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Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?
Author(s) -
Nawn Samarpan,
Banerjee Ashok
Publication year - 2018
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12238
Subject(s) - market liquidity , volatility (finance) , high frequency trading , order (exchange) , monetary economics , market maker , stock (firearms) , financial economics , stock exchange , economics , order book , stock market , price discovery , business , algorithmic trading , finance , mechanical engineering , paleontology , horse , biology , engineering , futures contract
We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.