High-frequency trading: a literature review

Virgilio, Gianluca Piero Maria

doi:10.1007/s11408-019-00331-6

Cited by 26 publications

(4 citation statements)

References 51 publications

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“…One of the most discussed causality issues in HFT financial literature is whether HFT causes changes in volatility or if changes in volatility attract high-frequency activity. Results have been found in both directions (Virgilio 2019), and it is still a topic of study. In our study, we investigate this by applying Granger causality between realized volatility and the HFT proxy.…”

Section: Price Efficiencymentioning

confidence: 96%

“…The prevalence of algorithmic trading (AT), and high-frequency activity (HFT) in particular, has increased significantly in recent years (Baron et al 2019;Borch 2016;Cvitanic and Kirilenko 2012;Dall'Amico et al 2019;Golub et al 2012;Hasbrouck and Saar 2013;Jovanovic and Menkveld 2012). Financial literature has thoroughly studied market quality parameters and their relationship with AT and HFT, although inconclusive findings have been reached regarding volatility, liquidity, price efficiency, and transaction costs related to HFT's impact and behavior (Virgilio 2019). If one conclusion can be drawn it is that technology and new AT and HFT trading strategies significantly affect market microstructure (O'Hara 2015).…”

Section: Introductionmentioning

confidence: 99%

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Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

Ferreruela

Martín

2022

JRFM

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The recent emergence of COVID-19 and the subsequent short-selling restriction (SSR) imposed on some equity markets provide us with a unique framework to analyze the effects of this kind of measure on market quality in the context of increasingly automated equity markets. We contribute to the literature by analyzing the microstructure and quality parameters of the Spanish equity market during COVID-19 and SSR. We study four subperiods, namely pre-crisis, turmoil, SSR, and first de-escalation periods, by means of a tick-by-tick dataset and the complete limit order book (LOB). We observe the following impact of the SSR on the constituents of IBEX 35: (1) the SSR did comply partially with its aim at an intraday level regarding volatility, but liquidity was reduced; (2) liquidity deterioration affected more the sell than the buy side of the LOB; (3) high-frequency activity (HFT) diminished during SSR, reinforcing volatility; (4) negative effects on liquidity and HFT diminished and disappeared as the ban was lifted; (5) HFT unidirectionally Granger causes 1 min realized volatility while the natural logarithm of the slope of the LOB bidirectionally Granger causes 1 min realized volatility.

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Section: Price Efficiencymentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

Ferreruela

Martín

2022

JRFM

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“…In an attempt to organize ideas and findings within the diverse topics regarding HFT and its impacts, several survey and discussion studies have been conducted in the last decade. These studies cover HFT strategies and profits (Goldstein et al, 2014) as well as market efficiency and welfare (Stiglitz, 2014), while the focus is mostly on HFT impacts on market quality and the regulatory actions (Prewitt, 2012;Jones, 2013;Biais and Foucault, 2014;O'Hara, 2015;Chung and Lee, 2016;Virgilio, 2019).…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

Ersan

Dalgic

Ekinci

et al. 2021

Alanya Akademik Bakış

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High-frequency trading (HFT) has been dominating the activity in developed financial markets in the last two decades. Despite its recent formation, the literature on the impacts of HFT on financial markets and participants is broad. However, there are ongoing debates and unanswered questions within many subtopics. We survey through the research towards HFT effects on liquidity in an attempt to explain the coexistence of evidence regarding both the positive and the negative impacts of HFT. We name two main factors leading to mixed results. Former concerns the negative market conditions such as intraday shocks, through which HFT trading patterns may sharply change. Latter regards the certain characteristics of HFT liquidity provision with the potential to present externalities for the market.2 BYX exchange utilized by CBOE reduced order processing times by around sevenfold from 445 microseconds in 2009 to 64 microseconds in 2018 (Baldauf and Mollner, 2020). HFT firms are in an arms race that leads them to send data from exchanges to electronic traders in latencies as small as 4 nanoseconds (Sprothen, 2016). 3 Throughout the text, we use 'high-frequency trading (trader)' and 'low-latency trading (trader)' interchangeably. We use 'HFT' as the acronym for high-frequency trading; 'HFTs' as the acronym for high-frequency traders; and non-HFTs as the acronym for low-frequency traders (traders other than high-frequency traders).

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“…A la luz de los recientes avances en este tema, los resultados indican que durante la apertura y el cierre de los mercados se produce un elevado volumen de posiciones ejecutadas por estrategias de negociación de alta frecuencia. Esto podría llevar, por un lado, a proporcionar liquidez y controlar la volatilidad del mercado en beneficio de los inversores minoristas y, por otro, afectar a las estrategias de negociación de todos los inversores y al funcionamiento del mercado [242,243]. Aunque todavía se requieren estudios formales sobre estos efectos, el volumen de datos que reportan este tipo de operaciones, además de proporcionar información detallada sobre los activos que pueden resultar atractivos para los inversores y especuladores, podría revelar pautas en los precios que pueden ayudar a predecir a corto plazo la evolución y la dirección de los mercados.…”

Section: Estrategias De Negociaciónunclassified

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

Argotty Erazo

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Predecir los precios y las tendencias de los instrumentos financieros para mejorar la efectividad de las decisiones de inversión es un reto clave para la industria financiera y los agentes implicados. Aunque se han utilizado muchas técnicas eficaces de inteligencia artificial en el análisis de las series de tiempo, el problema de predecir la dirección del movimiento de los tipos de cambio en el mercado Forex aún requiere soluciones parsimoniosas, descifrables y precisas. Desde la perspectiva del análisis técnico, esta investigación presenta una metodología novedosa para clasificar la dirección de la tendencia de los tipos de cambio. La metodología utiliza puntos de inflexión y datos de mercado que miden la acción de los precios, junto con las diferencias multidimensionales entre tendencias, para construir una función lineal discriminante (LDA).La metodología propuesta consta de cinco fases: preparación de datos, selección de características, detección de estructuras subyacentes, formulación de una función discriminante lineal y evaluación del desempeño del modelo con datos dentro y fuera de la muestra. Los experimentos se realizaron con datos de mercado del tipo de cambio euro-dólar en marcos de tiempo de 15 minutos y 1 semana, y una colección de puntos de inflexión del mercado (ET) definidos por un modelo de negociación algorítmico. El período de muestra va desde enero de 1999 hasta abril de 2023.En contraste con algunos trabajos notables publicados en la literatura científica como la Memoria a Corto Plazo (LSTM), el Aprendizaje Profundo por Refuerzo (DRL), el Análisis Wavelet (WA), el Análisis de Sentimiento de Contenido Textual, las Máquinas de Vectores de Soporte (SVM) y los Algoritmos Genéticos (GA), la metodología propuesta logró una precisión de clasificación del 98.77% con datos fuera de muestra. Estos resultados respaldan la construcción de métodos de clasificación interpretables, generalizables, precisos y parsimoniosos, lo que sugiere mejoras significativas en el rendimiento financiero y la reducción del riesgo en las estrategias de negociación. Además, esta metodología es aplicable en la selección de variables y se adapta fácilmente a otros activos financieros.

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High-frequency trading: a literature review

Cited by 26 publications

References 51 publications

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

High-Frequency Trading and its Impact on Market Liquidity: A Review of Literature

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

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