We are witnessing a new industrial revolution driven by digital data, computation, and automation. 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. If successfully processed and managed, Big Data has the potential to spur new products, services, and practices as well as new scientific methodologies. One European sector that could greatly benefit from the use of Big Data is Finance. To exploit this potential, banks and other financial institutions must be able to handle and process massive heterogeneous data sets in a fast and robust manner.
BigDataFinance ITN, “Training for Big Data in Financial Research and Risk Management”, has provided doctoral training in sophisticated data-driven risk management and research at the crossroads of Finance and Big Data for 13 Early State Researchers (ESR). The main training objective of BigDataFinance were to meet the increasing commercial demand for well-trained researchers with experience in both Big Data techniques and Finance. The main research object was to develop and implement new quantitative models and econometric methods for empirical financial research and risk management by bridging the gap between research methodologies in Finance and Data Science. To achieve the objectives, the emphasis was put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. This program provided new realistic data-driven scientific approaches that will be requisite in finance.
