Professor Mark Salmon

Currently teaching Applied Asset Management on the MPhil. Economics and Finance at Cambridge University, he is also a Visiting Professor at Imperial College and Director of Research in High Frequency Trading in the Centre for Advanced Financial Engineering, (CAFÉ) and an advisor to  Lansdowne Partners, Austria. From 2010-2015 he acted as senior scientist for BHDG Systematic Trading a managed futures CTA and until 2018 he advised Old Mutual Global Investors.

Up to September 2011 he was Professor of Finance at Warwick Business School and Director of the Financial Econometrics Research Unit and Warwick Finance Research Institute. Previously he  has held appointments as Deutsche Morgan Grenfell Professor of Financial Markets at Cass Business School, London, and Professor of Economics and Chair of the Economics Department at the European University Institute, Florence along with visiting positions at Nuffield College, Oxford, Princeton, Paris I Sorbonne, the Mathematics Department of Kings College London, Aix-Marseille, Bordeaux IV and Illinois. He has published widely in the leading international journals in Statistics, Economics and Finance. He has supervised 35 PhD students through to completion and has generated over £4 million in research funding from a range of research council and private sources.

He has also worked in the financial sector for some twenty five years, served as a consultant to a number of city institutions and was until recently an advisor to the Bank of England for 6 years. He was a member of a "Task Force" set up by the European Commission to consider exchange rate policy for the EURO. He has been a member of the European Financial Markets Advisory Panel and has worked with the National Bank of Hungary on transition policies towards membership of the European Union. He  has acted as senior scientist for BHDG Systematic, a managed futures fund and has been an advisor to Old Mutual Global Investors, with respect to a global market neutral equity fund, Deutsche Asset Management on Fixed Income strategies and a number of similar positions within the finance industry.

His general research interests lie in the theory and application of Statistical Methods and Financial Econometrics, the development of trading and predictive analytics including machine learning,

He is currently working on the design of robust asset allocation strategies using parametric portfolios and signal identification developing the use of causal machine learning combined with elastic net and related relaxation methods for minimal dimension selection. An important part of this is the use of post model selective inference to control the FDR ( False Discovery Rate) and more generally counterfactual or invariant causal prediction given the observational time series found in finance. Relating this potentially critical issue for the application of machine learning in Finance is driving his research at the moment.

He has also worked on the analysis of high frequency data, modelling order books and associated execution strategies based on Hawkes processes and duration based volatility;  measuring herding and sentiment, some aspects of International Macroeconomics- FX strategies, on the impact of Knightian Uncertainty on position sizing and volatility timing. He is also interested in structural volatility prediction, Dynamic Score models in predictive analytics, Hierarchical mixtures of experts; state dependent logistic mixtures - essentially self adaptive mixture models and consequent execution strategies with pockets of predictability and finally Multivariate Dynamic Time Warping.

He has written papers, researched and applied strategies in all asset classes, futures, stocks, fixed income and FX, and has co-authored and/or edited 6 books and is currently writing a new book on Asset Management.

Recent Papers:
For a fuller list of papers see my page on

  •  The information content of a limit order book: The case of an FX market Journal of Financial Markets , Volume 15, Issue 1, February 2012, Pages 1–28 with Roman Kozhan-  on most cited papers  list in Journal of Financial Markets
  • Intensity based volatility and covariance estimation via Hawkes processes, with Bingbing Li (August 2012)
  • Market Impact functions and optimal execution implied by Hawkes processes, with Bingbing Li (September 2012)
  • Sentiment , Beta Herding and Cross-sectional Asset returns, with Soosung Hwang, (September 2012)
  • Andreea-Ingrid Funie, Mark Salmon and Wayne Luk, A Hybrid Genetic-Programming Swarm-Optimisation Approach for Examining the Nature and Stability of High Frequency Trading Strategies, 2014 13th International Conference on Machine Learning and Applications, IEEE, pp. 29-34, 2014
  • Andreea-Ingrid Funie, Paul Grigoras, Pavel Burovskiy, Wayne Luk, and Mark Salmon,Reconfigurable acceleration of fitness evaluation in trading strategies, IEEE 26th International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 210-217, 2015
  • Evaluating Strategy Performance, paper presented at London Quant Group, May 14th, 2015
  • Herding and Cross-sectional asset returns, August 2015, with Soosung Hwang, submitted
  • Strategy Confidence Sets, accounting for data mining bias. May 2016
  • .Kurek, Maciej, Becker, Tobias, Guo, Ce, Denholm, Stewart, Funie, Andreea-Ingrid, Salmon, Mark, Todman, Tim and Luk, Wayne, Self-aware Hardware Acceleration of Financial Applications on a Heterogeneous Cluster, Self-aware Computing Systems: An Engineering Approach, Springer International Publishing, pp. 241-260, 2016.
  • Andreea-Ingrid Funie, Liucheng Guo, Xinyu Niu, Wayne Luk and Mark Salmon, Custom Framework for Run-Time Trading Strategies, Applied Reconfigurable Computing, ARC 2017, Lecture Notes in Computer Science, Springer, Vol 10216, pp. 154-167, 2017
  • Andreea-Ingrid Funie, Paul Grigoras, Pavel Burovskiy, Wayne Luk, and Mark Salmon, Run-time Reconfigurable Acceleration for Genetic Programming Fitness Evaluation in Trading Strategies, Journal of Signal Processing Systems, Vol. 90(1), pp. 39-52, 2018
  • Under-confidence, Pessimism and the Low beta Anomaly, with Soosung Hwang, 2017.
  • Next steps in Machine Learning for Finance, 2018, Ravenpack London Conference.
  • The importance of Causal Machine Learning in Asset Management, 2019, presented at Goldman Sachs, Thalesians, RIDE Royal Holloway London, CME Group, at different times during 2019.
  • Causality and Machine Learning in Asset Management, 2020, working paper.