Spoofing and Price Manipulation in Order-Driven Markets

Q3 Mathematics
Á. Cartea, S. Jaimungal, Yixuan Wang
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引用次数: 8

Abstract

ABSTRACT We model the trading strategy of an investor who spoofs the limit order book (LOB) to increase the revenue obtained from selling a position in a security. The strategy employs, in addition to sell limit orders (LOs) and sell market orders (MOs), a large number of spoof buy LOs to manipulate the volume imbalance of the LOB. Spoofing is illegal, so the strategy trades off the gains that originate from spoofing against the expected financial losses due to a fine imposed by the financial authorities. As the fine increases, the investor relies less on spoofing, and if the fine is large, the investor does not spoof the LOB. The arrival rate of buy MOs increases because other traders interpret the spoofed buy-heavy LOB as an upward pressure on prices. When the fine is low, spoofing considerably increases the revenues from liquidating a position. Spoofing increases the PnL because (i) the investor employs fewer MOs to draw the inventory to zero and benefits from roundtrip trades, which stem from spoof buy LOs that are ‘inadvertently’ filled and subsequently unwound with sell LOs; and (ii) the midprice trends upward when the book is buy-heavy; therefore the spoofer sells the asset at better prices.
订单驱动市场中的欺骗和价格操纵
我们模拟了一个投资者的交易策略,他欺骗限价单(LOB)来增加从出售证券头寸中获得的收入。该策略除了使用卖出限价单和卖出市价单外,还使用大量的欺骗性买入限价单来操纵LOB的成交量不平衡。欺骗是非法的,因此该策略将欺诈带来的收益与因金融当局的罚款而导致的预期经济损失进行权衡。随着罚款的增加,投资者对欺骗的依赖减少,如果罚款很大,投资者不会欺骗LOB。因为其他交易者将伪造的大量买入的LOB解释为对价格的上行压力,所以买入最大限度的到达率会增加。当罚款较低时,欺骗会大大增加平仓的收入。欺骗增加了PnL,因为(i)投资者使用更少的mo将库存降至零,并从往返交易中获益,往返交易源于欺骗的买入LOs,这些LOs“无意中”被填满,随后被卖出LOs平仓;(2)在买入较多的情况下,中间价呈上升趋势;因此欺诈者以更好的价格出售资产。
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来源期刊
Applied Mathematical Finance
Applied Mathematical Finance Economics, Econometrics and Finance-Finance
CiteScore
2.30
自引率
0.00%
发文量
6
期刊介绍: The journal encourages the confident use of applied mathematics and mathematical modelling in finance. The journal publishes papers on the following: •modelling of financial and economic primitives (interest rates, asset prices etc); •modelling market behaviour; •modelling market imperfections; •pricing of financial derivative securities; •hedging strategies; •numerical methods; •financial engineering.
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