A brief introduction to Big Data and Signal Processing

The chaotic nature of financial big datasets requires in depth analysis of their properties. These properties vary from past information and signal filtering to statistical inference and arbitrage identification. There are some general approaches that one should take into consideration when dealing with big chunks of data. To capitalize on the Big Data, information has […]

13.11.2017 Read more

BigDataFinance Conference – Presen..

BigDataFinance conference presentations from Oct 4th and 5th 2017 ar..

19.10.2017 Read more

Mid-Term Review Meeting 6th of October..

Mid-term review meeting for BigDataFinance project consortium members ..

05.10.2017 Read more

big data in finance

BigDataFinance 2015–2019, a H2020 Marie Sklodowska-Curie Innovative Training Network “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 researchers. The main objectives are

i) to meet an increasing commercial demand for well-trained researchers experienced in both Big Data techniques and Finance and

ii) to develop and implement new quantitative and econometric methods for empirical finance and risk management with large and complex datasets.

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. Banks and other financial institutions must be able to manage, process, and use massive heterogeneous data sets in a fast and robust manner for successful risk management; nonetheless, financial research and training has been slow to address the data revolution.

Compared to the USA, Europe is still at an early stage of adopting Big Data technologies and services. Immediate action is required to seize opportunities to exploit the huge potential of Big Data within the European financial world. This world-class network consists of eight academic participants and six companies, representing banks, asset management companies, and data and solution providers.

The proposed research is relevant both academically and practically, because the program is built around real challenges faced both by the academic and private sector partners. To bridge research and practice, all researchers contribute to the private sector via secondments. BigDataFinance provides the European financial community with specialists with state-of-the-art skills in finance and data-analysis to facilitate the adoption of reliable and realistic methods in the industry. This increases the financial strength of banks and other financial institutions in Europe.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675044.

Archives

BigDataFinance conference: register by 1st of October!

  BigDataFinance Conferen..   BigDataFinance Conference Bring Three, Go Free! Bring along three colleag..

29.09.2017 Read more

Ubiquitous Scaling Laws and Irrelevant Time

“Who owns the informatio.. “Who owns the information, he owns the world”. This phrase became fa..

19.07.2017 Read more

A Brief Discussion About Utilizing High-Frequency News Data In Finance

In the last two decades a lot .. In the last two decades a lot of endeavors have been made to develop tools in an..

10.07.2017 Read more

Complex Networks in Financial Markets

In most natural and engineered.. In most natural and engineered systems, a set of entities interact with each oth..

31.05.2017 Read more

An introduction to BigData in Finance: the econometric point of view

Ultra-high-frequency data are .. Ultra-high-frequency data are probably the perfect representative for the financ..

13.04.2017 Read more

Blog introduction

People in modern societies are.. People in modern societies are leaving behind a vast amount of data that can be ..

11.04.2017 Read more

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