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How particle physics could prevent financial fraud

A new collaboration agreement sees CERN data analytics used to help protect commodity and financial markets from fraud

CERN PHOTOWALK 2010 - Computer Centre - Roger Claus
Similarities between the billions of particle collisions in CERN’s Large Hadron Collider and the high-speed trading on commodity futures markets have led to a new research collaboration (Image: Roger Claus/CERN)

Every day, commodity markets trade millions of food ingredients and more, so detecting fraud can be challenging. A new collaboration agreement between CERN, the Commodity Risk Management Expertise Center (CORMEC) and Wageningen University & Research (WUR) will now use advanced data analytics from particle physics to help protect commodity and financial markets from fraud. The insights gained could be used by governments and regulators to improve market stability.

During a visit to CERN, WUR economics professor Joost Pennings realised the similarities between the billions of particle collisions in CERN’s Large Hadron Collider and the high-speed trading on commodity futures markets. Most transactions, or collisions, show no anomalies. But when they do, this may lead to new ground-breaking insights for both economists and physicists.

Hence this new collaboration plans to combat fluctuations in markets caused by anomalies, by combining the unique commodity and financial market data and understanding from CORMEC and WUR with CERN’s ROOT data analysis expertise and techniques.

“We see that ROOT, CERN’s scientific big data analytics framework, has the potential to be a game changer for finance data analytics. It's exciting that ROOT can serve society also outside its core domain of high-energy physics.”

– Dr Axel Naumann, CERN senior applied physicist and ROOT project leader

This collaboration will use data analytics to diagnose manipulation in commodity and financial markets. This should enable regulators to create safer and more stable environments for trading, leading to improved regulation and market integrity. The research may also lead to new diagnostic tools for predicting financial instability, which will indirectly help risk management.

The three-year knowledge-transfer project is named HighLo (High Energy Physics Tools in Limit Order Book Analysis) and is supported by the Province of Limburg in the Netherlands. The first results are expected at the end of 2020.