Human vs. high-frequency traders, penny jumping, and tick size

Mahmoodzadeh, Soheil; Gençay, Ramazan

doi:10.1016/j.jbankfin.2017.08.015

Cited by 19 publications

(4 citation statements)

References 26 publications

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“…The outcomes reported align with outcomes from prior studies conducted by Frino, Mollica, and Zhang (2015) and Mahmoodzadeh and Gençay (2017), as well as Hagströmer and Nordén (2013). These studies collectively support the assertion that smaller tick prices indeed have an impact on the escalation of High-Frequency Trading (HFT) activity.…”

Section: Tick Price and Hftsupporting

confidence: 80%

The influence of tick prices on high-frequency trading on market quality in LQ-45 Indonesia

Riyanto,

Sriyana,

Arifin

et al. 2024

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This research aims to determine the influence of tick prices on high-frequency trading on market quality in LQ-45 Indonesia. This study employs a quantitative approach. This study employed the Fixed Effect, Random Effect and Common Effect. The research data source used secondary data, with 37 stocks identified that met the criteria for High-Frequency Trading (HFT). The data analysis technique utilized panel data method. Three findings were revealed in the results of this research: first, it demonstrates a significant impact of tick price on High-Frequency Trading (HFT) activity; second, it reveals a positive influence of HFT on market quality, as evidenced by the metrics of spread, volatility, and risk-adjusted return. HFT activity in this research substantiates a positive impact on market quality, measured through spread, volatility, and risk-adjusted return. However, when assessing market quality based on liquidity, the research indicated that HFT volume and trades exert a noteworthy impact on diminishing market liquidity; and third, High-Frequency Trading (HFT) generally exhibits a notable impact on reducing spreads, even though its strength is not conclusively proven in the HFT volume test. The practical implications of this research offer insights into the positive impact of tick prices on the escalation of High-Frequency Trading (HFT) activity.

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Section: Tick Price and Hftsupporting

confidence: 80%

The influence of tick prices on high-frequency trading on market quality in LQ-45 Indonesia

Riyanto,

Sriyana,

Arifin

et al. 2024

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“…Once a human trader places an order at a round number with last digit zero, on the ask side, algorithmic “queue jumping is done by submitting an order with last digit 9 and the buy side by an order with last digit 1… Automated traders queue-jump first the manual trader quote then leap frog each other as they compete for top of the book” [ 79 ]. Algorithms advanced into the more detailed aspect of reality where humans did not [ 85 ]. Summing up, a study group of 14 central banks comes to the conclusion that “while algorithms can relatively easily handle the extra digit, human traders find it more difficult to adapt” [ 19 ].…”

Section: Discussion and Interpretationmentioning

confidence: 99%

How Complexity and Uncertainty Grew with Algorithmic Trading

Hilbert

Darmon

2020

Entropy

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The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka ‘pip-trading’). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty.

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“…authentic traders use round numbers as cognitive reference points (Rosch, 1975) to simplify and save effort in the decision-making and evaluation (Ikenberry and Weston, 2008;Kuo et al, 2015;Lacetera, Pope, and Sydnor, 2012), distinguishing authentic trades from algorithmic trades (Mahmoodzadeh and Gençay, 2017;O'Hara, Yao, and Ye, 2014). Wash traders typically use automated trading programs, particularly when fake orders feature small transaction size yet substantial aggregate amounts (Vigna and Osipovich, 2018;Rodgers, 2019).…”

Section: Trade Size Clusteringmentioning

confidence: 99%

Crypto Wash Trading

Cong,

Li,

Tang

et al. 2023

Management Science

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We present the first systematic approach to detect fake transactions on cryptocurrency exchanges by exploiting robust statistical and behavioral regularities associated with authentic trading. Our sample consists of 29 centralized exchanges, among which the regulated ones feature transaction patterns consistently observed in financial markets and nature. In contrast, unregulated exchanges display abnormal first significant digit distributions, size rounding, and transaction tail distributions, indicating widespread manipulation unlikely driven by a specific trading strategy or exchange heterogeneity. We then quantify the wash trading on each unregulated exchange, which averaged more than 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and user base), market conditions, and regulation. Overall, our study cautions against potential market manipulations on centralized crypto exchanges with concentrated power and limited disclosure requirements and highlights the importance of fintech regulation. This paper was accepted by David Simchi-Levi, special issue of Management Science: Blockchains and crypto economics. Funding: This research was partly funded by the Ewing Marion Kauffman Foundation [Grant G-201907-6995], the National Natural Science Foundation of China [Grants 72192802, 72192800, and 72192801], Ripple’s University Blockchain Research Initiative (UBRI), and the FinTech at Cornell Initiative. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.02709 .

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Human vs. high-frequency traders, penny jumping, and tick size

Cited by 19 publications

References 26 publications

The influence of tick prices on high-frequency trading on market quality in LQ-45 Indonesia

The influence of tick prices on high-frequency trading on market quality in LQ-45 Indonesia

How Complexity and Uncertainty Grew with Algorithmic Trading

Crypto Wash Trading

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