Artificial intelligence (AI) has grown in presence in asset management and has
revolutionized the sector in many ways. It has improved portfolio management, trading, and …

Managing the prediction of metrics in high‐frequency financial markets is a challenging task.
An efficient way is by monitoring the dynamics of a limit order book to identify the information …

This paper develops a new neural network architecture for modeling spatial distributions (ie
distributions on R d) which is more computationally efficient than a traditional fully …

A limit order book is essentially a file on a computer that contains all orders sent to the
market, along with their characteristics such as the sign of the order, price, quantity and a …

We propose a machine learning framework to capture the dynamics of high-frequency limit
order books in financial equity markets and automate real-time prediction of metrics such as …

Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are
well suited to forecasting and sequence classification. They have been applied extensively …

Limit order books are a fundamental and widespread market mechanism. This paper
investigates the use of conditional generative models for order book simulation. For …

Machine learning (ML) methods are attracting considerable attention among academics in
the field of finance. However, it is commonly believed that ML has not transformed the asset …

Mid-price movement prediction based on the limit order book data is a challenging task due
to the complexity and dynamics of the limit order book. So far, there have been very limited …

Nowadays, with the availability of massive amount of trade data collected, the dynamics of
the financial markets pose both a challenge and an opportunity for high frequency traders. In …