Informed Traders and Limit Order Markets

Goettler, Ronald L.; Parlour, Christine A.; Rajan, Uday

doi:10.2139/ssrn.933358

Cited by 55 publications

(82 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Papers taking this approach include Parlour (1998), Foucault (1999, Foucault, Kadan, and Kandel (2005), Goettler, Parlour, and Rajan (2005), Rosu (2009), andButi, Rindi, Wen, andWerner (2011), among others. Chakravarty and Holden (1995), Kaniel and Liu (2006), and Goettler, Parlour, and Rajan (2009) allow informed traders, but incorporating a dark pool into their models is far from trivial. For a literature review of limit order books, see Parlour and Seppi (2008).…”

Section: Markets and Tradersmentioning

confidence: 99%

Do Dark Pools Harm Price Discovery?

2012

View full text Add to dashboard Cite

Dark pools are equity trading systems that do not publicly display orders. Dark pools offer potential price improvements but do not guarantee execution. Informed traders tend to trade in the same direction, crowd on the heavy side of the market, and face a higher execution risk in the dark pool, relative to uninformed traders. Consequently, exchanges are more attractive to informed traders, and dark pools are more attractive to uninformed traders. Under certain conditions, adding a dark pool alongside an exchange concentrates price-relevant information into the exchange and improves price discovery. Improved price discovery coincides with reduced exchange liquidity.

show abstract

Section: Markets and Tradersmentioning

confidence: 99%

Do Dark Pools Harm Price Discovery?

2012

View full text Add to dashboard Cite

show abstract

“…Parameter κ ∈ [0, 1] specifies the degree to which the fundamental reverts back to the meanr, and u t ∼ N 0, σ 2 s is a random shock at time t. The private component for agent i is a vector i representing differences in the agent's private benefits of trading given its net position, similar to the model of Goettler et al (2009). This private valuation vector reflects individual preferences in the marginal value of the security (e.g., due to risk aversion, outside portfolio holdings of related securities, or immediate liquidity needs), as well as preferences regarding urgency to trade.…”

Section: Valuation Modelmentioning

confidence: 99%

Latency arbitrage in fragmented markets: A strategic agent-based analysis

Wah¹,

Wellman

2017

View full text Add to dashboard Cite

Abstract.We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We employ a simple model of latency arbitrage in which a single security is traded on two exchanges, with price quotes available to regular traders only after some delay. An infinitely fast arbitrageur reaps profits when the two markets diverge due to this latency in cross-market communication. Using an agent-based approach, we simulate interactions between high-frequency and zero-intelligence trading agents. From simulation data over a large space of strategy combinations, we estimate game models and compute strategic equilibria in a variety of market environments. We then evaluate allocative efficiency and market liquidity in equilibrium, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.

show abstract

“…Our study is methodologically associated with the state-of-the-art microstructure models for limit order markets developed by Goettler et al (2005Goettler et al ( , 2009. Goettler et al (2005Goettler et al ( , 2009) introduce dynamic models in which investors have to make asynchronous trading decisions, depending on their information set and the market structure, in which the equilibrium is obtained numerically as in our study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…12 Goettler et al (2009) also show that a limit order market is a 'volatility multiplier'. 13 For instance, 73% of the trading volume on the U.S. stock market in 2009 can be attributed to highfrequency proprietary trading; as compared to practically zero in the mid-1990s (see Hendershott et al, 2011).…”

mentioning

confidence: 97%

“…Given the asynchronous nature of the game, we solve the equilibrium using the algorithm introduced by Pakes and McGuire (2001), which was originally proposed for industrial organization problems with sequential decisions. This algorithm provides a Markov-perfect equilibrium which has been successfully implanted into a dynamic model for limit order markets by Goettler et al (2005Goettler et al ( , 2009, although without exploring the effects of HFT on market quality and stability as in our study. Once we obtain the model equilibrium, we simulate time series of orders and trades, and the complete evolution of the order book as in an authentic limit order market.…”

mentioning

confidence: 99%

See 1 more Smart Citation

How Fast Can You Trade? High Frequency Trading in Dynamic Limit Order Markets

Bernales¹

2013

SSRN Journal

View full text Add to dashboard Cite

We consider a dynamic equilibrium model of high frequency trading (HFT). The model is a stochastic sequential game with endogenous trading decisions in a limit order market. There are two types of agents: fast and slow traders. Fast traders have speed advantages in terms of analysing information and the low-latency transmission of orders. Nevertheless, slow traders can observe and learn from the signals disclosed by fast traders in the market activity. We find that HFT improves market quality by increasing informational efficiency and liquidity. Fast traders make larger average trading profits than slow traders; however the trading profits of slow traders are higher when there is HFT in the market than in the case where HFT is absent. This is due to both the improvements in market quality induced by HFT and the learning process followed by slow traders, which increases the effectiveness of their trading strategies. We report that a cancellation fee, which has already been imposed by some exchanges, may affect negatively the market quality. Finally, we show that HFT traders may have incentives to manipulate market volatility since they can make larger profits through limit orders when we simulate a volatility shock. . 4 In addition, traders can re-enter at the market multiple times to revise and to modify previous trading strategies. However, agents 2 The fundamental value of the asset can be thought of as the discounted value of expected future dividends. 3 This assumption is supported by previous empirical studies on HFT, which show that fast traders are better informed than other market participants (see, e.g., Hendershott and Riordan, 2010;Brogaard, 2010;Kirilenko et al., 2011; and Brogaard et al., 2011). In addition, similar assumptions have already been used in HFT theoretical models by Biais et al. (2012a), Foucault et al. (2012, and Martinez and Roşu (2011). 4 The expected time between arrivals for high frequency traders is lower than for slow traders, since the expected value of an exponentially distributed variable , , with parameter is 1/ .3 cannot instantaneously modify trading decisions due to the fact that cognition limits prevent them from continuously monitoring the market; thus trading plans are 'sticky' (see, e.g., Biais et al., 2012b). Nevertheless, high frequency traders have the possibility of evaluating market changes and modifying previous trading strategies much faster than slow traders. Thus, fast traders and slow traders re-enter at the market according to two Poisson processes at rate and , respectively, where .Currently, the exchanges in which we can find HFT are fully, or at least partially, organized as limit order markets (e.g. BATS U.S. stock exchange, NYSE, NASDAQ, London Stock Exchange, NYSE Euronext, BATS Chi-X Europe). 5 Consequently, the microstructure features and particularities of these types of trading venues should be considered when evaluating the effects of HFT on market quality and stability. 6 Therefore, we consider a limit order market in our dynamic equil...

show abstract

Informed Traders and Limit Order Markets

Cited by 55 publications

References 36 publications

Do Dark Pools Harm Price Discovery?

Do Dark Pools Harm Price Discovery?

Latency arbitrage in fragmented markets: A strategic agent-based analysis

How Fast Can You Trade? High Frequency Trading in Dynamic Limit Order Markets

Contact Info

Product

Resources

About