Modelling high-frequency limit order book dynamics with support vector machines

Citations

Cited by:

1. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
2. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators," Papers 1907.09452, arXiv.org.
3. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
4. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
   • Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
5. Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
6. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.
7. Ymir Mäkinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2033-2050, December.
8. Jiwon Jung & Kiseop Lee, 2024. "Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book," Papers 2409.02277, arXiv.org.
9. Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Deep Adaptive Input Normalization for Time Series Forecasting," Papers 1902.07892, arXiv.org, revised Sep 2019.
10. Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
11. Faisal I Qureshi, 2018. "Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach," Papers 1901.10534, arXiv.org.
12. Fan Fang & Waichung Chung & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & Fan Wu, 2020. "Ascertaining price formation in cryptocurrency markets with DeepLearning," Papers 2003.00803, arXiv.org.
13. Avraam Tsantekidis & Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Using Deep Learning for price prediction by exploiting stationary limit order book features," Papers 1810.09965, arXiv.org.
14. Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "Quantformer: from attention to profit with a quantitative transformer trading strategy," Papers 2404.00424, arXiv.org, revised Oct 2024.
15. Ymir Makinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data," Papers 1810.10845, arXiv.org.
16. Xuekui Zhang & Yuying Huang & Ke Xu & Li Xing, 2023. "Novel modelling strategies for high-frequency stock trading data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
17. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "Towards Robust Representation of Limit Orders Books for Deep Learning Models," Papers 2110.05479, arXiv.org, revised Dec 2022.
18. Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data," Papers 1901.08280, arXiv.org.
19. Ilia Zaznov & Julian Kunkel & Alfonso Dufour & Atta Badii, 2022. "Predicting Stock Price Changes Based on the Limit Order Book: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-33, April.
20. Luca Mucciante & Alessio Sancetta, 2023. "Estimation of an Order Book Dependent Hawkes Process for Large Datasets," Papers 2307.09077, arXiv.org.
21. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
22. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
23. Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
24. Kong Ao & Zhu Hongliang, 2018. "Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques," Journal of Systems Science and Information, De Gruyter, vol. 6(2), pages 120-133, April.
25. Matthew F Dixon, 2017. "Sequence Classification of the Limit Order Book using Recurrent Neural Networks," Papers 1707.05642, arXiv.org.
26. Juho Kanniainen & Ye Yue, 2019. "The Arrival of News and Return Jumps in Stock Markets: A Nonparametric Approach," Papers 1901.02691, arXiv.org.
27. Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022. "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers 2203.03613, arXiv.org, revised Jan 2023.
28. Mynbaev, Kairat, 2020. "Using full limit order book for price jump prediction," MPRA Paper 101684, University Library of Munich, Germany.
29. Dorota Toczydlowska & Gareth W. Peters, 2018. "Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics," Econometrics, MDPI, vol. 6(3), pages 1-45, July.
30. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.