Introduction to Machine Learning and Network Analytics in Finance Minitrack

Recent years have seen a rapid evolution of methodologies in artificial intelligence and machine learning, and as a result, increasingly widespread use of these techniques in different domains. One of the most important application areas is finance, offering challenging problems for researchers and practitioners. In the early stages of the use of machine learning in finance, traditional quantitative data, e.g. historical time series, has been the main focus of analysis to develop various prediction models and to shed new light of stock markets, economic behaviour etc. More recently, following general trends in machine learning, contributions utilizing unstructured, specifically textual, data have started to appear and the number of these applications increases steadily. Previously untouched data sources, such as news articles, company announcements or even social media comments, can be utilized to improve or complement traditional financial data analytics tools. The selected contributions included in the minitrack offer novel contributions in applying machine learning tools, with the main focus on utilizing textual data. The contributions not only develop new predictive models, improving for example traditional neural network approaches, but rigorously test them on large reallife datasets illustrating the relevance of machine learning tools in finance.