Centre for Productivity and Performance


Professor Baibing Li BSc (Yunnan), MSc (Vrije Brussel), PhD (Shanghai Jiao Tong)

Photo of Professor Baibing Li

Professor of Business Statistics and Management Science

Bayesian statistical modelling; data mining; statistical modelling for financial markets; transportation and traffic studies

Baibing is Professor of Business Statistics and Management Science at the School of Business and Economics.  Prior to his current appointment, he was a Lecturer in Statistics in the School of Mathematics and Statistics at Newcastle University. In 2004, he moved to Loughborough University, as a Lecturer in the School of Business and Economics, where he was subsequently appointed as Reader in 2007, and then Professor in 2011. He is programme director of Management Sciences.

Baibing’s research focuses on statistical machine learning and data mining, covering a wide range of topics such as predictive modelling, dimension reduction, and forecasting, as well as applications to various business and management problems in transport studies and financial markets.

Baibing’s research focuses on statistical machine learning and data mining, covering a wide range of topics such as predictive modelling, dimension reduction, and forecasting.

In his recent theoretical research on state space models and Markov switching processes, Baibing develops new algorithms to deal with state estimation/forecasting problems for stochastic nonlinear dynamic systems when new data is collected in real time.  His research interests also cover predictive modelling, dimension reduction methods, kernel methods, and mixture models.

In the recent years much of his work has involved applications to transport studies and financial markets. The former includes transportation demand analysis, travel behaviour modelling, and intelligent transportation systems. The latter involves statistical modelling for financial markets, hedge fund performances, etc.

Baibing enjoys supervising his PhD students, whose research topics span a wide range of business and management problems such as, for example, stochastic frontier analysis for economic growth, traffic flow modelling and analysis with high-frequency data, liquidity timing analysis of hedge funds, asset portfolio allocation, and financial market modelling and forecasting via Markov switching approach, hedge funds performances and market competition. Baibing welcomes applications for PhD research in various areas of business analytics, such as financial market modelling and forecasting, data mining and applications in marketing.  

Baibing is Member of editorial advisory board of Transportation Research Part B (Methodological). In the recent years, Baibing served as external examiner at Manchester Business School (2011-2014) and UCD College of Business, University College Dublin (2015-2018). He is a reviewer for a number of journals including Journal of the Royal Statistical Society: Series A, Series B, and Series CTransportation Research: Part B and Part CIEEE Transactions on Automatic ControlJournal of the Operational Research SocietyEuropean Journal of Operational Research, etc.

Baibing has published research articles in a wide range of outlets. His recent representative publications include:

  • Li, B, Liu, C, Chen, W-H (2017) An Auxiliary Particle Filtering Algorithm with Inequality Constraints, IEEE Transactions on Automatic Control, 62(9), pp.4639-4646.
  • Li, B and Hensher, D (2017) Risky Weighting in Discrete Choice, Transportation Research Part B: Methodological, 102, pp.1-21.
  • Chou, HI, Li, B, Yin, X, Zhao, J (2017) Variables in Dollar Terms Versus in Rate Terms: The Case of Market Feedback on Merger Negotiations, International Review of Financial Analysis, 49, pp.138-145.
  • Luo, J, Tee, K, Li, B (2017) Timing the Liquidity in the Foreign Exchange Market: Did the Hedge Funds Do It?, Journal of Multinational Financial Management, 40, pp.47-62.
  • Li, B, Luo, J, Tee, K (2017) The Market Liquidity Timing Skills of Debt-Oriented Hedge Funds, European Financial Management, 23(1), pp.32-54.