How European Market State Developed during COVID-19

In our previous blog post, we highlighted that the US markets had moved from a passive state (book dynamics lead by passive orders) to an active state (dynamics lead by aggressive orders heavily hitting the top of the book, with high volumes, low order resting time and wider spreads) during the Covid-19 crisis (especially in March).

 

The data in the US showed that, due to the market volatility created around uncertainty of the potential impact of Covid-19, traders were willing to execute orders quickly and aggressively regardless of higher implied trading costs (wider spreads). This change in dynamic highlights that the opportunity cost of not executing trades in volatile markets outweighed the cost of crossing wide spreads in veritable size.

In this new interactive animation, we look at the 200 most liquid stocks for Europe across the 4 biggest markets, France, Germany, Switzerland and the UK. The effect of Covid19 volatility on Europe in aggregate mirrors the impact seen in the US. Click on the legend to select & de-select As with the US, European stocks moved from a passive state, with longer resting times, tighter spreads and lower volumes executed, to a more aggressive state during the height of the COVID-19 crisis. Towards the end of April, market state reverted back to longer order resting time and lower volumes, however spreads remained wide even into the start of May.

Examining the data on a country level, bubbles are clustering per market, top Swiss equities (depicted in orange) which had a longer average resting time than German stocks before the crisis, continued to do so throughout the crisis, suggesting a less fundamental change in market state than the German or British markets.

The different markets show bubbles repartition along straight lines with the same slope in that log-log plot - in that respect the data presented here is less noisy than what we had observed in the US. Those straight lines in a log-log plot, suggest a power law relation between the number of executions and the resting time of the orders. Therefore one can infer, should such an observation be made at an early enough stage, a level of predictability about the direction of travel of market state. A widening of spreads, coupled with shorter average resting times, can be indicative of a market state where traders are happy to take slippage to better position themselves against wider market swings.

Aggressive market states, such as those depicted by our plot, should prove rich pickings for market participants who have the technology and data architecture in place to identify and capitalise on wider spreads and higher volumes. Sudden market swings, which seem unpredictable to the naked eye, uncover themselves through access to granular market data & analytics and the tools to analyse them. At BMLL we provide our clients with the data and analytics they need to understand how markets behave and outperform their peers.

The plot is interactive allowing users to select individual securities by double clicking once on the ticker symbol in the legend and hover over each bubble to visualise the underlying data.

Notes: The Y-axis represents the average order resting time The X-axis represents the number of executions The bubble size represents bid-offer spreads.