RNCP Code: 35913
The TSM Master in Finance among the top 10 Masters in Finance that are EFMD-Accredited worldwide
The European Foundation for Management Development (EFMD) Accreditation is the most thorough programme accreditation system for business and management programmes with an international perspective. According to EFMD: "The TSM Master in Finance is a robust academic programme with a commendable connection with the world of practice. The programme demonstrates several examples of best practices among which the use of ILOs to develop the provision but also to conduct programme delivery and assessment. The programme also offers a unique academic approach combined with the purpose of applying knowledge and skills to solving real-life problems and practical learning opportunities through internships."
The Master in “Finance and Information Technology” (FIT) provides students with skills in both finance and financial IT.
Financial technology, also known as “fintech”, aims at proposing solutions to all actors involved in financial decisions, such as financial institutions, firms and single individuals using specialized IT tools.
The Master provides a rigorous preparation in all fields of finance together with a solid knowledge of the most recent IT tools such as business intelligence and blockchain. This double competence prepares students for jobs both in corporate finance and financial markets where an understanding of IT is essential. Through practical cases and projects, students develop complementary practical skills like problem solving, communication and business etiquette.
Students pursuing the Finance and Information Technology track will learn to:
- Analyze and exploit big data to extract information to take financial decisions;
- Implement and upgrade business processes which collect information to support company’s decisions;
- Design and implement business intelligence systems applied to financial contexts.
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Asset pricing
ECTS : 5.00
HCM : 30.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
By the end of this course, students should be able to:
- Apply consumption-based asset pricing models to assess risk premia
- Explain the market efficiency hypothesis and its empirical validity
- Discuss the use and role of factor models
- Apply asset pricing models to evaluate investment performance
- Compute the value of fixed income instruments
- Choose the right fixed income instrument according to a financial objective
- Prérequis
Students are expected to have a minimum preparation in mathematics, statistics and econometrics. An introductory course of Asset Pricing is a plus.
Students are expected to have a basic knowledge of standard financial instruments (bonds, forward contracts, options).
Corporate finance
ECTS : 5.00
HCM : 30.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
At the end of this course, students should be able to:
- Apply standard conceptual frameworks used in finance to major corporate events (like e.g. IPOs, M&A, dividend distribution, corporate governance decisions, early stage fund raising,...)
- Evaluate the financial implications of these events
- Identify the ethical issues at stake for corporations
- Appreciate the role of corporate governance.
- Provide concise summaries of complex cases in both written and oral form
- Work effectively in a group
- Prérequis
Foundations on corporate finance theory (Modigliani-Miller, trade-off theory, agency issues, asymmetric information and financial decisions).
Basics of corporate valuation and accounting (financial statements, valuation methods: DCFs, multiples, cost of capital).
Financial econometrics
ECTS : 5.00
HCM : 30.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
On completion of this course, students should be able to:
- Be familiar with the econometrics techniques used in financial analysis (e.g. event studies, differences- in-differences, time series analysis, panel data analysis)
- Develop an econometric model to test an economic hypothesis
- Implement such an econometric model using standard econometric software (e.g. identify the data, create variables, apply econometric techniques)
- Interpret the outcomes of empirical analyses
- Present the results of the empirical analysis in a professional manner.
- Prérequis
Intermediate knowledge in finance theory and in econometrics.
Information technology for finance
ECTS : 5.00
HCM : 30.00
Python for Finance
ECTS : 4.00
HCM : 24.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
By the end of the course, students should be able to:
- Write a basic Python program;
- Apply different programming paradigms like functional and object oriented programming;
- Use versioning control software;
- Read in and manipulate structured data using dedicated libraries;
- Create some basic models and algorithms in a structured and documented way;
- Synthesize information and present the results in a written form.
- Prérequis
Laptop or access to university computers.
Basic understanding of math and logic.
Introduction to SAP
HCM : 12.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
After this training, students should be able to:
- Explain the main concepts of an ERP and discuss its opportunities and challenges
- Describe the structure of a SAP system
- Navigate through a SAP system
- Execute simple SAP transactions
- Synthesize information and present the results in a written form
- Work effectively in team
- Prérequis
Basic knowledge in financial and management accounting
Basic knowledge in information technology
FinTech
ECTS : 4.00
HCM : 24.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
By the end of the course, students should be able to:
- Articulate the mechanism behind the blockchain
- Collect and analyse blockchain data
- Assess applications of blockchain to financial services
- Synthesise information and make focused presentation
- Describe how peer-to-peer lending platforms work
- Assess to what extent peer-to-peer lending complements traditional banking
- Prérequis
A basic understanding of the financial services industry. General knowledge of personal computer; Excel (intermediate); Python (beginner).
Big Data in Finance
ECTS : 4.00
HCM : 24.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
By the end of this course, students should be able to:
- Extract useful information for business improvement from structured and unstructured huge datasets
- Numerically evaluate a model
- Manipulate huge financial databases
- Write efficient codes for any empirical issue
- Prérequis
Students should have followed a programming class, a statistics or econometrics course or its equivalent.
Students must have their own laptops with the following software:
R: https://www.r-project.org/about.html
Rstudio: https://www.rstudio.com/products/rstudio/download2/
Microsoft Machine Learning Server: https://docs.microsoft.com/en-us/machine-learning-server/install/machine-learning-server-install
Financial Information Systems
ECTS : 4.00
HCM : 24.00
Langue d'enseignement : Anglais
Economics for finance
ECTS : 5.00
HCM : 30.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
Upon completion of this course, students will be able to:
- Describe the drivers of international trade and capital flows
- Analyze the functioning of global financial markets (exchange rates, parity relations, international arbitrage)
- Master the fundamental tools for international risk management
- Work with economic models that underpin theories of intermediation and corporate finance
- Understand the interactions between financial markets and financial decisions
- Undertake a model-based analysis of financial decision-making by companies, investors and intermediaries
- Apply ethical considerations to global issues
- Provide concise summaries of complex cases in written form
- Prérequis
Previous exposure to basic finance concepts is a plus.
Internship / Entrepreneurial project
ECTS : 15.00
Langue d'enseignement : Anglais
Machine Learning for Finance using Python
ECTS : 4.00
HCM : 24.00
HTD : 0.00
Langue d'enseignement : Anglais
Financial reporting
ECTS : 4.00
HCM : 24.00
International Financial Reporting Standards (IFRS)
HCM : 12.00
Langue d'enseignement : Anglais
- Objectifs pédagogiques
At the end of the course, students should be able to:
- Describe principles and characteristics of IFRS/IAS for consolidated financial statements
- Evaluate differences between IFRS/IAS and GAAPs
- Apply intricate IFRS/IAS requirements (e.g. IAS 12, IAS 21)
- Execute consolidation of simple financial statements
- Prérequis
A first course in financial accounting and consolidated financial statements.
Statutory audit
HCM : 12.00
Langue d'enseignement : Anglais
Prerequisites for all candidates:
→ Successful completion of 240 ECTS
→ An English language test for non-English speakers (B2 required). List of the English language certificates accepted here.
→ Examination of candidate’s application and possible interview
Please note that candidates who have completed the TSM M1 in Finance are given priority.
Need help? Consult our FAQs or contact assistance-admission@tsm-education.fr
This Master is a programme in partnership with Toulouse School of Management (TSM) and Toulouse School of Economics (TSE).
TSM offers theoretical, high-quality instruction with significant relevance to the industry. During the internships I did for my Master in Finance, employers showed a strong interest in this programme. After graduating, I had no issues finding a first job on par with the positions offered to students graduating from engineering schools. Close interactions with the teachers, who were available and committed to our success, is a real asset of the Finance and Information Technology Master.
Marina GRIMAUD
Class 2013/2014 | SAP Finance functional Consultant
92%* of students have found a job within 3 months of graduation.
27%* of students have found a job abroad.
The average annual net salary two years after graduation was above 40,000€*.
Career opportunities:
- IT system consultant in Finance
- Business Analyst
- Data analyst
- Financial IT manager
- Project manager
Preferred sectors: consulting firms, IT service industry, merchant and investment banks, large companies.
*Source: 2016, 2017, 2018 graduate survey 2 years after completing the M2 FIT.
