Price pressures

Hendershott, Terrence; Menkveld, Albert J.

doi:10.1016/j.jfineco.2014.08.001

Cited by 285 publications

(144 citation statements)

References 54 publications

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“…For equity markets, empirical evidence in support of the inventory theory has been found by Hansch et al (1998) and Hendershott and Menkveld (2013). These papers detect significant price pressures from dealers' inventories.…”

Section: Potential Sources Of Yield Effects Of Auctionsmentioning

confidence: 89%

Price Effects of Sovereign Debt Auctions in the Euro-Zone: The Role of the Crisis

et al. 2013

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Section: Potential Sources Of Yield Effects Of Auctionsmentioning

confidence: 89%

Price Effects of Sovereign Debt Auctions in the Euro-Zone: The Role of the Crisis

et al. 2013

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“…For the second aspect, based on the previous work by Menkveld and Hendershott [3,10], we introduce a parameter named net threshold, denoted as NT to supervise the net position of HFTs, denoted as np. Suppose V i represents the trading volume in session i and NT is proportional to average trading volume in first periods, so after periods, NT is calculated and works as follows:…”

Section: Strategy For Order Quantitymentioning

confidence: 99%

Exploring Market Making Strategy for High Frequency Trading: An Agent-Based Approach

Xiong

Yamada

Terano

2015

Proceedings of the International Conference on Social Modeling and Simulation, Plus Econophysics Colloquium 2014

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This paper utilizes agent-based simulation to explore market making strategy for high frequency traders (HFTs) and tests its performance under competition environments. After proposing a model representing HFTs' activities in financial market when they act as market makers, we carry out simulations to explore how order price and order quantity affect HFTs' profits and risks. As the result, we find that offering prices around last trading price, as well as taking advantage of order imbalance, increases HFTs' returns. On the other hand, our results show utilizing adaptive order size based on previous order execution rate and setting a net threshold based on average trading volume helps to control the risks of end-of-day inventory. In addition, we introduce the competition environments of increased competitors and decreased latency, so as to see how these factors affect the performance of market making strategy.

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“…The Kalman filter decomposes discrete datasets, such as time series of prices, into both a de-noised fundamental price and a noise component. Utilizing the Kalman filter to decompose market prices is a widely-used approach for financial time series (Brogaard, Hendershott, and Riordan, 2014;Haven, Liu, and Shen, 2012;Hendershott and Menkveld, 2014;Hendershott, Menkveld, Li, and Seasholes, 2013;Lopes and Tsay, 2011;Schwartz and Smith, 2000;Wong, 2010). We describe the mechanisms of the Kalman filter in the following.…”

Section: Price Decompositionmentioning

confidence: 99%