Universal features of price formation in financial markets: perspectives from Deep Learning

Citations

Cited by:

1. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
2. Allison Koenecke & Amita Gajewar, 2019. "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers 1904.12887, arXiv.org, revised Jul 2019.
3. Gábor Petneházi & József Gáll, 2019. "Exploring the predictability of range‐based volatility estimators using recurrent neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 109-116, July.
4. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
5. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
6. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
   • Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction Of Investor Interest: a Supervised Clustering Approach," Working Papers hal-02276055, HAL.
7. Aiusha Sangadiev & Rodrigo Rivera-Castro & Kirill Stepanov & Andrey Poddubny & Kirill Bubenchikov & Nikita Bekezin & Polina Pilyugina & Evgeny Burnaev, 2020. "DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data," Papers 2008.12152, arXiv.org.
8. Ivan Peñaloza & Pablo Padilla, 2022. "A Pricing Method in a Constrained Market with Differential Informational Frameworks," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1055-1100, October.
9. 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.
10. Deniz Ersan & Chifumi Nishioka & Ansgar Scherp, 2020. "Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500," Journal of Computational Social Science, Springer, vol. 3(1), pages 103-133, April.
11. Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
12. Qi Zhao, 2020. "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers 2010.07404, arXiv.org.
13. 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.
14. 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.
15. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
16. Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
17. Luca De Gennaro Aquino & Carole Bernard, 2019. "Bounds on Multi-asset Derivatives via Neural Networks," Papers 1911.05523, arXiv.org, revised Nov 2020.
18. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
19. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
20. Christa Cuchiero & Wahid Khosrawi & Josef Teichmann, 2020. "A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models," Risks, MDPI, vol. 8(4), pages 1-31, September.
21. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
22. Paul Bilokon & Yitao Qiu, 2023. "Transformers versus LSTMs for electronic trading," Papers 2309.11400, arXiv.org.
23. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
24. 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.
25. Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023. "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers 2310.10500, arXiv.org, revised Mar 2024.
26. Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
27. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
28. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    • Baptiste Barreau & Laurent Carlier & Damien Challet, 2021. "Deep Prediction Of Investor Interest: a Supervised Clustering Approach," Post-Print hal-02276055, HAL.
29. Juho Kanniainen & Ye Yue, 2019. "The Arrival of News and Return Jumps in Stock Markets: A Nonparametric Approach," Papers 1901.02691, arXiv.org.
30. Arthur le Calvez & Dave Cliff, 2018. "Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market," Papers 1811.02880, arXiv.org.