of them are members of the research team DRM-Finance.

  The aim of this course is to present the different steps of an academic career and to offer a view on recent researches in finance performed by the members of the team. This is an open view on what could be done after the M2 104, as well as on the state of the art in finance.

  This course is mandatory for the students enrolled in the cursus Phd Qualifying Year. It is optional for all others.

  Assessment :

  None

• Formation to R programming (option)

  Formation to R programming (option)

SEMINARS - Select 8 courses

• Strategies and actors on financial markets

  Strategies and actors on financial markets

  Ects : 3

  Lecturer :
  AMINE RABOUN
  

  Total hours : 21

  Coefficient : 1
• Microstructure of financial markets

  Microstructure of financial markets

  Ects : 3

  Lecturer :
  JEROME DUGAST
  

  Total hours : 21

  Overview :

  The field of market microstructure combines theoretical modeling, institutional knowledge, and empirical analysis to understand how prices result from the interactions of traders in financial markets. The course aims to acquaint students with (i) the canonical models in microstructure, and (ii) econometric models used to test the predictions of those models.

   

  Course structure:

  1. Trading Mechanisms
  2. Measuring Liquidity
  3. Price Dynamics and Liquidity
  4. Trade Size and Market Depth
  5. Empirical Analysis

  Coefficient : 1

  Learning outcomes :

  Master the concepts of financial markets microstructure

  Assessment :

  Evaluation: assignment and final exam

  Bibliography-recommended reading

  Foucault, Thierry, Marco Pagano, and Ailsa Röell, Market Liquidity: Theory, Evidence, and Policy, Oxford University Press, 2013.

• Advanced corporate finance

  Advanced corporate finance

  Ects : 3

  Lecturer :
  GILLES CHEMLA
  

  Total hours : 21

  Overview :

  Recent developments in the theory of corporate finance To follow this course, the students must validate the course "Corporate finance". Course objective : the main objective of the course is to familiarize students with a number of important, recent results and subjects that have been added to the theory of corporate finance. A second important objective is to provide an overview of some of the modelling issues faced and of the methods that are currently employed in the area of corporate finance

  Coefficient : 1

  Learning outcomes :

  The students will master the most recent reserach issues in corporate finance, with specific insights into modelling.

• Behavioral Finance

  Behavioral Finance

  Ects : 3

  Lecturer :
  ALBERTO MANCONI
  

  Total hours : 21

  Overview :

  Introduce students to this relatively new sub-discipline of finance which incorporates insights from cognitive and social psychology into finance. In the past 20 years behavioral finance has emerged as an important stream of thinking in finance. Relaxing the traditional assumptions of finance models has proved a fruitful way of understanding financial decision-making.

  Course outline: The course will go through:

  • The cognitive biases evidenced by cognitive psychologists;
  • Financial anomalies and their interpretations through a behavioral finance lens;
  • The implications of behavioral finance for investors and corporate financial policy.

  Coefficient : Coefficient 1 (M2 Research in Finance) and Coefficient 1.5 (M2 Financial Markets)

  Learning outcomes :

  Relaxing the traditional assumptions of finance models has proved a fruit ful way of understanding financial decision-making and anomalies found in empirical tests.

  Assessment :

  Students will present a state-of-the art research paper among a selection of papers chosen by the instructors.

  Bibliography-recommended reading

  Daniel Kahneman, Paul Slovic, and Amos Tversky (eds.), Judgment under uncertainty: Heuristics and biases, Cambridge: Cambridge University Press, 1982. Richard Thaler, ed., Advances in behavioral finance, New York: Russell Sage Foundation, 1993. Richard Thaler, ed., Advances in behavioral finance, Volume II, New York: Russell Sage Foundation, 2005. Shleifer, Inefficient markets : an introduction to behavioral finance, Oxford, Oxford University Press 2000.

• Advanced empirical corporate finance

  Advanced empirical corporate finance

  Ects : 3

  Lecturer :
  RYAN WILLIAMS
  

  Total hours : 24

  Overview :

  Case studies, academic papers, and Stata.

  Coefficient : 1

  Recommended prerequisites :

  Knowledge of mathematical statistics.

  Require prerequisites :

  Corporate finance.

  Learning outcomes :

  This is a practical course in applied econometrics. We will be reviewing and replicating famous academic papers. You will learn how to use Stata to manipulate data, run regressions, use more complex estimation methods. We will discuss methods to determine causality.

  The final goal is to produce a student who is comfortable performing an empirical research project from the data collection phase to the presentation of the final results.

  Assessment :

  Final project + presentation of an empirical question in corporate finance.

• Empirical Asset Pricing (it is strongly advised to have some knowledge in Python for this course)

  Empirical Asset Pricing (it is strongly advised to have some knowledge in Python for this course)

  Ects : 3

  Lecturer :
  JUAN FELIPE IMBET JIMENEZ
  

  Total hours : 21

  Overview :

  The course will cover the necessary tools in order to conduct independent research in asset pricing, focusing on the relation between theoretical and empirical explanations of prices, and risk. We will cover asset pricing anomalies, consumption based asset pricing, intermediary asset pricing, and production based asset pricing.

  Coefficient : 1

  Recommended prerequisites :

  Econometrics, Asset Pricing Theory, Linear Algebra, and Macroeconomics

  Learning outcomes :

  Understanding of theory and empirics of asset pricing research, with a focus on how to bring models to the data.

  Assessment :

  Project 20%

  Final Exam 70%

• Advanced Market Microstructure

  Advanced Market Microstructure

  Ects : 3

  Lecturer :
  FABRICE RIVA
  CAROLE GRESSE
  SABRINA BUTI
  JEROME DUGAST
  

  Total hours : 21

  Overview :

  1. Liquidity and Asset Prices

  2. Limit Order Markets and OTC Markets: a Review of Theory

  3. Market Transparency

  4. Market Fragmentation

  5. Algorithmic Trading and High-Frequency Trading

  6. Microstructure and Corporate Finance

  7. Information Technologies, Big Data, and Financial Markets

  Coefficient : 1

  Learning outcomes :

  The course aims to acquaint students with advanced topics in market microstructure.

  Assessment :

  Referee reports or paper replications

• Time series (it is strongly advised to have some knowledge in R for this course)

  Time series (it is strongly advised to have some knowledge in R for this course)

  Ects : 3

  Lecturer :
  YANNICK LE PEN
  

  Total hours : 21

  Overview :

  This course will present the modelling and forecasting of time series. We will expose the main concepts and methodsapplied to univariate time series : stationnarity and unit roots, ARIMA models, univariate volatility models, forecasting. We will also present the methods for multivariate framework : VAR, Cointegration and VECM, Multivariate GARCH. The learning goal of this course is that students become able to engage in and conduct original research. It is also toprepare them to be professionals in careers that require training in econometrics.

   

  Outline

  1. Univariate time series modelling and forecasting Stationnarity and unit roots, unit root tests, ARIMA models : estimation, testing
  2. Univariate volatility models ARCH, GARCH models and their extensions
  3. Multivariate times series models VAR models, Causality, Impulse-Response analysis, Cointegration, VECM
  4. Multivariate GARCH models BEKK, CCC and DCCmodels

  Software

   

  The software that will be used in this course is R. No prior knowledge of this software package is assumed. This package will be introduced in lectures and in the problem sets as the course proceeds. Students are asked to install R and RStudioDesktop :

  1. R can be found on https://pbil.univ-lyon1.fr/CRAN/
  2. RStudio Desktop can be found on https://www.rstudio.com/products/rstudio/download/

  Coefficient : 1

  Require prerequisites :

  The course assumes familiarity with statistics, probability and basic econometrics.

  Learning outcomes :

  After this course, the students should be able to produce their own empirical study with time series. They also should have acquired sufficient knowledge to read and understand more complex time series econometric methods.

  Assessment :

  The grade is based on an individual project.

  Bibliography-recommended reading

  Brooks, C., Introductory Econometr cs for F nance, Cambridge University Press, 3rd edition 2014. Ghysels, E. and M. Marcellino,A ed Econom c Forecast ng s ng me er es Methods, Oxford University Press, 2018. Mills, T., et R.N. Markellos, R.N., he Econometr c Mode ng of F nanc a me er es, Cambridge University Press ; 3ème Édition, 2008

   

  Additional references

  Campbell, J., A. Lo and C. MacKinlay, he Econometr cs of F nanc a Mar ets, Princeton Uni- versity Press, 1997 Bauwens L., Hafner C. et S. Laurent, Handboo of Vo at ty Mode s and the r A cat ons, John Wiley & Sons, 2012. Taylor, S. J., Asset Pr ce Dynam cs) Vo at ty and Pred ct on, Princeton University Press, 2007. Jondeau, E., Poon S.-H. et M.Rockinger, F nanc a mode ng under non-gauss an d str but ons, Springer. Linton, O., F nanc a Econometr cs) Mode s and Methods, Cambridge University Press, 2019

• Data management (Bloc 2/3 of the Certificate "Fundamentals of Data Science")

  Data management (Bloc 2/3 of the Certificate "Fundamentals of Data Science")

  Ects : 3

  Lecturer :
  BERTRAND HASSANI
  

  Total hours : 21

  Overview :

  Data science is an interdisciplinary field that is rapidly evolving. Many companies have widely adopted machine learning and artificial intelligence methods to power many applications that have captured the imagination of society at large. Data systems and data engineering are an inevitable part of all these large-scale data-driven applications and decisions, as ML/AI methods are powered by massive collections of potentially heterogeneous and messy datasets and, as such, should be managed as part of an organization's overall data lifecycle.

  This course corresponds to the third block of the Certificate “Fundamentals of Data Science”. This Certificate is designed to train and familiarize professionals with the key technologies in this interdisciplinary field, with the aim of enabling them to take full advantage of the opportunities offered by data science and to become active players in this field within of their organizations. This is an accelerated training focused on the key modules of the profession of data scientist, in particular the management of massive data and machine learning.

   

  Course outline:

   

  Module 1 : Why shall we engage a Data transformation program (1h)*

  - Introduction - The Role of Data in a company - Review of the evolution of Data topics - Data value chain - Presentation of the pillars of a Data transformation ( challenges / objectives) - The Data Strategy - Data Management & Governance - Analytics - IT - Project to Product team

   

  *Module 2: Data Management (4h)*

  - General presentation of the main concept of the framework (DAMA) - Structure & organization (roles & responsibilities) - Lineage & Metadata: data knowledge - The importance of data quality - Privacy / GDPR - Data types and their characteristics - Structured - Unstructured data - Examples of arc hitectures - Main tools to manipulate Data

   

  *Module 3: Case Analysis - From Theory to Practice: data retrieved from

   

  both a database and an excel file. (10h)*

   

  - From integration to visualization - Integration of data from Excel file via Python - Representation, cleaning, recoding - Aggregates - Merge and join

   

  - *Practical work

  1

  *

  - Data integration from the database - Relational model - Introduction to SQL - SQL in Python

   

  - *Practical work

  2

  *

  - Beyond SQL, other possible cases (NoSQL) - Problems encountered when reconciling data (duplication, quality, veracity) > Can you put your trust into your data

   

  - *Practical work

  3

  *

  - Using Artificial Intelligence to explore to power of Data - Presentation of some use cases and explanation: how does that work practically ? - Demonstration (prediction / recommendation) - Visualization: transforming data into information and then knowledge for an informed and effective decision making process

   

  Lecturer: Bretrand Hassani

  Coefficient : 1
• Machine Learning : empirical applications for finance (Bloc 3/3 of the Certificate "Fundamentals of Data Science")

  Machine Learning : empirical applications for finance (Bloc 3/3 of the Certificate "Fundamentals of Data Science")

  Ects : 3

  Lecturer :
  HOUCINE SENOUSSI
  

  Total hours : 21

  Overview :

  Basics of ML

  • Definitions, approaches and applications.
  • Data mining (DM) : definitions and links with ML.
  • Classification and regression problems.
  • Building and evaluating an ML model.
  • Presentation of the main approaches of ML/DM.
  • Application I.

   

  Decision Trees :

  • Definitions and algorithms.
  • Advanced methods based on DL : Bagging, Boostring and Random forests.
  • Application II : Making a decision in finance.

   

  Neural networks:

  • Definitions.
  • Learning in NN : grandient descent and Backpropagation.
  • Advanced methods based on NN (Deep learning).
  • Application III : : Stock pricing.

   

  Reinforcement Learning :

  • Definitions : Agents and environnments.
  • Markovian Decision Process (MDP).
  • Policies and optimal policies.
  • Q-learning.
  • Application IV : Trading.

  Coefficient : 1

  Require prerequisites :

  Python programming language.

  Learning outcomes :

  Building Machine Learning (ML) models for Finance problems. Using ML Python library (and in particular sickit-learn).

  Assessment :

  Two/Three assignments (building a model + Python programming).

• Alternative Finance

  Alternative Finance

  Ects : 3

  Lecturer :
  MARIUS FRUNZA
  FABIAN ASTIC
  

  Total hours : 21

  Overview :

  The aim of this course is to propose an out-of the box perspective upon the financial markets and to explore the financial universe beyond the traditional investments like equity, bonds, currency… . We will focus the course on the products and technics used at the fringe of finance including crowfunding, peer-2-peer finance, shadow banking, Bitcoin, social and environmental impact products….

  Throughout this course students will learn about alternative investment supports and alternative financing solutions. The objectives of this lecture are:

  • To understand the mechanism of alternative risks: global warming, catastrophic events including on the economy, …
  • To explore new area including Environmental, Social, and Governance (ESG) Investment, cryptocurrency etc..
  • To get familiar with modelling methods specific to alternative finance.

  Course outline:

  1. Alternative finance 101 Two faces of the same coin: as investors or as issuers. 2. Modelling methods for alternative finance

  • Introduction to the non-Gaussian universe
  • Real Option Theory
  • Extreme value theory

  3. Crypto-currencies: an alternative financial universe 4. Environmental, Social, and Governance (ESG) Investment 5. Crypto-currency : an alternative financial universe. 6. Alternative capital markets and Fintechs: Focus on Crowdfunding and P2P finance 7. Alternative Risk Transfer

  • Climate risks
  • Insurance and re-insurance. Focus on CAT Bonds

  8. Fintech workshop (industry view)

  Coefficient : 1.5

  Require prerequisites :

  Students must be enrolled in courses Applied Time series and must have past Introduction to Financial Econometrics, Financial Derivatives.

  Learning outcomes :

  Knowledge on the modelling methods specific to alternative finance.

  Assessment :

  Project

  Bibliography-recommended reading

  • Alexandridis, A. K. and A. D. Zapranis, 2013: Weather Derivatives, Springer, 300 pages.
  • Barrieu P., and L. Albertini, 2009: The Handbook of Insurance-Linked Securities, Wiley, 398 pages.
  • Frunza, M., 2010: Carbon allowances: A new financial asset, Editions universitaires europeennes, 164 pages.
  • Guthrie G., Real Options in Theory and Practice, 2009, OUP, 432 pages.  
• Blockchains and Cryptocurrencies

  Blockchains and Cryptocurrencies

  Ects : 3

  Lecturer :
  LOUIS BERTUCCI
  

  Total hours : 21

  Overview :

  Although blockchain technology is a fairly recent concept, the rate of innovation in this space has been tremendous over the past years. This class will give students an overview of the fundamental concepts needed to properly understand most aspects around blockchains, with a focus on the Bitcoin and Ethereum blockchains. We will also cover the most recent advanced topics including : Consensus Algorithms (Proof-of-Work vs Proof-of-Stake), the scaling problem, Smart contracts as well as a detailed approach of Decentralized Finance (DeFi), Token economics (Fungible and Non-Fungible Tokens) and CDBC. The academic literature is also very dynamic and this class will heavily rely on this literature to explain in depth the main concepts.

  Although an academic approach will help students get a solid knowledge about blockchains, this class will also incorporate some practical training, including low-level bitcoin transaction scripting and smart contract development/deployment/interaction with Solidity.

  Even if this class is not directed to computer scientists, students will be expected to make the effort to learn about the most important computer science primitives needed to understand the economics of blockchain. Such primitives will be taught in class.

  Coefficient : 1

  Require prerequisites :

  Coding skills : Python development. Some knowledge in Javascript will also be a plus as Solidity, the most popular smart-contract development language, has a Javascript-like syntax, but this is not mandatory.

  Knowledge in basic economics and Game Theory will also be a plus although not mandatory.

  Learning outcomes :

  Students are expected to get an in-depth understanding of the functioning of any blockchain and DeFi projects, as well as an awareness of most of the current important issues and recent developments. Students will also be exposed to the most important papers in the literature as well as some knowledge on practical aspects like the basics of smart contract development. Students are not expected to become smart contract developers but rather to know the basics of it, how it works and ultimately to be able to interact with actual smart contract developers.

  Assessment :

  Oral presentation (critical assessment of a chosen blockchain or DeFi project), Homework (coding, paper review) and/or final exam.

  Bibliography-recommended reading

  Books :

  • Andreas Antonopoulos, Mastering Bitcoin, 2nd edition, O’Reily, 2017
  • Andreas Antonopoulos, Gavin Wood, Mastering Ethereum, 1st edition, 2018
  • Primavera De Filippi, Aaron Wright, Blockchain and the Law : The Rule of Code, Harvard University Press, 2018
  • Campbell Harvey, Ashwin Ramachandran, Joey Santoro, DeFi and the Future of Finance, 1st edition, Wiley, 2021
• Financial macroeconomics

  Financial macroeconomics

  Ects : 3

  Lecturer :
  VERONIKA SELEZNEVA
  

  Overview :

  This 24-hour course is a graduate-level introduction to financial macroeconomics. The main objective of this course is to provide students with a rigourous approach to the basic ingredients behind any macroeconomic model, i.e the consumption/demand and production/supply sides. In an intuitive approach, students are first thaught the standard techniques of dynamic programming. The traditional consumer's decision problem is then covered, potentially but not exclusively through the lens of this newly exposed method. Before studying real business cycle models as a whole and therefore being able to investigate why aggregate economic activity fluctuates in a general equilibrium setting, students learn about the neoclassical theory of investment (i.e the Ramsay model). Finally, to better understand the links between output and inflation and if time allows, students are introduced to the role played by money and the importance of prices. In particular, the New Keynesian framework with its price and/or wage rigidities allows students to analyze the costs and benefits of price stability and the inherent role of central banks.

  Coefficient : 0.5 (M1 Finance - Formation Initiale)

  Assessment :

  Pré-requis obligatoires

  Basic notions in intermediate macroeconomics (IS/LM model, etc)

   

  Bibliographie, lectures recommandées

  There is no textbook that covers all the material of the course. Useful textbooks (even though some of them are really technical and should not scare the students) nevertheless include : - Blanchard, O.J and Fischer, S. (1989), Lectures on Macroeconomics, Cambridge, MA : MIT Press. - Obstfeld, M. and Rogoff, K (1996), Foundations of International Macroeconomics, Cambridge, MA : MIT Press  

• Artificial Intelligence for finance

  Artificial Intelligence for finance

  Ects : 3

  Lecturer :
  BERTRAND HASSANI
  HOUCINE SENOUSSI
  

  Total hours : 21

  Coefficient : 1

METHODOLOGY OF RESEARCH AND MASTER’S THESIS - Select either "Applied Master’s Thesis + Internship" (6 ECTS) or “Research Master’s Thesis” (6 ECTS)

• Applied Master's thesis

  Applied Master's thesis

  Ects : 3

  Lecturer :
  DELPHINE LAUTIER
  

  Overview :

  The purpose of the applied master's thesis is, for the student: • to anchor an autonomy in learning • to enhance his or her capacity for innovation • to provide education through research • to write an ariginal piece of work, on contemporary questions

  The interest of such a work is that the student: • Acquires a distinctive competence on a specific subject • This specific skill should be an asset for his or her professional career

  Coefficient : 4

  Learning outcomes :

  The aim of the Master’s thesis is to produce an original piece of research work on a clearly defined topic within the investigative field of contemporary finance, under the guidance of one of the Masters’ Professors.

  For the students of the M2 104 who intend to have a career in the finance industry , the Master’s thesis is a unique opportunity to demonstrate their scientific expertise in the field of finance. Experience shows that employers highly value this research approach.

  The students who choose the applied master’s thesis must also find an internship. The subject of the internship may or may not be related to the subject of the master’s thesis.

  Assessment :

  The Applied Master's thesis must be received by the supervisor in the end of June, and defended before mid-July.  

• Internship

  Internship

  Ects : 3

  Lecturer :
  DELPHINE LAUTIER
  

  Overview :

  The M2 104 receives a wealth of internship offers from companies and past Master ’ s students, which helps students find the right internship every year. During the year, several meetings are organized with large financial institutions. The 104 also benefits from its linkedin group.

  Coefficient : Validation (pas de note)

  Learning outcomes :

  The students of the M2 104 who are interested in a career in the finance industry can choose the applied research option, that includes an applied research master's thesis plus an internship.

  The subject of the internship can be related to the subject of the Master’s thesis, but it is not mandatory.

  The length of the internship is a minimum of 3 months. Most of the time, it lasts 6 months or more.

  The internship can not start before the end of the courses. Most of the time it starts in April and is carried out in a financial institution or in the finance department of a firm.

  Assessment :

  The internship is validated by D. Lautier. Its subject must be in line with the knowledge acquired by the students during the year.

• Research Master's thesis or Research project

  Research Master's thesis or Research project

  Ects : 6

  Lecturer :
  DELPHINE LAUTIER
  

  Overview :

  Students are required to write a Master's thesis on an innovative topic, which enables them to gain true expertise on their subject.

  Coefficient : 4

  Learning outcomes :

  The research Master's thesis is designed for the students who wish to pursue a career in research after the M2 104. This could be done in a private firm or by applying to a PhD program in Finance (at Dauphine, or in another academic institution).

  The research master's thesis is also an opportunity to meet a thesis director who might become the supervisor of the PhD thesis at Dauphine PSL

  Writing a research Master’s thesis is mandatory for all students enrolled in the PhD Qualifying Year. All other students can choose between either a research Master’s thesis or the combination of an applied Master’s thesis and an internship.

  When a student chooses the research master's thesis, there is no need, for him ou her, to do an internship. There are, however, some possibilities to do a short research internship in our research lab (at DRM-Finance).

  Assessment :

  When the student wants to enrol in the PhD program in finance of PSL - University Paris Dauphine, he/she need to write a research project, which is due in May. The thesis supervisor should receive the research master ’ s thesis in the beginning of September. The defense of the thesis must occur before the 15th of September.

• Seminar on research methodology (mandatory)

  Seminar on research methodology (mandatory)

  Lecturer :
  DELPHINE LAUTIER
  

  Total hours : 23

  Overview :

  In this seminar, the students will learn:

  • to define a research subject;
  • to select, read and use the articles related to their subject
  • to organize the content of their Master's thesis, and to write their review of the litterature.

  Coefficient : 4

  Learning outcomes :

  This course is an introduction to the methodology of research through the writing of the Master's thesis. Throughout the year, the students of the M2 104 will have to work on their master’s thesis, which is a very important part of their formation.

  The aim of the Master’s thesis is to produce an original piece of research work on a clearly defined topic within the investigative field of contemporary finance, under the guidance of one of the Masters’ Professors.

  - For students considering a career in the industry, the Master’s thesis is a unique opportunity to demonstrate their scientific expertise in the field of finance and experience shows that employers highly value this research approach.

  - For students considering a doctoral thesis, the Master’s thesis enables them to get an initial feel for what research involves and is often the foundation for further investigation for a student’s doctorate.

  Assessment :

  The seminar is mandatory for all students of the M2 104. Their grades depend on the assiduity of the student and his /her ability to produce in due time, five documents. Each document represents one step in the writing of the Master’s thesis.

  Document n°1 reveals the preferences of the students about the research subjects in which they are especially interested

  Document n°2 gives a definition of the subject, in accordance with the supervisor of the Master's thesis

  Document n°3 is a synthesis about the main references that will be needed to write the Master's thesis

  Document n°4 is a first draft of the review of the litterature

  Document n°5 is the Master's thesis

  Bibliography-recommended reading

  Adresse du site de l'enseignant : https://sites.google.com/site/delphinelautierpageweb/

PROFESSIONNAL TRAINING

• Formation Alumnye

  Formation Alumnye

  Lecturer :
  DELPHINE LAUTIER
  

  Total hours : 6

  Overview :

  This short-term course is a professional formation proposed by the society AlumnEye. It is scheduled in the very beginning of the year, in September. It offers a coaching to the students who intend to pursue in the finance industry after the M2 104, especially those considering an international career, and who are willing to prepare themselves to the recruiting processes and the professional interviews in the fields of banking, finance and consulting. The fees that the students must pay to attend this formation are offered by the University. The number of places is limited to those really interested in this formation.  
• AMF Certification (On line course)

  AMF Certification (On line course)

  Lecturer :
  DELPHINE LAUTIER
  

  Overview :

  Content of the certification: - Know the general principles of banking and financial law - Identify the role and operation of the various financial actors - State the main principles of French financial regulation - Master the fundamentals of the monetary and financial code and the general regulations of the AMF - Understand and explain the rules on client protection and the legal and ethical framework governing financial transactions, - Know the different means of payment and describe their main characteristics: cards, checks, transfers, direct debits. - Inform a client about the different types of financial instruments - Distinguish the different types of financial instruments used by customer - Know the organization and role of financial markets - Ability to read business financial statements - Get an overview of tax rules for businesses and individuals

  Learning outcomes :

  The AMF General Regulation requires investment services providers to verify that persons exercising certain functions under their authority or on their behalf have a minimum level of knowledge in 12 areas relating to the regulatory and ethical environment and financial techniques.

  The AMF Certification is an online course proposed by an institution certified by the AMF. The M2 104 gives the possibility to follow it and to validate the exam, during the second semester.

  Assessment :

  One line examination