Investment in high-frequency trading technology: A real options approach

Delaney, Laura

doi:10.1016/j.ejor.2018.03.025

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…High-frequency trading Over the last decades, financial markets have changed significantly and become fragmented. Hence, traders can search across many markets, but this requires costly infrastructure and technology to make trading profitable [8]. In addition, high speed trading is now considered to be a crucial part of the trading technology.…”

Section: Related Workmentioning

confidence: 99%

To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders

Kokabisaghi¹,

Pauwels²,

Dorsman³

2019

Preprint

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In this paper we re-analyse the transition from sure to probabilistic sniping as explored in Menkveld and Zoican [14]. In that paper, the authors introduce a stylized version of a competitive game in which high frequency traders (HFTs) interact with each other and liquidity traders. The authors show that risk aversion plays an important role in the transition from sure to mixed (or probabilistic) sniping. In this paper, we re-interpret and extend these conclusions in the context of repeated games and highlight some differences in results. In particular, we identify situations in which probabilistic sniping is genuinely profitable that are qualitatively different from the ones obtained in [14].

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Section: Related Workmentioning

confidence: 99%

To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders

Kokabisaghi¹,

Pauwels²,

Dorsman³

2019

Preprint

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“…Biais et al (2015) analyze the effect of an arms race and show that a higher speed triggers more severe adverse selection for slow traders. Delaney (2018) describes the speed decision of HFTs as a model of irreversible investment with an optimal stopping time, while Bongaerts and Van Achter (2016) view it from a perspective of high-frequency market making. 6 However, the speed decision in these models is discrete (i.e., being fast or not), and they abstract away from addressing the implications of the equilibrium level of speed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the spread works as an endogenous upward-sloping cost of being faster. To our knowledge, the existing models of the speed decision of HFTs, such as those by Foucault et al (2003Foucault et al ( , 2016, and Delaney (2018), deal only with the exogenous sunk cost of speed. By contrast, we propose endogenous cost as a means through which a speed bump affects the speed decision of HFTs.…”

Section: Introductionmentioning

confidence: 99%

Strategic Speed Choice by High-Frequency Traders Under Speed Bumps

Aoyagi

2018

SSRN Journal

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We study how high-frequency traders (HFTs) strategically decide their speed level in a market with a random speed bump. If HFTs recognize the market impact of their speed decision, they perceive a wider bid-ask spread as an endogenous upward-sloping cost of being faster. We find that the speed elasticity of the bid-ask spread (slope of the endogenous cost function) negatively depends on the expected length of a speed bump since a longer delay makes market makers insensitive to HFTs' speed increment. Hence, speed bumps promote the investment of HFTs in high-speed technology by reducing the marginal cost of getting faster, undermining their intended purpose of protecting market makers. Depending on the expected length of a bump, an arms race among HFTs exhibits both complementarity and substitution. These findings explain the ambiguous empirical results regarding speed bumps and adverse selection for market makers.

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“…Traditional studies have proposed algorithms to predict stock trends based on machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs) [5][6][7][8]. Recently, scholars have begun to adopt well-known deep learning techniques, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks [9,10], for stock price prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

et al. 2021

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The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

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Investment in high-frequency trading technology: A real options approach

Cited by 13 publications

References 28 publications

To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders

To snipe or not to snipe, that is the question! Transitions in sniping behaviour among competing algorithmic traders

Strategic Speed Choice by High-Frequency Traders Under Speed Bumps

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

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