High Frequency Trading and Stock Herding

In financial markets, the potential tendency of traders to follow some type of consensus action is referred to as herding. Traders might herd intentionally (i.e. they intend to mimic others’ behaviour or follow the market consensus), or they might herd unintentionally (spurious herding). The literature shows mixed evidence of herding which mainly focuses on human traders, while herding evidence from non-human traders such as algorithmic traders and high frequency traders is absent from the herding literature. Therefore, in this thesis, the role of high frequency trading (hereafter HFT) in herding is discussed in the context of a single market and the most popular exchanges around the world. The thesis employs quotes and trade volumes to proxy HFT in the US equity market and provide evidence that HFT induces spurious herding when trading intensity is high. Moreover, the colocation start date and HFT effective date are used from ten of the most popular global exchanges to proxy the emergence of HFT and estimate the effect of HFT on herding. Again, it is shown empirically that the emergence of HFT induces herding even during the financial crisis period. Finally, the implementation of MiFID II from the beginning of 2018 allows access to data which flags algorithmic trading under different traders. Instead of using different methods to proxy algorithmic trading, we can therefore identify each algorithmic trade and estimate the effect of different traders (i.e., human traders, algorithmic traders, and market makers) on herding. The results also demonstrate significant evidence of herding. Overall, the thesis shows that HFT induces herding, given the increasing trading intensity. To best of my knowledge, this is the first time the herding literature has examined HFT and algorithmic trading or shown such findings.