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ORCIDStefan Zohren
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2020 – today
- 2024
[c15]Xinpeng Hong, Changgang Zheng, Stefan Zohren, Noa Zilberman:
Accelerating Machine Learning for Trading Using Programmable Switches. ECAI 2024: 3429-3436- [c14]Dragos Gorduza
, Yaxuan Kong, Xiaowen Dong, Stefan Zohren:
Extracting Alpha from Financial Analyst Networks. ICAIF 2024: 397-405 - [c13]Wee Ling Tan, Stephen Roberts, Stefan Zohren:
Deep Learning for Options Trading: An End-To-End Approach. ICAIF 2024: 487-495 - [c12]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka:
The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. KDD 2024: 6733-6734 - [c11]Felix Drinkall, Eghbal Rahimikia, Janet B. Pierrehumbert, Stefan Zohren:
Time Machine GPT. NAACL-HLT (Findings) 2024: 3281-3292 - [i43]Felix Drinkall, Eghbal Rahimikia, Janet B. Pierrehumbert, Stefan Zohren:
Time Machine GPT. CoRR abs/2404.18543 (2024) - [i42]Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, Stefan Zohren:
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges. CoRR abs/2406.11903 (2024) - [i41]Yoontae Hwang, Stefan Zohren, Yongjae Lee:
Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management. CoRR abs/2407.13751 (2024) - [i40]Felix Drinkall, Janet B. Pierrehumbert, Stefan Zohren:
Traditional Methods Outperform Generative LLMs at Forecasting Credit Ratings. CoRR abs/2407.17624 (2024) - [i39]Wee Ling Tan, Stephen J. Roberts, Stefan Zohren:
Deep Learning for Options Trading: An End-To-End Approach. CoRR abs/2407.21791 (2024) - [i38]Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang, Peng Sun, Qingsong Wen:
Unlocking the Power of LSTM for Long Term Time Series Forecasting. CoRR abs/2408.10006 (2024) - 2023
- [j7]Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. Entropy 25(6): 884 (2023) - [j6]Peer Nagy, Jan-Peter Calliess, Stefan Zohren:
Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets. Frontiers Artif. Intell. 6 (2023) - [c10]Xinpeng Hong, Changgang Zheng, Stefan Zohren, Noa Zilberman:
LOBIN: In-Network Machine Learning for Limit Order Books. HPSR 2023: 159-166 - [c9]Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob N. Foerster:
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network. ICAIF 2023: 91-99 - [c8]Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren:
Dynamic Time Warping for Lead-Lag Relationship Detection in Lagged Multi-Factor Models. ICAIF 2023: 454-462 - [c7]Sascha Yves Frey, Kang Li, Peer Nagy, Silvia Sapora, Christopher Lu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu:
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. ICAIF 2023: 583-591 - [i37]Samuel Kessler, Adam D. Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts:
On Sequential Bayesian Inference for Continual Learning. CoRR abs/2301.01828 (2023) - [i36]Peer Nagy, Jan-Peter Calliess, Stefan Zohren:
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets. CoRR abs/2301.08688 (2023) - [i35]Wee Ling Tan, Stephen J. Roberts, Stefan Zohren:
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies. CoRR abs/2302.10175 (2023) - [i34]Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren:
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models. CoRR abs/2305.06704 (2023) - [i33]Xingyue Pu, Stephen J. Roberts, Xiaowen Dong, Stefan Zohren:
Network Momentum across Asset Classes. CoRR abs/2308.11294 (2023) - [i32]Xingyue Pu, Stefan Zohren, Stephen J. Roberts, Xiaowen Dong:
Learning to Learn Financial Networks for Optimising Momentum Strategies. CoRR abs/2308.12212 (2023) - [i31]Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob N. Foerster, Anisoara Calinescu:
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading. CoRR abs/2308.13289 (2023) - [i30]Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob N. Foerster:
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network. CoRR abs/2309.00638 (2023) - [i29]Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren:
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies. CoRR abs/2310.10500 (2023) - 2022
- [j5]Diego Granziol, Binxin Ru, Xiaowen Dong, Stefan Zohren, Michael A. Osborne, Stephen J. Roberts:
Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications. Algorithms 15(6): 209 (2022) - [j4]Diego Granziol, Stefan Zohren, Stephen Roberts:
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training. J. Mach. Learn. Res. 23: 173:1-173:65 (2022) - [c6]Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts:
Same State, Different Task: Continual Reinforcement Learning without Interference. AAAI 2022: 7143-7151 - [c5]Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert:
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts. NAACL-HLT 2022: 1471-1484 - [c4]Xinpeng Hong, Changgang Zheng, Stefan Zohren, Noa Zilberman:
Linnet: limit order books within switches. SIGCOMM Posters and Demos 2022: 37-39 - [i28]Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert:
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts. CoRR abs/2205.10408 (2022) - [i27]Daniel Poh, Stephen J. Roberts, Stefan Zohren:
Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity. CoRR abs/2208.09968 (2022) - [i26]Fernando Moreno-Pino, Stefan Zohren:
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions. CoRR abs/2210.04797 (2022) - [i25]Dragos Gorduza, Xiaowen Dong, Stefan Zohren:
Understanding stock market instability via graph auto-encoders. CoRR abs/2212.04974 (2022) - 2021
- [c3]Peter Belcak, Jan-Peter Calliess, Stefan Zohren:
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects. MABS 2021: 42-56 - [c2]Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen J. Roberts:
Hierarchical Indian buffet neural networks for Bayesian continual learning. UAI 2021: 749-759 - [i24]Zihao Zhang, Bryan Lim, Stefan Zohren:
Deep Learning for Market by Order Data. CoRR abs/2102.08811 (2021) - [i23]Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. CoRR abs/2105.10019 (2021) - [i22]Zihao Zhang, Stefan Zohren:
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units. CoRR abs/2105.10430 (2021) - [i21]Kieran Wood, Stephen J. Roberts, Stefan Zohren:
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection. CoRR abs/2105.13727 (2021) - [i20]Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts:
Same State, Different Task: Continual Reinforcement Learning without Interference. CoRR abs/2106.02940 (2021) - [i19]Eghbal Rahimikia, Stefan Zohren, Ser-Huang Poon:
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding. CoRR abs/2108.00480 (2021) - [i18]Kieran Wood, Sven Giegerich, Stephen J. Roberts, Stefan Zohren:
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture. CoRR abs/2112.08534 (2021) - 2020
- [j3]Joschka Roffe, Stefan Zohren, Dominic Horsman, Nicholas Chancellor:
Quantum Codes From Classical Graphical Models. IEEE Trans. Inf. Theory 66(1): 130-146 (2020) - [c1]Bryan Lim, Stefan Zohren, Stephen Roberts:
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. IJCNN 2020: 1-8 - [i17]Bryan Lim, Stefan Zohren, Stephen Roberts:
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. CoRR abs/2002.02008 (2020) - [i16]Bryan Lim, Stefan Zohren:
Time Series Forecasting With Deep Learning: A Survey. CoRR abs/2004.13408 (2020) - [i15]Zihao Zhang, Stefan Zohren, Stephen Roberts:
Deep Learning for Portfolio Optimisation. CoRR abs/2005.13665 (2020) - [i14]Peter Belcak, Jan-Peter Calliess, Stefan Zohren:
Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects. CoRR abs/2008.07871 (2020) - [i13]Xingchen Wan, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, Xiaowen Dong:
Sentiment Diffusion in Financial News Networks and Associated Market Movements. CoRR abs/2011.06430 (2020) - [i12]Vincent W. C. Tan, Stefan Zohren:
Large Non-Stationary Noisy Covariance Matrices: A Cross-Validation Approach. CoRR abs/2012.05757 (2020) - [i11]Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Building Cross-Sectional Systematic Strategies By Learning to Rank. CoRR abs/2012.07149 (2020)
2010 – 2019
- 2019
- [j2]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. Entropy 21(6): 551 (2019) - [j1]Zihao Zhang, Stefan Zohren, Stephen J. Roberts:
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. IEEE Trans. Signal Process. 67(11): 3001-3012 (2019) - [i10]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. CoRR abs/1901.08096 (2019) - [i9]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Enhancing Time Series Momentum Strategies Using Deep Neural Networks. CoRR abs/1904.04912 (2019) - [i8]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. CoRR abs/1905.09691 (2019) - [i7]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning. CoRR abs/1906.01101 (2019) - [i6]Zihao Zhang, Stefan Zohren, Stephen J. Roberts:
Deep Reinforcement Learning for Trading. CoRR abs/1911.10107 (2019) - [i5]Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts:
Indian Buffet Neural Networks for Continual Learning. CoRR abs/1912.02290 (2019) - [i4]Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
A Maximum Entropy approach to Massive Graph Spectra. CoRR abs/1912.09068 (2019) - 2018
- [i3]Mariano Chouza, Stephen J. Roberts, Stefan Zohren:
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning. CoRR abs/1803.09119 (2018) - [i2]Diego Granziol, Bin Xin Ru, Stefan Zohren, Xiaowen Dong, Michael A. Osborne, Stephen J. Roberts:
Entropic Spectral Learning in Large Scale Networks. CoRR abs/1804.06802 (2018) - [i1]Arnold Salas, Stefan Zohren, Stephen J. Roberts:
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods. CoRR abs/1811.03679 (2018)
Coauthor Index
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