Periodic Reporting for period 2 - BigDataFinance (Training for Big Data in Financial Research and Risk Management)
Reporting period: 2017-10-01 to 2019-09-30
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.
(i) provides sophisticated methods in data science for financial modelling and risk management and pay special attention to machine learning and knowledge extraction algorithms;
(ii) uses and develops methods and visualisation approaches with complex networks to increase understanding of the phenomena in financial markets arising from large and diverse data sets;
(iii) provides new econometric methods augmented with textual data sources and (ultra-)high-frequency stock market data for advanced risk management;
(iv) develops and publish an open financial sentiment index based on social media data and an open software to model and predict macroeconomic indicators;
(v) implements new Big Data applications with machine learning and knowledge extraction techniques for advanced financial risk and investment management in real business environments.
These objectives stimulate practitioners to exploit data-driven and robust risk management models and abandon less realistic ones for safer operation with financial instruments and better focus on the real properties of financial and economic variables arising from real data. On the academic side, BigDataFinance promotes the use of massive integrated data sources to develop innovative financial models and methods reassessing the unrealistic assumptions so far applied with homogenous data sets. These objectives will be vitally important to the financial sector, solution providers, and researchers.