Large Option Trades, Market Makers, and Limit Orders

CFA Digest ◽  
1997 ◽  
Vol 27 (2) ◽  
pp. 47-48
Author(s):  
Terence M. Lim
Keyword(s):  
2012 ◽  
Vol 1 (4) ◽  
pp. 139-164
Author(s):  
Austin Murphy ◽  
Hong Qian ◽  
Yun Zhu ◽  
Ranadeb Chaudhuri

This research finds some empirical evidence that the sale of stock without delivering shares can contribute to pressuring down the equity prices of companies seeking to raise capital. By allowing for the delayed effects on prices of limit orders by naked shorts, a significant negative impact on equity value per share is discovered but only for naked short selling by market makers and only on stocks of firms in urgent need of external financing.


2018 ◽  
Vol 6 (3-4) ◽  
pp. 1-26
Author(s):  
Hoda Heidari ◽  
Sébastien Lahaie ◽  
David M. Pennock ◽  
Jennifer Wortman Vaughan

2018 ◽  
Vol 55 (3) ◽  
pp. 667-681
Author(s):  
Vít Peržina ◽  
Jan M. Swart

AbstractWe consider a simple model for the evolution of a limit order book in which limit orders of unit size arrive according to independent Poisson processes. The frequencies of buy limit orders below a given price level, respectively sell limit orders above a given level, are described by fixed demand and supply functions. Buy (respectively, sell) limit orders that arrive above (respectively, below) the current ask (respectively, bid) price are converted into market orders. There is no cancellation of limit orders. This model has been independently reinvented by several authors, including Stigler (1964), and Luckock (2003), who calculated the equilibrium distribution of the bid and ask prices. We extend the model by introducing market makers that simultaneously place both a buy and sell limit order at the current bid and ask price. We show that introducing market makers reduces the spread, which in the original model was unrealistically large. In particular, we calculate the exact rate at which market makers need to place orders in order to close the spread completely. If this rate is exceeded, we show that the price settles at a random level that, in general, does not correspond to the Walrasian equilibrium price.


2019 ◽  
Vol 32 (12) ◽  
pp. 4997-5047 ◽  
Author(s):  
Marcin Kacperczyk ◽  
Emiliano S Pagnotta

Abstract Using over 5,000 trades unequivocally based on nonpublic information about firm fundamentals, we find that asymmetric information proxies display abnormal values on days with informed trading. Volatility and volume are abnormally high, whereas illiquidity is low, in equity and option markets. Daily returns reflect the sign of private signals, but bid-ask spreads are lower when informed investors trade. Market makers’ learning under event uncertainty and limit orders help explain these findings. The cross-section of information duration indicates that traders select days with high uninformed volume. Evidence from the U.S. SEC Whistleblower Reward Program and the FINRA involvement addresses selection concerns. Received January 11, 2017; editorial decision December 17, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.


1996 ◽  
Vol 9 (3) ◽  
pp. 977-1002 ◽  
Author(s):  
Henk Berkman
Keyword(s):  

2017 ◽  
Vol 03 (01) ◽  
pp. 1750009 ◽  
Author(s):  
Charles-Albert Lehalle ◽  
Othmane Mounjid

This paper is split in three parts: first, we use labeled trade data to exhibit how market participants’ decisions depend on liquidity imbalance; then, we develop a stochastic control framework where agents monitor limit orders, by exploiting liquidity imbalance, to reduce adverse selection. For limit orders, we need optimal strategies essentially to find a balance between fast execution and avoiding adverse selection: if the price has chances to go down, the probability to be filled is high, but it is better to wait a little more to get a better price. In a third part, we show how the added value of exploiting liquidity imbalance is eroded by latency: being able to predict future liquidity consuming flows is of less use if you do not have enough time to cancel and reinsert your limit orders. There is thus a rationale for market makers to be as fast as possible to reduce adverse selection. Latency costs of our limit order driven strategy can be measured numerically. To authors’ knowledge, this paper is the first to make the connection between empirical evidences, a stochastic framework for limit orders including adverse selection, and the cost of latency. Our work is a first step to shed light on the role played by latency and adverse selection in optimal limit order placement.


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