International Students Coordinator
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The «Advanced Studies and Research in Finance» program is a one-year course whose main objectives are:
to prepare students to doctorate level
to prepare for the CFA® exam
to provide a solid background and training to pursue careers in the financial, banking, insurance and corporate sectors as research analysts, financial consultants or executives...
Students will acquire an up-to-date knowledge and become experts in a field of specialization.
The program allows students to choose elective topics, depending on their academic project (research or CFA preparation).
The program and supervision of students focusing on research are supported by the Research Center for Economics and Management (CREM), the only research center dedicated to Economics and Management Sciences Western France.
CREM is accredited by the National Center for Scientific Research (CNRS).
The program is taught exclusively in English. It consists of eight mandatory core modules. Students attend several seminars on financial issues, taught by invited professors and professionals from the private sector.
Students also benefit from intensive classes of French language.
Please note: in a process of continuous improvement, training contents is subject to possible changes.
Détail du programme
The aim of the course is to understand
- the main models of portfolio optimization
- portfolio insurance strategies
Skills to be acquired
The students will be able to imlement portfolio optimization and portfolio insurance strategies
Chapter 1. Portfolio management
§ 1. Portfolio optimization
§ 2. The Black-Litterman methodology (strategic and tactical allocation)
Applications on Excel and Bloomberg
Chapter 2. Advanced methods of portfolio management: Portfolio Insurance Strategies
§ 1. Option based Portfolio Insurance (OBPI strategies)
§ 2. Constant Proportion Portfolio Insurance (CPPI strategies)
Applications on Excel
Financial theory level Master 1
The aim of the course is to explore in-depth the properties of options and those of the option markets. One will insist especially on the management of options and on the implied volatility and related concepts. These subjects are rarely covered that way.
The students will be able to understand the challenge faced by the option sellers and many aspects of the option markets…
This course presents the foundation of corporate finance with an emphasis on capital structure decisions. The main objective of the course is to provide the conceptual background for understanding and analyzing the capital structure of firms in the market environnement.
This course will cover important topics and recent developments in capital structure theory. The goal of this class is to familiarize you both with original papers in the field and current researches in this areas.
✓ Chapter 1 - The financing mix question: practical point of view
✓Chapter 2 – The MM’s theorem
✓ Chapter 3 – The costs of financial distress
✓ Chapter 4 – The signaling theory and informational asymmetries
✓Chapter 5 – The agency theory
Chapter 6 – The tradeoff model
✓ Chapter 1 – Equity financing
✓ Chapter 2 – Debt Financing
✓ Chapter 3 – Hybrid Financing
Damodaran, Corporate Finance: Theory and practice, 2nd edition, 2010
Almeida, Heitor, and Thomas Philippon, 2007, The Risk-Adjusted Cost of Financial Distress, Journal of Finance 62, 2557-2586
Anderson, RW, and S Sundaresan, 1996, Design and Valuation of Debt Contracts, Review of Financial Studies 9:1, 37-68
Graham, John R., 2000, How Big Are the Tax Benefits of Debt?, Journal of Finance 55, 1901-1941.
Jensen, Michael C., and William H. Meckling, 1976, Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure, Journal of Financial Economics 3, no. 4 (October), pp. 305-360
Leary, Mark T., and Michael R. Roberts, 2009, The Pecking Order, Debt Capacity, and Information Asymmetry, Journal of Financial Economics 95, 332-355.
Modigliani, Franco, and Merton H. Miller, 1958, The Cost of Capital, Corporation
Finance and the Theory of Investment, American Economic Review 48:261-297
Myers, Steward C., and Nicholas S. Majluf, 1984, Corporate Financing and Investment Decisions when Firms Have Information that Investors do not Have, Journal of Financial Economics 13, no. 2 (June), pp. 187-224
Shyam-Sunder, Lakshmi, and Stewart C Myers, 1999, Testing Static Trade-Off Against Pecking Order Models of Capital Structure, Journal of Financial Economics 51:2, 219-244.
Strebulaev, Ilya A., 2007, Do Tests of Capital Structure Theory Mean What They Say?, Journal of Finance 62:4, 1747-1787
Written exam (2h)
The aim of the course is to provide students with the knowledge necessary to understand, analyse and make corporate financial decisions. Tools and techniques will be taught through theoretical presentation and practical applications.
Critical analysis will be encouraged by in-class discussion about the limits of the techniques employed and the way to overcome them.
- Part I: Investing decisions
Part II: Financing decisions
Part III: Payout policy
Assessment method: final exam (questions within the final exam for both corporate finance courses)
- Data Envelopment Analysis, A comprehensive Text with Models, Applications,References and DEA- Solver Software, W.W. Cooper, L.M. Seiford, K. Tone, Springer, 2008.
- Gestion des risques et institutions financiers, J. Hull, Pearson, 2008.
- Data Envelopment Analysis, Theory, Methodology and Applications, A. Charnes, W. Cooper, A. Lewin, M. Seiford, Kluwer, 2000.
The aim of the course is to:
- Discover VBA with excel and to code on VBA
- Master the basics of programming
- 3h: Presentation and theoretical overview of VBA
-6h : Series of 20 exercises on macros
-6h : Series of 20 exercises on functions
Risk management has become progressively more important for all corporations in the last few decades. Financial institutions such as banks and insurance companies are concerned with providing a good trade-off between return and systemic (non-diversifiable) risk for their investors. They are also concerned with total risks (systemic plus non-systemic) because of the bankruptcy costs arguments. However, there is another reason why most financial institutions carefully monitor total risks. This is that regulators require them to ensure that the probability of a bank or an insurance company experiencing severe financial difficulties is low.
Risk management is the process of identification, analysis and acceptance or mitigation of uncertainty in investment decisions. In other words, the role of risk management is to understand the risks of an investment that the company is currently taking and the risks it plans to take in the future. It must decide whether the risks are acceptable and if they are not acceptable, what action should be taken given its investment objectives and risk tolerance.
There are two broad risk management strategies open to a financial institution. One approach is to identify risks one by one and handle each one separately. This is referred to as risk decomposition. The other is to reduce risks by being well diversifying. This is referred to as risk aggregation. The objective of this course is therefore twofold. The first one is to give a view of how risk measurement techniques are quantified in almost every single market. Each technique is deeply associated with its specific market and cannot be applied directly to other markets. The second objective (main objective of the course) is to present an integrated way to deal with different markets and different risks and to combine all of the factors in a single number which is a good indicator of the overall risk level: Value at risk.
1. An overview of risk measurement techniques
2. Value at risk
Linear regression is the most basic tool of an econometrician and is widely used throughout finance and economics. It attempts to model the relationship between two or more variables by fitting a linear equation to observed data. Using the suitable methods and techniques, the objectives of regression analysis are to analyze movements in an economic variable by reference to movements in one or more other economic variables. Linear regression’s success is owed to two key features: the availability of simple closed form estimators and the ease and directness of interpretation.
There are two types of linear regression, simple linear regression and multiple linear regressions. In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regressions, two or more independent variables are used to predict the value of a dependent variable.
There are broadly three types of data that can be employed in quantitative analysis of financial problems: time series data, cross-sectional data, and panel data. A cross-sectional regression is a type of regression in which the explained and explanatory variables are associated with one period or point in time. This type of cross-sectional analysis is in contrast to a time-series regression in which the variables are considered to be associated with a sequence of points in time. Panel data have the dimensions of both time series and cross-sections. The panel data, also called longitudinal data or cross-sectional time series data, are data where multiple cases (people, firms, countries, etc.) were observed at two or more time periods. Following these distinguished types of data, the course is divided into three units.
In the empirical work, this course uses R software to analyze the relationship between variables. R is an open-source software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
Regression analysis with cross-sectional data and time series data
The aim of the course is to give to students solid foundations in portfolio management.
The aim of the course is to give an overview of real option theory and applications.
-Real option Valuation : basic
Real option valuation : advanced
Option to wait
Comments of papers on real option theory
Project : comments of papers.
The aim of the course is to present financial statement analysis from the point of view of the primary users of financial statements: equity and credit analysts.
The accrual method of accounting and its implications for financial reporting: income statement and balance sheet
Cash flow statement and cash flow analysis
Ratio analysis: advantages and limits
Analysis of inventories
Long-lived assets: capitalization versus expensing decision
Allocation of capitalized costs to operations (depreciation, impairment and restructuring)
Long-term liabilities: analysis of varying forms of debt, off-balance-sheet financing techniques (leases)
The aim of the course is to give students a general understanding of the numerical methods that have early been introduced in Finance to deal with situations where it is impossible to derive analytical results. Among the broad set of available numerical methods, the course will insist on the Monte Carlo simulation technique (and variants) as well as a number of tree approaches such as the binomial tree and the trinomial tree. My aim is also to highlight and illustrate (through examples) both the opportunities and the challenge these approaches represent. For instance, the simulation approach essentially relies on our capacity to “discretize” the continuous process (hypothesized for modelling the dynamics of the underlying asset price). The tree approach must be carefully adapted to account for path-dependency. As a by-product, I will present in this course a number of exotic options for which no analytical pricing formula exists.
The aim of the course is to present the core material, tools and concepts of Mathematical Finance. These devices are very useful in Finance to model the financial market, to describe the dynamics of asset prices, to price financial assets and in some cases to understand how people decide. After a general overview of useful probabilistic concepts, one will insist on some elements picked from stochastic processes and stochastic calculus. One will study and apply the Brownian motions, the Levy processes, the Ito’s lemma and the Girsanov theorem among many other things. This course is taught in parallel with the “Computational Finance” course. As a result students can have some deep understanding and intuitions of what stochastic processes can provide.
The Master of Finance - Advanced Studies and Research in Finance is an English-taught Master 2 program, leading to a Master's degree fully accredited by the French state.
In order to apply, students should hold a 4-year Bachelor’s, or a Master 1 or Master 2 or a 4 or 5-year business school diploma.
Holders of a 3-year Bachelor's Degree are not eligible to apply.
Due to the postponement of all international Masters taught in English to the 2020-2021 academic year, students with a good command of French (B2 at least) are welcome to apply for the Master Finance, parcours Études et Recherche en Finance.
Tuition fee for the academic year 2021-2022 is:
Included in this tuition fee:
Not included in this tuition fee:
French students and international French-speaking students are welcome to contact us for more information on the fee applicable to their situation.
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