Introduction to the Issue on Financial Signal Processing and Machine Learning for Electronic Trading

The twelve papers in this special issue presents relevant research contributions from the disciplines of finance, mathematics, data science and engineering to facilitate scientific cross-fertilization. It will also serve the signal processing community to be exposed to the state of the art in mathematical finance, financial engineering, financial signal processing an electronic trading, and to foster future research in this emerging area. The main themes of this special issue include using tools from machine learning and signal processing that help to address some of the main problems arising in quantitative finance: modeling risk and correlations of financial instruments and their baskets, returns and liquidity, and problems involving risk-aware resource allocation -namely portfolio optimization. These problems involve tools from convex and discrete optimization, non-parametric statistics, time-series modeling,graph theory and high-dimensional covariance estimation.