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Research project

Modelling Complex Uncertain Environments

Project overview

Making predictions of future outcomes in an uncertain environment is a difficult but important task, as these predictions are often required for making informed choices by individuals, companies and Governments. The project developed innovative approaches to modelling information such that predictions could be developed which outperformed market predictions in a complex setting where prediction markets had been thought to use information efficiently. The project revealed that certain types of data are often under-utilised when making predictions, notably complex data which requires some pre-processing, time-related data and more novel data, which it appears market participants are slow to utilise. The project resulted in a number insights regarding the reasons for mis-pricing in prediction markets and means of correcting for likely over-reactions to certain types of information. The project resulted in several papers being published in leading international journals and formed the basis for a number of subsequent PhD theses.


Lead researcher

Professor Ming-Chien SUNG

Professor of Risk And Decision Sciences

Research interests

  • Financial technology (Fintech) and cryptocurrency analysis
  • Risk analysis using Big Data
  • Artificial intelligence (AI)
Other researchers

Professor Frank McGroarty

Chair in Computational Finance & FinTech

Research interests

  • Empirical Market Microstructure
  • Computational Finance
  • Systematic Mispricing

Emeritus Professor Johnnie Johnson

Research interests

  • Risk taking behaviour
  • Use of information in betting markets
  • Decision-making under uncertainty

Dr Tiejun Ma

Associate Professor

Research interests

  • Financial Risk Modelling
  • Investors Behaviour Modelling
  • Fintech

Collaborating research institutes, centres and groups

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