Blog introduction


People in modern societies are leaving behind a vast amount of data that can be analysed and exploited in new and unprecedented ways to understand and model financial markets for better risk management. These data sets of high-volume, high-velocity, and high-variety information, directly relevant to the financial sector, arise from various sources. The sources include social media and news services being heterogeneous and unstructured, and electronic financial markets that generate terabytes of structured ultra-high-frequency limit order book data each day. The resulting datasets are so large and complex that such “Big Data” is becoming difficult to process with the current data management tools and methods. On the other hand, this data could provide valuable information to validate financial strategies, manage risks, and make decisions.

In BigDataFinance Marie Sklodowska-Curie Network, we recruited 13 smart and talented early-stage researchers who are preparing their PhD thesis on research projects. In this blog, they elaborate their research and thoughts at the cross-roads of Quantitative Finance, Big Data, and Data Science.

Juho Kanniainen
Prof., Coordinator of BigDataFinance