Community Research and Development Information Service - CORDIS

H2020

BigDataFinance Report Summary

Project ID: 675044
Funded under: H2020-EU.1.3.1.

Periodic Reporting for period 1 - BigDataFinance (Training for Big Data in Financial Research and Risk Management)

Reporting period: 2015-10-01 to 2017-09-30

Summary of the context and overall objectives of the project

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”, provides 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 is to meet the increasing commercial demand for well-trained researchers with experience in both Big Data techniques and Finance. The main research object is 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 is put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. This program provides new realistic data-driven scientific approaches that will be requisite in finance.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Early State Researchers are recruited successfully. During the first period, the fellows have published 8 working papers and additionally there are 22 unpublished papers in progress. The consortium has organized five events: Training event on Data Science in Finance (Tampere), Summer School for Introduction to Econometrics and Empirical Modelling of Financial Markets (Aarhus), Workshop and Conference on Complex Networks in Finance (Zürich), Training Event on Textual Data in Finance (Dubrovnik), and Conference on Big Data in Finance (London). Additionally, training on complementary skills, such as ethics and scientific writing, is provided. All the specified milestones and deliverables are achieved.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Financial research with Big Data needs multidisciplinary collaboration and new researcher profiles with the right competences to apply both modern data engineering techniques and state-of-the-art econometric methods to the financial domain and to progress towards computational methods with an emphasis on extensive use of real data. Especially financial risk management can no longer rely on pure traditional approaches, which often make unrealistic assumptions for the sake of soluble theoretical models over agreement with empirical data. To address these challenges, BigDataFinance consortium is expected to

(i) provide sophisticated methods in data science for financial modelling and risk management and pay special attention to machine learning and knowledge extraction algorithms;
(ii) use and develop methods and visualisation approaches with complex networks to increase understanding of the phenomena in financial markets arising from large and diverse data sets;
(iii) provide new econometric methods augmented with textual data sources and (ultra-)high-frequency stock market data for advanced risk management;
(iv) develop and publish an open financial sentiment index based on social media data and an open software to model and predict macroeconomic indicators;
(v) implement new Big Data applications with machine learning and knowledge extraction techniques for advanced financial risk and investment management in real business environments.

These objectives are expected to 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. We anticipate that these objectives will be vitally important to the financial sector, solution providers, and researchers.

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