Are high–frequency traders informed?

Anagnostidis, Panagiotis; Fontaine, Patrice; Varsakelis, Christos

doi:10.1016/j.econmod.2020.08.013

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…Continuous bilateral quotations are given to gain profit from the difference between buying and selling prices or gain cost advantage thanks to the market maker function. Although HFT's income-seeking activities have positive contributions such as increasing transaction volumes and liquidity in the markets, improving price efficiency with their bids and prices incorporate information more efficiently [1][2][3], as a result of selling pressure with high order cancellations in markets where prices fall, they increase the severity of fluctuations in the markets. In times of market stability, heightened levels of HFT engagement correlate with a reduction in the volatility of stock prices.…”

Section: Introductionmentioning

confidence: 99%

The Intraday High-Frequency Trading with Different Data Ranges: A Comparative Study with Artificial Neural Network and Vector Autoregressive Models

Koy,

Çolak

2023

AAES

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With the High-Frequency Trading process, which is a subclass of algorithmic trading transactions, intraday information has increasing importance. Traditional statistical methods often fall short in capturing the intricate patterns and volatility inherent in such high-frequency data. In contrast, ANN models demonstrate remarkable capability in handling these challenges, and VAR models provide insights into short-term relationships among variables. This study highlights the importance of using both ANN and VAR models for processing these short time intervals. BIST100 index which is the main index of Borsa Istanbul is predicted with two different models in different data ranges with artificial neural network models and vector auto regression models. Both generated ANN models successfully complete the training stages, with extremely high precision, and exhibit exceptionally low error values in their predictions. Although both models are effective, the evidence favors the model evaluated using 5-minute data for both the training and prediction phases of artificial neural network models. However, the relative importance of 15-minute data in explaining the variation of BIST100 is higher. Moreover, the VAR model results indicate that the short-term relationship between variables can be influenced by the range of data and the 15-minute interval data of the variables play a more significant role in explaining the BIST100 index over the longer term.

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Section: Introductionmentioning

confidence: 99%

The Intraday High-Frequency Trading with Different Data Ranges: A Comparative Study with Artificial Neural Network and Vector Autoregressive Models

Koy,

Çolak

2023

AAES

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“…There are increasing literature using high sampling frequency because lower sampling frequency may result in information loss (see Hansen and Lunde (2006) ; Bandi and Russell (2006) ). For example, by the use of high-frequency data, Erdemlioglu et al (2021) and Anag-nostidis, Fontaine and Varsakelis (2020) studied the prediction of financial market risk and the monitoring of moral hazard in financial markets, respectively.…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID-19 on commodity options market: Evidence from China

Chen

Hao

2022

Economic Modelling

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The Role of Pre-Opening Mechanisms in Fragmented Markets

2016

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and CEPR 4 1 We are very grateful to Paul Besson (from Kepler-Chevreux), Dion Bongaerts, Catherine d'Hondt, Thierry Foucault, Carole Gresse, Alexander Guembel, Marius Zoican and seminar participants at the 3rd Forum "Market Microstructure: Confronting Many Viewpoints" (Paris) for providing useful comments. We especially thank Eurofidai-Bedofih for providing us the data. Laurence Lescourret is research fellow at CREST. Financial support from the ANR (ANR-16-CE26-0008) is gratefully acknowledged. Part of this research was carried out while Sophie Moinas was appointed Judith C. and William G. Bollinger Visiting Professor at the Wharton School, University of Pennsylvania, and benefited from the support from the CNRS.

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Are high–frequency traders informed?

Cited by 6 publications

References 53 publications

The Intraday High-Frequency Trading with Different Data Ranges: A Comparative Study with Artificial Neural Network and Vector Autoregressive Models

The Intraday High-Frequency Trading with Different Data Ranges: A Comparative Study with Artificial Neural Network and Vector Autoregressive Models

The impact of COVID-19 on commodity options market: Evidence from China

The Role of Pre-Opening Mechanisms in Fragmented Markets

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