Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

Nawn Samarpan; Banerjee Ashok

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Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

机译：专有算法交易者在市场压力期间退出流动性吗？

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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.

机译：我们调查专有算法交易员在限制阶市场促进流动性方面的作用。利用来自印度国家证券交易所的订单级数据，我们发现专有算法交易员在高短期股票特异性波动性和极端股票价格运动中增加了限额令供应。即使在高市场波动的时期，它们也不会降低他们的流动性供应。我们将高频交易员的订单定义为从专有算法交易员的订单的子类，这些交易员在不到三毫秒的时间内修改。高频交易的行为模拟其父类的行为。这与快速交易者在出现的速度出现时留下市场的理论是不一致的，尽管当短期波动性的增加比暂时的信息增加时，它们的极限顺序供应行为变得较弱。代理算法交易员和少算法交易者通过减少压力情况下的流动性供应来表现与专有算法交易者相反。市场上的交易者的存在可能灌输对他们不利选择的恐惧。我们记录了代理算法交易商的订单失衡与未来的短期回报呈正相关，而专有算法交易商的订单失衡与未来的短期回报呈负相关。

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《Financial management》 |2019年第2期|641-676|共36页
作者
Nawn Samarpan; Banerjee Ashok;
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Indian Inst Management Udaipur Dept Finance & Accounting Udaipur Rajasthan India;

Indian Inst Management Calcutta Dept Finance & Control Kolkata W Bengal India;

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专利

1. Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress? [J] . Nawn Samarpan, Banerjee Ashok Financial management . 2019,第2期

机译：专有算法交易员会在市场压力期间撤回流动性吗？
2. A Game of Hide-and-Seek between Proprietary and Buy-Side Algorithmic Traders: Causal links with Market Quality [J] . Arumugam Devika, Prasanna P. Krishna Applied Economics . 2021,第40a42期

机译：专有和买边算法交易商之间的隐藏和寻求游戏：具有市场质量的因果关系
3. Who Makes Markets? Liquidity Providers Versus Algorithmic Traders [J] . JOON CHAE, JAEUK KHIL, EUN JUNG LEE The journal of futures markets . 2013,第5期

机译：谁创造市场？流动性提供者与算法交易者
4. Energy futures market liquidity: An analysis of the roles of commercial and noncommercial traders in the NYMEX futures energy complex [C] . Ronald D. Ripple, Roselyne Joyeux 31st IAEE annual international conference . 2008

机译：能源期货市场的流动性：商业和非商业交易者在纽约商品交易所期货能源综合体中的作用分析
5. Liquidity and traders' behaviors in financial markets. [D] . Serban, Laura Elena. 2010

机译：金融市场中的流动性和交易者的行为。
6. Do algorithm traders mitigate insider trading profits?: Evidence from the Thai stock market [O] . Nopparat Wongsinhirun, Pattanaporn Chatjuthamard, Sirimon Treepongkaruna, 2021

机译：算法交易商减轻内幕交易利润吗？：来自泰国股市的证据
7. We develop a sequential trade model of Iceberg order execution in a limit order book. The Iceberg-trader has the freedom to expose his trading intentions or (partially) shield the true order size against other market participants. Order exposure can cause drastic market reactions (“market impact”) in the end leading to higher transaction costs. On the other hand the Iceberg trader faces a loss-in-priority when he hides his intentions, as most electronic limit order books penalize the usage of hidden liquidity. Thus the Iceberg-trader is faced with the problem to find the right trade-off. Our model provides optimal exposure strategies for Iceberg traders in limit order book markets. In particular, we provide a range of analytical statements that are in line with recent empirical findings on the determinants of trader’s exposure strategies. In this framework, we also study the market impact also market impact of limit orders. We provide optimal exposure profiles for a range of hightech stocks from the US SP500 and how they scale with the state-of-the-book. We finally test the Iceberg’s performance against the limit orders and find that Iceberg orders can significantly enhance trade performance by up to 60. [O] . Gökhan Cebiro˜glu, Ulrich Horst 100

机译：我们在限价订单簿中开发了冰山订单执行的顺序交易模型。 Iceberg-trader可以自由地揭露他的交易意图，或者（部分）屏蔽其他市场参与者的真实订单规模。订单暴露最终会导致市场反应激烈（“市场影响”），导致交易成本上升。另一方面，冰山交易员在隐藏他的意图时面临优先权损失，因为大多数电子限价订单都会惩罚隐藏流动性的使用。因此，冰山商人面临着找到正确权衡的问题。我们的模型为限额订单市场中的冰山交易者提供了最佳的风险敞口策略。特别是，我们提供了一系列分析性陈述，这些陈述与最近关于交易者暴露策略决定因素的实证研究结果一致。在此框架下，我们还研究了市场影响以及限价订单的市场影响。我们为来自美国标准普尔500指数的一系列高科技股票提供最佳曝光配置文件，以及它们如何随着本书的规模扩展。我们最终根据极限订单测试了冰山的表现，发现冰山订单可以显着提升贸易业绩高达60％。

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

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