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 …

A novel approach is presented for predicting the mean-mid stock price by utilizing high-
frequency and complex limit order book (LOB) data as inputs for machine learning …

We exploit cutting-edge deep learning methodologies to explore the predictability of high-
frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on …

The prediction of financial markets is a challenging yet important task. In modern
electronically driven markets, traditional time‐series econometric methods often appear …

This paper proposes a method to compute ex-ante trading costs at the intraday level from
limit order books. Using nearly 500 of the largest traded companies in the NYSE ArcaBook …

I study the strategic choice of informed traders for market vs. limit orders by analyzing the
informational content of the limit order book. In particular, I examine intraday return …

This paper focuses on liquidity modelling to explore the world of limit order book markets. By
trying to predict the bid-ask spread across a month of active trading days, we aim to compare …

The vast amount of information characterizing nowadays's high-frequency financial datasets
poses both opportunities and challenges. Among the opportunities, existing methods can be …

This thesis consists of three essays that attempt to provide novel empirical analyses of
important problems in finance. The first essay deals with the returns of actively managed …

A Work Project, presented as part of the requirements for the Award of a Master Degree in
Finance from the NOVA – School of Bu Page 1 A Work Project, presented as part of the …