Information, learning and High–Frequency Trading in electronic call auction markets

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This paper investigates the impact of High Frequency quoting on the efficiency
of prices in order driven call auction markets. Based on the framework of noisy
rational expectations equilibria, we first provide a theoretical model where HFT
and non–HFT traders coexist in a transparent order–driven call market. As the
pre–call order batching procedure evolves in time, HFTs improve the precision
of their signal by collecting public information through various electronic net-
works. To this extent, price efficiency is accelerated significantly as additional
information is impounded into prices. Moreover, the model predicts that price
efficiency is positively related to the number of HFTs in the market. To test
empirically the prediction of our model, we utilize a unique set of intraday data
that includes HFT flagged messages, enabling us to distinguish between com-
puter and human trading. Our empirical analysis provides evidence that HFTs
contribute significantly to price efficiency, corroborating our theoretical analysis.

Author(s):

Panagiotis Anagnostidis    
EUROFIDAI
France

Patrice Fontaine    
EUROFIDAI
France

Christos Varsakelis    
Université catholique de Louvain
Belgium