no code implementations • 29 Apr 2024 • Felix Drinkall, Eghbal Rahimikia, Janet B. Pierrehumbert, Stefan Zohren
Large language models (LLMs) are often trained on extensive, temporally indiscriminate text corpora, reflecting the lack of datasets with temporal metadata.
2 code implementations • 16 Oct 2023 • Kieran Wood, Samuel Kessler, Stephen J. Roberts, Stefan Zohren
To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes.
no code implementations • 15 Sep 2023 • Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other.
no code implementations • 5 Sep 2023 • Trent Spears, Stefan Zohren, Stephen Roberts
We study an empirical trading strategy respectful of transaction costs, and demonstrate performance over a long history of 29 years, for both a linear and a non-linear state space model.
no code implementations • 25 Aug 2023 • Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, Anisoara Calinescu
Financial exchanges across the world use limit order books (LOBs) to process orders and match trades.
no code implementations • 23 Aug 2023 • Peer Nagy, Sascha Frey, Silvia Sapora, Kang Li, Anisoara Calinescu, Stefan Zohren, Jakob Foerster
Overall, our results invite the use and extension of the model in the direction of autoregressive large financial models for the generation of high-frequency financial data and we commit to open-sourcing our code to facilitate future research.
no code implementations • 23 Aug 2023 • Xingyue, Pu, Stefan Zohren, Stephen Roberts, Xiaowen Dong
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns.
no code implementations • 22 Aug 2023 • Xingyue, Pu, Stephen Roberts, Xiaowen Dong, Stefan Zohren
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets.
no code implementations • 18 Aug 2023 • Valentina Semenova, Dragos Gorduza, William Wildi, Xiaowen Dong, Stefan Zohren
Our initial experiments decompose the forum using a large language topic model and network tools.
no code implementations • 25 Jul 2023 • Tom Liu, Stefan Zohren
In this paper, we introduce Multi-Factor Inception Networks (MFIN), an end-to-end framework for systematic trading with multiple assets and factors.
no code implementations • 7 Jul 2023 • Tom Liu, Stephen Roberts, Stefan Zohren
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies.
no code implementations • 8 Jun 2023 • Alvaro Arroyo, Alvaro Cartea, Fernando Moreno-Pino, Stefan Zohren
Essential to this choice is the fill probability of a passive limit order placed in the LOB.
no code implementations • 11 May 2023 • Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren
In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering.
1 code implementation • 20 Feb 2023 • Wee Ling Tan, Stephen Roberts, Stefan Zohren
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time.
no code implementations • 31 Jan 2023 • Trent Spears, Stefan Zohren, Stephen Roberts
We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory.
no code implementations • 20 Jan 2023 • Peer Nagy, Jan-Peter Calliess, Stefan Zohren
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders.
1 code implementation • 4 Jan 2023 • Samuel Kessler, Adam Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
no code implementations • 23 Sep 2022 • Fernando Moreno-Pino, Stefan Zohren
Volatility forecasts play a central role among equity risk measures.
no code implementations • 21 Aug 2022 • Daniel Poh, Stephen Roberts, Stefan Zohren
Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures.
no code implementations • NAACL 2022 • Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert
We present a novel approach incorporating transformer-based language models into infectious disease modelling.
no code implementations • 22 Feb 2022 • Nikan Firoozye, Vincent Tan, Stefan Zohren
This paper presents a novel framework for analyzing the optimal asset and signal combination problem.
3 code implementations • 16 Dec 2021 • Kieran Wood, Sven Giegerich, Stephen Roberts, Stefan Zohren
We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
no code implementations • pproximateinference AABI Symposium 2022 • Samuel Kessler, Adam D. Cobb, Stefan Zohren, Stephen J. Roberts
Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks.
no code implementations • 17 Nov 2021 • Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren
The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.
no code implementations • 1 Aug 2021 • Eghbal Rahimikia, Stefan Zohren, Ser-Huang Poon
This study develops FinText, a financial word embedding compiled from 15 years of business news archives.
1 code implementation • 5 Jun 2021 • Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, Stephen J. Roberts
In this paper we formalize this "interference" as distinct from the problem of forgetting.
2 code implementations • 28 May 2021 • Kieran Wood, Stephen Roberts, Stefan Zohren
Back-testing our model over the period 1995-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of one-third.
2 code implementations • 21 May 2021 • Zihao Zhang, Stefan Zohren
We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques.
1 code implementation • 20 May 2021 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction.
no code implementations • 17 Feb 2021 • Zihao Zhang, Bryan Lim, Stefan Zohren
Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information.
no code implementations • 13 Dec 2020 • Daniel Poh, Bryan Lim, Stefan Zohren, Stephen Roberts
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction.
no code implementations • 10 Dec 2020 • Vincent Tan, Stefan Zohren
By correcting the biases in the sample eigenvalues and aligning our estimator to more recent risk, we demonstrate that our estimator performs well in large dimensions against existing state-of-the-art static and dynamic covariance shrinkage estimators through simulations and with an empirical application in active portfolio management.
no code implementations • 18 Aug 2020 • Peter Belcak, Jan-Peter Calliess, Stefan Zohren
As a simple illustration, we employ our toolbox to investigate the role of the order processing delay in normal trading and for the scenario of a significant price change.
no code implementations • 31 Jul 2020 • Trent Spears, Stefan Zohren, Stephen Roberts
In this work we show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades.
1 code implementation • 16 Jun 2020 • Diego Granziol, Stefan Zohren, Stephen Roberts
Whilst the linear scaling for stochastic gradient descent has been derived under more restrictive conditions, which we generalise, the square root scaling rule for adaptive optimisers is, to our knowledge, completely novel.
2 code implementations • 27 May 2020 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt deep learning models to directly optimise the portfolio Sharpe ratio.
no code implementations • 28 Apr 2020 • Bryan Lim, Stefan Zohren
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains.
no code implementations • 23 Jan 2020 • Bryan Lim, Stefan Zohren, Stephen Roberts
Detecting changes in asset co-movements is of much importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical correlations.
no code implementations • ICLR 2020 • Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson
This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.
no code implementations • 19 Dec 2019 • Diego Granziol, Robin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing.
no code implementations • 4 Dec 2019 • Samuel Kessler, Vu Nguyen, Stefan Zohren, Stephen Roberts
We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically.
no code implementations • 22 Nov 2019 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts.
no code implementations • 3 Jun 2019 • Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Doing, Michael Osborne, Stephen Roberts
Efficient approximation lies at the heart of large-scale machine learning problems.
no code implementations • 23 May 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimisation methods.
1 code implementation • 9 Apr 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule.
1 code implementation • 23 Jan 2019 • Bryan Lim, Stefan Zohren, Stephen Roberts
Testing this on three real-world time series datasets, we demonstrate that the decoupled representations learnt not only improve the accuracy of one-step-ahead forecasts while providing realistic uncertainty estimates, but also facilitate multistep prediction through the separation of encoder stages.
1 code implementation • 8 Nov 2018 • Samuel Kessler, Arnold Salas, Vincent W. C. Tan, Stefan Zohren, Stephen Roberts
We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimisation algorithms such as AdaGrad and Adam.
4 code implementations • 10 Aug 2018 • Zihao Zhang, Stefan Zohren, Stephen Roberts
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.
Computational Finance
no code implementations • 18 Apr 2018 • Diego Granziol, Binxin Ru, Stefan Zohren, Xiaowen Dong, Michael Osborne, Stephen Roberts
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network.
no code implementations • 24 Mar 2018 • Mariano Chouza, Stephen Roberts, Stefan Zohren
Besides complementing our analytical findings with numerical results from simulated Gaussian random fields, we also compare it to loss functions obtained from optimisation problems on synthetic and real data sets by proposing a "black box" random field toy-model for a deep neural network loss function.