Search Results for author:
Found 49 papers, 14 papers with code
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
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.
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other.
On statistical arbitrage under a conditional factor model of equity returns
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.
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading
Financial exchanges across the world use limit order books (LOBs) to process orders and match trades.
Learning to Learn Financial Networks for Optimising Momentum Strategies
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns.
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
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.
Network Momentum across Asset Classes
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets.
Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB
Our initial experiments decompose the forum using a large language topic model and network tools.
Multi-Factor Inception: What to Do with All of These Features?
In this paper, we introduce Multi-Factor Inception Networks (MFIN), an end-to-end framework for systematic trading with multiple assets and factors.
Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies.
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
Essential to this choice is the fill probability of a passive limit order placed in the LOB.
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
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.
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies
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.
View fusion vis-à-vis a Bayesian interpretation of Black-Litterman for portfolio allocation
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.
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets
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.
On Sequential Bayesian Inference for Continual Learning
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.
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
Volatility forecasts play a central role among equity risk measures.
Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity
Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures.
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
We present a novel approach incorporating transformer-based language models into infectious disease modelling.
Canonical Portfolios: Optimal Asset and Signal Combination
This paper presents a novel framework for analyzing the optimal asset and signal combination problem.
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies.
Can Sequential Bayesian Inference Solve Continual Learning?
Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks.
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
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.
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
This study develops FinText, a financial word embedding compiled from 15 years of business news archives.
Same State, Different Task: Continual Reinforcement Learning without Interference
In this paper we formalize this "interference" as distinct from the problem of forgetting.
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection
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.
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units
We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques.
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention
The performance of a cross-sectional currency strategy depends crucially on accurately ranking instruments prior to portfolio construction.
Deep Learning for Market by Order Data
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.
Building Cross-Sectional Systematic Strategies By Learning to Rank
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction.
Estimation of Large Financial Covariances: A Cross-Validation Approach
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.
Fast Agent-Based Simulation Framework with Applications to Reinforcement Learning and the Study of Trading Latency Effects
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.
Investment sizing with deep learning prediction uncertainties for high-frequency Eurodollar futures trading
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.
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
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.
Deep Learning for Portfolio Optimization
We adopt deep learning models to directly optimise the portfolio Sharpe ratio.
Time Series Forecasting With Deep Learning: A Survey
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains.
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio
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.
Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods
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.
A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing.
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning
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.
Deep Reinforcement Learning for Trading
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts.
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Efficient approximation lies at the heart of large-scale machine learning problems.
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
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.
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
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.
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
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.
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods
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.
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.
Computational Finance
Entropic Spectral Learning for Large-Scale Graphs
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network.
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning
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.
