Abstract
Financial markets have come across a phenomenal adoption of advanced and complex technologies in the pursuit of efficient markets. Algorithmic Trading (AT) is one of the prominent moves in this direction and is widely adopted across world markets. The existing literature on AT and its impact on markets is still in the nascent stage primarily due to the inability of most of the markets to directly identify AT. In this study, we directly identify AT and examine its impact on trade sizes which has a key impact on liquidity and price impact of trades. We also use the inverse of Order-to-Trade (1/OTR) ratio as a measure of algorithmic trading efficiency and examine its relationship with size. It is expected that AT has the capability to break large orders into smaller sizes in order to access liquidity and reduce price impact. In this study, we provide empirical evidence for the size effects of AT with direct identification of AT.
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