292 citations · 466 across the 8 of their papers we have counts for
9 papers
DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets
Wanyun Zhou, Saizhuo Wang, Mihai Cucuringu +5
The lead-lag effect, where the price movement of one asset systematically precedes that of another, has been widely observed in financial markets and conveys valuable predictive si…
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
Chao Zhang, Zihao Zhang, Mihai Cucuringu +1
We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasti…
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units
Zihao Zhang, Stefan Zohren
We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques. Unlike standard structures where a single prediction is made, we adopt…
Deep Learning for Market by Order Data
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 info…
Deep Learning for Portfolio Optimization
Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us…
Deep Reinforcement Learning for Trading
Zihao Zhang, Stefan Zohren, Stephen Roberts
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatil…
Extending Deep Learning Models for Limit Order Books to Quantile Regression
Zihao Zhang, Stefan Zohren, Stephen Roberts
We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-ser…
BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, Stephen Roberts
We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data so…
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
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. The architecture utilises convolutional filters to captur…