Robeco is an asset manager that manages over 200 billion Euro in quantitative and fundamental investment strategies. The implementation of these strategies results in sizeable transactions, so understanding liquidity and transaction cost drivers is key for optimal implementation. Consequently, Robeco has built up unique data sets related to liquidity and trading under a plethora of market conditions. The aim of these thesis projects is to improve Robeco’s in-house models for assessing market liquidity, market impact, i.e., to what extent the trade influences market prices.
Modelling liquidity and market impact comes with challenges. Most important is the low signal to noise ratio caused by random market movements. Other challenges include imbalances in the data set, and stability of estimates. One direction to explore in this thesis is evaluating the existing models, and engineering features that would improve them.