Using news for finding firm relations and implications on financial markets

Finding correlations between publicly traded companies is a topic of interest for a variety of actors on the financial markets. Many financial institutions use it to predict asset returns, while the regulators want to the know how the risk of default will spread through the market in times of crises. The classical approach towards building […]

14.08.2019 Read more

An introduction to sentiment analyses..

The general idea behind sentiment analyses in finance is the existence..

12.08.2019 Read more

Financial Data Analysis: Machine Learnin..

Financial markets are complex, interconnected and deterministically ch..

07.08.2019 Read more

International Conference on Data Science in Finance

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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

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Large, Unstructured, Noisy Data in Finance

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Seeking Postdocs for Data Science in Finance

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PhD dissertation of Giorgio Mirone

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Big Data Finance: PhD Thesis in Three Minutes

BigDataFinance Early Stage Res.. BigDataFinance Early Stage Research Vladimir Petrov, based at University of Zür..

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