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Research Areas

Quantitative Finance

Quantitative Finance covers all applications of quantitative methods to finance (mathematics, statistics, computational methods). The activities of the research team are in different fields:

 

  • Decision under risk and ambiguity.
  • Heterogeneous agents in financial economics.
  • Financial markets and asset pricing.
  • Intertemporal asset pricing.
  • Asset pricing and allocation.
  • Portfolio theory.
  • Risk measures.
  • Pension funds.
  • Performance measurement.
  • Hedge funds.
  • Risk models and model risks.
  • Financial econometrics.
  • Volatility and Risk Management.
  • Systemic Risk.
  • Management and regulation of financial risks.
  • New product design.
  • Valuation of derivative products.
  • Real options/investment under uncertainty.
  • Modeling of commodity prices
  • International Finance.
  • Performance of machine learning models.
  • Forecasting methods and scenario simulations.
  • Economics of R&D.

Data Science

Data Science addresses the boundaries between humans, computers, and real and artificial worlds: what people see, how they interact with what they see and how they analyze data and understand patterns from what they see using machine learning or statistical methods. Effective research in this domain integrates many areas of Computer Science. “Data” as a subject also introduces major economic, cultural and social challenges, from new business models to the protection of personal data. Each of the functions of a business, from finance to HR, can study these challenges from their own viewpoint. But it is relevant to develop a forum where each of these specialties can meet and compare their approach to develop transversal methods and projects, and possibly a common vision. In particular, the research team aims at becoming a vehicle for corporate partnerships with the school, where collecting and using data become central dimensions in business strategy.

The activities of the research team are in different fields:

 

  • Data Mining
  • Machine Learning
  • Internet of Things
  • Performance Assessment
  • Network Analysis
  • Quality of Service and Quality of Experience
  • data visualization, social media analysis, natural language processing, network analysis, data mash-ups
  • Scientometrics
  • Artificial Intelligence
  • Human-Machine Interaction
  • Human-Robot Interaction
  • Digital Marketing
  • Artificial Intelligence
  • Multiagent Systems
  • Automated Auctions
  • Knowledge Management
  • Digital learning 
  • Integration of new technologies in teaching
  • Digital work environments
  • Digital natives

Management Science

The research team performs basic and applied research for solving complex problems arising in Decision Systems. The focus is on computational intelligence approaches and optimization methodologies tailored to specific practical applications such as logistics/transportation, customer service, manpower planning or yield/revenue management. The specific research areas where the team is conducting research are shown below:

  

  • Interface research between operations management and information systems
  • Big data-driven logistics systems
  • IT- Enabled Intelligent Manufacturing, Industry 4.0, Internet of Things
  • Facility layout and planning, Warehousing
  • Sustainable operations
  • AI-based smart products and service
  • DEA-based performance evaluation of information and operations systems
  • Supply Chain Management
  • CSR and Supply Chain
  • CSR and performance
  • CSR Reporting and performance
  • CSR and governance
  • Supply Chain management
  • Sustainable Supply Chain Management
  • B2B relationships
  • Econometric models to quantify the impact of marketing actions
  • Multicriteria Decision Aid
  • Economics of innovation

Adresse

emlyon business school
23 Avenue Guy de Collongue,
69130 Écully

Contactez-nous

Nicole Robin
robin@em-lyon.com